pdfs.semanticscholar.orgAnnual Report 2007- Department ELEC I CONTENTS CHAPTER 1. Introduction to...

Annual Report 2007- Department ELEC I

CONTENTS

CHAPTER 1. Introduction to the department ELEC ........1

1. Introduction to the long term strategy of the department ELEC 1

2. Research TEAMS of the department ELEC: Overview of the 5 teams ............................................................................. 2A. Team A: Physical communication, estimation of parameters in distributed

systems and laboratory of marine acoustics. ......................................2B. Team B: system identification and parameter estimation of linear and

non-linear systems .........................................................................3C. Team C: the study of microwave systems .........................................3D. Team D: the IMEC/VUB-ELEC laboratory for microwave microelectronic

measurements and modelling (4M) ...................................................3E. Team E: Identification of biogeochemical signals & systems (IBiSS) ......3

3. Staff of ELEC (Status 01/01/2008) ..................................... 4A. General director ............................................................................4B. Team A: Physical communication, estimation of parameters in distributed

systems and laboratory of marine acoustics .......................................4C. Team B: System identification and parameter estimation of linear and non-

linear systems ...............................................................................7D. Team C: The study of microwave systems ..................................... 10E. Team D: The IMEC/VUB laboratory for microwave microelectronic

measurements and modelling (4M) ................................................. 12F. Team E: Identification of biogeochemical signals & systems (IBiSS) ... 13G. Technical and administrative staff .................................................. 14H. Professor Emeritus ...................................................................... 17I. PhD-students from other departments, universities, institutions... ....... 18J. Industrial Partnership ................................................................... 18K. National or international contacts ................................................... 18

A. Visiting professors/researchers .........................................................18B. Scientific missions ...........................................................................20

L. List of phone numbers and e-mail addresses (Status 01/01/2008) ...... 27

4. Organisation chart of department ELEC ..............................28

5. Functional organisation of the dept. ELEC ...........................29

6. List of the most important measurement equipment ............31A. Signal Generators ........................................................................ 31B. Spectrum Analysers, Impedance Analysers, Network Analysers .......... 31C. Digitizers .................................................................................... 31D. Miscellaneous ............................................................................. 32E. Underwater Acoustics ................................................................... 32

II Contents

7. Financial support .............................................................33

8. Awards ..........................................................................36A. Grade of Fellow (IEEE) ................................................................. 36B. Grade of Senior Member (IEEE) ..................................................... 36C. IEEE I&M Outstanding Young Engineer Award .................................. 36D. Awards granted by the VUB, on the proposition of the department ELEC 36

CHAPTER 2. Short Description of the Research Projects/Team ..................................................... 37

1. Team a: automatic measurement systems and laboratory of underwater acoustics .......................................................37A. Introduction to Team A ................................................................ 37B. Short Description of the Research Projects of Team A ....................... 38

A. positioning technologies used in LBS (Location Based Services) focusing on WiMAX networks. ............................................................. 38

B. RESOLUTION ENHANCING LOCALIZATION TECHNIQUES FOR LOCATION-BASED SERVICES ............................................................................. 38

C. Design and evaluation of Multiple-Input-Multiple-Output channel models for DSL systems..................................................................... 40

D. Loop Qualification and Capacity Estimation of DSL Lines ....................... 41E. Detecting faults on critical instrumentation loops of gas turbines............ 43

2. team b: system identification and parameter estimation .......45A. Introduction to Team B ................................................................ 45

A. Identification of Linear Systems ........................................................ 45B. Identification of nonlinear Systems ................................................... 46

B. Short description of the research projects of Team B ........................ 48A. METHUSALEM: Centre for Data Based Modelling and Model Quality

Assessment...................................................................................... 48B. Study of the LTI relations between the outputs of two coupled Wiener

systems and its application to the generation of initial estimates for Wiener-Hammerstein systems ............................................................ 49

C. Identification of a crystal detector using a block structured nonlinear feedback model ................................................................................ 51

D. Using ANOVA in a Microwave Round-Robin Comparison ........................ 51E. Improved FRF measurements in the presence of nonlinear distortions

via overlap ....................................................................................... 53F. Non-parametric Power Spectrum Estimation with Circular Overlap .......... 55G. Identification of Stable models From Noisy MeasurementS.................... 57H. Measuring and modelling weakly nonlinear, slowly time-varying systems 58I. Initial Estimates for Wiener-Hammerstein Models Using the Best

Linear Approximation ........................................................................ 60J. THE USE OF NEUTRON NOISE ANALYSIS TECHNIQUES FOR THE

DETERMINATION OF THE MODERATOR TEMPERATURE COEFFICIENT OF A PRESSURIZED WATER REACTOR.................................................. 62

K. Continuous-time operational modal analysis in the presence of harmonic disturbances..................................................................... 64

L. Blind maximum likelihood identification of Wiener and Hammerstein

Annual Report 2007- Department ELEC III

block structures ................................................................................66M. Experimental stability analysis of nonlinear systems.............................67N. AIC Model Selection for Short Data Records.........................................68O. Nonlinear analysis of flutter...............................................................70P. THE USE OF INVERSE MODELLING IN ILC METHODS.............................72

3. Team C: The study of Microwave Systems ..........................74A. Introduction to Measurement, Modelling and Identification of Microwave

Systems ...................................................................................... 74B. Short Description of the Research Projects of TEAM C ....................... 75

A. Advanced calibration and verification setups for nonlinear RF-devices......75B. New measurement and modeling techniques for the nonideal nonlinear

behavior of microwave multiple port systems ........................................76C. New Measurement and Modelling Techniques for RFIC’s ........................78

4. Team D: The imec/vub-elec laboratory for microwave microelectronic measurements and modelling (4m) ..............81A. Introduction to the IMEC Laboratory: Microwave Microelectronic

Measurements and Modelling ......................................................... 81B. Short description of the research projects of Team D ........................ 81

A. Advanced circuit-analysis and modeling. .............................................81B. Design automation, analysis and modeling of multirate discrete-time

systems ......................................................................................... 83C. Experimental analysis of the coupling mechanisms between a 4GHz PPA and

a 5-7GHz LC-VCO..............................................................................84D. Measuring, modeling and realization of high-frequency amplifiers...........85E. Sampling and discrete-time signal processing for SDR...........................87F. Design of analog CMOS circuits for mm-wave applications .....................87

5. Team E: Identification of biogeochemical signals & systems (IBiSS) ...........................................................................89A. Introduction to Team E ................................................................. 89B. Short description of the research projects of team E ......................... 90

A. Climate reconstruction based on archaeological shells: a non-linear multi-proxy approach.........................................................................90

B. Identification of a harmonic signal in the presence of additive noise, an unknown time base distortion, and an averaging effect ......................91

6. Spin-off: NMDG Engineering .............................................93A. Vision ........................................................................................ 93B. At a glance ................................................................................. 93C. Markets ...................................................................................... 93D. Products and Services .................................................................. 94E. Large-Signal Network Analyser Products together with Maury Microwave 94F. Characterization Service ............................................................... 95G. Measurement-based Behavioural Modeling Service ........................... 95H. HF Measurement System Consulting .............................................. 96I. LSNA History ............................................................................... 96J. About NMDG ................................................................................ 97

IV Contents

CHAPTER 3. Education ........................................... 99

1. The introduction of the bachelor-master structure ................99

2. Courses lectured in the faculty of engineering .....................99A. Lectures and practical courses ....................................................... 99B. Minor “Measuring, Modelling, and Simulation of Dynamic Systems” offered

within the 2nd Master Electronics and Information Technology ......... 101C. Workshop Information and Communication Technology (“ICT”) ........ 102D. Optional courses ....................................................................... 105

3. Courses lectured in the faculty of Science .........................106A. Lectures and practical courses ..................................................... 106B. Post-graduates .......................................................................... 106

4. National and international courses ...................................107A. National courses: ...................................................................... 107B. International courses: ................................................................ 109C. Advanced university education .................................................... 110

CHAPTER 4. Bibliography ......................................111

1. BookS ..........................................................................111

2. Journal papers (since 2007) ............................................111

3. Conference papers (since 2007) ......................................115

4. abstracts (since 2007) ...................................................121

5. Workshops (since 2007) .................................................125

6. Scientific lectures (since 2007) ........................................127

7. Patents ........................................................................127

8. Doctoral dissertations ....................................................128

9. Thesis tot het behalen van het aggregaat van het hoger onderwijs ......................................................................135

Annual Report 2007- Department ELEC V

CHAPTER 5. Location of the university (VUB) and the dept. ELEC ............................................ 137

A. Arrival by car: ........................................................................... 137B. From the Brussels National airport at Zaventem: ........................... 138C. Arrival by train: ......................................................................... 139D. Arrival by subway (€ 1.50/ticket): ............................................... 139

Annual Report 2007- Department ELEC 1

CHAPTER 1. Introduction to the department ELEC

1. INTRODUCTION TO THE LONG TERM STRATEGY OF THE DEPARTMENT ELEC

‘ELEC’ stands for “Fundamental Electricity and Instrumentation” (in Dutch: “Algemene

Elektriciteit en Instrumentatie”) and the name corresponds to the educational and research

tasks and objectives of the department. The main research activity of the department is the

development of new measurement techniques using advanced signal processing methods,

embedded in an identification framework. When we make a measurement, we have to

make a number of decisions: firstly a model for the considered part of reality is proposed

(e.g. for a resistance measurement Ohm’s law can be selected, describing the relation

between the voltage across the resistor and the current through it); next a number of

measurements is made (e.g. a number of current and voltage measurements); finally the

quantities of interest are extracted from these measurements by matching the model to the

data. Often an intuitive approach is used. However, in the presence of measurement errors

this can lead to a very poor and even dangerous behaviour: the user wouldn't remark that

something is going seriously wrong. This is the major motivation for the development of the

identification theory. It offers a systematic approach to ‘optimally’ fit mathematical models

to experimental data, eliminating stochastic distortions as much as possible. As such it can

be considered as the modern formulation of the measurement problem, and for that reason

the identification approach is the “file rouge” in most of the activities of the department.

Each measurement (or identification session) consists of a series of basic steps:

- Collect information about the system;

- Select a (non) parametric model structure to represent the system;

- Select the model parameters to fit the model as good as possible to the measurements

(this requires a “goodness of fit” criterion);

- Validate the selected model.

Most of the research activities of the department are related to one of these problems, but

this does not narrow our focus. At this moment we deal with a very wide scope of appli-

cation fields:

- Systems covering the frequency range from a few mHz up to 50 GHz,

- Linear systems and non-linear systems,

- Lumped systems and distributed systems.

2 Introduction to the department ELEC

We applied the measurement and modelling techniques to the identification of electrical

machines (frequency range 0.01 Hz till 1 kHz, linear models, 2 inputs and 2 outputs),

mechanical vibrating systems (frequency range below 5 kHz, linear or non-linear, up to 2

inputs/2 outputs), electronic circuits and filters (frequency range up to 5 MHz, linear and

non-linear models, single input/single output or multiple input/multiple output), under-

water acoustics (frequency range up to a few MHz, 1 input and 2 outputs), distributed

systems (telecommunication lines, up to a few hundreds MHz), microwave applications

(frequency range up to 50 GHz, non-linear, 6-port measurements). Since a few years we

apply those methods also to the analysis of biological samples used as records of global

climate change. For some of these applications the efforts are focused on the development

of new measurement instruments (measurement of telecommunication lines, non-linear

microwave analyser), for others we focused completely on the development of new data

processing and modelling techniques, or even worked on the underlying fundamental

theoretical aspects.

To cover this wide application range, we make use of an extensive measurement park. Most

of it nowadays consists of VXI-based data-acquisition systems, although we have also some

classical instruments like network and spectrum analysers. All these instruments are

computer controlled in a MatlabTM environment.

2. RESEARCH TEAMS OF THE DEPARTMENT ELEC: OVERVIEW OF THE 5 TEAMS

A. Team A: Physical communication, estimation of parameters in distributed systems and laboratory of marine acoustics.

• Identification of distributed systems

• Incorporation of knowledge engineering in complex measurement problems

• Application of information theory in data telecommunication by wire problems

(xDSL).

• Modelling and Identification of transmission lines and wireless channels (LOS and

NLOS)

• Wireless local loop

• Signal processing techniques related to measurement in Earth Science problems.

• Development of PC and PXI based instrumentation

• Underwater acoustic studies

• Environmental G.I.S. development

• 4G communication.

• Navigation techniques, including but not limited by, applications for Location Based

Services


• Positioning Techniques using the cellular network (focus on GSM)• Proactive Location-based Services (LBS) using multiple positioning technologies.

B. Team B: system identification and parameter estimation of linear and non-linear systems

• Study and development of basic concepts on identification theory

• Identification of linear and nonlinear concentrated and distributed systems

• Experiment design

• Time and frequency domain system identification

• Development and distribution of a system identification toolbox

C. Team C: the study of microwave systems

• General nonlinear measurement techniques

• Development of a measurement system for nonlinear microwave devices

• Modelling high frequency nonlinear systems

D. Team D: the IMEC/VUB-ELEC laboratory for microwave microelectronic measurements and modelling (4M)

• Efficient system level simulation techniques

• a high frequency non-linear network analyser

• identification and modelling techniques for both linear, non-linear and mixed ana-

log/digital systems

E. Team E: Identification of biogeochemical signals & systems (IBiSS)

• Pre- and post-signal processing of solid substrate measurements

• parameter estimation and constrained optimization of climatological and ecological

models

• validation of environmental models

The department ELEC is attached to the Faculty of Engineering of the “Vrije Universiteit

Brussel” (Brussels Free University).

Address: Vrije Universiteit Brussel, Department ELEC, Pleinlaan 2,

Building K, 6th floor, B-1050 Brussels, Belgium

Secretariat:

phone: +32 (0)2 629 29 47 or 27 67 e - mail: [email protected], [email protected]

Telefax: +32 (0)2 629 28 50 WWW-environment: http://wwwir.vub.ac.be/elec/


3. STAFF OF ELEC (STATUS 01/01/2008)

A. General director

B. Team A: Physical communication, estimation of parameters in distributed systems and laboratory of marine acoustics

Johan Schoukens (head of the department) received the degree of Electrical Engineer, and the degree of doctor in applied sciences, from the Vrije Universiteit Brussel (VUB), Brussels, Belgium in 1980 and 1985, respectively. Until September 2000 he was a Research Director of the Fund for Scientific Research - Flanders (FWO) and part time lecturer at the VUB in the Electrical Measurement Department (ELEC). Presently he is a full time professor at the same department (ELEC). His research interests are in the field of identification of linear and nonlinear systems.

Phone: +32 (0)2 629 29 44e-mail: [email protected]

Leo Van Biesen (coordinator of team A) was born in Elsene, Belgium, on August 31, 1955. He received the degree of Electro-Mechanical Engineer from the Vrije Universiteit Brussel (VUB), Brussels in 1978, and the Doctoral degree (PhD) from the same university in 1983. Currently he is a full senior professor. He teaches courses on fundamental electricity, electrical measurement techniques, signal theory, computer-controlled measurement systems, telecommunication, underwater acoustics and Geographical Information Systems for sustainable development of environments. His current interests are signal theory, modern spectral estimators, time domain reflectometry, wireless local loops, xDSL technologies, underwater acoustics, and expert systems for intelligent instrumentation. He has been chairman of IMEKO TC-7 from 1994-2000 and President Elect of IMEKO for the period 2000-2003 and the liaison Officer between the IEEE and IMEKO. Prof. Dr. Ir. Leo Van Biesen has been president of IMEKO until September 2006. He is also member of the board of FITCE Belgium and of USRSI Belgium.Phone: +32 (0)2 629 29 43e-mail: [email protected]


Mussa Bshara was born in Tartous, Syria, on February 4, 1966. He studied electrical engineering at Damascus University, where he graduated and obtained his B.Sc. in Electronics in 1990.He obtained his Master degree in signal processing and information security at university of posts and telecommunication, Beijing (China) in 2000.Currently he is working as a PhD student at Vrije Universiteit Brussel, ELEC department. His research deals with positioning technologies focusing on WiMAX networks.


Hernan Cordova was born in Guayaquil, December 19, 1977. In October 1999 he obtained graduated as a Bachelor of Science in Electrical Engineering at the Escuela Superior Politecnica del Litoral (ESPOL), Guayaquil. In august 2004, he finished at the same university a master degree program in Telecommunications Management. Since september 2004, he is going towards the PhD at the ELEC department being supervised by Prof. Van Biesen. His main interests are: Digital Signal processing and Fixed and Mobile Wireless Communications.


Nico Deblauwe Nico Deblauwe was born in Asse, Belgium, on May 7, 1980. He studied electrical engineering (minor in telecom) at the Vrije Universiteit Brussel (VUB), where he graduated as a “Burgerlijk Ingenieur” in 2003. He obtained as well his Danish Civilingeniør degree (Master in Electrical Engineering, minor in Mobile Communications) at Aalborg Universitet, in Denmark. Nico is attached to the Vrije Universiteit Brussel, department ELEC, funded by a research grant of the IWT. He has worked as a visiting researcher at the Ludwig-Maximilians-Universität in Munich. His research deals with positioning technologies for Location-based Services (LBS) in the field of navigation, focusing on mobile phone network based methods.


Wim Foubert was born in Geraardsbergen, on February 14, 1983. In July 2006, he received the degree of Electrical Engineering (option Electronics and Information Processing) from the Vrije Universiteit Brussel (VUB). He joined the department ELEC in December 2006, as a PhD student. His main research interests are on the design of new transmission channel models for xDSL systems.



Tesfazghi Ghebre Egziabeher was born in Eritrea (Asmara), on september 1, 1956. In 1980, he received the BSc. degree in Geology Addis Ababa University (Ethiopia). In 1992 he received the MSc. degree in Fundamental and Applied Quaternary Geology at the Vrije Universiteit Brussel, Belgium and in 1995 the MSc. degree in Applied Information Technology at the same university. Since 1995 his research work in the department ELEC (research fellow, IUAP-20) is focused on Modelling, Designing and Developing Multidisciplinary GeoDatabase GIS with the Physical Implementation of RDBMS in conjunction with CAD and different GIS applications for the development of Coastal / Marine Environment. In September 2005 he obtained the degree of Doctor in Sciences (PhD). Actually he is lecturing courses on ECOMAMA.Phone: +32 (0)2 629 27 68e-mail: [email protected]

Carine Neus was born in Anderlecht, Belgium, on March 16, 1983. She graduated as an Electrical Engineer (Burgerlijk Elektrotechnisch Ingenieur), option Telecommunication in July 2004 at the Vrije Universiteit Brussel. Carine joined the department ELEC as a research assistant on October 1st, 2004. Her main research is on the estimation of the line capacity with Single Ended Line Testing (applied to xDSL technologies).


Indira Nolivos Alvarez obtained her BSc. in Oceanography and Environmental Sciences at the Maritime Engineering and Marine Sciences Faculty - ESPOL (Ecuador, 2002). In September 2002, Indira started a two-year Master program in Water Resources Engineering (KUL and VUB universities, Belgium), which was completed successfully in September 2004. Currently, she has been selected for the project of Environmental Management Systems for Agriculture and Aquaculture, EMSAA, of the IUC-VLIR-ESPOL programme, to continue her Doctoral studies working with the design and implementation of a GIS-BASED INTELLIGENT/DECISION SUPPORT SYSTEM (DSS) for the Chaguana River Basin in El Oro province in Ecuador. The DSS will manage all the available and relevant information of the river basin using GIS and Artificial Intelligent tools to assess the decision makers. Thus, the final product will be developed in function of the requirements of the final users.Phone: +32 (0)2 629 27 68e-mail: [email protected]


C. Team B: System identification and parameter estimation of linear and nonlinear systems

Johan Schoukens (Head of the department and coordinator of team B) received the degree of Electrical Engineer, and the degree of doctor in applied sciences, from the Vrije Universiteit Brussel (VUB), Brussels, Belgium in 1980 and 1985, respectively. Until September 2000 he was a Research Director of the Fund for Scientific Research - Flanders (FWO) and part time lecturer at the VUB in the Electrical Measurement Department (ELEC). Presently he is a full time professor at the same department (ELEC). His research interests are in the field of identification of linear and nonlinear systems.


Kurt Barbé was born in Aalst on April 25, 1983. He received the degree in Mathematics (option Statistics) from the Vrije Universiteit Brussel (VUB), Brussels, Belgium, in 2005. Presently, he is working as a PhD research student at the VUB, department ELEC. His main interests are in the field of System Identification and its stochastic properties.


John Lataire was born in Etterbeek (Belgium) in 1983. He graduated as an Electrical Engineer in Electronics and Information Processing (profile Measurement and Modelling of Dynamic Systems) in July 2006 at the Vrije Universiteit Brussel. In August 2006 he joined the department ELEC as a PhD student. His main interests are the measurement and identification of slowly time varying, weakly nonlinear dynamic systems.



Lieve Lauwers was born in Jette, Belgium, on March 15, 1982. She received the degree of Electrical Engineering (option Photonics) in 2005 from the Vrije Universiteit Brussel (VUB), Brussels, Belgium. In August 2005 she joined the department ELEC of the VUB as a PhD student. Her research interests are in the field of nonlinear system identification.


Griet Monteyne was born in Vilvoorde, Belgium, on January 19, 1985. She graduated as an Electrotechnical-Mechanical Engineer (Burgerlijk Ingenieur) in July 2007 at the Vrije Universiteit Brussel (VUB), Brussels, Belgium. In October 2007 she joined the ELEC department of the VUB as a PhD student. Her research is in collaboration with the department ANS (Advanced Nuclear Systems) of the SCK-CEN in Mol, Belgium. Her research interests are in the field of noise analysis related to a Nuclear Power Plant.


Rik Pintelon was born in Gent, Belgium, on December 4, 1959. He received the degree of Electrotechnical-Mechanical Engineer (burgerlijk ingenieur) in July 1982, the degree of doctor in applied science in January 1988, and the qualification to teach at university level (geaggregeerde voor het hoger onderwijs) in April 1994, all from the Vrije Universiteit Brussel (VUB), Brussels, Belgium. Until September 2000 he was a Research Director of the Fund for Scientific Research - Flanders (FWO) and part time lecturer at the VUB in the Electrical Measurement Department (ELEC). Presently he is a full time professor at the same department (ELEC). His main research interests are in the field of parameter estimation / system identification, and signal processing.



Laurent Vanbeylen was born in Elsene, Belgium, on January 6, 1983. He graduated as an Electrical Engineer in Electronics and Information Processing in 2005 at the Vrije Universiteit Brussel. Laurent joined the department ELEC in August 2005, as a PhD student (on FWO-project FWOAL317). His main interests are the study of block oriented nonlinear systems, including identification and stability analysis.


Ben Van der Hasselt was born in Jette on July 14, 1985. He received the degree in Mathematics (option Applied Mathematics) from the Vrije Universiteit Brussel (VUB), Brussels, Belgium, in 2007. Presently, he is working as a PhD research student at the VUB, department ELEC. His main interests are in the field of system identification and its stochastic properties.


Mattijs Van de Walle was born in Bonheiden on the 25th of October, 1979. He received an engineering degree in electronics from the ‘De Nayer instituut’, Sint Katelijne Waver, Belgium, in 2002. After teaching electronics and telecommunication at the Vrij Technisch Instituut (VTI), leuven, for two years, he took up studying again to receive the degree of electrical engineer from the Vrije Universiteit Brussel (VUB), Brussels, Belgium, in 2007. Currently, he is working as a PhD research student at the VUB, department ELEC. The aim of his research is the analysis of flutter in a non-linear framework.

Phone: +32 (0)2 629 29 79 e-mail: [email protected]


D. Team C: The study of microwave systems

Anne Van Mulders was born in Jette on September 19, 1984. In July 2007, she received the degree of Mechanical Engineering from the Vrije Universiteit Brussel. She joined the department of ELEC in September 2007 as a PhD student. Her main research interests are in the field of nonlinear system identification

Phone: +32 (0)2 629 36 65 e-mail: [email protected]

Yves Rolain (coordinator of team C) (1961, Belgium) received the Electrical Engineering (Burgerlijk Ingenieur) degree in July 1984, the degree of computer sciences in 1986, and the PhD degree in applied sciences in 1993, all from the Vrije Universiteit Brussel (VUB), Brussels, Belgium. He is currently a research professor at the VUB in the department ELEC. His main interests are microwave measurements and modelling, applied digital signal processing and parameter estimation / system identification.


Alain Barel was born in Roeselare, Belgium, on July 27, 1946. He received the degree in Electrical Engineering from the Université Libre de Bruxelles, Belgium, in 1969, the Postgraduate degree in telecommunications from Rijks Universiteit Gent (State University of Gent), Belgium, in 1974, and the doctor of applied science from the Vrije Universiteit Brussel in 1976. He worked as assistant and Lecturer at the Vrije Universiteit Brussel (VUB). Currently he is 10% active at the department as Professor emeritus and teaches microwaves.



Liesbeth Gommé was born in Diest on October 20, 1982. Shegraduated as an Electrical Engineer, option Photonics in July 2005 at the Vrije Universiteit Brussel. Presently she is working as a PhD student at the department ELEC. Her main interests are in the field of advanced RF calibration and instrumentation setups


Koen Vandermot was born on the 10th of June 1981 and received the degree of Electrical Engineering (Burgerlijk Ingenieur), option Electronics in 2005 from the Vrije Universiteit Brussel (VUB), Brussels, Belgium. In August 2005 he joined the department ELEC of the VUB as a research assistant. His main research interests are on the design of new measurement and modelling methods for microwave multiport devices with focus on the nonideal behaviour.


Wendy Van Moer was born on the 12th of July 1974 and received the degree of Electrical Engineering (Burgerlijk Ingenieur), option Telecommunication in 1997 from the Vrije Universiteit Brussel (VUB), Brussels, Belgium. In August 1997 she joined the department ELEC of the VUB as a research assistant and in December 1997 she received an IWT scholarship. She received her PhD on June 1, 2001. From 01/01/2002 till 31/12/2004 she received an IWT post-doctoral scholarship. Presently, she is a part-time lecturer at the Vrije Universiteit Brussel and is working as a post-doctoral researcher of the FWO (Research Foundation-Flanders) on new measurement and modelling techniques for RFICs with focus on the nonlinear behaviour.



E. Team D: The IMEC/VUB laboratory for microwave microelectronic measurements and modelling (4M)

Gerd Vandersteen (coordinator of team D) was born in Belgium in 1968. He received his PhD and his degree of Electrical Engineering in respectively 1997 and 1991 from the Vrije Universiteit Brussel (VUB), Brussels, Belgium. He is presently a principal scientist in the wireless group at IMEC/NES. His main interest are in the field of modeling, measurement and simulation of nonlinear microwave devices.

Phone: +32 (0)2 629 29 44e-mail: [email protected]/[email protected]

Lynn Bos was born in Vilvoorde, Belgium, on August 16, 1983. She graduated as an Electrotechnical Engineer in Electronics and Information Processing in 2006 at the Vrije Universiteit Brussel (VUB), Belgium. In August 2006 she joined the ELEC department of the VUB as a PhD student. Her residence is at the group DESICS (Design Technology for Integrated Information & Communication Systems) of IMEC (Inter-university Microelectronic Centrum) in Leuven. Her research interests are in the field of analysis, modelling and design automation of analog discrete-time systems.

Phone: + 32 (0)2 629 36 65e-mail: [email protected]

Stephane Bronckers was born in Brussels, Belgium, 1982. He graduated as an Electrical Engineer in Electronics and Information Processing in 2005 (Master of Science of Engineering) at the Vrije Universiteit Brussel (VUB). In August 2005 he joined the department ELEC as a research assistant working towards the PhD degree. His residence is at the group DESICS (Design Technology for Integrated Information and Communication Systems) of IMEC (Inter-university Microelectronic Centrum) in Leuven. His research will focus on developing a methodology to improve the electromagnetic immunity of analog integrated circuits.

Phone: +32 (0)1628 85 65, + 32 (0)2 629 36 65 (ELEC)e-mail: [email protected]


F. Team E: Identification of biogeochemical signals & systems (IBiSS)

Ludwig De Locht was born in Leuven, on December 31, 1979. He graduated as an Electrical Engineer in Electronics and Information Processing in 2002 at the Vrije Universiteit Brussel (VUB). In August 2002 he joined the department ELEC as a research assistant. His residence was until December 2006 at the group WIRELESS of IMEC in Leuven. The purpose of his research was to give an analog designer insight in the nonlinear behaviour of power amplifiers for communication systems. This has been achieved by developing the appropriate simplified methods. In December 2002 he received an IWT scholarship, and he obtained the degree PhD in November 2007. Actually he’s working as a post-doc. researcher at the dept. ELEC.

Phone: + 32 (0)2 629 36 65e-mail: [email protected]

Karen Scheir was born in Jette, Belgium, 1982. She graduated as an Electrical Engineer in 2005 (Master of Science in Engineering) at the Vrije Universiteit Brussel (VUB). In August 2005 she joined the department ELEC as a research assistant working towards the PhD degree. Her residence is at the group DESICS (Design Technology for Integrated Information and Communication Systems) of IMEC (Inter-university Microelectronic Centrum) in Leuven. Her research will focus on the implementation in CMOS of analog components for millimetre wave receiver architectures.

Phone: e-mail: [email protected]

Maité Bauwens was born in Butare (Rwanda), on December 18, 1982. She received the degree in biology, option environmental biology, in July 2005 from the Vrije Universiteit Brussel (VUB), Brussels, Belgium. In November 2005 she joined the department ELEC of the VUB. Presently she is working as a PhD research student under the supervision of Fjo De Ridder and Frank Dehairs (Dept. Chemistry/VUB). Her research is in collaboration with the CALMARS project and is focused on applications of system identification techniques in the climate reconstruction.



G. Technical and administrative staff

Veerle Beelaerts was born in Brugge, Belgium, on February 27, 1981. She studied bio engineering, option chemistry at the Vrije Universiteit Brussel (VUB), where she graduated as a “Bio-ingenieur” in 2005. Presently she is working as a PhD research assistant under the supervision of Fedor De Ridder, Johan schoukens, Frank Dehairs and Rik Pintelon. Her research is in collaboration with the CALMARS project and is focused on identification of biochemical systems and signals.


Anouk de Brauwere was born in Etterbeek, on December 12, 1980. In June 2003 she received the degree of Masters in Chemistry from the Vrije Universiteit Brussel. Actually she is working as a PhD student at the Faculty of Sciences, at the department of Analytical and Environmental Chemistry being supervised by Prof. W. Baeyens and Prof. J. Schoukens. Her current research, in collaboration with the department ELEC, is focused on the application of system identification to the modelling and quantification of mixing processes in the ocean.


Wilfried Ceuppens was born in 1950 (Belgium). He graduated as an Industrial Engineer (Industrieel Ingenieur) in 1975. Since 1977 he is with the Vrije Universiteit Brussel (full time tenure) and since 1979 he is working at the department ELEC. He is mainly responsible for the mechanical design: planning, estimation and practical execution. He is in charge of the prototyping of RF µwave circuits. He is involved in the maintenance of the IT infrastructure of the department.

Phone: +32 (0)2 629 29 52/29 45e-mail: [email protected]


Wim Delcourte Wim Delcourte was born in 1962 (Belgium). He graduated as an Industrial Engineer (Industrieel Ingenieur) in 1987. Since May 1989 he is with the department ELEC of the Vrije Universiteit Brussel (VUB full time tenure). He is responsible for the design of electronic measurement instruments for research and education, for the repair and maintenance of complex measurement instruments and computers. He also takes care of the management of the computerrooms of the faculty.


Jenny Lievens was born on December 12, 1953 (Brussels, Belgium). She graduated from secretary school in 1971. Since October 1971 she is with the department ELEC (actually part-time). She is mainly responsible for the general secretariat and accounting of the department. Besides, she organises the social activities for the ELEC members.

Phone: +32-(0)2-629.29.47e-mail: [email protected]

Ann Pintelon was born in October 1963 (Belgium). In 1985 she received the MS degree in Physical Education (VUB). Since 1990 she has been with the department ELEC at the Vrije Universiteit Brussel (VUB) on a GOA contract (Measurement, Modelling and Identification of Dynamic Systems), and since 2008 she working on “Methusalem”: Centre for Data Based Modelling and Model Quality Assessment (4/5 tenure). She is mainly responsible for the scientific reports of the department ELEC (annual report, web-pages ELEC, …); the management of the infocentre (library, publication list, …); the administrative organisation of conferences and workshops; hosting of visiting researchers; and administrative support to the associated editors of IEEE-Instrumentation and Measurement and Automatica in the dept. ELEC.



Sven Reyniers was born in 1973 (Belgium). He has several years of ICT - experience in multinationals, including several international missions (France, Germany). Since 2006, he is working at the Vrije Universiteit Brussel (full time tenure) at the department ELEC. As system and network administrator he is responsible for the health and availability of the department servers and network.

Phone: ++32 (0)2 629 29 52/29 45e-mail: [email protected]

Jean-Pierre Weemaels was born in 1952 (Belgium). He received the degree of Electrician in 1970. Since 1973 he is with the Vrije Universiteit Brussel (VUB), and since 1985 he is with the department ELEC. He is responsible for the organisation of the student labs: design, maintenance and repair of the instruments and circuits. He also designs printed circuits. Since July 2006 he’s working part-time.



H. Professor Emeritus

Ronny Van Loon. Born in Antwerp, 1940, he obtained a degree in Physics at the ULB, and a PhD in Science at the VUB. He joined the VUB in 1970 as assistant, then Professor at the Faculty of Applied Science. In parallel with the teaching activities, he had research and logistic activities as a medical physicist at the university hospital AZ-VUB: from 1982 on in the Radiotherapy department focusing on EC granted projects in Hyperthermia, from 1989 on in the Radiology department involved in dosimetry and quality assurance. He directed the subgroup QUARAD (“Quality in Radiology”), a team involved in dosimetry, radiation protection and quality improvement in radiology. This group is acting in Belgium as reference centre for the Quality Assurance of the technical aspects of breast cancer screening. Prof. Van Loon is the Past-President of the Belgian Hospital Physicist Association, and member of the Board of the “Federal Agency of Nuclear Control” and the “Belgian Society of Radioprotection”. He was delegate of the VUB in the VLIR-cooperation and Development Cell, and coordinates a cooperation project in Hanoi (Vietnam) and in VLIR International Training programme on Medical Physics. Until September 2005, he was still 10% active at the department ELEC as professor emeritus. He also conrtribiutes to the IAEA's (International Atomic Energy Agency) teaching programs on radiation protection in medical applications of ionizing radiation. Presently he is scientific advisor at the “Belgian Museum of Radiology”, Brussels, and he is active as a voluntary researcher at the dept. ELEC.Phone: +32 (0)2 629 27.67e-mail: [email protected]

Alain Barel was born in Roeselare, Belgium, on July 27, 1946. He received the degree in Electrical Engineering from the Université Libre de Bruxelles, Belgium, in 1969, the Postgraduate degree in telecommunications from Rijks Universiteit Gent (State University of Gent), Belgium, in 1974, and the doctor of applied science from the Vrije Universiteit Brussel in 1976. He worked as assistant and Lecturer at the Vrije Universiteit Brussel (VUB). Currently he is 10% active at the department as Professor emeritus and teaches microwaves.



I. PhD-students from other departments, universities, institutions...

- M.Sc. Mohammed EL-BARKOUKY, IMEC-Leuven, promoter: P. WAMBACQ (IMEC), co-

promoter: Y. ROLAIN.

- M.Sc. Arnd GEIS, IMEC-Leuven, promoter: Y. Rolain, co-promoter: G. Vandersteen

- ir. Griet MONTEYNE, SCK-CEN, promotor: J. Schoukens, P. Baeten

J. Industrial Partnership

- Dr. ir. Alain GEENS, Association Vinçotte Nucleair

- ir. Frank LOUAGE, MSc Zobeida Cisneros Barros; Address Systems N.V.

- Dr. ir. Luc PEIRLINCKX, Phonetics Topographics, Belgium

- Lic. Marc PERSOONS, Tresco Navigation Systems

- ir. Serge TEMMERMAN, SEBA service nv (measuring instruments for telecom. cables)

- Dr. Frank UYTDENHOUWEN, Banama-Telecom

- Dr. ir. Marc VANDEN BOSSCHE, Dr. ir. Frans Verbeyst; NMDG Engineering bvba

K. National or international contacts

A. VISITING PROFESSORS/RESEARCHERS

Deistler Manfred, Technical University Vienna, Inst. Fur Okonometrie, Vienna, Austria: 20/03/2007 - 21/03/2007: presentation “The use of system identification techniques in the economic world”6/11/2007 - 8/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics, invited presentation “Modeling high dimensional time series by generalizedfactor models”

Delwiche Thomas, Université Libre de Bruxelles, Control Engineering Department, Belgium: 04/12/2007: presentation “Comparison of friction compensation methods in bilateral teleoperation”

Barbé André, Katholieke Universiteit Leuven, Dept. ESAT/SISTA, Louvain, Belgium: academic year 2007/08: visiting lecturer “Meten en modelleren van niet-lineaire systemen”.

Dobrowiecki Tadeusz, Budapest University of Technology and Economics, Depart. of Meas. and Instr. Engineering, Budapest, Hungary:06/02/2007 - 11/02/2007: bilateral cooperation “Identification of linear & nonlinear systems”.

Enqvist Martin, Linköping University, Div. of Automatic Control, Dept. of Electrical Eng. - ISY, Linköping,Sweden: 06/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics

Ferrero Andrea, Politecnico di Torino, Dipartimento di Elettronica: 29/10/2007 - 30/10/2007: member of jury at presentation PhD Ludwig De Locht

Filler Alexander, Technical University Vienna, Inst. Fur Okonometrie, Vienna, Austria:07/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics


Funovits Bernd, Technical University Vienna, Inst. Fur Okonometrie, Vienna, Austria:07/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics

Gevers Michel, Université Catholique de Louvain, CESAME, Louvain-la-Neuve, Belgium:23/03/2007, 07/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics and 13/12/2007: Discussion session: Informative experiments.

Godfrey Keith, University of Warwick, School of Engineering, Coventry, United Kingdom: 06/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling

Goodwin Graham, University of Newcastle, Department of Electrical and Computer Engineering, Australia: 12/03/2007 - 16/03/2007

Hallin Marc, Université Libre de Bruxelles, Faculté des Sciences - Mathématiques, Belgium: 07/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics, Invited presentation: “The General Dynamic Factor Model: Determining the Number of Factors”

Hjalmarsson Hakan, Royal Institute of TechnologyDept. Signals, Sensors, Systems (control group), Stockholm, Sweden: 06/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling inEngineering, Econometrics, and Statistics

Kerschen Gaetan, University of Liège, Structural Dynamics Laboratory Aerospace and Mechanical Engineering Department, Belgium:01/06/2007 - 01/06/2007: presentation “ Nonlinear System Identification in Structural Dynamics”

Ljung Lennart, Linköping University, Div. of Automatic Control, Dept. of Electrical Eng. - ISY, Linköping,Sweden: 26/02/2007 - 28/02/2007: microsymposium06/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics, Invited presentation “A general prediction error framework for identification of dynamic systems”

Lumori Mikaya, University of San Diego, Electrical Engineering, CA, USA04/07/2007 - 04/01/2008

Marconato Anna, University of Trento, Italy:10/09/2007 - 07/03/2008

Moreau Yves, Katholieke Universiteit Leuven, ESAT, Dept. Electr. Eng., Louvain, Belgium:academic year2007/08: visiting lecturer “Bio-informatica en datamining”.

Olivi Martine, INRIA - Sophia Antipolis, France:20/12/2007 - 21/12/2007: member of jury at presentation PhD Tom D’Haene

Poullet Jean-Baptiste, Katholieke Universiteit Leuven, Afdeling ESAT - SCD: SISTA/COSIC/DOCARCH:11/06/2007 - 15/06/2007: IAP research

Ruppel Peter, Ludwig-Maximilans-Universität München, Mobile and Distributed Systems Group, Germany: 17/09/2007 - 21/09/2007: Research cooperation in the field of combining GPS and GSM Cell-ID positioning; Short seminar (Mo 2007.Sep.17): “Introduction to Windows Mobile”

Sjoberg Jonas, Chalmers University of Technology, Signals & Systems, Göteborg, Sweden:25/11/2007 - 30/11/2007: Methusalem

Söderström Torsten, Uppsala UniversitySystems and Control, Dept. of Information Technology, Sweden06/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics


B. SCIENTIFIC MISSIONS

Kurt BARBÉ

Alain BAREL

Maite BAUWENS

Swevers Jan, KULeuven, Faculty of Applied Sciences, Department of Mechanical Engineering - PMA, Production Engin., Machine Design and Automation, Louvain, Belgium: academic year 2007/08: visitinglecturer “Gevorderde controletechnieken”

Van den Hof Paul, Signals, Systems and Control Group, Department of Applied Physics, Delft Universityof Technology, Delft, The Netherlands: 26/02/2007 - 28/02/2007: microsymposium

Wahlberg Bo, S3-Automatic Control, KTH (Kungl Tekniska Högskolan) Sweden:06/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics

Widanage Dhammika, University of Warwick, School of Engineering, Coventry, United Kingdom: 06/02/2007 - 09/02/200706/11/2007 - 08/11/2007: Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Estimating the power spectrum and sample variance of the fourier coefficientsusing overlapping sub-records”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Frequency domain errors-in-variables estimation of linear dynamic systems using data from overlapping sub-records”

29/09/07 03/10/07 16th ERNSI Workshop on System Identification, Venice, Italy, 2007: presentation of paper: “Improved Non-parametric Power Spectrum Estimation using Circular Overlap”

08/10/07 10/10/07 European Microwave Week 2007 (EUMW07), Munchen: presentation of paper: “Using ANOVA in a Round-Robin Comparison”

07/11/07 07/11/07 Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics, Vrije Universiteit Brussel

22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels: presentation of poster “Improved Non-parametric Power Spectrum Estimation using Circular Overlap”

19/02/07 23/02/07 University of Cantabria, Santander, Spain: Lectures at the 3rd TARGET Winterschool "CAD Implementation of Non-linear Device Model and Advanced Measurements"

04/07/07 06/07/07 Université de Limoges: opponent of ir. Jean-Pierre Teyssier concerningthe presentation of his PhD

31/01/07 31/01/07 Annual calmars meeting, Brussels, poster presentation, “A multi proxy model for the reconstruction of the environmental history using bivalve shell composition”

01/06/07 06/06/07 Paleosalt workshop, Barcelona: presentation of paper “A non-linear multi-proxyapproach for the reconstruction of the paleo-environment”

04/10/07 05/10/07 Bivalve workshop, Brest, France, Presentation of paper “The incorporation of trace elements and stable isotopes in Mytilus edulis: Black box versus white box modeling”


Veerle BEELAERTS

Patrick BOETS

Lynn BOS

Stefaan BRONCKERS

Mussa BSHARA

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Time averaging in solid substrates”

01/06/07 06/06/07 Paleosalt workshop, Barcelona: presentation of poster “Averaging in solid substrates”

04/10/07 05/10/07 Bivalve workshop, Brest, France, Presentation of paper “The elimination of averaging errors in proxy records”


29/08/07 02/09/07 FITCE’s 46th European Telecommunications Congress, Warsaw, 2007: presentation of paper “Wireless Broadband Communications in Rural and Remote Areas - a WiMAX Solution?”

27/11/07 27/11/07 Annual Electrabel Aero LM Users Meeting,, Linkebeek, Belgium: Demonstrationof measurement equipment

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Analysis and modelling of analog discrete-time systems using multisine excitations”

16/04/07 20/04/07 DATE 2007, Design, Automation and Test in Europe, Nice, France: presentationof paper “Simulation methodology and experimental verification for the analysis of substrate noise on LC-VCO's”

14/05/07 15/05/07 Meeting of the Chameleon RF project (STREP-project sponsored by the European Commission under the IST-Programme) in Delft, Nederland

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Measurement and modeling of the sensitivity of LC-VCO?s to substrate noise perturbations”

01/06/07 06/06/07 2007 IEEE Radio Frequency Integrated Circuits Symposium, Honolulu, Hawaii: Presentation of paper “On the P+ guard ring sizing strategy to shield against substrate noise”

09/10/07 10/10/07 Meeting of the Chameleon RF project (STREP-project sponsored by the European Commission under the IST-Programme) in Lisboa, Portugal: presentation of paper “On the P+ guard ring sizing strategy to shield against substrate noise”

19/10/07 20/10/07 Meeting of the Chameleon RF project (STREP-project sponsored by the European Commission under the IST-Programme) in Leuven, Belgium

11/12/07 12/12/07 Meeting of the Chameleon RF project (STREP-project sponsored by the European Commission under the IST-Programme) in Graz, Austria

10/09/07 11/09/07 Linköping University, Sweden: research meeting with experts at the Linköping University


04/12/07 04/12/07 Conference Broadband Europe 2007, Antwerp


Tom d’HAENE

Nico DEBLAUWE

Wim FOUBERT

Liesbeth GOMMÉ

08/01/07 22/01/07 INRIA Sophia-antipolis, France: Visit dept. APICS (Prof. Laurent Baratchart): comparison of two stabilisation methods

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Stable Approximations of Unstable Models”


27/11/06 30/06/07 Ludwig-Maximilians-Universität, Munchen, Dept. NDSG: Research cooperation in the frame of LBS

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “A positioning technique for GSM, using the signal strengths of one antenna site”

05/08/07 10/08/07 Mobiquitous 2007, Philadelphia: Presentation of paper “Combining GPS and GSM Cell-ID positioning for Proactive Location-based Services”

29/08/07 02/09/07 FITCE’s 46th European Telecommunications Congress, Warsaw, 2007: presentation of paper “Public Transport LBS: Combining GPS and GSM Positioning Techniques”

10/09/07 11/09/07 Linköping University, Sweden: research meeting with experts at the Linköping University and microseminar “Characteristics of Location-based Services, and Multi-technology Positioning”


22/11/07 27/11/07 IEEE International Conference on Signal Processing and Communications (Dubai): presentation of paper “An Angle Of Arrival Location Estimation Technique for Existing GSM Networks”

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Transmission line modelling exploiting 'common-mode' signals”

19/09/07 21/09/07 15th IMEKO TC4 International Symposium on Novelties in Electrical Measurements and Instrumentation, Iasi, Romani: Presentation of paper “TheEffects of In-Home Wiring For VDSL Transmission”

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Crystal detector as calibration standard for Large Signal Analysis”

16/04/07 16/04/07 IAP VI/4 DYSCO Workshop, Court-St.-Etienne, Belgium

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Validation of a crystal detector model for the calibration of the Large Signal Network Analyzer”


22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels: presentation of poster “RF pulse train generator for a dense frequency grid phase calibration”


John LATAIRE

Lieve LAUWERS

Mikaya LUMORI

Anna MARCONATO

Carine NEUS

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Frequency Domain Identification of Linear Slowly Time-Varying Systems”

16/04/07 19/04/07 DATE 2007, Design, Automation and Test in Europe, Nice, France: Preesentation of paper: “Optimizing Analog Filter Designs for Minimum Nonlinear Distortions Using Multisine Excitations”

29/09/07 03/10/07 16th ERNSI Workshop on System Identification, Venice, Italy: Presentation of poster “Frequency domain identification of slowly time-varying systems”


22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels: presentation of poster “Estimating the Input/Output Measurement Noise of Linear, Slowly Time-varying Systems”

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Some practical applications of a nonlinear block structure identification procedure”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Some practical applications of a nonlinear block structure identification procedure”

29/09/07 03/10/07 16th ERNSI Workshop on System Identification, Venice, Italy: Presentation of poster “Generating initial estimates for a Wiener-Hammerstein system via partial fraction expansion of the Best Linear Approximation”


22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels: presentation of poster “Initializing the linear blocks of a Wiener-Hammerstein system? Use the Best Linear Approximation”


22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels



13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “A Frequency Domain Identification Algorithm for Single-Ended Line Measurements”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Transfer Function Estimation of DigitalSubscriber Lines with Single Ended Line Testing”


Johan PADUART

Rik PINTELON

Yves ROLAIN

22/06/07 30/06/07 IEEE International Conference on Communications (ICC 2007), Glasgow (UK): Presentation of poster “Channel Capacity Estimation of Digital Subscriber Lines:a Frequency Domain Approach”


27/11/07 27/11/07 Annual Electrabel Aero LM Users Meeting,, Linkebeek, Belgium: Presentation ofproject: "Time domain reflectometry (TDR) on Critical Instrumentation Loops for Gas Turbines and demonstration of measurement equipment

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “On the Equivalence between some Block Oriented Nonlinear Models and the Nonlinear Polynomial State Space Model”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “On the Equivalence between some Block Oriented Nonlinear Models and the Nonlinear Polynomial State Space Model”


13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Member organizing commitee, Chairman Plenary Lecture “Parametric Time Domain Methods for Non-Stationary Random Vibration Identification: An Overview of Methods and Applications”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Asymptotic Uncertainty of Transfer Function Estimates Using Non-Parametric Noise Models”

29/09/07 03/10/07 16th ERNSI Workshop on System Identification, Venice, Italy

07/11/07 07/11/07 Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics, Vrije Universiteit Brussel: invited presentation “Basic Choices of and Interrelations between Time and Frequency Domain System Identification”

22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels: Organization of the workshop

07/02/07 07/02/07 Board meeting Network of Excellence "TARGET", Vienna, Austria


13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Special Session: A round-robin benchmark for the Large Signal Network Analyzer”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Measuring the Response of a Voltage Controlled Oscillator using the Large-Signal Network Analyser”

08/10/07 10/10/07 European Microwave Week 2007 (EUMW07), Munchen: presentation of paper: “Measurement of the Best Linear Approximation of nonlinear systems”




Karen SCHEIR

Johan SCHOUKENS

Leo VAN BIESEN

Gerd VANDERSTEEN

Mattijs VAN DE WALLE

Ben VAN DER HASSELT

Wendy VAN MOER

01/06/07 06/06/07 2007 IEEE Radio Frequency Integrated Circuits Symposium, Honolulu, Hawaii: Presentation of paper “Design and analysis of inductors for 60 GHz applicationsin a digital CMOS technology”

05/03/07 05/03/07 University of Warwick: scientific meeting with Keith Godfrey

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Member organizing commitee

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Identification of a crystal detector using a block structured nonlinear feedback model”

29/09/07 03/10/07 16th ERNSI Workshop on System Identification, Venice, Italy

07/11/07 07/11/07 Interdisciplinary Workshop on Data Driven Modelling in Engineering, Econometrics, and Statistics, Vrije Universiteit Brussel: organization of the workshop


28/03/07 08/04/07 Ecuador, ESPOL: IUS project

06/09/07 07/09/07 Adviaory Board IMEKO, Technical and Editoral Board meetings, General Council, Paris, 2007: Chairman of the Advisory board and participating at the Technical/Editoral board meetings and General Council meetings

19/09/07 21/09/07 15th IMEKO TC4 International Symposium on Novelties in Electrical Measurements and Instrumentation, Iasi, Romani

27/11/07 27/11/07 Annual Electrabel Aero LM Users Meeting,, Linkebeek, Belgium: Demonstrationof measurement equipment

04/12/07 04/12/07 Conference Broadband Europe 2007, Antwerp

16/04/07 20/04/07 DATE 2007, Design, Automation and Test in Europe, Nice, France: Session chair

08/10/07 10/10/07 European Microwave Week 2007 (EUMW07), Munchen: presentation of paper: “Scaling friendly design methodology for inductively-degenerated RF Low-NoiseAmplifiers”




13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Chairman Special Session


Anne VAN MULDERS

Laurent VANBEYLEN

Koen VANDERMOT

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “A Multisine based Calibration for Broadband Measurements”

08/10/07 10/10/07 European Microwave Week 2007 (EUMW07), Munchen: presentation of paper: “A Multisine based Calibration for Broadband Nonlinear Measurements”




13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Experimental stability analysis of nonlinear feedback systems”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Application of Blind Identification to Calibration of Sensors”

01/07/07 07/07/07 European Conference on Control (ECC 2007), Kos, Greece: presentation of paper “Blind Maximum Likelihood Identification of Wiener Systems”

29/09/07 03/10/07 16th ERNSI Workshop on System Identification, Venice, Italy: Presentation of poster “Experimental stability analysis of nonlinear feedback systems”


22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels: presentation of poster “Experimental stability analysis of nonlinear feedback systems”

13/03/07 15/03/07 26th Benelux Meeting, Vossemeren, Lommel, Belgium: Presentation of paper “Extending the Best Linear Approximation for Frequency Translating Systems”

30/04/07 04/05/07 IMTC 2007, IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland: presentation of paper “Extending the Best Linear Approximation for Frequency Translating Systems: The Best Mixer Approximation”


22/11/07 22/11/07 IAP VI/4 DYSCO workshop, Royal Military Academy, 1000 Brussels: presentation of poster “Introducing the Power-Scalable Best Mixer Approximation”


L. List of phone numbers and e-mail addresses (Status 01/01/2008)

NAME PHONE E-MAIL

K. BARBÉ +32(0)2 629 29 46 [email protected]. BAREL +32(0)2 629 29 49 [email protected]. BAUWENS +32(0)2 629 27 66 [email protected]. BEELAERTS +32(0)2 629 27 66 [email protected]. BOS +32(0)2 629 36 65 [email protected]. BRONCKERS +32 (0)1628 85 65 [email protected]. BSHARA +32(0)2 629 27 68 [email protected]. CEUPPENS +32(0)2 629 29 52 [email protected]. CORDOVA +32(0)2 629 27 68 [email protected]. DEBLAUWE +32(0)2 629 29 79 [email protected]. DE BRAUWERE +32(0)2 629 32 64 [email protected]. DELCOURTE +32(0)2 629 29 52 [email protected]. DELOCHT +32(0)2 629 36 65 [email protected]. FOUBERT +32(0)2 629 29 79 [email protected]. GHEBRE EGZIABEHER +32(0)2 629 27 68 [email protected]. GOMMÉ +32(0)2 629.28.68 [email protected]. LATAIRE +32(0)2 629 29 42 [email protected]. LAUWERS +32(0)2 629.29.53 [email protected]. LIEVENS +32(0)2 629 29 47 [email protected] M. LUMORI +32(0)2 629 29 42 [email protected]. MARCONATO +32(0)2 629 29 49 [email protected]. MONTEYNE +32(0)2 629 29 49 [email protected]. NEUS +32(0)2 629 29 79 [email protected]. NOVILOS ALVAREZ +32(0)2 629 27 68 [email protected]. PINTELON +32(0)2 629 27 67 [email protected]. PINTELON +32(0)2 629 29 44 [email protected]. REYNIERS +32(0)2 629 29 52 [email protected]. ROLAIN +32(0)2 629 29 44 [email protected]. SCHEIR +32 (0)1628 85 65 [email protected]. SCHOUKENS +32(0)2 629 29 44 [email protected]. VANBEYLEN +32(0)2 629 29 46 [email protected]. VAN BIESEN +32(0)2 629.29.43 [email protected]. VAN DER HASSELT +32(0)2 629 29 46 [email protected]. VANDERMOT +32(0)2 629.29.53 [email protected]. VANDERSTEEN +32(0)2 629.29.44 [email protected]. VAN DE WALLE +32(0)2 629 29 79 [email protected]. VAN LOON +32(0)2 629 27 67 [email protected]. VAN MOER +32(0)2 629.28.68 [email protected]. VAN MULDERS +32(0)2 629 36 65 [email protected]. WEEMAELS +32(0)2 629.29.52 [email protected]


4. ORGANISATION CHART OF DEPARTMENT ELEC

Team

C:

the

study

of

mic

row

ave

syst

ems

Pro

f. D

r. i

r. Y

ves

RO

LA

IN,

full-

tim

e pro

fess

or

Prof. D

r. ir. A

lain

BAREL,

Pro

fess

or

emer

itus

MSc.

Sal

ua D

AG

HAY I

WT s

hola

rship

(u

ntil 31/0

5/0

7)

ir. L

iesb

eth G

OM

MÉ,

FWO

sch

ola

rship

ir.

Koen

VAN

DERM

OT,

IW

T s

chola

rship

Dr. ir. W

endy

VAN

MO

ER,

post

doc.

Tech

nic

al

an

d A

dm

inis

trati

ve S

taff

:in

g.

Wilfr

ied

CE

UP

PE

NS

, in

du

str.

en

gin

eer

ing

. W

im D

ELC

OU

RT

E,

ind

ust

rial en

gin

eer

Mrs

. Je

nn

y L

IEV

EN

S,

part

-tim

e s

ecr

eta

ryM

Sc.

An

n P

INTE

LO

N,

4/

5 a

dm

inis

trati

ve

coo

rdin

ato

r (I

LiN

OS

)in

g.

Sven

RE

YN

IER

S,

netw

ork

man

ag

er

(ILiN

OS

, si

nce

01

/0

1/

06

)M

r. J

ean

-Pie

rre W

EEM

AELS

, te

chn

icia

n

Team

D:

the

IMEC/V

UB-E

LEC lab

for

µ-w

ave

elec

tronic

mea

sure

men

ts a

nd m

odel

ling

Dr.

ir.

Gerd

VA

ND

ER

STEEN

, se

nio

r re

sear

cher

IM

EC

ir. L

ynn B

OS,

IWT s

chola

rship

ir. S

tephan

e BRO

NCKERS,

IWT s

chola

rship

ir. L

udw

ig D

E L

OCH

T, I

WT s

chola

rship

ir. K

aren

SCH

EIR

, IW

T s

chola

rship

Team

A:

estim

ation o

f para

met

ers

in d

istr

ibute

d

syst

. an

d lab

of m

arin

e ac

oust

ics

Pro

f. D

r. i

r. L

eo

VA

N B

IES

EN

, fu

ll-tim

e pro

fess

or

Dr. ir. P

atrick

BO

ETS,

pos

t doc

(until 31/1

2/0

7)

MSc.

Muss

a BSH

ARA,

PhD

stu

den

tBSc.

Her

nan C

ORD

OVA,

PhD

stu

den

tir.

Nic

o D

E B

LAU

WE,

IWT s

chola

rship

ir.

Wim

FO

UBERT,

PhD

sch

ola

rship

(si

nce

01/0

9/0

6)

Dr.Te

sfaz

ghi G

HEBRE E

GZIA

BEH

ER,

post

doc.

ir. C

arin

e N

EU

S,

rese

arc

h a

ssis

tent

MSc.

Indira

NO

LIVO

S A

LVAREZ,

PhD

st

uden

t

Team

E:

Iden

tifica

tion o

f bio

chem

ical

sig

nal

s sy

stem

s

Dr. A

nouk

DE B

RO

UW

ER,

post

doc

(Fac

ulty

of

Sci

ence

s, D

ept.

of

Anal

ytic

al &

Envi

ronm

enta

l Chem

istr

y)M

Sc.

Mai

té B

AU

WEN

S,

PhD

stu

den

tM

Sc.

Vee

rle

BEELA

ERTS,

PhD

stu

den

t

Hea

d of

the

Dep

artm

ent

ELEC

Prof

. Dr.

ir. J

ohan

SC

HO

UK

ENS

Team

B:

Sys

tem

Iden

tifica

tion &

Par

amet

er

Est

imat

ion

of

lin

ear

and n

onlin

ear

syst

ems

Pro

f. D

r. ir.

Jo

han

SC

HO

UK

EN

S,

full-

tim

e pro

fess

or

MSc.

Kurt

BARBÉ,

rese

arc

h a

ssis

tant

ir. T

om

D’H

AEN

E,

PhD

shola

rship

(until 31/1

2/0

7)

ir. J

ohn L

ATA

IRE,

FWO

sch

ola

rship

ir. L

ieve

LAU

WERS,

rese

arc

h a

ssis

tant

ir. G

riet

MO

NTEYN

E,

PhD

sholla

rship

sin

ce 1

/10/2

007

ir. J

ohan

PAD

UART,

PhD

shola

rship

(until 31/1

0/0

7)

Prof. D

r. ir. R

ik P

INTELO

N,

fulll

-tim

e pro

fess

or

ir. L

aure

nt

VAN

BEYLE

N,

PhD

shola

rship

MSc.

Ben

VAN

DER H

ASSELT

, Ph

D s

hola

rship

(s

ince

01/0

9/2

007)

ir. M

attijs

VAN

DE W

ALL

E,

PhD

shola

rship

(s

ince

01/0

9/2

07)

ir. A

nne

VAN

MU

LDERS,

PhD

shola

rship

(s

ince

01/0

9/2

007)


5. FUNCTIONAL ORGANISATION OF THE DEPT. ELEC

- Cours

esEduca

tional

sta

ff-

Labs

and E

xerc

ises

Sci

entific

staff

- La

b-e

xerc

ises

Coord

inat

or

Rik

Pin

telo

n-

Lab a

ssis

tance

J.P.

Wee

mae

lsW

. Ceu

ppen

sW

. D

elco

urt

e

- In

stru

men

t an

d a

cces

sory

ove

rvie

wJ.

P. W

eem

aels

- Li

bra

ryM

agaz

ine

over

view

Lite

ratu

re o

verv

iew

Spar

e m

anual

sThes

is b

ooks

Dat

a books

- Cours

e note

s-

Lab.

not

es-

Inte

rnal note

s-

Public

ation lis

t-

Annual

Rep

ort

- In

vento

ry c

om

pute

r st

atio

ns

- In

vento

ry s

oft

war

e-

updat

a R&

D d

atab

ase

- Cen

tral

izat

ion

Corr

esponden

cePe

rson

nel

file

s-

Copy

card

s-

Thes

is p

rinto

uts

- Adm

inis

trat

ion

Purc

has

esContr

acts

- O

ffic

e m

ater

ial st

ock

- G

ener

al s

ecre

tariat

- Acc

ounting

- Ele

ctro

nic

Des

ign a

ssis

tance

J.P.

Wee

mael

sW

. D

elco

urt

e-

Mec

hanic

al D

esig

n a

ssis

tance

W.

Ceu

ppen

s-

Did

actica

l D

esig

n a

ssis

tance

J.P.

Wee

mael

s-

Purc

has

e Ele

ctro

nic

Com

ponen

tsJ.

P. W

eem

ael

s-

Purc

has

e G

ener

al C

om

ponen

tsW

. Ceu

ppen

s-

Inst

rum

ent

Mai

nte

nan

ce C

oord

in.

W.

Del

court

e-

Inst

rum

ent

Inve

nto

ry &

Loan

ser

v.W

. D

elco

urt

e-

Off

ice/

Lab

. ac

com

modat

ion

J.P.

Wee

mael

s

Head

of

dep

art

men

t ELEC

J. S

chouke

ns

Tech

nic

al ass

ista

nce

Ad

min

istr

ati

on

Info

cen

ter

Ed

uca

tio

n

J. L

ieve

ns

A.

Pin

telo

n

A.

Pin

telo

n


- Annual

Rep

ort

- in

tern

et -

ELE

C h

om

e pag

e-

PC w

ork

stat

ions

ELE

CY.

Rola

inW

. Ceu

ppen

sS.

Rey

nie

rs-

Support

net

work

ELE

CY.

Rola

inW

. Ceu

ppen

sS.

Rey

nie

rs

- Jo

b s

tuden

ts-

Follo

w-u

p-

schola

rship

s-

Res

earc

h c

ontr

acts

- co

ntr

act

renew

al-

visi

ting r

esea

rcher

s/pro

fess

ors

- Coord

inat

ion

Thes

is/P

hD

follo

w-u

pIn

dust

rial

conta

cts

Job s

tuden

ts f

ollo

w-u

pSta

ff f

ollo

w-u

pL.

Van

Bie

sen -

tea

m A

J. S

chouke

ns

- te

am B

A.

Bar

el -

tea

m C

G.

Van

der

stee

n -

tea

m D

F. D

e Rid

der

- t

eam

E-

Res

earc

h w

ork

Res

earc

h s

taff

Rese

arc

h O

rgan

izati

on

Pers

on

nel

Su

pp

ort

to

oth

er

bo

die

s, in

tern

al o

r exte

rnal to

th

e V

UB

A.

Pin

telo

n

Co

mp

ute

r S

up

po

rt

Pu

bli

c R

ela

tio

ns

Nati

on

al/

inte

rnati

on

al co

nta

cts

- O

rganis

ation o

f w

ork

shops,

confe

rence

s, …

A.

Bar

elJ.

Sch

ouke

ns

L. V

an B

iese

nA.

Pin

telo

n

A.

Pin

telo

n

- AU

TOM

ATIC

A -

IFA

CJ.

Sch

ouke

ns,

ass

oci

ate

editor

A.

Pin

telo

n,

adm

inis

trat

ion,

follo

w-u

p-

IEEE I

nst

rum

enta

tion &

Mea

sure

men

t Soci

ety,

Adco

mR.

Pin

telo

n,

Y. R

ola

in,

G.

Van

der

stee

n,

W.

Van

Moer

as

soci

ate

editors

- IM

EKO

: L. V

an B

iese

n,

Pres

iden

t, L

iais

on O

ffic

er t

o IE

EE

IMEKO

Gen

eral

Counci

l L.

Van B

iese

n,

Chai

rman

IMEKO

Tec

hnic

al B

oar

d:

L. V

an B

iese

n,

mem

ber

IMEKO

Editorial

Boar

d:

L. V

an B

iese

n,

mem

ber

IM

EKO

Advi

sory

Boar

d:

L. V

an B

iese

n,

mem

ber

- ELS

EVIE

R -

IM

EKO

Journ

al “M

easu

rem

ent”

L. V

an B

iese

n,

asso

ciat

e ed

itor

- Fa

culty

of Ele

ctrica

l Engin

eeri

ng

J. S

chouke

ns,

vic

e-ch

airm

an o

f Res

earc

h a

nd

inve

stm

ent

counci

lJ.

Sch

ouke

ns:

mem

ber

boar

d

facu

lty

L. V

an B

iese

n,

Chai

rman

Counci

l Bac

hel

or

Stu

die

sR.

Pin

telo

n,

chai

rman

PhD

counci

lY.

Rola

in,

chai

rman

IT c

ounci

lW

. D

elco

urt

e, s

upport

of

syst

em a

dm

inis

trat

or

- The

Roy

al A

cadem

ies

of

Sci

ence

and t

he

Art

s of

Bel

giu

mM

ember

of

Nat

ional

Com

mitte

e of Rad

io-E

lect

rici

ty:

L. V

an B

iese

n-

VLI

R-c

ooper

atio

nR.

Van

Loon,

del

egat

e of

the

Rec

tor

- Vrije

Univ

ersi

teit B

russ

elJ.

Sch

ouke

ns,

mem

ber

res

earc

h b

oar

dJ.

Sch

ouke

ns,

mem

ber

res

earc

h c

ounci

l (O

ZR)

J. S

chouke

ns,

mem

ber

of

Sen

ate

- VU

B-O

RBAM

fac

ulty

educa

tional

counci

l fo

r Bac

hel

or

in

Engin

eering S

cien

ce s

tudie

sL.

Van

Bie

sen;

chai

rman


6. LIST OF THE MOST IMPORTANT MEASUREMENT EQUIPMENT

A. Signal Generators

• HP E1445A VXI Arbitrary Waveform Generator, fmax < 40 MHz (3x)

• HP E1340A VXI Arbitrary Waveform Generator, fmax < 42 MHz (3x)

• Synthesizer/Function Generator, Agilent 33120A (2x)

• NI 5411 AWG

• Noise Source, HP 346B, 10 MHz-18 GHz

• Agilent, 4142B, DC power supply

• HP E1434A VXI Arbitrary Waveform Generator, 4 channel source,

fsmax: 65 KHz

• HP, Signal Generator, HP 83650B, 45 MHz - 40 GHz

• Tektronix, AWG710, 4 GHz

• Tektronix AWG 7052, 5GHz Arbitrary Waveform Generator

• Rhode & Schwarz, Vector Signal Generator, SMIQ06B, 300 kHz - 6.46 GHz

• Agilent 33250A, NI 5411

• Agilent 81101A, Pulse generator, 50MHz

B. Spectrum Analysers, Impedance Analysers, Network Analysers

• 2 channel Dynamic Signal Analyser, HP 3562, 100 kHz (2x)

• Impedance Analyser, HP 4192A, 5 Hz - 13 MHz

• Vector Impedance Meter, HP 4193 A, 0.4 - 110 MHz

• Spectrum Analyser, HP 8566B, 100 Hz-22 GHz

• µwave Network analyser, HP 8510 B, 45 MHz - 50 GHz

• µwave Nonlinear Network analyser, Agilent 85120A K60

• Noise Gain Analyser, Eaton 2075 B, 10 MHz - 1800 MHz

• Network Analyser, HP 8753 C, 300 kHz - 6 GHz

• Spectrum Analyser, HP 8565 E, 9 kHz - 50 GHz

• PNA Network Analyser, Agilent, 5 0MHz - 50 GHz

• Impedance Analyzer, Agilent E4991A, 10MHz - 3GHz

C. Digitizers

• 4 channel digitizer, Nicolet 490, 200 MHz, 8/12 bit

• 4 channel Digital Sampling oscilloscope, HP 54120T, 20 GHz, 11 bit

• 1 channel, HP E1430A VXI ADC 10 MHz, 16 bit (10x)

• 1 channel, HP E1437A VXI ADC 20 MHz, 16 bit (4x)

• 2 channel, HP E1429B VXI ADC 20 MHZ, 12 bit (2x)

• 8 channel, HP E1433A VXI ADC 196 KHz


• 2 NI 5911 flexres digitizer

• TDS 3032 digital phosphor oscilloscope

D. Miscellaneous

• dual programmable filter, Difa PDF 3700, 100 kHz

• dual adjustable filter, Wavetek, 100kHz

• logic state analyser HP 1645A

• µwave power meter, HP436A, 10 MHz- 18 GHz

• 4 VXI racks +4 MXI controllers + Digital cards

(2x Agilent E4841A + 1 Agilent E4805A)

• HP E1450, VXI timing module

• HP E1446A, VXI power module generator

• Wafer Probe Station

• Polytec Optical Fibre Vibrometer

Velocity range (Doppler interferometer): 1, 5, 25, 125, 1000 mm/s/V

Displacement range (Fringe Counter): 2, 8, 20, 80, 320, 1280, 5120 µm/V

• 2 PXI mainframes + MXI controller + embedded controller

• Custom-built measurement setup for making geo-referenced GSM network meas-

urements

• 4 hTC P3600 smart phones (equipped with 2G, 3G, Bluetooth, WiFi and GPS)

• 2 JRC DGPS 200

E. Underwater Acoustics

• Raytheon V860 echosounder

• B&K hydrophones, amplifiers etc.

• Panametrics transducers (500 kHz, 1MHz)

• D-GP5 Beacon Receiver KODEN (KBR-90)

• 2 watertanks + positioning system


7. FINANCIAL SUPPORT

Sponsor (project leader)

Duration project

Activity Approx. amount (in €)

DWTC232(R. Pintelon)

2007-2011 Dynamical systems, control and optimization (DYSCO)

400000

EU187 (A. Barel)

2004-2007 Top amplifier research groups in a European team: TARGET

206385

FP6 Network of Excellence (F. De Ridder)

2005-2007 EUROCEANS network of excellence 12000

FWOAL289(R. Pintelon)

2004-2007 Operational Modal Analyse in the presense of eXogeneous inputsignals (OMAX )

293000

FWOAL291 (Y. Rolain)

2004-2007 Adaptive Identification of compact scale rational models for complex dynamic linear systems.

55000

FWOAL297(J. Schoukens, J. Sweversa)

2004-2007 Nonlinear Multivariable Systems 190300

FWOAL317(J. Schoukens, J. Suykensb)

2005-2008 Identification of nonlinear systems and applications on high frequency systems

281200

FWOAL349 (J. Schoukens, F. De Ridder)

2005-2007 EuroCLIMATE - Development, calibration and application of independent salinity proxies (PaleoSalt), Salinity effects on proxy incorporation in bivalves

55000

FWOAL356(R. Pintelon)

2006-2009 Measurement, modelling and control of nonlinear slow varying dynamical systems

203.600

FWOAL420(L. Van Biesen)

2007-2010 Design and evaluation of DSL-systems, with exploitation of common mode signals

196600

FWOTM420(Y. Rolain)

2007-2009 Advanced Calibration and instrumentation set-ups for non-linear RF components

3720

FWOTM423(R. Pintelon)

2007-2009 Measurement and modelling of weak non-linear, slow time varying dynamical systems

3720

FWOTM445(W. Van Moer)

2007-2010 Identification of multiport nonlinear microwave systems

4000

GOA54(J. Schoukens)

2007-2007 Measurement, Modelling and Identification of Dynamic Systems

287468

HOA9(J. Schoukens)

2006-2007 Euroclimate 160000

IWT202(scholarship Nico Deblauwe, promoter L. Van Biesen)

2004-2007 Resolution enhancing localization techniques for navigation

134872


l

l

l

l

IWT295 (scholarship Koen Vandermot, promoter Y. Rolain)

2006-2007 Measurement & modelling of µ-wave multiplier systems and their nonlinear behaviour

67440

IWT296(scholarship Stephane Bronckers, promoter Y. Rolain)

2006-2007 Improvement of electromagnetic immunity

67440

IWT297(scholarship Karen Scheir, promoter Y. Rolain)

2006-2007 Design of analog CMOS circuits at 60 GHz

67440

IWT340(scholarship Lynn Bos, promoter Y. rolain)

2007-2008 Automatic design, analysing and modelling of multirate analog discrete time-systems

7436

Legal Expertise (L. Van Biesen) since 1995 Expert to the court confidentia

License Identification toolbox (J. Schoukens)

since 1994 Identification confidentia

NDA29 (L. Van Biesen) since 2004 Cellular positioning confidentia

NDA65 (A. Barel)

since 2005 Mutual non-disclosure agreement (NDA) :project for a RDS TMC reciever box

confidentia

OZR978 (R. Pintelon)

2004-2007 Operational Modal Analyse in the presense of eXogeneous inputsignals (OMAX )

40000

OZR 979 (Y. Rolain)

2004-2007 Adaptive Identification of compact scale rational models for complex dynamic linear systems

24000

OZR1271 (J. Schoukens)

2006-2007 Identification of nonlinear syst. & application on high frequency syst.

32500

OZR1275 (L. Van Biesen)

2006-2007 4G wireless communication to support LBS (Location Based Services) services for navigation

8000

OZR1459(R. Pintelon)

2007-2008 Measurement and modelling of weak non-linear, slow time varying dynamical systems

2871

OZR1460(Y. Rolain)

2007-2008 Analog design methods to improve the electromagnetic immunity

6000

OZR1461(Y. Rolain)

2007-2008 Combined GPS + Galileo Satellite Positioning Precision

6000

OZR1462(Y. Rolain)

2007 Measurement, modelling and realisation of complex high frequent capacity amplifiers

4000

OZR1463(Y. Rolain)

2007-2008 Design of analog CMOS circuits for millimerwave applications

6000


Duration project



l

OZR1464(Y. Rolain)

2007-2008 Design of new methods for the measurement and modelling of microwave multi port systems and their nonlinear behaviour

6000

OZR1465(Y. rolain)

2007-2008 Technology-aware reliable design in sub 90nm RF and microwave broadband CMOS circuits with ESD functionality

10000

OZR1466(L. Van Biesen)

2007-2008 DSL-Loopexplorer: accurate identificatie van de telefoonlijnopbouw van een abonnee voor een SDSL communicatie. via SELT (Single Eded Line Testing)

6532


2007-2008 Design of 4G wireless communication to support new LBS

4313

OZR1531(Y. Rolain)

2007 PhD scholarship L. Gommé 22500


2007 PhD scholarship W. Foubert 30000

OZRMETH1(J. Schoukens)

2007-2013 Center for Dat Based Modelling and Model Quality Assessment.

751100

WDGO621(L. Van Biesen)

2007-2008 Time Domain Reflectometry (TDR) on critical Instrumentation loops for gasturbines

142886

WDGO647(J. Schoukens)

2007-2011 Coaching PhD Griet Monteyne “Application of noise methods for PWR’s”

183373

WDCO16 (R. Van Loon) 2004-2009 Lectures IAEA (Nuclear Energy Society, Vienna)

2564

WDV12 GAMAX Kft(J. Schoukens)

since 2000 Graphical user interface for the frequency domain system identification toolset.

confidentia2/3 ELEC

1/3 patentfund

a. Katholieke Universiteit Leuven, Department of Mechanical Engineering - PMAb. Katholieke Universiteit Leuven, Department Elektrotechniek - ESAT-SISTA


Duration project



8. AWARDS

A. Grade of Fellow (IEEE)

The Institute of Electrical and Electronic Engineers, Inc. elected the grade of fellow to:

- Johan Schoukens: for contributions to frequency domain system identification and the

integration of measurement, signal processing and estimation theory (1997)

- Rik Pintelon: for fundamental research in frequency domain system identification and its

applications in instrumentation, control and signal processing (1998)

- Yves Rolain: for contributions to measurement and modeling of nonlinear microwave

devices (2005)

B. Grade of Senior Member (IEEE)

In recognition of professional standing the Officers and Board of Directors of the IEEE

certify that:

- Gerd Vandersteen;

- Wendy Van Moer

have been elected to the grade of Senior Member (20 January 2007)

C. IEEE I&M Outstanding Young Engineer Award

IEEE Instrumentation and Measurement Society presents the “2006 Outstanding Young

Engineer Award” to Wendy Van Moer, For outstanding contributions to nonlinear circuit

theory, Poland, Warsaw, May 2007

D. Awards granted by the VUB, on the proposition of the department ELEC

- Title of Doctor Honoris Causa to Prof. P. Eykhoff (Technische Universiteit Eindhoven)

on April 4, 1990 (VUB, Brussels)

- Medal of Excellence to William Hewlett and David Packard on March 3, 1995 (VUB,

Brussels)

- Medal of Excellence to Joseph F. Keithley on June 4, 1996 (Gothic Town Hall of

Brussels)

- Title of Doctor Honoris Causa to Prof. M. Gevers (Université Catholique de Louvain -

CESAME) on November 28, 2001 (VUB, Brussels)


CHAPTER 2. Short Description of the Research Projects/Team

1. TEAM A: AUTOMATIC MEASUREMENT SYSTEMS AND LABORATORY OF UNDERWATER ACOUSTICS

A. Introduction to Team A

The activities of team A directly related to fundamental electricity, deal with the set-up of

computer controlled measurement systems, the design of new intelligent instruments, the

processing of the measured data (DSP and algorithmic treatment) and the implementation

of A.I.- techniques in instruments for the automatic interpretation of the acquired measure-

ments. The technical applications areas are quite diverse and cover areas in the field of

electrical and electronic systems, but a lot of special care is also given to earth science

applications.

This team is also active in telecommunication projects and studies, which deal with voice

coded transmissions (telephony) and with robust coded digital transmission (ADSL, HDSL,

VDSL). Communication channels are modelled, both for their electrical properties and noise

behaviour and with respect to the carried information (Information Theory).

Moreover, in the field of underwater acoustics important research projects are developed

since 1985. Fundamental as well as applied research is carried out in the field of the

modelling of marine systems, marine acoustics, sub bottom profiling and sediment classifi-

cation.

Marine environmental studies based on GIS-techniques are of prime importance in this

group as well.

Prof. Dr. ir. L. Van Biesen is the Belgian delegate in the board of IMEKO since 1993 and

chairman of the Technical Committee TC-7 on Measurement Science since 1994, up to

2000, has been vice-chairman of the Technical Committee TC-19 on Environmental

Measurements since 1999, President-Elect of IMEKO (2000-2003) and was the President of

IMEKO (2003-2006).

38 Short description of the research projects/team

B. Short Description of the Research Projects of Team A

A. POSITIONING TECHNOLOGIES USED IN LBS (LOCATION BASED SERVICES) FOCUSING ON WIMAX NETWORKS.MUSSA BSHARA

WiMAX supports short-range communications among the terminals (mesh network);

measuring the relative distances between the subscriber stations in a WiMAX mesh network

provides valuable information about terminals relative positions, which lead to

improvement in localization accuracy in the existing wireless networks. The distance

between two subscriber stations can be obtained by measuring the received signal strength

and knowing the path loss model of their environment.

From now on, we will refer to a subscriber station as a node. The nodes can be split in two

groups: mobile nodes (moving terminals) and static nodes (fixed computers using

(WiMAX). Mobile nodes can be pedestrians, bicyclists or cars. In the latter two cases it is

rather safe to assume that they only make use of the road network. Pedestrians however

can be anywhere (in buildings, at higher floors).

Currently, my work is twofold:

1. Developing a positioning path loss model for the Brussels environment, this model is

distinctly different from the path loss model used in communications due to the low

number of parameters, and the goal to use it is for positioning. I have made

measurements and obtained a first-cut path loss model, this model suffers from

insufficient measurement accuracy due to using WiMAX modem readings. To overcome

this problem; more measurements will be conducted using a powerful spectrum

analyser to obtain precise measurements.

Estimating the range (or distance) from measurements in a multi-path environment is

challenging and the multipath effect has to be modelled and taken into consideration.

2. Working on advanced filtering techniques used in positioning and tracking; I made

simulations using Matlab and obtained good results. The outcomes of simulations should

be validated using measurements on real network.

B. RESOLUTION ENHANCING LOCALIZATION TECHNIQUES FOR LOCATION-BASED SERVICESNICO DEBLAUWE, LEO VAN BIESEN

Location-based Services (LBS) are steadily gaining importance; see for instance the

commercially available car navigation systems. For LBS, localization technology is an

important component, besides (data) communication and content providers. Currently GPS

is the most utilized localization technology; however this technique tends to fail in the areas

where it is most needed: in urban areas. In these zones, a dense GSM network is available,

which offers possibilities for an alternative positioning technique that doesn’t fail.

This research project consists of two parts: enhancing the possibilities of GSM-based

positioning technologies on the one hand, and applying these techniques on the other

hand.


b

Positioning techniques using the cellular network

The first part of this project deals with enhancing the accuracy for positioning technologies

that make use of the possibilities of existing GSM networks. An extensive and country-wide

study of the accuracy of GSM Cell-ID was made, focusing on the achievable improvement

when Enhanced Cell-ID (i.e. the Timing Advance parameter) would be applied. Visualization

tools were created, e.g. refer to figure 1.a, so that analysis results could be coupled back to

the reality. By using the Received Signal Strengths (RSS) of multiple antennas, it is possible

to obtain more accurate position estimates. The main aspects that this project dealt with

were the directive antenna patterns and differential treatment of the measurements. The

latter was to cope with the slow and fast fading effects, which is still one of the dominant

problems for RSS-based positioning.

FIGURE 1. a: A visualization of the covered area of three sectored cells at one BTS location in the neighbourhood of the VUB campus. b: Accuracy of an angular positioning technique making use of only 1 BS (the rombus), for positioning a car (circle) moving along a road (line). Instead of one position es-

timate, an estimation distribution is plotted.

Proactive LBS using multiple positioning technologies

The second part of this project deals with combining the technology with the LBS

framework and thus rendering it usable for real positioning applications. The position-

dependent constraints of the application are examined and where possible relaxed, taking

into account pre-knowledge about the application's behaviour. In cooperation with Peter

Ruppel (LMU, Munich), GSM Cell-ID has been combined dynamically with GPS for a

proactive LBS middle ware platform, refer to figure 2 for some field test results with the

prototype. Additionally, we show that GSM-based positioning could be sufficient for a broad

class of LBS and navigation applications. The result is a public-transport LBS that makes

solely use of the GSM network for the positioning aspects.

a


C. DESIGN AND EVALUATION OF MULTIPLE-INPUT-MULTIPLE-OUTPUT CHANNEL MODELS FOR DSL SYSTEMSWIM FOUBERT, PATRICK BOETS

Digital Subscriber Line (DSL)

technology provides broadband

service over ‘twisted pair’ copper

wires of the existing telephone

network. Current DSL systems

make only use of differential

mode (DM) signals, which refers

to the electrical voltage difference

between the two wires (see Figure 3). In next generation DSL technologies, the

common mode (DM) signal will also be exploited. This signal corresponds to the

arithmetic mean of the voltages between each wire and ground. The signal can

also be extracted between two wire pairs. Using also the common mode signal in

addition to the differential mode signal, the capacity can be three times higher

than the conventional differential mode only capacity!

FIGURE 2. Example of proximity-separation detection with two users. User one (red) makes a tour and user two (yellow) stays semi-static at a square. A combination of GSM Cell-ID and

GPS was used, the test is performed in Munich.

FIGURE 3. The definition of the DM signals and the CM signal


In order to use also the common mode

signal, we need reliable transmission line

models which take also this signal into

account. The derivation of the new models

is based on the multi conductor trans-

mission lines (MTL) theory. This theory is

only valid for uniform line segments. Figure

4 shows how the behaviour of a binder with

twisted pairs can be approximated. The

representation contains a resistors R,

inductors L, conductors G and capacitances

C. For our model, we need accurate values for all of them. To become good approximations

for the R and L values, we use the series impedance which is derived by Belevitch. Beside

the skin effect, also the proximity effect is taken into account. Before we further improve

the model, we have to be sure that this formula is valid. Several measurements have been

done to verify this formula. There were a lot of challenges to cope with for the validation.

First, the length of the transmission line is limited because of some practical mechanical

problems. This length constraint makes it difficult to measure the one-port quantities

because the instrument must operate in a range of just a few decibels due to the limited

line attenuation. Due to the construction constraint, there will also be calibration errors.

Also drift and coupling with the environment makes the validation very difficult. The final

setup is 2m long and consists of 4 copper wires with a very small diameter.

The multi conductor transmission lines theory is only valid for uniform line segments. But, if

we want to allow wire twists, strands and variations our cable is not uniform at all. To

overcome this problem, the system will be split up in very short line segments. Each

segment is considered to be uniform and is represented by a transmission matrix. The

overall cable description is obtained by multiplication of the different matrices. In this way,

it’s easy to increase the number of pairs, change the twist rate, stranding or isolation just

by changing one or more parameters in the model.

These models are operative for a binder where signal transfer exists from the differential

mode to the common mode and vice versa. It’s also possible to model the disturbance

caused by the environment on our transmission line, and in the other way to see how the

transmission line can disturb the environment.

D. LOOP QUALIFICATION AND CAPACITY ESTIMATION OF DSL LINESCARINE NEUS, PATRICK BOETS, LEO VAN BIESEN

Although fibre is being deployed steadily in the telephone network for broadbandservices, complete penetration will take several decades, particularly to smallerbusinesses and residences. This motivates using the omnipresent coppertelephone network as effectively as possible during the transition period. This isalready widely done by the telephone operators, by providing xDSL services(ADSL, VDSL,...).

FIGURE 4. The MTL equivalence for a binder with twisted pairs


In the provisioning of xDSL services, the quality of the loop between the central office and

the customer plays an important role. xDSL signals are attenuated and distorted during

transmission through this loop, particularly at high frequencies. This attenuation and

distortion can be predicted if the loop make up is known. The loop topology is usually made

up of multiple copper cable segments. The goal of this research is to determine the make-

up of the subscriber loop, being the length of the cable sections and the type of cables.

When the received noise is known, the xDSL’s bit rate and performance level can then be

precisely estimated. The capacity will be different for every customer, because each loop

has its own peculiar characteristics. It is important for the operators to have a tool to

qualify accurately whether a particular telephone line will be suited for xDSL application, as

the deployment of a DSL system demands an initial investment.

Estimating the capacity is not a simple task, since the telephone plant is a very challenging

element to model accurately: topologies vary widely, the loop changes when repairs or

upgrades are performed, and detailed topological information is difficult to obtain. More

rover, the attenuation and distortion on the subscriber loop are also influenced by

environmental factors like temperature, humidity or corrosion. Usually records of the loop

make-up are stored into data bases, but in some cases, the information is incomplete, out

of date or even missing. As a consequence, the offered bit rates are nowadays overlay

conservative.

This research aims at developing a tool to estimate the loop make-up from measurements

solely at the central office side, to avoid dispatching a technician to the customer’s site.

This is done by sending in a signal on the subscriber loop under consideration and

measuring the reflections at the central office. These reflections give us information about

the loop topology and analysing them allows determining the loop make-up. Besides the

limited a priori knowledge of the loop, there are several other factors which complicate the

modelling and identification process: dispersion, high attenuation, power constraints,...

Moreover, if the measurement is to be done by the DSL-modem (preferred implemen-

tation), the low-frequent voice band signals are suppressed. Unfortunately, this will also

severely distort the reflections. Therefore, in contrast to previous approaches [1], a new

method is under development which is less influenced by these drawbacks. By working in

central office customer

subscriber loop

Ingoing signal

time

voltageReflections

FIGURE 5. Basic principle of reflectometry


the frequency domain instead of the classical time domain, the new approach is able to deal

with suppressed or missing frequencies. By applying well chosen processing steps, the

dispersion is strongly reduced. Figure 6 compares both methods.

Reference

[1] P. Boets, T. Bostoen, L. Van Biesen and T. Pollet, “Pre-Processing of Signals for Single-EndedSubscriber Line Testing”, IEEE Trans. Instrum. Meas., vol. 55, no. 5, pp.1509-1518, Oct. 2006.

E. DETECTING FAULTS ON CRITICAL INSTRUMENTATION LOOPS OF GAS TURBINESCARINE NEUS, PATRICK BOETS, LEO VAN BIESEN, RUDY MOELANS*, GUIDO VAN DE VYVERE*(*) ELECTRABEL, MAINTENANCE COMPETENCE CENTER

Sensors on gas turbines are used to

monitor in real-time the temperature,

pressure, flow, actuator positions,

combustor dynamics and speed of the

turbines. They are indispensable for the

correct and safe turbine operation. The

sensors and cable networks that make

the connections with the industrial

controllers are under severe stress due

to the high temperature, vibration,

chemical pollution and oil intrusion. If a

sensor or the sensor cable network

malfunctions or breaks down, the turbine might drift away into the danger zone or must be

taken out of operation. This will cause severe costs for the electricity producer. Therefore, it

FIGURE 6. Comparison of classical time domain method (top) and new proposed frequency-domain approach (bottom), for a cascade of two

1000m line segments. Two reflections are visible, however the dispersion is strongly reduced with the new approach.

0

0

2

4

6x 10

-3[d

imen

sion

less

]

00

1

2

3

4

5x 10

-3

[dim

ensi

onle

ss]

50

50

100 150

s11,processed

100 150

s11,new

200

200

250time [µs]

250time [µs]

FIGURE 7. Picture of the designed prototype


is of great importance to detect beforehand the state of the sensors and the cabling in

order to take proper actions when the turbine undergoes its regular maintenance.

An innovative and unique measurement prototype has been realized to quickly monitor,

assess and maintain the sensor network on gas turbines. The measurement technique is

based on the time-domain reflectometry (TDR) principle. Hereto, a measurement device

injects a signal into the network at the location of the industrial controller. Every cable

discontinuity will cause a reflection, e.g. at a connection box or at the sensor itself. The

reflections propagate back to the measurement device and are digitized. The digitized

record is called a reflectogram and the recording can be performed in just a few seconds.

When the turbine is in maintenance, a reflectogram will be measured of the non-defective

sensor network and stored as a reference. The next time that the turbine undergoes its

regular maintenance a new reflectogram will be measured and then compared with the

reference reflectogram. If a significant difference can be noticed between those two

recordings, a software program will analyse the measurements and it will make a prediction

of the location and the nature of the problem. This means that by applying TDR, faults can

be localized with a resolution of a few centimetres and after processing of the data it

becomes possible to assess the actual state of the sensor. As such, actions can be taken to

remove the aged sensor or to inspect connection boxes on oil intrusion or bad contacts.


2. TEAM B: SYSTEM IDENTIFICATION AND PARAMETER ESTIMATION

A. Introduction to Team B

The main interest of the identification team is situated in the development of new identifi-

cation methods and their application to real life problems. The team is involved with many

aspects of the identification theory:

- experiment design

- development of estimators

- modelling problems

The identification of linear and nonlinear systems is studied.

Throughout our work we make the following choices:

- use of periodic excitations whenever it is possible, use random excitations if it is imposed

by the user.

- use of non parametric noise models to characterize the stochastic disturbances

- use of an errors-in-variables framework: all measured signals are assumed to be

disturbed by noise.

- believe your data, not your prejudices

It will not always be possible to act along these clear principles, but whenever we face a

choice, they are an important factor in our decision process.

Another important aspect of our general approach is that we start the process by gathering

system knowledge from the measurements. This gives in a very early phase of the identifi-

cation process an idea of the global complexity of the modelling problem. For the linear

systems this phase boils down to the non parametric measurement of the frequency

response function, which is usually of a high quality due to the periodic excitation approach.

It contains a lot of information about the system. For nonlinear systems the situation is not

that clear, and a number of ideas are under study at this moment.

A. IDENTIFICATION OF LINEAR SYSTEMS

In the eighties, ELiS, an estimator to identify single input single output (SISO) linear

dynamic systems in the presence of uncorrelated input/output noise, was developed at the

department ELEC. In a next step this estimator was generalized to multiple input/multiple

output systems in the presence of correlated input and output noise.

Since linear dynamic systems are used as a basic modelling tool in a very wide range of

applications, it is clear that this work has a lot of applications. We applied this modelling

approach to chemical problems (traction batteries, diffusion processes), power engineering

(electrical machines, power transformers; fault localisation on a cable), mechanical

problems (flutter analysis, flexible robot arm) measurement area (compensation of the

dynamics of an acquisition channel, modelling the dynamics of a sensor), and signal


processing (design of digital filters). In all these applications the identified transfer function

model was an intermediate step, for example leading to a better physical insight in the

structure of the DUT, or to be used to improve the quality of the process control.

Nowadays we are further developing the estimators, making them more robust and user

friendly, resulting in a wider applicability of ELiS. Moreover a lot of effort is spent to gener-

alize frequency domain identification methods so that they also can be applied under exact

the same conditions as time domain methods, but still offering the advantages of using a

non parametric noise model.

robustification:

- what happens if the true model is not contained in the considered model class, for

example when nonlinear distortions or unmodelled dynamics are present

- what can be done if no noise information is available

- identification of high order systems, identification of an over parametrized model

- generation of improved starting values

user friendly:

- is it possible to extract the noise information from the same measurements that are used

to identify the system without user interaction

- is it possible to guide the inexperienced user to a good solution of his identification

problem

- development of fully automated processing methods: from raw time domain data to

validated models (including automatic detection and processing of the periodicity of the

signals; extraction of non parametric noise models; model selection; model validation)

generalization:

- identification of MIMO systems: design of excitation signals, development of new

algorithms, constraint estimation (stability, positive definite systems)

- identification of continuous time models using arbitrary excitations

- developing a general theoretic framework linking time and frequency domain

identification

- generalization to other fields like electrochemical reactions (÷s) and microwave systems

(Richardson)

B. IDENTIFICATION OF NONLINEAR SYSTEMS

In the last 10 years we started to study more intensively identification of nonlinear

systems. It is not a good idea to define the class of models under study by a negation. Just

as it is an impossible task to make a study of the zoology of the ‘non elephants’, it is impos-

sible to grasp all nonlinear systems in one framework. We should be more specific and give

a positive definition of those systems that we will consider. In this project we focus on those

systems where a periodic input results in a periodic output with the same period as the

input (we call it PISPOT systems). This excludes a lot of phenomena like chaos, bifurcation,

but it is still a wide class, including hard nonlinear systems like clippers, relays, deep

saturation etc. Selecting a specific model structure for these system is still a very complex


task. Not only the nonlinear behaviour should be properly characterised, also the dynamics

should be captured. This requires that many choices from the user, compared to the linear

problem, that it is not a good idea to start immediately with a parametric model when

identifying a nonlinear system. Too many questions would remain unanswered. For that

reason, we prefer to start with a non parametric representation of the system, trying to get

a first insight in its behaviour. Only in a second step, we will eventually move to a specific

parametric model that might be well adapted to the given problem, using the previously

gained insights to select a dedicated model structure.

Our work on identification of nonlinear systems is split in two parts. In the first part we

study the impact of nonlinear distortions on the linear identification framework. In the

second part we aim at modelling the nonlinearities.

Linear modelling in the presence of nonlinear distortions

Linear models are successfully applied to a wide range of modelling problems although they

are based on very restrictive assumptions. The real world is not linear, and hence intrinsi-

cally the theory is not applicable. However, in practice, the linear approximations are useful

and offer important advantages:

- They result in useful models that give the user a lot of intuitive insight in his problem.

- Many design techniques, that can not be easily generalised to nonlinear models, are

available.

- Nonlinear model building is mostly difficult and time consuming, while the additional

performance that is obtained might be small. So it is not obvious that the gain in model

performance warrants the required efforts to build a nonlinear model.

- No general framework is available for nonlinear systems as it is for linear systems. Often

dedicated models are needed, complicating the development/use of general software

packages.

For these reasons we work on a generalised linear framework that can be used in a

nonlinear environment. Using this framework it is possible to get:

- A better understanding of the impact of nonlinear distortions on the model.

- Optimized measurement techniques that reduce the required measurement time

significantly.

- Generalised uncertainty bounds that describe the model variations due to the nonlinear

distortions. This allows for a better balanced design that accounts for the model

limitations.

- A lower risk of being fooled by the classical linear identification methods that are widely

used in commercial packages.

- Simple design rules that help to reduce the undesired impact of nonlinear distortions on

practical designs.

The whole approach is based on the observation that for random excitations, a nonlinear

system can be represented under very mild conditions by a linear system plus an additive

noise source. The linear system gives the best linear approximation of the output

for the considered class of excitation signals, while the noise source represents all

nonlinear effects that are not captured by the model.

GR jωk( )YS jωk( )


Identification of nonlinear systems

Nonlinear modelling is extremely difficult. The major reason for this problem is the

enormous variability that exists. Even when we zoom in to for example Volterra systems,

there are still many additional degrees of freedom compared to the modelling of linear

system. The only model structure question for SISO-transfer functions is the order of the

numerator and denominator. For SISO-Volterra systems, the situation is much more

complicated, because the model uses multiple frequency variables that can appear in any

possible combination. Hence no simple prior structure selection is possible. For that reason

it is our strong belief that the parametric modelling step should be preceded by a non

parametric one. First the user should get an impression of the nonlinear behaviour, and

only in the next step he can propose a parametric model structure.

For that reason we studied and still look at a number of non parametric methods like:

Restoring force method, the power transfer method, the non parametric Volterra models.

For the parametric modelling we follow different approaches: nonlinear block-structured

models and nonlinear state-space models

- Nonlinear block-structured models consists of linear dynamic blocks combined with static

nonlinear blocks. In its most general form feedback loops can be present. The major

advantage of this structure is that it still provides some physical insight in the system,

and it uses a ‘small’ number of parameters. The major disadvantage is that it is

extremely hard to find good initial estimates for the individual blocks.

- Nonlinear state-space models cover a large class of nonlinear systems with a very rich

behaviour. They can be considered as a potential candidate for ‘black-box’ modelling of

nonlinear systems. The major advantage of this structure is that it is much easier to find

reasonable initial estimates for the structure for systems with a dominating linear

behavior. The major disadvantage is the large number of parameters that are used.

These models provide also less physical insight.

- Finally we study also dedicated nonlinear structures to model a number of typical high-

frequency components like mixers. For these systems we use either Volterra-based

descriptions, or dedicated block-structured models.

B. Short description of the research projects of Team B

A. METHUSALEM: CENTRE FOR DATA BASED MODELLING AND MODEL QUALITY ASSESSMENTJOHAN SCHOUKENS, RIK PINTELON, YVES ROLAIN, GERD VANDERSTEEN, WENDY VAN MOER, LUDWIG DE LOCHT

This is a project setup by the Flemish government to give long term stability to established

research groups. The project runs for 7 years, with a budget of more than 500 000 Euro/

year.

The ultimate goal of the project is to set up and maintain a centre for system identification,

summarized in the motto:

“From data to model”


The aim of this centre is to develop, acquire and disseminate advanced system identifi-

cation methods.

Structure of the project

This project follows three lines towards this long term goal:

- Development of advanced identification methods for dynamic systems: Development of

robust and user friendly methods for the identification of (non)linear dynamic systems.

This is the home base of the identification team, and it consists of a ‘natural’ continuation

and extension of the activities of the team over the last 20 years.

- High risk new challenges: Start-up of completely new high risk research lines that need a

substantial development compared with the existing theory and methodology. It consists

of two sub-projects:

• Development of a ‘non-asymptotic’ identification theory: How to deal with model-

ling problems where the number of data points is in the same order as the number

of parameters?

• Identification in the presence of noise and model errors: Development of a new

paradigm that balances/tunes noise disturbances and model errors.

- Learning, dissemination, networking: The aim is to acquire and disseminate actively

knowledge from/to other fields by offering one year research grants for visitors.

• Learning: On the one hand we want to acquire system identification methods that

are developed in other fields like statistics, econometrics, and are unknown within

the control- and measurement society (learning), by hosting specialists of these

fields for longer periods.

• Dissemination: On the other hand, many scientific disciplines face the problem of

extracting mathematical models from experimental data, while this does not belong

to their core business. Here, we want to give an active support to transfer sound

identification methods to these groups, by hosting and paying PhD-students of ex-

ternal (VUB-)groups for a longer period.

• Networking: Extend the existing international network of post-doc and senior re-

searchers by combining short term and/or long term visits.

B. STUDY OF THE LTI RELATIONS BETWEEN THE OUTPUTS OF TWO COUPLED WIENER SYSTEMS AND ITS APPLICATION TO THE GENERATION OF INITIAL ESTIMATES FOR WIENER-HAMMERSTEIN SYSTEMSJ. SCHOUKENS, R. PINTELON, AND M. ENQVIST

This project consists of two parts. In the first, more theoretic part, two Wiener systems

driven by the same Gaussian noise excitation are considered (see Figure 1). For each of

these systems, the best linear approximation (BLA) of the output (in mean square sense) is

calculated, and the residuals, defined as the difference between the actual output and the

linearly simulated output is considered for both outputs. Next we studied the linear

relations that exist between these residuals. Explicit expressions are given as a function of

the dynamic blocks of both systems. From conceptual point of view, the switch from the

actual system outputs to the ‘nonlinear’ residuals is important. It allows to separate the

‘linear’ from the ‘nonlinear’ information. An illustration of this new insight is the generation


of more ‘efficient’ initial estimates: how to get initial estimates for the transfer function of

the dynamic blocks of a Wiener-Hammerstein system (see also Figure 2)? To do so, an

‘artificial’ nonlinear system is put in parallel to the Wiener-Hammerstein system. This

method is illustrated on experimental data in Figure 3.

e

G1

G2

f1

f2

p

q

FIGURE 1. Setup: two Hammerstein systems driven by the same white Gaussian noise source.

y0u0 R0 S0f0

αu2 1 α–( )u3+ r

p0 q0

FIGURE 2. Wiener-Hammerstein system (black), together with a user created auxiliary system (grey).

f0R0 S0

y0u0

f f

FIGURE 3. Experimental Wiener-Hammerstein system.

FIGURE 4. Initial estimates of the FRF’s of the linear dynamic blocks and

. Black line: the initial estimates; gray

line: the reference measurements. Notice that the bandwidth of the excitation is 10 kHz (left of the dashed line). The initial estimates are only available within the

excited frequency band.

R S,R0

S0

0 0.5 1 1.5 2 2.5

x 104

−120

−100

−80

−60

−40

−20

0

20

40

60

Frequency (Hz)

Am

plitu

de (

dB)


C. IDENTIFICATION OF A CRYSTAL DETECTOR USING A BLOCK STRUCTURED NONLINEAR FEEDBACK MODELJ. SCHOUKENS, L. GOMMÉ, W. VAN MOER, Y. ROLAIN

This project studies the identification

of a block structured nonlinear

Wiener-Hammerstein system that is

captured in the feed forward or the

feedback path of a closed loop

system. In open loop systems like

Wiener, Hammerstein, and Wiener-

Hammerstein system is no nonlinear

feedback present. Such systems can

not describe many phenomena that

are observed in practice: a shifting

resonance frequency or a changing damping as a function of the input amplitude of the

excitation. To include also these phenomena, a feedback around the nonlinearity should be

added. This leads to the system as shown in Figure 5. In this figure are linear

dynamic systems and is a static nonlinear system.

First nonparametric initial estimates are generated for the three dynamic blocks, modelled

by their frequency response function, and the static nonlinear system, described using

cubic splines. Next a parametric model is identified. Random or periodic excitations can be

used in the experiments. The method is illustrated on the identification of an Agilent

HP420C crystal detector. This device is used in microwave applications to measure the

envelope of a signal.

D. USING ANOVA IN A MICROWAVE ROUND-ROBIN COMPARISONKURT BARBÉ, WENDY VAN MOER AND YVES ROLAIN

The goal of this research is to validate the nonlinear measurements that are performed by a

wide variety of measurement set-ups present amongst different microwave laboratories,

performing nonlinear device characterization. If it is possible to show that all labs measure-

ments are equal up to the measurement uncertainty, the repeatability of the measurements

is proven as the hardware used by each lab is in essence unique.

The main idea used here is the same as in all other inter comparison tests. A device that it

is as resistant as possible to external parasitics is circulated between the labs. In nonlinear

measurements, the challenge is to measure the time-domain waveform of multi-harmonic

signals without any distortion. To this end, the instruments need to have a calibration that

corrects for the amplitude and phase distortions of the response over the frequency. To

assess the quality of these calibrated measurements, the device-under-test will therefore

need to produce repeatable multi harmonic signals and certainly needs to be an active

device. As there is no reference lab whose measurement accuracy is known, or is an order

of magnitude better than the other labs, there is no possibility to consider that the

behaviour of the test system is known in advance. This poses a serious challenge to the

r yG0

R0S0 f0

u

pq xz

FIGURE 5. : Examples of a block structured feed-back model. Top: nonlinearity in the feed forward path; bottom: nonlinearity in the feedback path.

G0 R0 S0, ,f0


comparison procedure, as the first question to be tackled is: What should we compare

with?

This problem can be solved in a number of ways. If the measurement setups can be

assumed to be perfectly equal, it is possible to compare the output of the device as

measured by the different labs, as one can safely assume that the applied inputs are

perfectly equal. Unfortunately, this assumption of equal set-ups does not hold here. As an

example, consider the demands that such an assumption puts on the RF source used in the

set-up. It requires not only the power and the frequency of the applied signal to be the

same, it also needs that spectral distortion to be equal in magnitude and phase! This is

clearly not an acceptable hypothesis.

Previous attempts [1] to solve this problem have taken the also seemingly quite straight-

forward approach to build a model of the device. If the model can be proven to be accurate,

one can then perform a comparison of measurement responses as follows. First, the signal

that is applied to the device is measured and is then applied as an input to the model of the

device. The model output and the measured output of the device can then safely be

compared. The boogey trap here is the extraction of the model itself. Either the model is a

white box model based on physical insight, and then it often neglects the parasitic effects to

remain tractable, or it is a black box model and then it requires measurements that are

accurate enough to perform the model extraction. Both approaches were already tried out

in the past and showed not to be very effective [1]. The result was that the measurements

of the labs were well clustered, but that the distance between model and measurement was

larger than the distance in between measurements.

To get around these issues, it was decided from the start to go for a model-free comparison

in this work. over the frequency. To assess the quality of these As one needs a criterion to

compare the measurements, it was decided to use a statistical approach. The comparison

then needs to be reformulated, to fit in a hypothesis test framework. In the classical way,

one tries to determine if the measurements of the same device taken by different instru-

ments are equal. However, we may also take an alternative formulation. Assuming that the

measurements are correct, one has to find an answer for the question: Are the measure-

ments of the labs generated by the same device?

The answer to this question can be given using the statistical method of the Analysis of

Variance ANOVA) ([2], [3]). The ANOVA technique has two major advantages: no reference

model or measurement is needed and ANOVA is directly interpretable.

This technique is very powerful and allows selecting homogeneous groups of laboratories,

without the need for a reference lab or model. Furthermore, ANOVA can situate at which

level the homogeneous groups differ from each other.

This research was funded by a post doctoral fellowship of the Research Foundation-Flanders

(FWO), by a grant of the Flemish Government (GOA-IMMI), the Belgian Program on Inter

university Poles of attraction initiated by the Belgian State, Prime Minister’s Office, Science

Policy programming (IUAP V/2) and the research council of the VUB (OZR). The research

reported in this paper was performed in the context of the Top Amplifier Research Groups

within a European Team (TARGET) network.


References

[1] K. A. Remley, D. C. DeGroot, J. A. Jargon, and K. Gupta, “A method to compare vector nonlinearnetwork analyzers,” in IEEE MTT-S International Microwave Symposium digest, June 2001, pp. 1667–1670.

[2] M. Kendall, A. Stuart, and J. Ord, The Advanced Theory of Statistics-Volume 3. Charles Griffin & Co Ltd.,1983.

[3] T. Sincich, A Course in Modern Business Statistics, Second Edition. Macmillan College Publishing CompanyInc., 1994.

E. IMPROVED FRF MEASUREMENTS IN THE PRESENCE OF NONLINEAR DISTORTIONS VIA OVERLAPKURT BARBÉ, RIK PINTELON, LAURENT VANBEYLEN AND JOHAN SCHOUKENS

Introduction

All real life systems are to some extent nonlinear. However in many applications a linear

system is postulated if the nonlinear distortion is small with respect to the measurement

noise, or if the system behaves linearly in the frequency band of interest. In many applica-

tions the nonlinear behaviour is apparent such that the use of the classical linear identifi-

cation framework fails. While the behaviour of the disturbing noise is well studied for linear

systems, [4], [5], [6], the situation becomes more involved for nonlinear systems.

When non-linearities are detected, the user wants to know how large they are with respect

to the measurement noise. One way to do this, [7] is by computing the Frequency

Response Function (FRF) over different periods of the periodic excitation signal and this for

multiple realizations of the input signal. For the excitation signal periodic Gaussian noise or

a random phase multi sine, [2], is considered, like in the classical case, [7]. The variability

of the FRF over the different periods provides the user with an estimate of the level of the

measurement noise and the variability of the FRF over the different realizations provides

the user with an estimate of the level of the nonlinearities. We propose a method that

allows the user

1. to extract the level of nonlinear distortion and the measurement noise

2. a significant reduction in measurement time with respect to the classical method, [7].

Main method

For the excitation signal

periodic Gaussian noise or a

random phase multi sine, a

sum of harmonically related

sines [5], is considered, like in

the classical case, [7]. We

propose to use two periods and

two realizations only to extract

the noise characteristics, by

computing the FRF over the

different periods, [8], [9], and over the different realizations via circular overlap (Figure 6).

This method provides the user with a significant reduction in measurement time with

respect to the classical method, [7] and a reduction in uncertainty on the estimate of the

FRF. The user gets these advantages for free as the main estimation procedure remains

FIGURE 6. Circular overlap with 2=3 overlap on two independent periods.


unchanged. In Figure 7 a simulation example is shown. The black curve is the estimated

FRF, the light gray curves are the level of the nonlinear distortion plus the measurement

noise and the dark gray curves are the level of the measurement noise. For the dashed

curves circular overlap was used, for the solid curves the classical method, [7], without

overlap, was used.

3 Conclusion

This allows the user for a specified measurement time to improve the frequency resolution

of the FRF or for a specified frequency resolution to reduce the measurement time. The

user receives these advantages for free as the estimation procedure remains unchanged.

This research was funded by a grant of the Flemish Government (GOA-54), Science Policy

programming (IUAP VI/4) and the OZR.

References

[4] L. Ljung, Identification of Linear Systems: Theory of the User. Upper Saddle River (NJ.):Prentice Hall, 1999.

[5] R. Pintelon and J. Schoukens, System Identification: a Frequency Domain Approach. Piscataway(NJ.): IEEE Press, 2001.

[6] T. Söderström and P. Stoica, System Identification. Prentice Hall, 1989.[7] R. Pintelon, W. V. Moer, and Y. Rolain, “Identification of linear systems in the presence of

nonlinear distortions,” IEEE Transactions on Instrumentation and Measurement, vol. 50, no. 4,pp. 855–863, 2001.

[8] J. Antoni and J. Schoukens, “A comprehensive study of the bias and variance of frequency-response-function measurements: Optimal window selection and overlapping strategies,”Automatica, vol. 43, pp. 1723–1736, 2007.

[9] K. Barbé, J. Schoukens, and R. Pintelon, “Frequency domain errors-in-variables estimation oflinear dynamic systems using data from overlapping sub-records,” IEEE Transactions onInstrumentation and Measurement, [Under Review].

0 0.1 0.2 0.3 0.4 0.5−100

−80

−60

−40

−20

0

Normalized Frequency

Mag

nitu

de [d

B]

FRF

NL−distortion+measurement noise

∆

∆

measurement noise

FIGURE 7. Simulation example on a Wiener nonlinear system, where D = 6dB the gain due to overlap.


F. NON-PARAMETRIC POWER SPECTRUM ESTIMATION WITH CIRCULAR OVERLAPKURT BARBÉ, JOHAN SCHOUKENS AND RIK PINTELON

An important application area of digital signal processing (DSP) is the power spectral

estimation of periodic and random signals. Speech recognition problems use spectrum

analysis as a preliminary measurement to perform speech bandwidth reduction and further

acoustic processing. Sonar systems use sophisticated spectrum analysis to locate subma-

rines and surface vessels. Spectral measurements in radars are used to obtain target

location and velocity information. The vast variety of measurements which spectrum

analysis encompasses, is perhaps limitless, and thus, the subject received a lot of attention

over the last 5 decades, [10]. It is important for all these that the measurement society

offers the best tools to extract this spectral information from finite length measurement

records.

There are mainly two classes of Power Spectrum Estimators (PSE): the parametric and the

non-parametric estimators. The class of parametric PSE, [11], tries to fit an auto regressive

(AR) model to the signal by minimizing a given cost function: Burg’s Entropy Method [12],

Yule-Walker method [13]. In contrast to parametric methods, non-parametric methods do

not make any assumptions on the data-generating process or model (e.g. auto regressive

model). There are 4 common non-parametric PSE available in the literature: the Perio-

dogram, the Modified Periodogram, Bartlett’s Method, and Welch’s Method, see [10].

However, the non-parametric PSE are modifications of the classical periodogram method

introduced by Schuster [14].

The periodogram is defined as, [10],

It is well known that the Periodogram is asymptotically unbiased, but inconsistent because

the variance does not tend to zero for large record lengths. One can show, [10],[15], that the variance on the periodogram for an ergodic weakly stationarysignal, [16], for : is asymptotically proportional to thesquare of the true power at frequency bin . The periodogram uses a rectangular time-

window, a weighting function to restrict the infinite time signal to a finite time horizon. The

Modified Periodogram, [10], uses a non-rectangular time window in order to decrease the

variance. By choosing a different truncation window, the width of the main lobe and the

amplitudes of the side lobes can be varied. On one hand, the width of main lobe determines

the spectral resolution.

On the other hand, the amplitudes of the side lobes determine the spectral masking effect.

Another way to enforce the variance to decrease is by averaging. Bartlett’s Method divides

the signal of length into segments, with each segment having length . The

Periodogram Method is then applied to each of the K segments. The average of the

resulting estimated power spectra is taken as the estimated power spectrum. One can show

that the variance is reduced by a factor , but a price in spectral resolution is paid, [10].

The Welch Method, [17], eliminates the trade off between spectral resolution and variance

Sxx(k) =1N

∣∣∣∣∣

N−1∑

n=0

x(n)e−2πjkn

N

∣∣∣∣∣

2

Sxx k( )

x n( ) n 0= N 1– Sxx2 k( )

k

N K L N K⁄=

K


in the Bartlett Method by allowing the segments to overlap. Furthermore, the truncation

window can also vary. Essentially, the Modified Periodogram Method is applied to each of

the overlapping segments and averaged out. However, the Welch estimator only uses

regular overlap as illustrated in Figure 8.

We propose a Power Spectrum Estimator by applying the Modified Periodogram Method to

each of the circular is an asymptotically unbiased estimator of the true PowerSpectrum. Further, we compared its performance with the Welch PSE. We showedthat asymptotic bias vanishes more slowly for the PSE with Circular Overlap thanfor the Welch PSE, but the standard deviation on the PSE with circular overlap canbe reduced with 20%. The user can receive this significant reduction in uncertaintyfor free as the main estimation process remains the same.

This research was funded by a grant of the Flemish Government (GOA-54), the Belgian

Program on Inter university Poles of attraction initiated by the Belgian State, PrimeMinister’s Office, Science Policy programming (IUAP VI/4) and the research councilof the VUB (OZR)

References

[10] J. G. Proakis and D. G. Manolakis, Digital signal processing: principles, algorithms, andapplications, 3rd ed. Prentice-Hall, Upper Saddle River (N.J.), 1996.

[11] P. Broersen, Automatic Autocorrelation and Spectral Analysis. Springer, Berlin, 2006.[12] J. Burg, “The relationship between maximum entropy spectra and maximum likelihood spectra,”

Geophysics, vol. 37, pp. 375–376, April 1972.[13] P. Stoica and R. Moses, Introduction to spectral analysis. Prentice-Hall, Upper Saddle River

(N.J.), 1997.

FIGURE 8. Overlapping sub-records extracted from 2 independent records with 2/3 overlap.

FIGURE 9. Circular Overlapping sub-records extracted from 2 independent records with 2/3 overlap.


[14] A. Schuster, “On the investigation of hidden periodicities,” Terrestrial Magnetism, vol. 3, pp. 13–41, 1898.

[15] D. Brillinger, Time series: data analysis and theory. Holt, Rinehart and Winston, London, 1975.[16] B. Porat, Digital processing of random signals: theory and methods. Prentice-Hall, Upper Saddle

River (N.J.), 1994.[17] P.Welch, “The use of fast fourier transform for the estimation of power spectra: A method based

on time averaging over short, modified periodograms,” IEEE Transactions on Audio andElectroacoustics, vol. 15, no. 2, pp. 70–73, june 1967.

G. IDENTIFICATION OF STABLE MODELS FROM NOISY MEASUREMENTST. D’HAENE, R. PINTELON

Introduction

In this project the stability enforcement algorithm of [18] is improved at several points. The

original stabilisation algorithm [18] consists of a two-step method. In the first step a (high)

degree model without stability constraints is estimated that passes the validation tests

(analysis cost function and whiteness test of the residuals). This step suppresses in an

optimal way the noise without introducing systematic errors. In the second step the

constraint will be added: The (high) degree model will be approximated by a stable one. For

this the weighted difference between the unconstrained model and the stable model is

minimized.

(EQ 1)

The big advantage of the two step procedure is that it provides models with uncertainty and

bias errors, which is not the case when stability is imposed during the noise removal.

The algorithm

Starting from stable initial values [18], one can calculated the error (EQ 1) to

calculate the step . This results in the new values that lower the cost function.

(EQ 2)

If is stable we start a new iteration, if not we decrease with a factor (EQ

2), until becomes stable.

The basic idea of this method is to leave some freedom to the transfer function parameters

(gain, poles, zeros) to lower the cost function. The reason why this algorithm creates better

models than the methods that generates the starting values, lays in the fact that the

created stable models are optimized in a user-defined frequency band, while the starting

values optimize a criterion over the whole frequency axis [20].

Improvements

The major drawback of this algorithm is that the poles that reach the constraint borders will

prevent the model to improve further. The reason for this is that the step will be reduced till

zero for every new iteration step. To overcome this problem the parameters are divided

in 2 groups and . fulfil the constraints and are allowed to be adapted by

the algorithm. contains all the parameters that violate the constraints; these will be

e sl Θ,( ) w sl( ) G sl( ) G sl Θ,( )–( )=

Θk k 0=( )δΘ Θk 1+

Θk 1+ Θk δΘ+=

G s Θk 1+,( ) δΘG s Θk 1+,( )

ΘΘV ΘF ΘV

ΘF


hold fixed. Now whenever a parameter of violate the constraints, it is moved to ,

so can be lowered by further adapting the remaining .

An equiripple stable model can also be imposed as follows:

(EQ 3)

with the user defined frequency band, where the user wants a good model through his/

here data. To find a minimax solution for a stable model based on the existing iterative

algorithm, an iterative reweighted least squares method is an obvious choice [19].

Next, a scheme to calculate high order stable approximations is proposed. There are two

easy ways to increase the order of the model without violating the stability constraints. By

adding or multiplying the stable transfer function with an other stable transfer

function that lowers the global error

.

(EQ 4)

In a first step is put in the iterative algorithm described in Section but only

is allowed to adapt. This step creates optimal starting values for

. The second step is a final optimization of where all

parameters are modified. This will further lower .

Conclusion

In this work an improved algorithm for finding stable approximations of unstable models

has been presented. In addition stable minimax solutions can be generated. The

performance of the improved algorithm is demonstrated on a real measurement example.

References

[18] T. D'haene R. Pintelon, G. Vandersteen, “An Iterative Method to Stabilize a Transfer Function inthe s- and z-domains”, IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 4,August, 2006, pp. 1192-1196.

[19] R. Vuerinckx, “Design of Digital Chebyshev Filters in the Complex Domain”, PhD thesis, VrijeUniversiteit Brussel, Brussels, February, 1998.

[20] Mari Jorge, “Modifications of rational transfer matrices to achieve positive realness”, SignalProcessing, no. 80, 2000, pp. 615-635.

H. MEASURING AND MODELLING WEAKLY NONLINEAR, SLOWLY TIME-VARYING SYSTEMSJ. LATAIRE, R. PINTELON

The framework of LTI (Linear Time-Invariant) systems has been shown to provide good

approximating models for a large number of real-life dynamic systems. However, the

assumption of time invariance is not always satisfied for some applications, such as

systems with a varying set point (e.g. flight flutter analysis) or systems with varying

parameters (e.g. pitting corrosion or metal deposition).

ΘV ΘF

e ΘV

minimizeΘ maxsl Ω∈ e sl Θ,( )( )

Ω

G sl Θ,( )Gextra sl Θextra,( )

etot sl( ) w sl( ) G sl( ) Gtot sl Θtot,( )–( )=

Gtot sl Θtot,( ) G sl Θ,( ) Gextra sl Θextra,( )+=

Gtot sl Θtot,( ) G sl Θ,( ) Gextra sl Θextra,( )⋅=

Gtot sl Θtot,( )ΘextraGextra sl Θextra,( ) Gtot sl Θtot,( )

Θtot etot sl( )


The goal of this project is to gain insight into the frequency domain behaviour of weakly

nonlinear, slowly time-varying systems. More specifically, the already well-established

errors-in-variables methods for identifying LTI systems is generalised to linear time-varying

(LTV) systems.

The proposed method models the systems under test by ordinary differential equations with

time-varying coefficients (piece-wise polynomials), as described by the following equation

(subscript 0 denotes noiseless signals):

with (EQ 5)

Equation (EQ 5) is applied to each time-piece (as

shown in Figure 10). Any linear time-varying

system can be approximated arbitrarily well by a

model of this class, as long as the chosen time-

pieces are taken short enough or the order of the

polynomials are high enough. Intuitively, the

frequency band where the time variations of the

parameters are important should be much lower

than the frequency band that is important for the

intended application.

Working in the frequency domain involves taking the Laplace transform of (EQ 5) and using

the approximating discrete fourrier transforms of the sampled measured input and output

signals. The identification performed in an errors-in-variables framework consists of finding

the parameters and that minimize the sum of squared differences between the left

and right hand side of (EQ 5) (called the equation error), weighted by the variance of this

difference. Frequency domain methods have the advantage that they can very easily

handle non-parametric models of the noise power spectrum to calculate the variance of the

equation error.

Multisines are used as excitation signals.

These signals have been shown to be

very useful in identification. The response

to a multi sine provides easily extractable

non-parametric information about the

system. For time-invariant systems they

help to detect nonlinear distortions, as

demonstrated in [21]. For linear time-

varying systems, they provide an idea of

the speed of the variation of the instanta-

neous frequency response function. Also,

a non-parametric estimate of the noise

level is more easily determined. This is

illustrated on the measured output

an t( )tn

n

dd y0 t( )

n 0=

Na

∑ bn t( )tn

n

dd u0 t( )

n 0=

Nb

∑=an t( ) an 0, an 1, t an 2, t2 …+ + +=

bn t( ) bn 0, bn 1, t bn 2, t2 …+ + +=

TP1 TP2 TP3 TP4 TP5

anbn

timeFIGURE 10. . Inside each time-piece (TP) the coefficients and are de-

scribed by a polynomial. Continuity constraints are applied at the bounda-

ries of these time-pieces.

an bn

an p, bn p,

5 10 15

−90

−70

−50

−30

−10

Frequency (kHz)

Out

put S

pect

rum

(dB

V)

FIGURE 11. . Output spectrum of a time-varying electronic circuit excited by a multisine. A distinction

is made between the excited lines (black arrows) and the non-excited lines (grey dots). An estimate of the noise is given by the black full line. The grey full line is the noise spectrum estimated using classical

LTI measurements.


spectrum of a second order electronic circuit with a time-varying resonance frequency

(Figure 11). A multi sine excitation creates regions with high Signal-to-Noise ratio (SNR)

around the excited frequency lines (denoted by the black arrows) and regions with low SNR

(in-between two excited frequencies). In the latter regions, data processing allows for

significant reduction of the influence of the deterministic signal, giving a fair estimate of the

noise power spectrum [22]. An estimate of the noise is given by the full black line. Note

that this estimate is obtained from one well-designed experiment only. This is important

since accurate repetition of measurements is difficult, if not impossible, when dealing with

time-varying systems. The estimate coincides quite well with the noise spectrum estimated

using classical LTI measurements tools of the same system where the time variations are

frozen (grey full line)..

The identification method has been

applied to a second order, mainly linear

electronic circuit, with time-varying

resonance frequency. The resulting

instantaneous frequency response

function is plotted as a function of time in

Figure 12.

References

[21] Pintelon R., G. Vandersteen, L. DeLocht, Y. Rolain and J. Schoukens(2004) - Experimental characterizationof operational amplifiers: a systemidentification approach - IEEE Trans.Instrum. Meas., vol. 53, no 3, pp. 854-876.

[22] Lataire J., Pintelon R. (2007) - Estimating a Non-parametric, Coloured Noise Model for Linear,Slowly Time-varying Systems - internal note.

I. INITIAL ESTIMATES FOR WIENER-HAMMERSTEIN MODELS USING THE BEST LINEAR APPROXIMATIONLIEVE LAUWERS, JOHAN SCHOUKENS, RIK PINTELON

A method is proposed to initialize the linear dynamic blocks of a Wiener-Hammerstein

model. The idea is to build these blocks from the poles and zeros of the Best Linear

Approximation of the system under test, which can easily be extracted from the data. This

approach results in an easy to solve problem (linear-in-the-parameters) from which initial

estimates for the linear dynamics can be obtained. The proposed method is applied to

measurements from an electronic Wiener-Hammerstein circuit.

A Wiener-Hammerstein model is a block-oriented structure that consists of two linear

dynamic systems and with a static nonlinearity in between (see Figure 13).

The most difficult step in the identification of such models is to generate starting values for

the linear dynamic blocks. Only few papers give hints how to obtain good initial estimates

0 1 2 3−35

−30

−25

−20

−15

−10

−5

frequency (kHz)

FR

F (

dB)

FIGURE 12. . The instantaneous frequency re-sponse function of the identified model at different time instants. The resonance frequency is clearly

evolving as a function of time.

t

G1 G2 f .( )

f p( )G1 G2

FIGURE 13. Wiener-Hammerstein system.

u yp q


[23],[24]. We present a non-iterative method to initialize the Wiener-Hammerstein model

by using the Best Linear Approximation of the system under test [25]-[27]. The

idea is to split the system in two subsystems (see Figure 14), and to write the linear

dynamic blocks as a linear combination of basis functions, and , which contain the

poles and zeros of , respectively:

(3)

The goal is then to find the proper coefficients of these basis functions.

In the following, we will briefly sum up the different steps of the initialization procedure.

• Determine : first nonparametrically, then parametrically.

• Expand and into partial fractions.

• From this expansion, construct basis functions for and .

• Use a general model structure (see Figure 15) to obtain a problem that is linear-in-

the-parameters .

• Set continuity: .

• Estimate and by solving the resulting Total Least Squares problem 28.

• Compose the initial estimates: the estimated linear parameters determine the

coefficients of the basis functions. and are then composed parametrically

by making the linear combination in (3). Furthermore, the linear output of the non-

linearity in each subsystem (see Figure 15) is an initial (nonparametric) estimate

for the corresponding intermediate signal or .

GBLA

FIGURE 14. Decomposed Wiener-Hammerstein system.

f1 f2u yz1 z2

G1polesGBLA⎝ ⎠⎛ ⎞ G2

1– zerosGBLA⎝ ⎠⎛ ⎞

Wi HiGBLA

G1 θ1L i( )Wi

i 1=

r

∑=

G21– θ2

L i( )Hii 1=

s

∑=⎩⎪⎪⎨⎪⎪⎧

θL

GBLA

GBLA GBLA1–

G1 G21–

θL θNL,( )

FIGURE 15. Generalized model structure.

Wr

…

W1

z1u

Hs

…

H1

z2 yθ2

L

θ2NL

θ1L

θ1NL

++

p q

z1 z2=

θL θNL

θL

G1 G21–

p q


u y

The proposed initialization method is successfully applied to input/output measurements

from an electronic circuit with a Wiener-Hammerstein structure [29].

The resulting initial estimates are shown in Figure 16.

We conclude that the pass band of both filters is retrieved, as well as the transmission zero

of the second filter. Hence, the initial estimates of both filters give us a good idea about the

dynamics of the linear blocks in the Wiener-Hammerstein structure. Also, the spline fit

through the scatter of points is of good quality: it coincides with the true nonlinearity in the

interval . However, outside this region the fit fails, because there the spline is used

in an extrapolating way. This will not significantly influence the initialization procedure,

since only a marginal number of samples resides outside the interval.

References

[23] P. Crama, J. Schoukens. Computing an Initial Estimate of a Wiener-Hammerstein System with aRandom Phase Multisine Excitation. IEEE Trans. Instrum. Meas., Vol. 54, No. 1, pp. 117-122,2005.

[24] J. Schoukens, R. Pintelon, J. Paduart, G. Vandersteen. Nonparametric Initial Estimates forWiener-Hammerstein Systems. Proc. 14th IFAC Symp. Syst. Identification, 2006, Newcastle,Australia, pp. 1033-1037.

[25] J. Schoukens, R. Pintelon, T. Dobrowiecki, Y. Rolain. Identification of linear systems withnonlinear distortions. Automatica, Vol. 41, No. 3, pp. 491-504, 2005.

[26] R. Pintelon, J. Schoukens (2001). System Identification. A Frequency domain approach. IEEEPress, New Jersey.

[27] J.S. Bendat, A.G. Piersol (1980). Engineering Applications of Correlations and Spectral Analysis.Wiley, New York.

[28] S. Van Huffel, J. Vandewalle (1991). The Total Least Squares Problem: Computational Aspectsand Analysis. Frontiers in Applied Mathematics. SIAM, Philadelphia.

[29] G. Vandersteen. Identification of linear and nonlinear systems in an error-in-variables leastsquares and total least squares framework. PhD Thesis, Vrije Universiteit Brussel, 1997.

J. THE USE OF NEUTRON NOISE ANALYSIS TECHNIQUES FOR THE DETERMINATION OF THE MODERATOR TEMPERATURE COEFFICIENT OF A PRESSURIZED WATER REACTORGRIET MONTEYNE

Scope

In Pressurized Water Reactors (PWRs), the water moderates and cools the reactor.

Neutrons must be moderated (slowed down) because slow neutrons are more likely to

cause fission of the enriched uranium. In order to keep the fission chain reaction running in

the reactor the average number of neutrons from one fission that cause another fission

0 5 10

−20

−10

0

Am

plitu

de [d

B]

Frequency [MHz]−2 −1 0 1 2

−2

−1

0

1

Out

put

Input0 5 10

−80

−60

−40

−20

0

Am

plitu

de [d

B]

Frequency [MHz]

FIGURE 16. Parametric initial estimates (grey line) for the different blocks of a physical Wiener-Hammerstein system. The true filter characteristics and the true nonlinearity are given by the black

line. The dark grey scatter of points is the nonparametric initial estimate of versus .p q

p q

1– 1[ , ]

1– 1[ , ]


must be equal to one. The reactivity of the reactor represents the deviation of the real

situation compared to this ideal situation.

The moderator temperature coefficient (MTC) is an important safety parameter in PWRs. It

reflects the reactivity feedback of a change in temperature of the water. It is defined as the

change of reactivity (δρ) induced by an inlet temperature change of the water, divided by

the core average water temperature change (δTmave) [30].

For safety reasons, this MTC is to be kept negative at all times. An increase in temperature

(due to an accidental situation) would hence reduce the reactivity of the core and will lead

to a stabilization of the temperature excursion. On the other hand, a reduction of the

temperature (over-cooling) would lead to an increase of the reactivity of the core.

During the operational cycle of a PWR, this coefficient will increase in absolute value (i.e.

become more negative) among other things, due to the decrease of the boron concen-

tration. The MTC has to be measured at least two times during a cycle: at the beginning, to

ensure that it is indeed negative and at the end of the cycle, to ensure that it does not go

beyond a reference value (to avoid a too large reactivity increase in case of a temperature

drop event).

The measurement at the beginning of the cycle is quite accurate and straightforward

because the reactor is not at full power at the time of the measurement. However, at the

end of the cycle, the measurement of the MTC is difficult. The reactor is at full power and

the classical measurements all involve a perturbation of the reactor configuration (boron

dilution, control rod withdrawal,…). The application of neutron noise analysis methods

would allow to estimate the MTC on-line (and at any moment during the cycle) without

having to perform such a perturbation of the reactor core.

This technique is based on the measurement and analysis of the power neutron- and

temperature-noise. By using the appropriate noise estimator, the MTC can be extracted

from these noise measurements. The power neutron fluctuations are induced by fluctua-

tions or oscillations of the reactor properties, i.e. displacement of core components,

temperature or density variations etc. [31]

Nevertheless, several attempts to monitor the MTC by noise analysis, revealed that the MTC

was systematically underestimated by a factor 2 to 5. It was demonstrated by Demazière

[3], both via modelling and measurements, that the deviation of the usual MTC noise

estimator from its expected value was mostly due to the spatially radially heterogeneous

structure of the moderator temperature noise. Therefore a new estimator was proposed.

This estimator shows promising results but more work needs to be done [32].

Objectives & work plan for the PhD work

The goal of the PhD research is to investigate the feasibility of neutron noise analysis

techniques to determine the Moderator Temperature Coefficient (MTC) with sufficient

MTC δρ

δTmave

-------------=


accuracy, and if successful, to implement this technique in one of the Belgian Nuclear Power

Plants. To reach these goals, the following objectives are defined:

• Study the different theoretical models and approaches;

• Validate the selected model via computer simulations;

• Set up the measurement chains including testing and validation of the hardware;

• Prove the feasibility by experiments at the BR1 reactor of SCK•CEN;

• Implement the technique at a Belgian reactor using the instrumentation present in

the reactor;

• Validate the technique in these conditions by comparing by other experiments or

reactor calculations;

• Define the recommendations for the use of this technique in other reactors or Gen-

eration III reactors.

References

[30] “Calculation and Measurement of the Moderator Temperature Coefficient of Reactivity for WaterModerated Power Reactors, an American National Standard”, ANSI/ANS-19.11-1997, AmericanNuclear Society, 1997

[31] I. Pázsit, “Transport Theory and Stochastic Processes, Lecture notes”, Chalmers University ofTechnology, Göteborg, Sweden, 2007

[32] C. Demazière, "Development of a Noise-Based Method for the Determination of the ModeratorTemperature Coefficient of Reactivity (MTC) in Pressurized Water Reactors (PWRs)", PhD thesis,Chalmers University of Technology, Göteborg, Sweden, 2007

K. CONTINUOUS-TIME OPERATIONAL MODAL ANALYSIS IN THE PRESENCE OF HARMONIC DISTURBANCES (R. PINTELON, B. PEETERS1 AND P. GUILLAUME2)

Operational modal analysis (OMA) allows to identify the modal parameters from the

measured response to unknown random perturbations of a mechanical structure in

operation. However, in all applications with rotating components (e.g. helicopters, turbines,

diesel motors, …), the structural vibration in operation is a combination of the response to

the random perturbation and the harmonic excitation due to the rotating components.

Classical OMA methods fail if the harmonic disturbance is close to, or coincides with a

resonance frequency of the structure. Therefore, these methods have been extended to

deal with harmonic disturbances with a known, fixed frequency. However, in many applica-

tions (e.g. helicopters, wind turbines, diesel motors, …) the frequencies of the harmonic

disturbances vary in time. This results in a broadening of the harmonic peaks in the

response spectrum that cannot be modelled accurately by a pole on the unit circle or a

sinewave with a constant frequency. If the harmonic disturbance with time-varying

frequency is close to an eigenfrequency of the structure, then the extended OMA proce-

dures fail to identify accurately the modal parameters. Hence, there is a need for OMA

methods that can deal with non-stationary harmonic disturbances.

This project presents three methods for suppressing the influence of harmonic disturbances

with unknown time-varying frequencies in operational modal analysis. The first method is

the simplest one and consists in cutting out the disturbing harmonics in the response

spectrum. The second method removes the disturbing harmonics via a non-parametric

1. LMS International2. VUB, dept. MECH


estimate of the time-varying frequency, and is used as starting value for the third method.

The third method suppresses the harmonic disturbance via a parametric frequency

modulated signal model that is identified simultaneously with the noise model. In

opposition to the classical discrete-time OMA algorithms, the random part of the response

is modelled here as continuous-time filtered band-limited white noise. It has the advan-

tages to match more closely the true physics and to allow for an easier calibration of the

systematic errors introduced by the measurement setup. The present project handles the

single output case only.

Figure 17 shows the acceleration spectrum measured at the gear box on the roof of a

helicopter which is flying at a forward speed of 148 km/h. One can clearly distinguish the

fundamental (3.47 Hz), the fifth (17.8 Hz), and the sixth (21.4 Hz) harmonic disturbance.

Note that the skirt of the fifth harmonic peak (17.8 Hz) almost completely masks the struc-

tural resonance at 14.5 Hz. It can be seen that the sum of a frequency modulated signal

(three signal and six phase harmonics) and continuous-time filtered band-limited white

noise (twelve zeroes and eight poles) explains the measurements very well. As a validation

test the instantaneous frequency extracted from the identified harmonic signal model is

compared in Figure 18 to the rotor speed derived numerically from the measured tacho

signal (1 pulse per rotation of the propellers). It can be seen that both the non-parametric

(starting value) and parametric estimate follow quite well the general trend of the

0 5 10 15 20 25−100

−50

0

50

Frequency (Hz)

Am

plit

ud

e (

dB

)

Measurement

0 5 10 15 20 25−100

−50

0

50

Frequency (Hz)

Am

plit

ud

e (

dB

)

Signal model

0 5 10 15 20 25−100

−50

0

50

Frequency (Hz)

Am

plit

ude (

dB

)

Noise model

0 5 10 15 20 25−100

−50

0

50

Frequency (Hz)

Am

plit

ude (

dB

)

Signal and noise model

FIGURE 17. : Acceleration spectrum (top left) at the gear box on the roof of a helicopter flying at a forward speed of 148 km/h. Top right: comparison between the measured acceleration (black), and the estimated frequency modulated harmonic signal model (light gray: model; and dark gray: stan-dard deviation model). Bottom left: comparison between the difference of the measured acceleration and the estimated signal model (black), and the estimated continuous-time noise model (light gray: model; and dark gray: standard deviation model). Bottom right: comparison between the measured (black) and modelled (light gray: signal + noise model; dark gray: standard deviation model) accel-

eration power spectrum.


measured frequency, but not the fast frequency variations. The reason for this is that the

data does not contain enough high frequency phase information: indeed only six phase

harmonic could be identified.

Reference

[33] Pintelon, R., B. Peeters, and P. Guillaume (2007). Continuous-time operational modal analysisin the presence of harmonic disturbances, Mechanical Systems and Signal Processing, in press.

L. BLIND MAXIMUM LIKELIHOOD IDENTIFICATION OF WIENER AND HAMMERSTEIN BLOCK STRUCTURESL. VANBEYLEN, R. PINTELON, AND J. SCHOUKENS

This work handles the identification of discrete-time nonlinear Wiener and Hammerstein

systems from output measurements only (= blind identification).

These models are widely used, because of their large number of applications (biological,

aerospace engineering, speech processing, telecommunications,...), their easy physical

interpretation, and their structural simplicity. Both models are formed by a cascade of a

linear time-invariant (LTI) dynamic system , and a static nonlinearity , in this or in

reversed order (see Figure 19). In contrast to the classical case, where both input and

output signals are available for measurement, blind identification does not need input

measurements. This is typically the case with sensors (e.g. accelerometers) where an

electrical signal is measured, while the actual quantity is not directly measurable.

.Instead, additional assumptions are

needed: the input is white Gaussian

noise, the output is observed without

errors, and the nonlinear characteristic

is invertible and has a non-zero (finite)

derivative. Also the linear filter is

assumed to be stable and minimum in

phase.

In both cases, a Gaussian maximum

likelihood estimator has been

constructed, and its Cramér-Rao lower

bound (allowing to calculate uncertainty

0 10 20 30 40

213.8

214.0

214.2

214.4

214.6

Time (s)

Roto

r speed (

rpm

)

0 10 20 30 400.00

0.02

0.04

0.06

Time (s)

Sta

nd

. d

ev.

(rp

m)

FIGURE 18. : Rotor speed of the helicopter. Left: comparison between the measured (black) and the estimated (dark gray: non-parametric; light gray: parametric) rotor speed. Right: standard devi-

ation of the parametric estimate of the rotor speed (dark gray).

H f

f H

fH

(a)

(b)

FIGURE 19. Block schemes of (a) the Wiener and (b) the Hammerstein model. Both are connections of a LTI system represented by and a static non-

linearity represented by .H

f


bounds on functions of the estimates) has been derived. A two-step procedure for gener-

ating high-quality starting values has been developed as well.

These methods are, e.g., suited for calibration of nonlinear measurement devices, or to

model certain non-Gaussian, coloured time series or model residuals.

Since the methods are not robust to output noise, we are currently investigating the case

with a white, Gaussian noise source at the output.

M. EXPERIMENTAL STABILITY ANALYSIS OF NONLINEAR SYSTEMSL. VANBEYLEN, J. SCHOUKENS

This research project concerns the development of a simple tool to verify if a nonlinear

system captured in a feedback loop, which is suspected to be possibly unstable, is stable or

not. This is done starting from experimental data on the physical feedback system, and

without building an additional model.

Although classical techniques

are available to guarantee

the stability of nonlinear

systems (e.g. Lyapunov

methods), the major problem

is that, in practice, when a

nonlinear model is

constructed (which is already

extremely complicated), it is

even harder to validate such

a nonlinear model. So there

is no guarantee that the

model is good everywhere,

and there is a large risk for

remaining model errors. Due

to these remaining model

errors, the nonlinear

feedback loop can become unstable. We will call a system stable, if an input with bounded

variance results in an output with bounded variance (power-related stability definition).

The idea is to split the nonlinear feedback system in two parts: (i) a (known) stable part

that is modelled and (ii) another (possibly unstable) remaining part, which accounts for all

Stable part

Remaining part Residual

Stationary randominput with bounded

variance

FIGURE 20. Block schematic representation of the equivalent model

−100 −50 0 50 100−100

−50

0

50

100

x1 = y

x2 =

dy/d

t

FIGURE 21. Phase portrait of the unperturbed second order nonlinear system. The border of the attractor is indicated in

grey.


unmodelled effects. Both are connected in parallel (sum) (see Figure 20). The output of the

latter will be called residual. If the excitation has a bounded variance, the variance of the

output of the first part will be finite, even when instability occurs in the nonlinear feedback

system. Therefore, in that case, the residual will have an infinite variance. Hence, a statis-

tical description of the residual is a key to stability analysis.

This can be done by means of an extreme value model, which is a classical tool for

describing rare extreme events. It has been shown that for a very wide class of distribu-

tions, the excesses over a sufficiently high threshold are generalized-Pareto distributed.

Finally, specific properties about the finiteness of the variance as function of the parameters

of the generalized-Pareto distribution are used to formulate conclusions about the stability.

Especially the shape parameter estimate and its variance estimate (Cramér-Rao lower

bound) are of main importance.The process is as follows:

a) data is collected

b) a stable model is identified

c) the residual is calculated

d) a generalized-Pareto distribution is fit onto the extreme values of the residuals

e) a statistical decision about stability is drawn (based on hypothesis testing) from the

shape parameter.

The link with the classical stability in state-space form is illustrated in Figure 21, repre-

senting the phase portrait of a second order nonlinear system formed by a linear system in

the feedforward branch and a cubic static nonlinearity in the feedback branch. Due to the

random input, the state is permanently ‘jumping’ in the phase plane. Since some of the

state trajectories are seen to drift away from the stable equilibrium in the origin, there is a

risk to jump on these trajectories, located outside the attractor. This is an alternative inter-

pretation to the instability as seen in this research

N. AIC MODEL SELECTION FOR SHORT DATA RECORDSBEN VAN DER HASSELT, ANOUK DE BRAUWERE, JOHAN SCHOUKENS, RIK PINTELON

Introduction

Our goal is to select the best model to describe a given short data record, i.e. where the

number of data is not significantly larger than the number of parameters. We have to cope

with a trade off between reducing systematic errors (complex models) and reducing the

noise sensitivity (simple models).

The properties of model selection are well understood if the number of data goes to .

But how can we extend the model selection rules to the finite situation?

A good model fits the data well. Hence, it will be associated with a low residual weighted

least squares (WLS) cost function

, (EQ 6)

N ∞

VWLS θ( ) e θ( )TW θ( ) e θ( )=


Where is the vector of addociated minimal residuals and is the

model. represents the estimated model parameters, found by minimizing the cost

function (EQ 6)

(EQ 7)

A balance between model flexibility and noise sensitivity is necessary. One model selection

strategy considers the minimization of a criterion of the geberal form ,

with the minimal value of the cost function as defined in (EQ 6)-(EQ 7), the

number of observations and the number of free model parameters. the penalty factor

corrects for overfitting. Formally, the best model should be the best

compromise betweed goodness-of-fit ( , decresing with ) and model variability

( , increasing with ).

Akaike Information Criterion (AIC)

Classically, in the prediction error framework, the AIC rule was presented for cases where

the noise model is estimated together with the plant model [35]. It was constructed to

perform well in the limit of large samples :

(EQ 8)

In [36], one has further modified this model selection cretierion in order to improve their

performance for short data records :

(EQ 9)

Model selection

suppose we want to choose between two models and to characterize a certain

dataset with Measurements. has parameters and has parameters

with . We also know that is the corrected model.

Then, for short data records, a modified AICS will select model iff

(EQ 10)

e θ( ) y y θ( )–= y θ( )

θ

θ minθ

VWLS θ( )arg=

VWLS θ( )p N nθ,( )

VWLS θ( ) N

nθ

p N nθ,( ) VWLS θ( )

VWLS θ( ) nθp N nθ,( ) nθ

N ∞→( )

VWLS θ( )N

--------------------- 12nθN

--------+⎝ ⎠⎛ ⎞

N n∞→( )

VWLS θ( )N nθ–

--------------------- 12nθ

N nθ–---------------+⎝ ⎠

⎛ ⎞

M1 M2N M1 n1 θ1 M2 n2

θ2 n1 n2< M2

M2

VWLS θ1ˆ( )

N n1–----------------------- 1

2n1N n1–---------------+⎝ ⎠

⎛ ⎞ VWLS θ2ˆ( )

N n2–----------------------- 1

2n2N n2–---------------+⎝ ⎠

⎛ ⎞>


Because is the correct model, we have , such that (EQ 10) can be

written as:

(EQ 11)

Moreover, will be selected by a -test [37] iff

(EQ 12)

Both righthandsides of (EQ 11) and (EQ 12) can be equated such that [34]:

(EQ 13)

For a given , and we can verify with which significance level an AICS model

selection corresponds.

References

[34] T. Söderström, P. Stoica (1988) System Identification, Prentice Hall, Englewood Cliffs.[35] L. Ljung (1999) System Identification: Theory for the User (2nd ed.), Prentice Hall, Upper

Saddle River.[36] F. De Ridder, R. Pintelon, J. Schoukens, D.P. Gillikin, Modified AIC and MDL Model Selection

Criteria for Short Data Records, IEEE Transactions on Instrumentation and Measurement, Vol.54, No. 1, February 2005.

[37] A. de Brauwere, F. De Ridder, R. Pintelon, M. Elskens, J. Schoukens, W. Baeyens, ModelSelection through a statistical analysis of the minimum of a weighted least squares costfunction, Elsevier Chemometrics and Intelligent Laboratory Systems 76 (2005) 163 173.

O. NONLINEAR ANALYSIS OF FLUTTERMATTIJS VAN DE WALLE

Introduction

Flutter is an aerolastic phenomenon

that makes structures oscillate when

surrounded by a moving fluidum.

Flutter can lead to fatigue and/or

failure on the wings, flaps and

fuselage of an airplane. Laborious

testing needs to be done on aircraft

to ensure safe operation within a

certain flight envelope. While flutter

is inherently nonlinear, most models use a linear framework to predict the critical speed at

M2 VWLS θ2( )ˆ N n2–≈

VWLS θ1( )ˆ N n1–( )1

2n2N n2–---------------+

12n1

N n1–---------------+

-------------------------

⎝ ⎠⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟⎛ ⎞

>

M2 χ2

VWLS θ1ˆ( ) χN n1 1 α–,–

2>

N n1–( )1

2n2N n2–---------------+

12n1

N n1–---------------+

-------------------------

⎝ ⎠⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟⎛ ⎞

χN n1 1 α–,–2=

N n1 n2

FIGURE 22. Experimental setup 2DOF


which oscillations might occur. The aim of this work is the construction of a nonlinear model

to more accurately predict the onset of oscillatory behaviour.

Experimental setup

Depicted in figure 1 is a schematic representation of the current two degree-of-freedom

setup used for wind tunnel testing. Rigid airfoil sections are being used for simplicity. The

plunge spring represents the bending stiffness of a real wing and the pitch spring repre-

sents the torsional stiffness. The test setup allows arbitrary adjustments to the stiffness in

both the pitch and the plunge direction.

Excitation methods

What sets this project apart from other flutter-related research is the necessity to impose

an excitation signal upon the structure [38]. This signal needs to have a user- defined

spectrum and insert an amount of power of the same order of magnitude (preferably

bigger) as the power inserted through turbulence and unsteady airflow during wind tunnel

tests. Several methods are currently considered to be viable:

• Electro dynamic shaker (fixed): while possibly the most straightforward and obvi-

ous means of excitation (the shaker being fixed, exciting the airfoil through a sting-

er), caution needs to be taken not to let the often violent behavior of the wing harm

the shaker.

• Electro dynamic shaker (inertial): a shaker can be mounted on the structure and

exert force inertially. This technique implies stringent constraints upon the weight

and size of the device and also still involves considerable risk to damage the device.

• Electro dynamic shaker (inertial + pneumatic/ hydraulic): to alleviate the two

above mentioned techniques from the constraints they impose, a dedicated wing

structure can be built that holds only the mass (not the shaker) for inertial excita-

tion. This mass can be controlled through an internal pneumatic or hydraulic sys-

tem. In this way, an optimal, external shaker can be used. Even though being the

most involved means of excitation, this method is expected to insert the most pow-

er into the structure within the bandwidth of interest.

• Compressed air: it has been shown that a jet of air, aimed at the structure can be

used as means of excitation and that, albeit being dependent upon the type of valve

used, a close to linear relation exists between pressure and force exerted on the

structure [39]. Furthermore, both proportional and on-off type valves can be used

to produce signals with controlled spectral content [40]. Pitfalls with thistechnique are the high pressure needed for a given amount of input powerand the inherent, hard to model, interaction between the airflow in thewind tunnel and the controlled excitation.

• Flap: a dedicated wing can be built that incorporates a moving flap eitheron top of the wing or at the trailing edge. The flap can be controlled with apiezoelectric transducer and, through the use of a pulse width modulatedcontrol signal, an excitation in the bandwidth of interest is achieved.


References

[38] R. Pintelon and J. Schoukens, System Identification: a Frequency Domain Approach, IEEEPress, 2001.

[39] S. Vanlanduit, F. Daerden, P. Guillaume, Experimental modal testing using pressurized airexcitation, Journal of Sound and Vibration, 299, 83–98, 2007.

[40] K. Godfrey, Perturbation Signals for System Identification, Prentice Hall, 1993.

P. THE USE OF INVERSE MODELLING IN ILC METHODSANNE VAN MULDERS, JAN SWEVERS*, JOHAN SCHOUKENS* K.U.LEUVEN

The goal of this project is the identification of local inverse nonlinear models. These models

will be used in Iterative Learning Control (ILC) to generate an updated input from the

observed output. The aim is to improve the performance of the ILC method in case of

strongly nonlinear systems.

I. Iterative Learning Control

ILC is a control method that iteratively improves the performance of a system. It can be

used for systems that execute the same task several times (under the same operating

conditions and with the same initial conditions), such as a robot arm that performs the

same part of a fabrication process over and over again. By using information from previous

iterations, the control input is modified in order to get a better approximation of the desired

trajectory. A widely used ILC learning algorithm [41] is

where denotes the control input with time index and iteration index .

is the performance or error signal, which describes the difference

between the desired and the measured output and are the Q-filter and

learning function, respectively. Both are a function of the forward time-shift operator

:

Application to nonlinear systems

If the inverse model of a nonlinear system would be available, convergence of the ILC

method could be faster than by using a linear approximation. That’s why identification of

the inverse model should be given due attention. That is the aim of this work.

Calculating the inverse model

Classic approach: A nonlinear model is used to calculate from a measured

by solving: . One possibility is to use to iteratively determine by

minimizing . Another option is to identify directly the inverse model

. Although this looks simple and attractive, some problems should be studied

in more detail, such as bias and stability.

uj 1+ k( ) Q q( ) uj k( ) L q( )ej k 1+( )+( )=

uj k( ) k j

ej k( ) yd k( ) yj k( )–=Q q( ) L q( )

q qx k( ) x k 1+( )=

y P u( )= ud

yd yd P ud( )= P ud

yd P u( )–

u Pinv y( )ˆ=


1. Bias errors can occur when noise is present on the input of the inverse system (i.e. the

output of the system). On the other hand, this noise is also present when a new input is

calculated from a measured output. A first study of this problem will be made.

2. Another difficulty is the possible instability of the inverse model (and/or system) in the

case of nonminimum phase systems. We will try to work in the frequency domain,

because there, the calculation of the inverse of an unstable linear system can be done

without any problems: the dynamic relations are reduced to algebraic relations between

the spectra of the input and output. This approach should be extended to nonlinear

systems. An alternative is to split up the transfer function into a minimum phase and a

maximum phase part and to apply causal filtering and anticausal filtering on resp. the

minimum phase and maximum phase part.

3. Nonlinear modelling is difficult. Therefore, we will only apply it where it’s necessary. The

idea is to identify a global linear model and to use local nonlinear models where the

linear model is insufficient.

PNLSS Models

In this work we will use Polynomial Nonlinear State Space Models to describe (approxi-

mately) the nonlinear systems. The expressions of the PNLSS models are simply an

extension of the linear state- and output equations, as can be seen below [42]:

and contain monomials in and . The matrices E and F contain the

coefficients associated

with these monomials.

The previously discussed methods should be generalised to be applicable on this nonlinear

structure.

References

[41] D. A. Bristow, M. Tharayil, A. G. Alleyne. A Survey of Iterative Learning Control A learning-based method for high-performance tracking control. IEEE control systems magazine, vol. 26,pp. 96-114, June 2006.

[42] J. Paduart. Identification of Nonlinear Systems using Nonlinear Polynomial State Space Models.PhD Thesis, Vrije Universiteit Brussel, 2007.

[43] O. Markusson. Model and System Inversion with Applications in Nonlinear System Identificationand Control. PhD Thesis, Royal Institute of Technology, Stockholm, Sweden, 2002

x k 1+( ) Ax k( ) Bu k( ) Eζ k( )+ +=

y k( ) Cx k( ) Du k( ) Fη k( )+ +=

η k( ) ζ k( ) x k( ) u k( )


3. TEAM C: THE STUDY OF MICROWAVE SYSTEMS

A. Introduction to Measurement, Modelling and Identification of Microwave Systems

The current trend in the microwave world is to model systems both in their linear and

nonlinear operating region. The linear models are used to describe small signal behaviour

while the nonlinear models are required to describe power efficiency or harmonic distortion.

Further more this modelling technique is measurement based and two-layered:

In a first step insight is gained in the device behaviour. It can be put in parallel with the

frequency response extraction for a linear system. In this step, a restriction of the class of

allowable nonlinear systems is needed. To allow to keep the Volterra model as a backbone,

the class of PISPO systems is introduced. A PISPO system is a system that, when excited

with a periodic waveform, outputs a periodic waveform with the same periodicity.

A preprocessing step based on the behaviour of PISPO systems and the available

measurement data allows a trained user to be able to understand the mechanisms behind

the general behaviour of the DUT. This approach is essentially a non-parametric modelling

step.

In a second step a parametric model is extracted for the device, that can then be used in a

simulator to predict the device behaviour. This can be put in parallel with the extraction of a

rational transfer function model in the linear case. Combination of the output of this model

with the first layer modelling allows to validate the model directly against the measure-

ments. Differences between model and measurement can then be used to decide which

model structure must be adopted based on a fair comparison.

Instrumentation contributions

For linear components, a normal vectorial network analyser clears the job, since these

devices are fully characterized by their s-matrix. For nonlinear devices or components, it is

of vital importance that the excitation conditions of the devices in test and operation condi-

tions match as closely as possible. Highly flexible instrumentation setups are therefore

required.

A flexible non-linear network analyser was developed to this end. The device combines the

advantages of reconfigurability and ease of use and provides an efficient and easy to use

data handling capability. As for today, the instrument handles fully absolute calibrated two-

and three-port measurements in CW, modulated and pulsed regime.

Modelling contributions

In the linear domain, the proposed modelling approach is different in the sense that it does

not rely on circuit descriptions. In the end, the proposed black-box approach delivers

models that properly describe the linear dynamics of the DUT, detect the presence of

modelling errors, have by far better numerical properties than their circuit-based equiva-

lents and operate almost automatically.


In the non-linear range, a first trial to simple, non-parametric modelling was performed. It

is promising in the sense that the approach yields simple, yet effective ways to estimate if a

system behaves as a static non-linearity or not and allows to determine which non-linear

energy transport mechanisms dominate the behaviour of the nonlinearity.

Using this knowledge allows to broaden the application spectrum of Volterra Input/Output

Maps (VIOMAP) models, as the otherwise tedious problem of model structure selection, can

be steered by the initial non-parametric results. Parametric models that cover a wide range

of frequency and power were then identified based on this previous knowledge.

As an application, the effect of the slow dynamics coming from thermal or biasing effects is

studied. A measurement based approach relies on the presence of 3 ports: namely the two

RF ports and the biasing/thermal port. Excitation on both RF input and the slow port is then

performed to determine the actual structure of these effects.

The different projects covering this topic in more detail are presented below.

The research-team C participates to the “6th European Frame Programme for Research,

Technological development and Demonstration” through a Network of Excellence called

“TARGET”. TARGET stands for “Top Amplifier Research Group in a European Team” and aims

to bundle the research efforts of 57 european research groups about design of solid state

power amplifiers (PA’s). The nonlinear aspects of the characterization of the PA's appears in

the Work Package 1.3.2. (see http://www.target-net.org) for which ELEC is the leader. This

Work Package fits into project C. (New Measurement and modelling techniques for rfci’s,

Instrumentation) of the following project descriptions.

B. Short Description of the Research Projects of TEAM C

A. ADVANCED CALIBRATION AND VERIFICATION SETUPS FOR NONLINEAR RF-DEVICESLIESBETH GOMMÉ, YVES ROLAIN

One of the challenges in modulated measurements resides in the calibration of the

instrument’s phase distortion for such signals. The calibration of waves composed of a

carrier and its harmonics relies on the well-established step recovery diode method. This

method cannot be used for spectra containing lines that do not lie on the harmonic grid: f0,

2f0... As a consequence, it cannot be used to calibrate a narrow band modulated signal. The

crystal detector (HP420C) was used as a reference element, as it translates the envelope of

the RF signal to IF frequencies.

The detector as calibration standard

Observe an amplitude modulated signal, xAM(t) as shown in Figure 1. The carrier frequency

is chosen on the coarse frequency grid, imposed by the SRD-method, while the modulation

tones determine the fine grid. If xAM(t) is now exciting an uncalibrated instrument, the

modulated signal will be distorted by the linear dynamics of the channel. (Figure 1a) In

order to quantify the phase distortion induced by the LSNA, the phase relations between

the tones in the excitation signal ought to be known exactly. This can be achieved by


F

measuring the response of the detector and, indirectly, the applied modulated tone. To this

end, we have modelled the crystal detector and this model will be used to retrieve the

modulated input signal by measurement of the detector’s response.

Model estimation and validation

As we have verified that downconversion of the detector is independent of the RF carrier

frequency, it is allowed to perform low frequency measurements to extract a baseband

block oriented nonlinear feedback model for the nonlinear detector. High frequency

validation measurements have been performed to confirm the model prediction capability

at RF [1]. The model consists of a linear direct path and a nonlinearity in the feedback path,

as is shown in Figure 1b. This baseband model has been validated using the setup in Figure

1a. The RF input, x, is measured and fed to the model to predict the low frequency

envelope. This modelled envelope is then compared to the ADC-measured low frequency

output, y. The measured (solid line) and modelled (dashed line) output envelopes are

shown in Figure 1c.

The mean deviation between the modelled and measured envelope is relatively small

compared to the measurement uncertainty. The difference in the beginning of the spectral

band of interest equals 1dB, as can be seen from Figure 1c. The spectral behaviour of the

modelled output coincides well with the measured output envelope.

Reference

[1] L. Gommé, J. Schoukens, Y. Rolain, W. Van Moer, Validation of a crystal detector model for thecalibration of the Large Signal Network Analyzer, 2007 IEEE Instrumentation and MeasurementTechnology Conference, 2007.

B. NEW MEASUREMENT AND MODELING TECHNIQUES FOR THE NONIDEAL NONLINEAR BEHAVIOR OF MICROWAVE MULTIPLE PORT SYSTEMSKOEN VANDERMOT

The purpose of this research project is to develop measurement-based black-box models

for multiport nonlinear microwave systems, in particularly mixers [2] and differential ampli-

fiers.

IGURE 1. a) Phase calibration setup, b) Detector model, c) Validation measurements


A good mixer model has to take into account

the ideal frequency translating behavior as

well as the non-ideal nonlinear perturbations

of the mixer. To this end, the Best Mixer

Approximation (BMA) is defined in [3]. This

BMA is the best approximation, in least

square sense, of the true behavior of the

mixer for a given class of excitation signals,

for example the class of Gaussian signals

with a fixed power spectrum. As a conse-

quence, the BMA depends on the power of

the input signal. A power-scalable BMA

model, that allows to model the behavior of

the mixer for any value of the input power

within an a priori specified range of powers,

will be estimated out of the input and output

signals of the mixer. To do so, a four-step

method will be used. In the first step, the

nonparametric BMA will be

estimated for different input powers within a

given range (see Figure 2). These BMAs will

be different since contains a

nonlinear term and, hence, is power

dependant.

In the next step, a parametric model for the BMA

is estimated:

(EQ 1)

where are the poles, the zeros, and is

the gain. As a consequence, a set of poles, zeros

and a gain can be found for every power of the

input signal:

(EQ 2)

0.2 0.22 0.24 0.26 0.281.4

1.6

1.8

2

2.2

2.4

Pha

se [r

ad]

Normalized Frequency

0.2 0.22 0.24 0.26 0.28−120

−100

−80

−60

−40

−20

0

Am

plitu

de [d

B]

GBMA

P

G∠ BMA

FIGURE 2. . Amplitude and phase of for different powers of the input signal. Also

the standard deviation of the simulation noise and the nonlinear stochastic contribu-

tions are represented. Dark grey symbols indicate higher powers of the input signal.

GBMA

NG

GS

P

⎭⎬⎫

GS

⎭⎬⎫

NG

GBMA

GBMA

−1 −0.5 0 0.5 1−1

−0.5

0

0.5

1

Imag

(z)

Real(z)

FIGURE 3. . Over the different powers of the input signal the poles (represented by crosses) and zeros (represented by circles) move. The different pole/zero trajectories are plotted by a black solid line. Dark grey symbols indicate higher powers of the input

signal.

GBMA ωk ωLO+( ) K

zk zb–( )

b 1=

nb

∏

zk pa–( )

a 1=

na

∏

-------------------------------=

pa zb K

Pi

z1i z2

i … znb

i p1i p2

i … pna

i Ki, , , , , , , ,⎩ ⎭⎨ ⎬⎧ ⎫


In the third step, the corresponding poles and zeros will be selected over the different input

powers . As a consequence, a set of locations for every pole and every zero

is found for different powers of the input signal.

In the last step, the location of the different poles/zeros and the value of the gain for any

power within the given power range will be estimated by a special interpolation method:

cubic smoothing spline. At the end, trajectories are found that describe

the move of every pole/zero for any power within the given power range. Also a gain

function is found. These trajectories and this gain function allow to define a 2-

dimensional high-level power-scalable BMA:

(EQ 3)

References

[2] S.A. Maas. Nonlinear Microwave Circuits, John Wiley & Sons, Boston, USA, 1988.[3] K. Vandermot, W. Van Moer, Y. Rolain and R. Pintelon. Extending the Best Linear Approximation

for Frequency Translating Systems: The Best Mixer Approximation, IEEE Instrumentation andMeasurement Technology Conference Proceedings, Warsaw, Poland, 2007, pp. 1-6.

[4] C. de Boor. A Practical Guide to Splines, Springer-Verlag, 1978.

C. NEW MEASUREMENT AND MODELLING TECHNIQUES FOR RFIC’S(WENDY VAN MOER, YVES ROLAIN)

The purpose of this research project is to develop

measurement based black-box models for multiple port

nonlinear microwave systems. Even if most microwave

systems are designed to have a linear behaviour, they are

often used in their nonlinear operation region. Hence, the

nonlinearities can not be neglected. Measuring these

nonlinearities requires an adapted measurement

instrument: a Large Signal Network Analyser (LSNA).

Based on these nonlinear measurements and their

stochastic properties, validated models for the nonlinear

behaviour can be obtained. To reach this goal, some

fundamental contributions are necessary on 4 different

aspects: the considered system classes, the measurement

instrumentation, the calibration and the modelling.

System Classes

A linear microwave system is fully described by: with ‘a’ the incident

waves and ‘b’ the reflected waves at the measurement ports of the Device Under Test

(DUT).

A first extension towards nonlinear systems is obtained when trading the system equation

Pi pa i( )zb i( ) Pi

pa P( ) zb P( ),

K P( )

GPBMA ωk ωLO+ P,( ) K P( )

zk zb P( )–( )

b 1=

nb P( )

∏

zk pa P( )–( )

a 1=

na P( )

∏

----------------------------------------=

b ω( ) S ω( ) a ω( )⋅=


to a polynomial relation: . Note that this nonlinear relationship can no

longer be formulated using a power independent S as for the linear case.

A second extension towards systems with bifurcations will make the polynomial system

equations even more general: . Measuring systems belonging to these

three different system classes requires different instrumentation and calibration setups.

Instrumentation

The behaviour of a linear system is measured using a Vectorial Network Analyser (VNA),

which is mainly a wave ratio meter. To characterise a nonlinear system, however, the

knowledge of the absolute waves is required. The VNA is therefore no longer the appro-

priate measurement instrument. The LSNA is able to simultaneously measure the incident

and reflected waves of the DUT in both amplitude and phase for multiple frequencies. As a

result this instrument is capable of measuring the nonlinear behavior of system.

In this project, the LSNA enhancements that are required to fulfil the needs of today’s

telecommunication applications will be worked out. The goal is to adapt the LSNA for

multiport measurements. This allows to investigate the nonideal nonlinear behaviour of

microwave multiport systems, in particularly mixers and differential amplifiers.

In order to validate the nonlinear measurements that are performed by a variety of

measurement set-ups, a round-robin has been setup between five different microwave

laboratories that are specialized in nonlinear device characterization. If the measurement

world would be ideal, the five measurement laboratories would return exactly the same

measurement results when measuring the same device-under-test. Unfortunately nothing

is ideal, which means that the measurements will differ due to f.e. measurement noise. If it

is possible to show that all the measurement results are identical within the measurement

uncertainty, the repeatability of the measurements is proven. In a first step, however, it is

important to know which measurement laboratories encountered measurement problems

during their measurement campaign, and can hence be excluded from the repeatability

test.

A nonlinear device is circulated amongst the five laboratories. The challenge is to measure

the time-domain waveform of multi-harmonic signals without any distortion. To this end,

the measurement equipment must have a calibration that negates the amplitude and phase

distortions of the response over the frequency range.

Since there is no reference lab whose measurement accuracy is known, or is an order of

magnitude better than the five laboratories, it is impossible to know the behavior of the test

system in advance, which poses a serious challenge to the comparison procedure.

We decided to use a model-free, statistical method to compare the measurements: the

Analysis of Variance (ANOVA) statistical method, one of the most popular and widely used

methods. The ANOVA technique has two major advantages: (i) no reference model or

measurement is needed and (ii) ANOVA is directly interpretable.

The technique is very powerful and facilitates selection of homogeneous groups of labora-

tories, without the need for a reference lab or model.

b ω( ) f a ω( )( )=

G a ω( ) b ω( ),( ) 0=


Calibration

Classical calibration techniques use single sinewave measurements to maximize the signal-

to-noise ratio. In large-signal characterisation, the large number of instrument states to

calibrate results in unacceptably long calibration times. The proposed approach uses

multisine excitation signals during the calibration, in order to reduce the calibration time,

while having only a marginal influence on the measured device response.

When performing large-signal characterisation, not only the measured system, but also the

excitation signal that is used, is of major importance. Depending on the type of excitation

signal (f.e. single sinewave or multisine), it becomes possible to get more or less infor-

mation about the nonlinear behaviour of the DUT. Or in other words, the excitation signal

must be ‘rich’ enough for the nonlinearities to reveal themselves. For instance, if a single

sinewave is used during the measurements, no information can be obtained about the

spectral regrowth mechanism of the nonlinear DUT. When a sinewave or a two-tone signal

is used during the large-signal characterisation, the number of frequencies to be calibrated

is relatively low. Hence, the calibration time is acceptable. However, when using broadband

multisines (sum of more than 50 sine waves), which allow to mimic the behaviour of the

DUT under wideband CDMA or OFDM excitation, the number of spectral components to

calibrate becomes relatively high and the time needed to perform a full calibration

increases accordingly to become unacceptably long.

To reduce the calibration time, a multisine based calibration procedure is proposed. Instead

of using one single sinewave when measuring the calibration standards, a multisine

excitation is applied. This multisine contains all the spectral lines that need to be calibrated.

As a result, all frequency components can be measured in only one single measurement

cycle. This will result in a speed up by a factor of with the number of

spectral lines to calibrate. Hence, a more accurate calibration is obtained as the drift is

lowered, under the condition that the signal-to-noise ratio of the calibration measurements

is high enough to clear the job.

Tcal Nspec⁄ Nspec


4. TEAM D: THE IMEC/VUB-ELEC LABORATORY FOR MICROWAVE MICROELECTRONIC MEASUREMENTS AND MODELLING (4M)

A. Introduction to the IMEC Laboratory: Microwave Microelectronic Measurements and Modelling

The long-term strategy of this team is to build a center of excellence in “Microwave, Micro-

electronic, Measurements and Modeling” (4M) through the cross-fertilization of the

expertise of dept. ELEC at the VUB in microwave measurements and modeling with the

expertise of IMEC in RF microelectronics. Such RF center of excellence requires the ability

of measuring and modeling RF devices. Hence, a diversity of measurement and modeling

problems arise when characterizing RF power amplifiers, mixers, ADCs... In addition to the

normal operation of the RF system, nonlinear distortions and distortions introduced by

digital switching of the on-chip digital circuits – also known as substrate noise – are

analyzed and measured. These measurements demand the combination of both microwave

measurement techniques and high-speed digital measurements.

Measurements and simulations are used to verify both the performance of circuit designs

and to verify the modeling results of the substrate noise modeling. They enable the

extraction and verification of linear and non-linear models for microelectronic circuits.

These extracted models can be used afterwards in the next design.

The research activities within the team are situated in the field of RF and mixed-signal

measurements and modeling. All activities are closely related to – and applied on – the

modeling, measurement and analysis of concrete analog circuits.

B. Short description of the research projects of Team D

A. ADVANCED CIRCUIT-ANALYSIS AND MODELING.GERD VANDERSTEEN

The amount of information extracted out a single experiment depends on three aspects:

the applied excitations; the performance of the simulation/measurement system; the post-

processing used.

The aim of this research is to apply well designed excitation signals to extract more infor-

mation than with classically used excitations. High-quality simulation techniques are

developed based on commercially available simulators and commercial design kits. All

state-of-the-art post-processing techniques are chosen to help the designer to interpret the

simulation results.

The nonlinear analysis of both weakly and strongly nonlinear circuits has been studied in

this context by applying specially designed multisine excitations which can qualify and


quantify the even and the odd nonlinear distortions [1-5]. The developed techniques make

it possible to

• continuously monitor the quality of the simulations;

• determine the best linear approximation of a nonlinear system based on transient

simulations and hence determine the dominant linear behavior of the nonlinear de-

vices.

The availability of accurate models are very important when design complex systems.

Therefore, modeling techniques are used when designing both active circuits [6] and when

modeling passive sub-circuits such as inductors or FBAR resonators [7-9]

The modeling of electromagnetic immunity is another important research topic. The main

focus is put onto the determination of the dominant contributors to the spurious response

of voltage controlled oscillators (VCO) [9-12]. Here, the validation of the simulations with

measurements is of high importance, since it is extremely difficult to predict/simulate the

non-ideal effects caused by parasitic couplings such as substrate noise coupling.

References

[1] Ludwig De Locht, “Measuring, modeling and realization of high-frequency amplifiers”, DoctoralDissertation, Vrije Universiteit Brussel, November 2007

[2] Rabijns, D.; Van Moer, W. and Vandersteen, G., “Using multisines to measure state-of-the-artanalog to digital converters.”, In: IEEE Trans. Instrumentation and Measurement., Vol. 56: (3),2007, pp.1012-1017.

[3] Rolain, Y.; Van Moer, W.; Schoukens, J. and Pintelon, R., “Measuring nonlinear differential RFamplifiers using one single-ended source.”, In: IEEE Trans. Instrumentation and Measurement.,Vol. 56: (3), 2007, pp.1042-1048.

[4] Borremans, J.; De Locht, L.; Wambacq, P. and Rolain, Y. “Nonlinearity analysis of analog/RFcircuits using combined multisine and volterra analysis.” In: Design, Automation and Test inEurope Conference - DATE., 16-20 April 2007; Nice, France, 2007, pp.261-266.

[5] Lataire, J.; Vandersteen, G. and Pintelon, R., “Optimizing analog filter designs for minimumnonlinear distortions using multisine excitations.”, In: Design, Automation and Test in EuropeConference - DATE., 16-20 April 2007; Nice, France, 2007, pp.267-272.

[6] Vandersteen, G.; Bos, L. and Dobrovolny, P., “Scaling friendly design methodology forinductively-degenerated common-source low noise amplifiers.”, In: European Conference onWireless Technology., 8-12 October 2007; München, Germany, 2007

[7] Scheir, K.; Wambacq, P.; Rolain, Y. and Vandersteen, G., “Design and analysis of inductors for60 GHz applications in a digital CMOS technology”, In: 69th ARFTG Microwave MeasurementConference., 3-8 June 2007; Honolulu, HI, USA, IEEE, 2007

[8] El-Barkouky, M.; Wambacq, P. and Rolain, Y. “FBAR overtone-based oscillators using 90 nmCMOS.” ,In: MEMSWAVE.,26-29 June 2007; Barcelona, Spain, Universitat Politecnica de Cat,2007, pp.49-52.

[9] El-Barkouky, M.; Wambacq, P. and Rolain, Y. “A low-power 6.3 GHz FBAR overtone-basedoscillators in 90 nm CMOS technology.”, In: 3rd Conference on Ph.D. Research inMicroelectronics and Electronics - PRIME., 2-5 July 2007; Bordeaux, France, UniversiteBordeaux 1 – Scienc, 2007, pp.61-64.

[10] Bronckers, S.; Soens, C.; Van der Plas, G.; Vandersteen, G. and Rolain, Y., ”Simulationmethodology and experimental verfication for the analysis of substrate noise on LC-VCO's”, In:Design, Automation and Test in Europe Conference - DATE.,16-20 April 2007; Nice, France,IEEE, 2007, pp.1520-1525.

[11] Bronckers, S.; Vandersteen, G.; Soens, C.; Van der Plas, G. and Rolain, Y., “Measurement andmodeling of the sensitivity of LC-VCO's to substrate noise perturbations.”, In: Proceedings IEEEInstrumentation and Measurement Technology Conference - IMTC.,1-3 May 2007; Warsaw,Poland, IEEE, 2007

[12] Bronckers, S.; Vandersteen, G.; Van der Plas, G. and Rolain, Y., “On the P+ guard ring sizingstrategy to shield against substrate noise.”, In: IEEE Radio Frequency Integrated CircuitsSymposium - RFIC., 3-8 June 2007; Honolulu, HI, USA, IEEE, 2007, pp.753-756.


B. DESIGN AUTOMATION, ANALYSIS AND MODELING OF MULTIRATE DISCRETE-TIME SYSTEMSLYNN BOS

Discrete-time systems are typically switched-

capacitor circuits. A basic building block is a

switched-capacitor integrator. It consists only of

switches, capacitors and amplifiers. The

transistors in the circuits are mainly used as

switches therefore the high-switching speed of

transistors in submicron technologies can be

exploited.

Multirate means that the sampling frequency can change throughout the system. By

selecting the optimal speed for each building block a trade-off can be made between power

consumption, performance and flexibility.

The goal of the first design is to apply the discrete-time and multirate principle on a

analog to digital converter.

A multirate lowers the power consumption. The first integrator is the most critical and

hence consumes the most power. Therefore the idea was to decrease the sampling

frequency of the first integrator and to increase the sampling frequency of the successive

integrators [13] The performance stays the same but the power consumption is decreased.

A multistage enables

flexibility towards dif-

ferent communication

standards. Each standard

demands a different

performance because of

the different bandwidth

and required resolution.

By disabling the appro-

priate stages, the cascade

is a more power

efficient solution for

multistandard radios than a high order designed for the toughest standard specifica-

tions [14].

With the technology scaling in submicron technologies, designing a good operational

amplifier is more and more difficult. Moreover the process variations in these technologies

are not negligible. Therefore a yield optimization algorithm was developed which sizes a

circuit while taking into account the process variations. An operational amplifier in 45 nm

IMEC technology was designed using this approach. This algorithm shows if switched-

capacitor filters remain feasible in deep submicron technologies. It is also very useful for

the design of the different scaled operational amplifiers necessary in ’s.

S1

Cs

CIin(t)

S2

F 1 F 2

out(t)

Σ∆

Σ∆

Σ∆

Σ∆

Σ∆

Σ∆

Σ∆


To predict the nonlinear behaviour of a discrete-time , it is very important to have a

good excitation signal together with an accurate nonlinear model. The problem however

with discrete-time systems is that the bandwidth of the input signal of the switched-

capacitor network is sometimes larger than the Nyquist frequency of the sampler due to

non-idealities. It doesn’t obey the Shannon-Nyquist theorem and so there is no clear

distinction between nonlinearities and aliasing components. Therefore a new multisine

excitation signal and a model for nonlinear analysis were developed. The multisine has

dedicated detection lines for the aliasing components and hence the best-linear approxi-

mation can be estimated. This approach was verified on simulations and measurements of

a switched-capacitor filter.

References

[13] [Colodro ‘02]: F. Colodro and A.Torralba, “Multirate sigma-delta modulators”, IEEE Trans.Circuits Syst. II, Analog Digit. Signal Process.,vol.49, no.3, pp.170-176, March 2002

[14] [Xotta ‘05]: A. Xotta, A. Gerosa and A. Neviani, “A Multi-Mode sigma-delta Analog-to-DigitalConverter for GSM, UMTS and WLAN”, Proc. Of ISCAS-05, Khobe, Japan, pp. 2551-2554, May2005

C. EXPERIMENTAL ANALYSIS OF THE COUPLING MECHANISMS BETWEEN A 4GHZ PPA AND A 5-7GHZ LC-VCOSTEPHANE BRONCKERS

RF transmitters are designed to convert baseband signals into RF signals using efficient

modulation techniques. A Voltage Controlled Oscillator (VCO) is thereby used as a Local

Oscillator (LO) for the up conversion. This modulated RF signal is then amplified and trans-

mitted by a power amplifier (PA). To maintain the high bandwidth of such transmitters it is

mandatory to minimize the length of the interconnects. Hence the VCO and the PA must be

placed close to each other on the same die.In such transmitters the PA acts as an aggressor

while the VCO acts as a victim. The transmitted RF signal couples into the VCO and causes

unwanted remodulation of the VCO and subsequent spectral spreading of the desired RF

signal. There are different possible coupling mechanisms that are responsible for possible

failure of the RFIC. Theoretically, the PA can couple resistively, magnetically and capaci-

tively with the VCO.

The PA and the VCO share the same die. Therefore they can couple resistively with each

other through the common substrate (see Figure 1)

Further, the PA couples magnetically with the VCO (see Figure 1). The VCO is often realized

as an LC-VCO. This topology is popular for its low phase noise and its low power

consumption when compared to ring oscillators. The oscillation frequency of such a VCO is

determined by the resonance frequency of its LC-tank. The frequency is tuned by a varactor

that changes the capacitance value in the resonant LC-tank. The inductor of the LC-VCO will

couple magnetically with the inductor present in the last stage of a PA. This inductor is part

of the output matching of the PA.

Furthermore, the PA and the VCO are connected to a Printed Circuit Board (PCB) through

bond wires. Those bond wires also behave as inductors. Consequently they can couple

magnetically with each other.

Σ∆


Finally, the PA can couple capacitively with the VCO through their respective traces (see

Figure 1). Capacitive coupling can occur both between the on-chip interconnects as the

interconnects on PCB.

This work determines the

dominant coupling mechanisms

between a CMOS 4GHz Pre

Power Amplifier (PPA) and an

CMOS 5-7GHz switched varactor

LC-VCO. Measurements show

that the coupling through the

common substrate is the

dominant coupling mechanism.

The remaining coupling is mainly

due to the magnetic coupling

between the bond wires.

Magnetic coupling between the

on-chip inductors can be

neglected because the simulated

mutual inductance is much lower

than the mutual inductance

between the bond wires..

CMOS 5-7GHz switched varactor

LC-VCO. Measurements show

that the coupling through the common substrate is the dominant coupling mechanism. The

remaining coupling is mainly due to the magnetic coupling between the bond wires.

Magnetic coupling between the on-chip inductors can be neglected because the simulated

mutual inductance is much lower than the mutual inductance between the bond wires.

D. MEASURING, MODELING AND REALIZATION OF HIGH-FREQUENCY AMPLIFIERSLUDWIG DE LOCHT

A designer of a front-end for a communication system has to take the constraints on the

linear and nonlinear behavior of the circuit or system into account. Designers are trained to

reason with linear systems, and therefore they are able to take the constraints on the linear

behavior correctly into account. The problem lies in dealing with the constraints on the

nonlinear behavior.

The constraints on the nonlinear behavior are verified by performing numerous simulations

or measurements. Although the results of these simulations or measurements give an

accurate representation of the circuit’s operation, no insight into the nonlinear operation of

the circuit is gained. This lack of insight is even more pronounced when complex modulated

signals are used. For complex circuits using complex modulation signals, it becomes impos-

sible to identify the sub-circuit contributing most to the overall nonlinear behavior using the

standard simulations or measurements.

FIGURE 1. Different coupling mechanisms between a LC-VCO and a PPA


To retrieve the insight in the nonlinear behavior, we introduced into circuit design specially

designed odd random-harmonic multi-tone signals and the best linear approximation. This

method can be used in an optimization loop. It is illustrated on the design of a wide-band

feedback Low-Noise Amplifier (LNA) of Figure 2 below.

The method first identifies transistor Mn2 as the transistor contributing most to the overall

nonlinear distortion. It does so by looking at the contribution of each transistor to the

output. This spectrum can then be split into a linear part (+), the odd-order nonlinear

distortion (o) and the even-order nonlinear distortion (*). It can be seen that the even-

order nonlinear distortion of transistor Mn2 is dominant (* is larger than o).

After the method optimizes the circuit’s performance by changing the biasing of the

transistors, the even-order nonlinear distortion decreased by 15dB. Each simulation took

only 279s of simulation time.

The proposed method is therefore ideal to verify and imply during the design the

constraints on the linear and the nonlinear behavior of a circuit for complex modulated

signals.

Before optimization

After optimization

Frequency (GHz)FIGURE 2. Design of a wide-band feedback

Low-Noise Amplifier (LNA)


E. SAMPLING AND DISCRETE-TIME SIGNAL PROCESSING FOR SDR ARND GEIS

Classical zero and low-IF

receiver architectures suitable

for Software Defined Radio

systems (SDR) are based on

quadrature mixing in combi-

nation with strong filtering in

the baseband to prevent aliases

from corrupting the signal’s

SNR during sampling in the

ADC. Filtering becomes even

more critical when considering

wide-band front-ends typically

used in software defined radio

receivers. Those wideband

systems necessitate a complex widely tunable filter circuitry when implemented in

continuous time. Sampled systems on the other hand provide bandwidth tunable filtering

by means of varying clock rate instead of switching large capacitor banks which translates

into low area front-end designs.

A system which employs sampling of signals which are spectrally surrounded by interfering

signals must provide strong anti-alias filtering which must be absorbed into the sampler.

Modern deep scaled CMOS processes come at the expense of shrinking voltage headroom

and increasing process variations. This has significant impact on the performance of analog

circuitry. At the same time the need for linear analog building blocks in the receiver chain

can be relieved by exploiting the superior high speed switching performance of deep scaled

CMOS transistors for frequency translation by means of RF band-pass/sub- sampling.

Decimation and selectivity can be implemented in the receiver chain as either active or

passive discrete time switched capacitor filter blocks.

An RF-sampling mixer followed by flexible discrete time filtering blocks for sample rate

decimation as well as improved selectivity was designed and validated in a UMC90 digital

process. Flexibility in the filtering stage was implemented in the discrete time domain as

programmable numbers of filter taps.

This PhD is targeted to exploit sampling and discrete-time signal processing for SDR.

Therefore, both RF & baseband sampling topologies are implemented and compared in the

context of software defined radio receivers.

F. DESIGN OF ANALOG CMOS CIRCUITS FOR MM-WAVE APPLICATIONS KAREN SCHEIR

The newest generations of CMOS devices are fast enough for operation in the millimeter-

wave frequency range. Their speed, together with their low cost potential, make scaled

CMOS the ideal technology for commercial high frequency applications (e.g.: short distance


communication in the 60 GHz band). Minimizing power and area consumption is crucial for

the development of commercial mobile applications. Additionally, one has to deal with a

scaled backend of poor quality at millimeter-wave frequencies. Consequently, mm-wave

CMOS design imposes a lot of challenges for a circuit designer.

Our main focus this year has been on benchmarking the performance of the key 60 GHz

receiver RF building blocks in a digital 90 nm CMOS technology. Previously obtained

knowledge about passive design and Voltage Controlled Oscillator (VCO) design has been

used to develop a quadrature VCO (QVCO) with a large tuningrange. Secondly, a Low Noise

Amplifier has been designed. An inductively degenerated single-stage cascode topology

was selected for optimal performance. Digitally controlled gain selection has been imple-

mented to anticipate for the varying air interface associated with indoor short-range

communication. As a last building block, the downconversion mixer has been studied. We

selected a single balanced Gilbert mixer with resistive loading. This mixer features an

inductor at the drain of the RF transistor to boost its conversion gain by tuning out the

parasitic capacitance at this node. .

In a second phase, we have studied several possibilities to implement beamforming with a

phased array realization. Phase shifting in the LO path came forward as an appropriate

method and was further elaborated. The final implementation is based on a global QVCO,

an LO distribution network and a digitally controlled phase selector/interpolator in each

antenna path.

To verify our research work, a 2-path beamforming receiver section has been implemented

in 90 nm digital CMOS technology. Measurement results confirm the good RF performance

for low power- and area consumption. The beamforming properties of the receiver have

also been successfully demonstrated

1400 µm

1000 µm

RF 1

RF 2

IF 1+ IF 1-

IF 2+ IF 2-

QV

CO

LO b

uffe

rs

Pha

se

sele

ctor

Mixer

LNA

LO routing

380 µm

RF 1

RF 2

IF 1+ IF 1-

IF 2+ IF 2-

QV

CO

LO b

uffe

rs

Pha

se

sele

ctor

Mixer

LNA

LO routing

380 µm

FIGURE 3. Chip microphotograph of the 2-path phased array receiver front-end section with a zoom-in on the several RF building blocks.


5. TEAM E: IDENTIFICATION OF BIOGEOCHEMICAL SIGNALS & SYSTEMS (IBISS)

A. Introduction to Team E

The long term goal of this team is to build measurement based models for environmental

systems. The current trend is to build more complex models in order to simulate the ‘real’

world, while a ‘good’ model is not necessarily the most complex model. On the contrary, it

only needs that complexity, which is supported by experimental measurements. It has to be

able to describe all significant variation in the data, without modelling the stochastic

measurement uncertainties. These models are used for two purposes: (i) to extract the

maximum amount of significant information out of noisy measurements; (ii) to make future

predictions.

Suppose, for instance, that we have a climate model with a set of estimated parameters

and an experimental record. Now, I would like to bring in an additional physical process,

which would make the model more realistic. What will happen?

With every modelled process, some model parameters are associated. Tuning these param-

eters will match the extended model better on the record. So, our model has become more

realistic. However, uncertainties are associated with these additional model parameters and

these additional uncertainties will lower the prediction horizon of the model. So, a higher

complexity could be a disadvantage for a model. Briefly, a first step in the search for a good

climate model is the determination of its complexity. To reach this goal, the modeller needs

the experimental data.

On the other hand of the spectrum, experimentalists are measuring climate/ ecological

changes in the past. However, the past cannot be directly measured. Instead, proxies in

sediments, biogenic carbonates, ice cores etc.… are measured and consequently, the

history is revealed as function of a distance, while we would like to have a time series. The

latter can be introduced by comparing the measured record with a model, containing the

time. The simplest models can be 14C-dating curves, but any model containing time can do

the trick. So, at this stage, every experimentalist will need models and modellers and some

evident questions arising are: which model to choose? How are the model parameters

tuned (on a record, not yet dated or on a previously dated one)? How are errors propa-

gating trough this interaction between models and measurements?

These two examples show that a horizontal orientated approach, with specialists in

measuring and others in modelling, has its limits. For this reason, we have chosen to

approach some important questions in environmental reconstruction and prediction in a

vertical manner, where we follow the information stream from the bottom up, from the

experimental set-up, the calibration of the instrument, dating the records, over tuning the

model parameters, till the selection of the appropriate model. Such a quantitative formu-

lation of the measurement problem is precisely what has been developed in the system


identification community and our first short term goal is the introduction of this approach in

the community of environmental sciences.

B. Short description of the research projects of team E

A. CLIMATE RECONSTRUCTION BASED ON ARCHAEOLOGICAL SHELLS: A NON-LINEAR MULTI-PROXY APPROACHMAITE BAUWENS

In accordance with the trends seen in previous years, also 2007 beaded some temperature

records. We just lived the warmest winter and the warmest spring since the beginning of all

temperature measurements. This seems to be convincing evidence so that most people

begin to believe in global warming. The question remains: what do we know about the

temperature before the start of the measurements? Not too much to be honest. To know

more about it, we need on one hand good proxy-archives (= sensors) in which a clear

proxy-signal is seen and on the other hand a good interpretation of the signal to recon-

struct the paleoclimate.

Several years of biogeochemical research on bivalve shells resulted in clear proxy-signals

with the promising potential for reconstruction of yearly variations in the paleoclimate

around estuarine systems. But the interpretation of the different proxy signals is still

problematic. The classical linear transfer function from proxy to environmental parameter

failed for most proxies, and if they seem to work (e.g. for δ18O in relation to temperature)

the signal-to-noise ratio is so high that the uncertainty on the reconstructed temperature is

higher than the recent temperature changes.

FIGURE 1. A.: Initial estimation of of a non-linear transfer function for every proxy over time. B.: rewriting the transfer functions in one n-dimensional function (there are as many dimensions as proxies). C. Linking of a measurement (chemical signature) to a unique time-position. D. Link-ing of the time-position to the corresponding environmental parameter(s). E. Validation of the method, comparing the measured environmental parameter(s) to the predicted environmental

parameter(s).


This is why we developed a non-linear multi-proxy approach. This approach consists in the

construction of a manifold in a multi-dimensional space, as an intermediate step, from

which in a second step a temperature is obtained. At this moment two types of models are

used to describe the manifold; (1) a spline model and (2) a polynomial model. Both models

describe the variations in the chemical signature of the shell during a one year cycle. The

shortest distance from another measurement (e.g. in an archeological shell) to the model

will give a time estimation, which can be linked to several environmental parameters. Initial

temperature reconstructions were promising, and after some ameliorations on the basic

principals (normalization, time-basis movements and weighting factor for proxies) the

reconstruction can even be called good. But still the model is not generic and we have to

find out which proxies are useful for witch environmental parameters.

B. IDENTIFICATION OF A HARMONIC SIGNAL IN THE PRESENCE OF ADDITIVE NOISE, AN UNKNOWN TIME BASE DISTORTION, AND AN AVERAGING EFFECTVEERLE BEELAERTS

Introduction

Natural archives are recorders of global climate changes. Data acquisition from natural

archives mostly involves sampling solid substrates. This imposes two problems:

Problem 1: The signals are sampled at a non-equidistant time grid.

Problem 2: When solid substrates are sampled, these samples are taken over a volume in

distance. As a consequence, when the continuous signal is sampled, it will be averaged over

the volume of the sample.

Existing methods [1] do not take into account problem 2 and, henceunderestimate the

amplitudes of the harmonics.

The aim of this work is to provide an efficient identification algorithm to identify the non-

linearities in the distance-time relationship, called time base distortions, and to correct for

the averaging effects.

Approach

A parametric model is proposed which estimates

a harmonic signal in the presence of additive

noise, a time base distortion and an averaging

effect. In a first approach the averaging effects

are assumed to be in one direction only, i.e. the

direction of the axis on which the measurements

were performed.

The following signal model for the averaged

signal is proposed:

(EQ 1)

FIGURE 2. Convention. δ:distance over which is averaged; ∆: distance between two samples; n: sample position, ; x: distance, x(n)=∆n; t(n): unknown time

variable.

n 1 … N, , ∈

y n θ,( ) ∆δ--- y m θ,( ) md

n 1∆2δ-------–

n 1∆2δ-------+

∫=


where m is the position, ; are the unknown parameters and is

the continuous signal we want to identify.

The time base is modelled as follows [1].

(EQ 2)

Here is the sampling period, the sampling frequency, and the

unknown time base distortion (TBD). In this work a splines approximation of the TBD is

chosen:

(EQ 3)

where, is a vector of unknown time base distortion parameters, and is a set of

splines. The estimates of the unknown parameters were obtained with a nonlinear least

squares algorithm.

Results

The vessel density measured in the mangrove

tree R mucronata is used to illustrate the

method. The samples were collected and

prepared according to [2]. The vessel density

is a proxy for the rain fall in tropical regions.

The data record is 23 samples long and covers

3.3 years.

A signal model consisting of 2 harmonics ;

and a time base distortion model with 4 time

base distortion parameters are used. The

constructed time base can be seen in Figure 3.

As expected, a yearly periodicity is visible in

Figure 3b.

The correction for the averaging effect is shown in Figure 3c. The amplitude increased with

11.18%.

References

[1] F. De Ridder, R. Pintelon, J. Schoukens, and A. Verheyden, “Reduction of the Gibbszphenomenon applided on non-harmonic time base distortions,” IEEE Trans. Instrum. Meas. vol.54, pp 1118-1125, 2005.

[2] A. Verheyden, F. De Ridder, N. Schmitz, H. Beeckman, and N. Koedam, “High-resolution timeseries of vessel density in Kenian mangrove trees reveal a link with climate,” New Phytologist,vol. 167, pp. 425-435, 2005.

x m( ) ∆m= θ y m θ,( )

t m( ) mTs g m( )Ts+=

Ts 1 fs⁄= fs g m( )

g m( ) bll 1=

b

∑= ∅l m( )

b ∅

FIGURE 3. Vessel density in mangrove trees

A

b


6. SPIN-OFF: NMDG ENGINEERING

Contact Person:

Marc Vanden Bossche

Director

email:

[email protected]

A. Vision

NMDG is committed to becoming the market leader in “next-generation characterization

and analysis tools” for the high-frequency electronics and communication market. Secondly

NMDG wants to be the preferred partner of test engineers who need to improve and accel-

erate their design and test processes, yet face the challenges of non-linear component

behaviour.

In order to change the design and test paradigm, NMDG is developing, selling and

supporting next- generation products focused on fast, complete and accurate characteri-

zation and analysis of high-frequency active and high-speed digital components. These

products combine existing instruments with new hardware supported by reliable and easy-

to-use software, while being fully integrated with the customer’s processes. To anticipate

the ever-growing complexity of design, the need for realistic models and in order to

eliminate the effect of the measurement set-up on product specifications, these products

will be seamlessly combined with simulation tools.

B. At a glance

NMDG is a young, dynamic high-tech company offering next-generation characterization

and analysis tools and services to a wide range of customers in the high – frequency

electronics and communication market. NMDG provides these customers a competitive

advantage by reducing development time and cutting test costs while improving yield and

reliability.

Leveraging the experience of a team of highly qualified high-frequency measurement and

simulation technology professionals, NMDG is presently focusing on four revenue gener-

ators: products, characterization services, measurement-based behavioural modelling

services and system integration services.

A close and personalized interaction with the customer is of paramount importance to

NMDG.

C. Markets

The markets covered by NMDG solutions reach from “sand” to “system” and correspond to

the markets that were penetrated by the S-parameter measurement capability.


“From Sand”

Using realistic and complete measurement information, provided by the NMDG products

and services, semiconductor manufacturers can verify, improve and certify their large-

signal models, before releasing their design kits. This is a guarantee for design cycle

reduction. By gaining complete insight in the behaviour of their transistors through the

extension of their present source- and load-pull systems with measurement capabilities

from NMDG, power amplifier designers and manufacturers can achieve new performance

levels with their components. Also, using the products of NMDG in combination with the

proper tuner solutions, source- and load-pull measurements become a matter of minutes

increasing the throughput of testing components.

“To System”

By extending vector signal generator – analyser set-ups with the NMDG products, it is

possible to characterize and analyse single-ended and differential devices, possibly in a

non- 50 Ohm environment.

Amongst others, measurement-based behavioural models can be created for chip manufac-

turers improving the simulation accuracy and reducing model uncertainty in the design

cycle.

Finally, NMDG is convinced that its products will play an important role in the high-speed

digital market place as it adopts future RF techniques.

D. Products and Services

Based on accurate characterization of HF active components under realistic conditions, the

NMDG solutions streamline the R&D development and test processes, which result in:

• High measurement accuracy and efficiency

• Faster designs and testing

• Significant cost savings

E. Large-Signal Network Analyser Products together with Maury Microwave

The MT4463, a product sold worldwide by Maury Microwave and

empowered by NMDG, characterizes the non-linear behaviour of

RF, microwave and fast analogue switching components in a

network analyser sense, based on large-signal network analyser

technology. The MT4463 goes beyond pass/fail tests where

classic measurement approaches fail to give insight. The

instrument aids in understanding what occurs at the component

level under near realistic circumstances.

The large-signal network analyser measures the complete

voltage and current or incident/reflected wave behaviour under


small- (Sparameters) and large-signal conditions. Independent of the type of component,

from transistor to system, a one-tone or periodic modulation can be applied under

mismatched conditions while the complete behaviour is measured and represented in

different ways.

In contrast to the almost complete measurement capabilities of

the MT4463, the VNAPlus, also presently sold worldwide

through Maury Microwave, is an entry-level add-on kit for

Agilent PNA’s to provide it with complete harmonic characteri-

zation capability.

Both the MT4463 and the VNAPlus can be combined with the

Maury tuners and the Maury Automated Test System (ATS)

software to operate in a non-50 Ohm environment.

F. Characterization Service

Make sure your transistor libraries pass the "design test" using certified design kits.

The NMDG characterization service enables the user to explore the capabilities of a larges-

ignal network analyser without having to invest in the MT4463 and VNAPlus.

The NMDG characterization service allows to:

• Study the true and complete high-frequency behaviour of components under

realistic large-signal RF excitation and mismatch conditions

• Eliminate the uncertainty of the impact of the measurement environment

• Investigate the reliability of components under realistic high-frequency conditions

• Reduce the design time by making sure that the designers use LSNA-certified

models

Also available are:

• Hot S-parameter measurements

• Stability analysis under hot conditions

• Measurement of impedance of sources, oscillators under hot conditions

• Conversion matrices

• Characterization of frequency translating devices, e.g. mixers

• In-circuit measurements using high-impedance probes

G. Measurement-based Behavioural Modeling Service

Simulate your active HF components under mismatched conditions fast and accurately.

The Standard Service includes:

• Extraction of a measurement-based behavioural model at the specified fundamental

frequency for a given input power range and a range of termination impedances


• Predicts the complete voltage - current or incident - reflected wave behaviour at

the input and output port of the component, taking into account the effect of source

and load impedance

• The bias circuitry is assumed to be part of the component

• Modulation prediction is possible under quasi-static assumption

• Connection into ADS from Agilent Technologies

• Components can be connectorised, in fixture (incl. de-embedding) or on wafer

Through customization of the measurement set-up and adaptation of the algorithms, it is

possible to extend the behavioural model with some harmonic information and to analyse

the effect of memory effects through the bias circuitry.

NMDG also provides services to connect measurements into different simulation environ-

ments and to integrate them in the design process.

H. HF Measurement System Consulting

NMDG extends your measurement set-ups or develops set-ups beyond the capabilities of

regular instrumentation to characterize and analyse active HF components in R&D and test

environments.

The customer benefits from NMDG’s unique expertise in HF active component characteri-

zation, instrument control, algorithm and GUI development.

NMDG's expertise takes away the burden to develop, adapt and support measurement

systems, and makes sure that your engineers stay focused on design and manufacturing.

I. LSNA History

Large-signal network analysis (LSNA) provides complete and accurate characterization of

non-linear devices under realistic operating conditions using one measurement set-up with

a single connection.

These measurements reveal device characteristics which could not be seen before with

other commercially available equipment.

The technology provides a step-by-step analysis of the device behaviour with increasing

signal complexity, to qualify and improve transistor models and improve system level

designs. The large-signal network analysis technology specifically addresses the non-linear

large-signal measurement needs of the semiconductor R&D and manufacturing customers

to characterize amplifiers, transistors and other active components in cell phones, base

stations and aerospace/defence equipment.

Many years of research work by the Hewlett Packard and later Agilent NMDG team at

Agilent Technologies in cooperation with different research institutes led to what LSNA

technology is today.

Under license from Agilent Technologies, the technology resulted in the commercial reali-

zation of the MT4463. This product is currently produced and sold worldwide by Maury


Microwave with the help of NMDG. The latter also provides presages, installation and post

sales support while continuing development of future generations.

J. About NMDG

NMDG’s ground breaking technology had its origins at the department ELEC of the Vrije

Universiteit Brussel in the eary nineties when some employees took part in the creation of

the large-signal network analyser technology at that department. The concepts and

processes to calibrate and measure non-linear high-frequency active components have

earned a worldwide recognition.

In order to offer better, more complete products NMDG continues its research and devel-

opment via partnerships and co-operation with leading academic and research institutes.

In July 2006, NMDG attracted an important research grant from the Flemish government

through an IWT project. This helped the initial acceleration of the company.

During the summer 2007 a venture capital seed funding enables the company to accelerate

its own product development plans, including research and development of large - signal

network analysis tools for RF, microwave and high-speed digital components. This will also

allow expanding its commercial team while strengthening relationships with its customers

and partners.

For further information, please visit us at: www.nmdg.be


CHAPTER 3. Education

1. THE INTRODUCTION OF THE BACHELOR-MASTER STRUCTURE

The academic year 2004-2005 started with the introduction of the “bachelor - master”

structure (replacing the candidate - licentiate structure). The initiative for this thorough

interference in the programmes of higher education was the Bologna Declaration. The

Ministers of Education of 31 European Countries gave in 1999 the start to uniform the

higher education in Europe. Although the Bologna process creates convergence, the funda-

mental principles of autonomy and diversity are still respected. The aim of the Bologna

Declaration is to improve in Europe the exchangeability of degrees, the free mobility of

students, quality assurance and a flexible study package by introducing credit systems.

The new structure and programme is applied to the first year of engineering. The students

that started last year in the candidate - licentiate structure, will finish their study in the

former system, unless they have to stay down a grade.

2. COURSES LECTURED IN THE FACULTY OF ENGINEERING

A. Lectures and practical courses

Lectures and practical coursesa Credits

Toegepaste Elektriciteit (Prof. L. Van Biesen)Basic Electricity: In this course electricity is treated as a part of fundamental physics. It is a basic course, so that emphasis is made on the description and the mathematical modelling of the physique principles, which were obtained historically from experimental physics. 2nd Bachelor IR 7

Statistiek voor ingenieurs (Prof. J. Schoukens)Probability and statistics: The aim of this course is to give the students a 'hands on' training on probability and statistics2nd Bachelor IR 3

100 Education

Systeem- en controletheorie (Prof. J. Schoukens and Prof. R. Pintelon)System- and control theory: Understanding the behaviour of linear dynamic systems and verify this insight by experiments. Knowing the basic aspects of control theory 3rd Bachelor IR 5

Electromagnetisme (Prof. A. Barel and Prof. Y. Rolain)Electromagnetism: Transfer to the student of knowledge and abilities of advanced electromagnetic problems. The theoretical knowledge is brought to the students by solving analytically some propagation problems of electromagnetism. The emphasis is laid on the generic character of the chosen solution to prepare the student to an active type of assimilation of the matter. This means clearly that the student must be able to solve these problems by himself. Abilities are brought to the student in the domain of the high-frequency techniques to be sure he will be able of handling transmission line technologies. These are: the Smith-Chart, the Time Domain Reflectometry (TDR), the technique of localizing defaults and miss matches by interpretation of standing waves, Stub Matching techniques.3rd Bachelor IR 6

Netwerken en filters (Prof. R. Pintelon)Network analysis and synthesis: analyses of linear and non-linear dynamic networks; synthesis of active and passive filters3rd Bachelor IR 7

Meten en identificeren (Prof. J. Schoukens, Prof. Y. Rolain)Measurement and Identification: A course aiming to teach how to measure, and how to built mathematic models starting from noisy data, measurements. Extension of the basic knowledge of the measurement problem towards- frequency domain- signal conditioning and computer controlled measurement systems. - Acquiring hands-on experience with complex measurement devices.1st Master IR 7

Signaaltheorie (Prof. L. Van Biesen)Signal theory: introduction to signal theory, detection theory, information theory and modulation.1st Master IR 5

Hoogfrequent elektronica en antennes (Prof. Y. Rolain)High-frequency electronics and antennas: Introduction of wave propagation and antenna theory in practical set-ups. Basic instruments for microwaves measurements. Design of microstrip and active structures including practical realisation. 1st Master IR 6

Digital Video Broadcastingb (Prof. L. Van Biesen)1st and 2nd Master IR 3

Transmissiemedia en -systemen (Prof. L. Van Biesen)Electrical transmission, media and systems: Complementary course to the fundamental course of signal theory. Advanced study of radio communications and analog and digital modulation techniques. Description of practical designs (discrete hardware as well as ICs). Continued study of electrical transmission media and their practical applications: symmetric and asymmetric electrical transmission lines, bundles of pairs and electrical busses. Application of xDSL over POTS local loop, Telenet and teledistribution2.2E/EL-TC, 3.3E/EL-TC, 5E/EL-IN-TC 3

Navigatie en Intelligente Voertuigen (Prof. L. Van Biesen, Prof. H. Sahli)Navigation and Intelligent vehicles4WK 6



B. Minor “Measuring, Modelling, and Simulation of Dynamic Systems” offered within the 2nd Master Electronics and Information Technology

Design of new systems and products is a complex process with a central role for the

engineer. A good physical insight is combined with experimental results to come eventually

to the best products (lowest price, good quality, no pollution) to concur the competition.

Each step in this design process requires dedicated tools. We should collect good experi-

mental data, often collected under poor conditions (course: Industrial measurement

techniques). From these measurements a wide variety of models is extracted for the

designer (course: Identification of dynamic systems) that should meet many requirements.

Stem, beeld, navigatie en telemetrie I + II (Prof. L. Van Biesen)Voice, image coding, media and systems I + II: Part 1 of the course consists of an introduction to telephony (communication by wires) and covers also a fundamental study of electrical transmission lines and their applications. The design of a telephony switchboard system is studied and different scenarios in case of saturation are compared and elaborated. Part II: Applications and theory on telecommunication in which the transmission of voice (speech, audio, music, digital telephony,...) and images (TV video, digital photos) are important. Introduction to the (automated) navigation in airborne and ship transportation and overview of instrumentation for assistant guidance in over land navigation (motorcycle, car, truck, bus,...) Students will gain insight in a specific technology by investigating a modern, successful technology, or one, which has a promising future, by self study. The skills to prepare and to present a technical talk using the most advanced multimedia will be thought.5E/TC, 2.2E/TC, 3.3E/TC 4

Capita selecta TelecomCapita Selecta Telecom: To bring the students in contact with the latest evolutions inthe technology of telecommunication in stimulating the exchange between in the fieldspecialists and the students. The 'Capita Selecta' are reviewed and organised eachyear in close cooperation with the industry. After completion of this course, thestudents have acquired knowledge on industrial research projects and on theprocesses to market and deploy new products and services.2nd Master IR 3

a. The language of tuition of all these courses is Dutchb. This course will start in 2008-2009

Course (Dutch or English) credits

An introduction to system identification (Prof. J. Schoukens) 4

Measuring and modelling of nonlinear systems (Prof. J. Schoukens) 4

Industrial measurement environments (Prof. L. Van Biesen) 4

Identification of dynamic systems (Prof. R. Pintelon) 4

Advanced control methods (Prof. R. Pintelon and Prof. J. Swevers, KULeuven) 4

CAE-tools for the design of analog electronic circuits (Prof. G. Vandersteen, IMEC) 4

Design and characterization of high-frequency (nonlinear) systems (Prof. W. Van Moer) 4

Bio-informatics and datamining (Prof. J. Schoukens and Prof. Y. Moreau) 4


102 Education

Sometimes the models are used in simulation tools like ‘Spice’, or they are used to measure

parameters that are difficult to access directly, like the damping of a wing, the time

constants of an electrical machine. Models are also at the basis of control design (for

example the active suspension of a car). A lot of commercial software packages are

available on the market to support the design process, but using these tools without under-

standing can result in a bad or poor design or even create dangerous situations. In a

simulation package, many user parameters are set to default values that are not always

suitable for the specific situation, and this can jeopardize the results completely (course:

CAE-tools for the design of analog electronic circuits). During the design, it is very tempting

to rely on linear models because they are very intuitive, easy to use, many rules of thumb

are available, and it is not too difficult to extract them from measurements. However,

nature is not linear. What is the quality of the design under these conditions? Is the stability

analysis of the controller still valid? How to design a controller in the presence of nonlinear

distortions? How is the bit-error-rate of a high speed communication link affected by a

nonlinear amplifier? The courses Measuring and modelling of nonlinear systems and

Advanced control methods help to answer these questions. These ideas are practically

applied in microwave designs where transistors are often pushed in their nonlinear

operating region for power efficiency reasons (course: Design and characterization of high-

frequency (nonlinear) systems). As an engineer, we are overwhelmed with information that

is often out of the scope or our interest. The course on Bioinformatics and datamining

opens a new scientific field that is directed to the problem how to extract the desired infor-

mation out of enormous amounts of data. How can we turn data into information?

C. Workshop Information and Communication Technology (“ICT”)

A workshop named “ICT” has been introduced in the fourth semester of the Bachelor of

Engineering at the Faculty of Engineering VUB. It is the consequence of the introduction of

a Bachelor-Master structure into our old european “Polytechnic School” engineering

education structure.

At VUB, the bachelor programme, which counts 6 semesters, is identical for all the

engineering students except that semesters 5 and 6 are specific to the speciality they

choose.

Several workshops illustrating these specialities have been introduced in the semester 4

during the academic year 2002-2003. One of them is the “Workshop ICT”.

Prof. Jacques Tiberghien (Dept. INFO) and Prof. Alain Barel (Dept. ELEC) are the promoters

of the new workshop and with the help of the technical and academic staff of the Dept.

INFO, ELEC and ETRO, the workshop have been designed and run for the first time between

February and May 2003.

The task.

Design the control of a water tower as a “project based learning” pedagogy.


A water tower is a very simple installation which delivers drinking-water to the inhabitants

of a town through a distribution network [Figure 4]

It is obvious that building a large water tower brings two advantages: the distribution is

passive, i.e. it continues to work even when there is no more electric power available and

the distribution pressure shows only small variations with the volume of water left in the

tower.

.

FIGURE 4. Installation of Water Tower

The owner or the operator of the water tower must manually or preferably automatically

care to keep the level of water in the water tower as constant as possible in spite of the

fluctuations on the flow of water imposed by the users.The task of the workshop consists in

the design of the automatic control system that commands the approvision pump of the

water tower.

Four reduced scale laboratory water towers have been build and foreseen with the same

sensors and actuators as the real water towers [Figure 5]

These sensors and actuators are connected to a computer (a PC) which is in charge of the

control.

No deep theory about control is requested in this workshop, only common sense is needed

to write a good working control algorithm.

The students are grouped in entities of 6 to 7 working on the same project.

All groups have to reach the same goal, but have to choose their equipment:

- which type of sensor or actuator

Water tower

Users

Underground water tank

104 Education

- which type of electronic interface

- which type of PC and software

Each group can spend a fictitious credit of 10 kEuro to buy the chosen equipment.

The evaluation of each group is based on the obtained technical performances during a test

run and on the amount of credit used.

FIGURE 5. .Scale laboratory water tower

Aim of the workshop and pedagogy.

The aim of the workshop is to show the students a broad overview of the different aspects

of electro technics in its ICT taste and initiate them to the first possible and simple applica-

tions of basic concepts of mechanics, electricity and chemistry already learned in the

semesters 1 to 3.

Also we choose to do it as a project-based learning pedagogy because it opens a lot of

human aspects of the engineering job, like the ability to express (oral and written), to work

in group, to manage a work plan and to manage a timing.

wheel

potentiometer Computer

Interface

Camera

float

Tank


D. Optional courses

Optional coursesa

a. The language of tuition of all these courses is Dutch

credits

Netwerkentheorie I Theory of Networks I4E/FO, 5E/EL-IN-TC-FO 3

SatellietcommunicatieSatellite communication4E/FO, 5E/EL-IN-TC-FO 3

Gevorderde SysteemidentificatieAdvanced System Identification4E/FO, 5E/EL-IN-TC-FO 4

Onderwater akoestiekUnderwater Acoustics4E/FO, 5E/EL-IN-TC-FO 3

Numerische methodes in elektromagnetismeNumerical methods in electromagnetics4E/FO, 5E/EL-IN-TC-FO 4

Ontwerp en realisatie van microgolfkringenDesign and realisation of microwave circuits4E/FO, 5E/EL-IN-TC-FO 5

Meten en modelleren van niet lineaire systemen (Dutch or English)Measurement and modelling of nonlinear systems4E/FO, 5E/EL-IN-TC-FO 5

106 Education

3. COURSES LECTURED IN THE FACULTY OF SCIENCE

A. LECTURES AND PRACTICAL COURSES

B. Post-graduates

Lectures and practical coursesa

a. The language of tuition of all these courses is Dutch

credits

Elektriciteitsleer Principles of electricityBio-ingenieurs, 1ste jaar van de graadb

b. Bio-engineers, first year of degree

5

Geographical Information Systems (Prof. L. Van Biesen)This course explicitly contributes to the following competences of the curriculum of Master of Ecological Marine Management: General1 Developing the own learning process 1 Learning to work in a team1 Presenting and transferring the acquired knowledge1 Reporting in various waysDomain specific1 Developing practical management skills1 Planning and conducting marine research in an autonomous way1 Understanding, judging and interpreting research results1 Analytical and problem-solving thinking1 Using research supporting tools (e.g. Biostatistics, GIS etc.) 1 Critically evaluating and integrating multidisciplinary scientific information 1 Translating scientific information into advice for sustainable marine management1 Communicating scientific findings to various kinds of audiencesLearning targets and goalsAfter finishing this course, the student should:î Be able to design a geographical information system appropriate to a given environmental problemî Apply it to perform spatial analysis , interpret , visualize and report spatial information graphically and tabularî Enable to query, identify spatially anomalous zones and suggest measures to mitigate adverse effects and sensitivity of outcropped problemsî Model and Integrate heterogeneous raw data and perform intercalated Geostatistcal and Geographical analysis î Develop a working methodology of understanding, manipulating and managing a structured spatial database data holistically.2nd Master of Ecological Marine Management 3

Graduates Colleges and practical courses hrs.col/

hrs.ex.

Bijzondere licentie in de biomedische en klinische ingenieurstechnieken (BMKIT)Postgraduate in biomedical and clinical engineering techniques.

Inleidende vakkenIntroduction courses- Toegepaste electriciteit (Applied electricity)

3


4. NATIONAL AND INTERNATIONAL COURSES

A. National courses:

5 day course on ‘Basics of DSP’

Organised by: J. Schoukens and Y. Rolain

Location: IMEC, Leuven, Fall 2001

# attendees: 20

The main goal of this course is to give an introduction to general DSP and filter design and

realization for FIR and IIR filters, for engineers that have only limited or no basic knowledge

of DSP. Care was taken to emphasize the applications and the practical problem solving,

including numerical and implementation aspects and the non linearity induced by round off.

Enough theoretical background was given to ease further reading about the topic.

10 day course on ‘System identification: from data to model’

Organised by: BEST Summer Course 20021

Location: Vrije Universiteit Brussel, August 30-September 13, 2002


KernvakkenCentral courses- Elektrische meettechnieken en bio-elektrische instrumentatie (Electrical measurement techniques and bio-electrical instrumentation)

3


Kernvakken medische fysicaCentral courses medical physics- Klinische dosimetrie (Clinical dosimetry)

4

ECOMAMA: M.Sc. in Ecology Marine Managementa

GIS for sustainable management, part II

6

Master in information technology (I.T.)Second year: Specialisation carto-graphy and geographical information systems

Optional courses- Computer-guided instrumentation- Signal theory- Optical fibres- Telecommunication by wire- Introduction to opto- and µ-electronics: electronics

33333

a. Note: The languages of tuition are Dutch and English: most of the courses are taught in English, and textbooks are available for the other courses.

1. BEST, “Board of European Students of Technology”, was officially founded in 1989 in Berlin, as an answer to the need for a structure to promote communication and exchange between technology students throughout Europe.þ It is a non-profit, apolitical organisation of European Engineer stu-dents.

Graduates Colleges and practical courses hrs.col/

hrs.ex.

108 Education

Lectured by: J. Schoukens, Y. Rolain and P. Guillaume

# attendees: 20

The BEST Summer Course 2002 deals about system identification, which is “the determi-

nation, on the basis of input and output, of a system within a specified class of systems, to

which the system under test is equivalent” (L. Zadeh, 1962). This theory, which is used in

every field of science, is extensively studied by the well-respected research group of the

department “ELEC” of the faculty of applied sciences.

Goal of the course: In order to test a system, engineers must produce a test model.

Creating a mathematical model of a system from noisy data is a daunting task that faces

many engineers in numerous fields including communications and signal processing,

mechanical vibrations, electronics. This course presents a general approach to this

problem, with both practical examples and theoretical discussions that give the student a

sound understanding of the subject and of the pitfalls that might occur on the road from

raw data to validated model.

Identificatie van systemen (Identification of Systems)

Organised by: University of Gent, “Instituut voor permanente vorming”

Location: IVPV - UGent, Technologiepark, 9052 Gent-Zwijnaarde

Lectured by: Johan Schoukens and Yves Rolain

Dates: 7, 14 and 21 December 2004

Meten en modelleren is een basisactiviteit van vele ingenieurs: modellen worden gebruikt

tijdens het ontwerp, in simulatoren en in eindproducten. Het modelleringsproces is een

complexe activiteit die in 4 grote delen kan worden opgesplitst: verzamelen van de experi-

mentele data; opstellen van een model; in overeenstemming brengen van een model en

data; validatie van de resultaten. Systeemidentificatie biedt een systematische, optimale

oplossing en wordt in deze module bestudeerd, met als toepassing the identificeren van

dynamische systemen. Hierbij wordt de klemtoon gelegd op het aanbrengen van de ideeën,

ondersteund door uitgewerkte Matlab illustraties.

De behandelde topics zijn

• Systeemidentificatie: wat? waarom?

Een verhelderend voorbeeld

Goede schatters/slechte schatters, wat mag je ervan verwachten?

• Niet-parametrische identificatie van frequentieresponse functies

Basisidee: van tijdsignaal tot frequentierespons (FRF)

Experiment design: keuze van de excitatiesignalen, ruisgevoeligheid, uitmiddelen

Nietlineaire distorties: detectie, kwalificatie en quantificatie

• Parametrische identificatie van de transferfunctie

Basisidee: van data tot model

Tijdsdomein- en frequentiedomein-identicatie

• Identificatie van tijdsvariërende systemen

Basisidee

Balans volgsnelheid/ruisgevoeligheid


B. International courses:

Short Course on Non-Linear Measurement Basics

Organised by: A. Barel, Y. Rolain and L. Dunleavy

Sponsored by: IEEE MTT technical committee for education

IEEE MTT technical committee for measurements

Location IEEE MTT IMS 2001, Phoenix, Arizona, 2001.

# attendees: 110

In this 1 day course, the fundamentals of nonlinear measurements for RF and microwave

are explained. Specific properties and needs are introduced as a generalisation of the linear

counterparts. Examples taken from simulation and measurement are added. The course is

specially suited for engineers familiar with the linear techniques that want an ‘easy start’ in

the nonlinear measurement world.

NIST ARFTG short course on nonlinear measurements

Organised by: Y. Rolain and A. Barel

Sponsored by: ARFTG and National Institute for Standards and Technology

(Boulder, Colorado)

Location: San Diego (CA), 2001

# attendees: 30

In this course slot, the aspects of the non linearity that matter in the analysis/design of

telecommunication channels are highlighted. A special care was taken to show the most

obvious problems if a non linearity is treated as if it were a linear system. The advantages

and drawbacks of the currently used figures of merit for the nonlinear behaviour are shown.

The main goal of this course is to ease a critical analysis of the parameters that are

currently used for the specification of telecommunication devices.

System Identification

Organised by: DISC1 Graduate School in Systems and Control

Lectured by: P. Van den Hof (T.U. Delft), J. Schoukens and R. Pintelon (VUB)

Location: Utrecht, The Netherlands, Spring 2001 (6 days)

# attendees: 20

Course on System Identification for Dutch Phd Researchers

NIST ARFTG short course on nonlinear measurements

Organised by: Y. Rolain and A. Barel

Sponsored by: ARFTG and National Institute for Standards and Technology

(Boulder, Colorado)

Location: Washington DC (USA), Fall 2002

# attendees: 35

1. Dutch institute of Systems and Control

110 Education

In this course slot, the aspects of the non linearity that matter in the analysis/design of

telecommunication channels are highlighted. A special care was taken to show the most

obvious problems if a non linearity is treated as if it were a linear system. The advantages

and drawbacks of the currently used figures of merit for the nonlinear behaviour are shown.

The main goal of this course is to ease a critical analysis of the parameters that are

currently used for the specification of telecommunication devices.

Characterisation of Multiport Systems through 3-port LSNA Measurements

Location: Seminar at NIST, Boulder, CO, December 2003

Lectured by: Wendy Van Moer

# attendees: 20

The use of multisines

Location: Seminar at NIST, Boulder, CO, December 2003

Lectured by: Daan Rabijns

# attendees: 20

GIS training in SEAFDEC, Thailand

Location: Samut Prakan SEAFDEC/Training Department, Thailand, Bangkok,

February-March, 2005

Lectured by: Tesfazghi Ghebre Egziabeher

The theme of the course was interrelated to the use of Geographic Information System for

Fishery Management. Participants of the course were members of the Southeast Asian

Fisheries Development and Training Center (SEAFDC/TD), who were professionally engaged

in the fishing industry.

C. Advanced university education

Flemish Interuniversity Council (Vlaamse Interuniversitaire Raad - Vl.I.R.):

International Training Programmes 2002-2004:

Training in Medical Imaging, Radiation Therapy and Nuclear Medicine and Related Radiation

Protection (ITP 2002-2004).

Contact person and details:

- Prof. R. Van Loon, VUB/dept. ELEC


CHAPTER 4. Bibliography1

1. BOOKS

b1 Identification of Linear Systems: A Practical Guideline to Accurate ModelingJ. Schoukens, R. PintelonPergamon Press - London, 1991 (ISBN 0-08-040734-X)The book is concentrated on the problem of accurate modelling of linear time invariant systems. Thesemodels can be continuous time (Laplace-domain) or discrete time (Z-domain). The complete experimentalprocedure is discussed: how to create optimal experiments (optimization of excitation signals), how toestimate the model parameters from the measurements, how to select between different models, etc. Theseproblems are thoroughly discussed in the first section of the book. A profound theoretical development ofthe proposed identification algorithm is also made in this section. The second part consists of detailedillustrations of the proposed algorithms on practical problems: modelling an electronic, electrical, acoustic,mechanical system. Finally the book is completed with a practical guideline to help the user making thecorrect choices.The book is intended for all those dealing with “practical” modelling problems and have to combinemeasurements and theory. A second group of interested people are those involved with identification theory.

b2 System Identification: A Frequency Domain ApproachRik Pintelon, Johan SchoukensIEEE Press, Piscataway, NJ, 2001 (ISBN 0-7803-6000-1)How does one model a linear dynamic system from noisy data This book presents a general approach to thisproblem, with both practical examples and theoretical discussions that give the reader a soundunderstanding of the subject and of the pitfalls that might occur on the road from raw data to validatedmodel. The emphasis is on robust methods that can be used with a minimum of user interaction.System Identification: A Frequency Domain Approach is written for practising engineers and scientists whodo not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learnmore about the theoretical aspects of the proofs. Several of the introductory chapters are suitable forundergraduates. Each chapter begins with an abstract and ends with exercises, and examples are giventhroughout

2. JOURNAL PAPERS (SINCE 2007)

ip279 Comment on: Paleoclimatic inference from stable isotope profiles ofaccretionary biogenic hardparts—a quantitative approach to the evaluationof imcomplete data, by Wilkinson, B.H., Ivany, L.C., 2002. Palaeogeogr.Palaeocl. Palaeoecol. 185, 95-114Fjo De Ridder, Anouk de Brauwere, Rik Pintelon, Johan Schoukens, Frank Dehairs,Willy Bayens, Bruce H. WilkinsonPalaeogeography, Palaeoclimatology, Palaeoecology, 248 (2007), pp. 473-476The time base reconstruction method proposed by Wilkinson and Ivany [Paleoclimatic inference from stableisotope profiles of accretionary biogenic hardparts—a quantitative approach to the evaluation of incompletedata. palaeogeogr. Palaeocl., Palaeoecol. 185 (2002), 95-114] can be further refined. The have shown thatvariations in accretion rate can be reconstructed from variations in the period of the measured signal. Here,it is shown that such variations influence not only the period, but also the phase of the signal. So, a refinedestimator for the time base is constructed, which takes both period and phase variations into account.

ip280 Estimation of heteroscedastic measurement noise variancesAnouk de Brauwere, Rik Pintelon, Fjo De Ridder, Johan Schoukens, Willy Bayens

1. More publications of the department ELEC can be found on: http://www.vub.ac.be/infovoor/onderzoekers/research/team_pub.php?team_code=elecor http://wwwir.vub.ac.be/elec/Papers%20on%20web/index.html

112 Bibliography

Chemometrics and Intelligent Laboratory Systems 86 (2007), pp. 130-138For any quantitative data interpretion it is crucial to have information about the noise variances.Unfortunately, this information is often unavailable a priori. We propose a procedure to estimate the noisevariances starting from the residuals. The method takes two difficulties into account. (i) The noise can beheteroscedastic (not constant over the measurement domain). This implies that one number is not enoughanymore to characterise the total noise variance structure. (ii) The initial model used to generate theresiduals may be inperfect. As a consequence, the residuals contain more than only stochastic information.The outcome of our procedure is an estimate of the noise variances which depends on the freedom over themeasurement domain. Indeed, as a consequence of the heteroscedastic noise, the model parameters will befitted more to those data with low uncertainty and most of the degrees of freedom are lost at these locations

ip281 A water tower to introduce electronic engineering and computer scienceJacques Tiberghien, Nico Deblauwe, Alain BarelMeasurement 40 (2007), pp. 192-201During the fourth semester, just before they have to choose among one of the engineering disciplines taughtat the Vrije Universiteit Brussel, students are confronted with four different practical problems, one perdiscipline. These problem based learning courses are intended to familiarize students with the most typicalaspects of each dicipline, in order to facilitate a conscious choice. This paper describes the project related to"Electronics and Information Technology" that essentially focuses on computer based process control andgives a summary of the experience accummulated with the project over three years.

ip282 Extraction of a quantitative reaction mechanism from linear sweepvoltammograms obtained on a rotating disk electrode. Part II: Applicationto the redoxcouple Fe(CN)3-6/Fe(CN)4-6Els Tourwé, Tom Breugelmans, Rik Pintelon, Annick HubinJournal of Electroanalytical Chemistry 609 (2007), pp. 1-7A methodology to quantitatively determine the mass and charge tranfer parameters of an electrochemicalreaction that was proposed in the first part of this series of articles is used in this work for the study of thereduction of ferriccyanide to ferrocyanide at a platinum electrode. A validation of the previously describedmethod is aimed for. It is concluded that the reaction can be described by a simple electron transfer and thevalues of the charge transfer parameters are: aox = 5.12E-01 ± 8.04E-03, kox = 1.43E-08m/s ± 2.31E-09m/s and kred= 1.74E-00m/s ± 2.87E-01m/s. The diffusion coefficients are Dred= 8.07E-10m2/s ± 2.23E-17m2/s and Dox= 8.31E-10m2/s ± 2.75E-17m2/s.

ip283 Extraction of a quantitative reaction mechanism from linear sweepvoltammograms obtained on a rotating disk electrodeTourwé, E., T. Breugelmans, R. Pintelon, and A. HubinWIT Transactions on Engineering Sciences - Simulation of Electrochemical ProcessesII, Vol. 54, pp. 173-182.A new methodology to quantitatively determine the mass and charge transfer parameters of anelectrochemical reaction is applied successfully in this work for the study of the reduction of ferricyanide toferrocyanide at a platinum electrode, rotating at 1000 rpm.

ip284 Frequency domain maximum likelihood estimation of linear dynamicerrors-in-variables modelsR. Pintelon, and J. SchoukensAutomatica 43 (2007), 621-630This paper studies the linear dynamic errors-in-variables problem in the frequency domain. First theidentifiability is shown under relaxed conditions. Next a frequency domain Gaussian maximum likelihood(ML) estimator is constructed that can handle discrete-time as well as continuous-time models on (a) part(s)of the unit circle or imaginary axis. The ML estimates are calculated via a computational simple and numericalstable Newton-Gauss minimization scheme. Finally the Cramr-Rao lower bound is derived.

ip285 Water mass distributions in the Southern Ocean derived from a parametricanalysis of mixing water massesAnouk de Brauwere, Stéphanie H.M. Jacquet, Fjo De Ridder, Frank Dehairs, RikPintelon, Johan Schoukens, Willy BaeyensJournal of Geophysical Research, Vol 112, C02021, 2007An empirical method to reconstruct distributions of mixing water masses is developed and applied to twoSouthern Ocean (Australian sector) data sets. The method is a parametric extension of the well-knownOptimum Multiparameter (OMP) analysis, therefore called POMP (parametric OMP). The main developmentconsists of parameterizing the mixing fractions as a function of position (e.g. latitude and depth) andestimating the parameters of the resulting function, instead of estimating the mixing fractions for everysampling point individually. Because this enables to reduce the total number of unknowns to be estimated,the proposed adjustments result in more robust estimates of the mixing fractions, as illustrated on theSouthern Ocean CLIVAR-SR3 data set (early spring 2001). An additional consequence of the new workingscheme is that the mixing distributions are smoothed. It is emphasized that the degree of smoothing shouldbe chosen with care in order not to neglect any significant features. Statistical rules to do this are applied.Application of the POMP method to the second Southern Ocean data set (SAZ’98, summer 1998) revealedthe importance of a careful selection of the mixing source water types. This study was limited to thesubantarctic region and upper 1000 m, where variations in water mass characteristics and distributions aremost important. Indeed, it seems necessary to redefine the source water characteristics for each data set.The direction of these changes suggests that interannual variability or long term changes of water massesoverwhelm the seasonal variability.


ip286 Box-Jenkins identification revisited — Part III: multivariable systemsR. Pintelon, J. Schoukens, P. GuillaumeAutomatica 43 (2007), pp. 868-875Part I of this series of three papers handles the identification of single input single output Box-Jenkins modelson arbitrary frequency grids in an open and closed loop setting. Part II discusses the computational aspectsand illustrates the theory on simulations and a real life problem. This paper extends the results of Part I andII to multiple input multiple output systems. Contrary to the classical time domain approach, the presentedtechnique does not require symbolic calculus for multiple output polynomial Box-Jenkins models.

ip287 Initial Estimates for the Dynamics of a Hammerstein SystemJ. Schoukens, W. D. Widanage, K. R. Godfrey, R. PintelonAutomatica 43 (2007), pp. 1296-1301This paper studies the generation of initial estimates for the dynamic part of a Hammerstein model. It willbe shown that ARMAX or Box-Jenkins models result in better initial estimates than ARX or output-error (OE)models even in the absence of disturbing noise. This will be proven by noticing that a static nonlinear systemcan be replaced by a static gain plus a nonlinear noise source that acts in a completely similar way todisturbing noise for the study of the second-order properties of the estimators in the prediction errorframework.

ip288 Uncertainty calculation in (operational) modal analysisR. Pintelon, P. Guillaume, J. SchoukensMechanical Systems and Signal Processing, 21 (2007), pp. 2359-2373In (operational) modal analysis the modal parameters of a structure are identified from the response of thatstructure to (unmeasurable operational) perturbations. A key issue that remains to be solved is thecalculation of uncertainty bounds on the estimated modal parameters. The preesent paper fills this gap. Thetheory is illustrated by means of a simulation and a real measurement example (operational modal analysisof a bridge).

ip289 Linear Approximation of Weakly Nonlinear MIMO SystemsTadeusz P. Dobrowiecki, Johan SchoukensIEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 3, June 2007,pp. 887-894A nonlinear multiple-intput-multiple-output (MIMO) Volterra system, measured with random multisinesignals, can be replaced by an approximating linear MIMO system followed by nonlinear-noise sources. Thisapproximation is a straightforward extension of the approximation to single-input-single-output Volterrasystems. For a two-input-two-output cubic system, an optimized measurement strategy is also proposed toreduce as much as possible the influence of nonlinear noise on the measurement of the nonparametricfrequency-response function.

ip290 Using Multisines to Mesure State-of-the-art Analog to Digital ConvertersD. Rabijns, W. Van Moer and G. VandersteenIEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 3, June 2007,pp. 1012-1017Multisines have proven to be very useful for fast and accurate measurements, particulary when studying thenonlinear behavior of a device under test. This paper will deal with some problems that can arise whendesigning experiments for measuring state-of-the-art analog to ditital converters (ADCs) with multisines.Special care has been taken to the measurement setup, particulary with the generation and verification ofthe test signals. The verification of the test signals requires either a more performant ADC or alternativemeasurement methods such as a spectrum Analyzer. By means of real measurement examples, it is shownthat most existing techniques used for analog measurements can be reused after an adaptation to the newproposed measurement setup. It will, e.g., be shown that the loss of information introduced by using aspectrum analyzer leads to bounds onto the nonlinear distortions instead of the absolute value.

ip291 Measuring Nonlinear Differential RF Amplifiers using one Single-EndedSourceYves Rolain, Wendy Van Moer, Johan Schoukens and Rik PintelonIEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 3, June 2007,pp. 1042-1048A measurement procedure for the nonlinear baseband response of a differential amplifier is proposed usingthe large-signal network analyzer. The setup uses only 1 single-ended RF generator: A simplifiednonparametric model for the frequency response of the device and its nonlinear contributions over a widepower range is proposed, measured and validated experimentally.

ip292 Public Transport LBS: Combining GPS and GSM Positioning TechniquesNico Deblauwe and Leo Van BiesenThe Journal of The Communications Network, Volume 6, Part 3, July-September2007, pp. 1-7Location-based Services (LBS) are being hyped again. The first generation of applications was not fullycapable of doing justice to the user's demands, mainly to technical limitations. Portable Information Devices(PID) have improved greatly over the latest years, allowing the second generation of LBS to fulfill theexpectations. This paper presents the case of an LBS application for public transport, focusing on thepositioning component. As modern PIDs are equipped with several technologies, a choice needs to be made,taking into account the limited battery power of the device and strigent accuracy demands of the application.This paper examines the possibilities for GPS, GSM Cell-ID, and RSS-based positioning for both the User

114 Bibliography

Application and Fleet Management. Accuracy measures on the individual technologies are given, andinteraction schemes are presented.

ip293 Identifying the Structure of Nonlinear Perturbations in Mixers usingMultisine SignalsKoen Vandermot, Wendy Van Moer, Yves Rolain and Johan SchoukensIEEE Instrumentation & Measurement Magazine, Vol. 10, No. 5, October 2007, pp.32-39Telecommunication transceivers (e.g., radio, Global System for Mobile communications [GSM] Bluetooth,wireless local area network [WLAN] etc.) use mixers to translate the frequencies of signals carryinginformation. Mathematically, this ideal nonlinear frequency translation behavior corresponds to amultiplication. Real mixers also present “nonideal” nonlinear behavior that result in the creation of higher-order harmonics, additional intermodulation products, and even linear spurious response terms. For highperformance designs, a microwave designer needs to know how close a real mixer is t an ideal frequencytranslator. If it is possible to understand where the nonideal nonlinearities are located in the mixer, thedesign can be optimized to reduce unwanted perturbation levels. To answer these concerns, we formallydefine an ideal mixer as a block-oriented model that contains a perfect multiplier but that is surrounded bytwo linear time-invariant systems. This model must then be extended to take into account the nonlinearities.There are two possible locations in the signal path that might introduce nonlinearity - the input path or theoutput path of the mixer. To identify the position of the nonlinearity, a custom designed multisine signal canbe used to excite the signal path of the mixer. This special random-phase multisine signal with a randomharmonic grid contains several carefully selected spectral lines that are left unexited. These “holes” in thespectrum of the signal act as detectors for the presence and the location of nonlinearities that deviate fromthe ideal. First we test the proposed detection algorithm in a simulation to show that it can work in an ideallycontrolled environment. Afterwards, we perform measurements on a real mixer excited with a special oddrandomphase multisine with a two-port large signal network analyzer (LSNA) and detect the position of theunwanted nonlinearities in the device model.

ip294 Fast Measurements of Quantization Distortions in DSP AlgorithmsJohan Paduart, Johan Schoukens and Yves RolainIEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 5, October2007, pp. 1917-1923A measurement technique is proposed to characterize the nonidealities of digital signal processing algorithmswhich are induced by quantization effects, overflows (fixed point), or nonlinear distortions in one singleexperiment/simulation. The main idea is to apply specially designed multisine excitations in order todiscriminate between the output of the ideal system and the contributions of the systems’s nonidealities.This approach allows the comparison and quantification of the quality of different implementationalternatives. Applications of this method include but are not limited to, digital filters, fast Fourier transforms,and audio codecs.

ip295 Determining the Reciprocity of Mixers through 3-port Large SignalNetwork Analyser MeasurementsWendy Van Moer and Yves RolainIEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 5, October2007, pp. 2051-5056In this paper, a method to determine the reciprocity of radio-frequency mixers is proposed based on fullycalibrated three-port large signal network analyser measurements. Besides revealing the reciprocity of themixers, these type of measurements can also be used in yielding the match conditions at all ports as well asmagnitude, phase and group-delay response of the conversion loss of the mixer.

ip296 A comprehensive study of the bias and variance of frequency-response-function measurements: Optimal window selection and overlappingstrategiesJ. Antoni, J. SchoukensAutomatica, 43(10), pp. 1723-1736, October 2007This paper investigates the measurement errors involved in measuring frequency response functions fromweighted-overlapped segment averaging, a technique that has become a standard in modern spectralanalysers due to its computational advantages. A particular attention is paid to leakage errors, for which thisprocedure has been frequently criticised. Exact and asymptotic expressions for the bias and variance areprovided, whose minimisation enables the derivation of the optimal settings to be used with this procedure.Our main finding is that a Half-sine or Diff window with 2/3 overlap achieves the best compromise to reduceleakage errors, and this is independently of the system frequency response function. This conclusion is to becontrasted with the customary habit of using a Hanning window with 1/2 overlap.

ip297 Measuring a linear approximation to weakly nonlinear MIMO systemsT. Dobrowiecki, J. SchoukensAutomatica, 43(10), pp. 1737-1751, October 2007The paper addresses the problem of preserving the same LTI approximation of a nonlinear MIMO (multiple-input multiple-output) system. It is shown that when a nonlinear MIMO system is modeled by amultidimensional Volterra series, periodic noise and random multisines are equivalent excitations to theclassical Gaussian noise, in a sense that they yield in the limit, as the number of the harmonics M -> infinity,the same linear approximation to the nonlinear MIMO system. This result extends previous results derivedfor nonlinear SISO (single-input single-output) systems. Based upon the analysis of the variability of themeasured FRF (frequency response function) due to the presence of the nonlinearities and the randomnessof the excitations, a new class of equivalent input signals is proposed, allowing for a lower variance of thenonlinear FRF measurements, while the same linear approximation is retrieved.


ip298 Asymptotic Uncertainty of Transfer Function Estimates Using Non-Parametric Noise ModelsR. Pintelon, and M. HongIEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 6, December2007, pp. 2599-2605Identification of parametric transfer function models from noisy input/output observations is an importanttask in many engineering applications. Aside from the parametric model, the estimation algorithm usedshould also provide accurate confidence bounds. In addition it is important to know whether the proposedestimation algorithm has the lowest possible uncertainty within the class of consistent estimators. This paperhandles these issues for the frequency domain Gaussian maximum likelihood estimator of rational transferfunction models within an errors-in-variables framework.

3. CONFERENCE PAPERS (SINCE 2007)

c606 On a parametric method to reconstruct distributions of mixing watermassesAnouk de Brauwere, Fjo De Ridder, Stéphanie H. M. Jacquet, Frank Dehairs, RikPintelon, Johan Schoukens, Willy Baeyens14th National Australian Meteorological and Oceanographic Society (AMOS)Conference (5-8 feb 2007), Adelaide, AustraliaMixing distributions of water masses in the ocean can be reconstructed by measuring conservative tracerson a grid and optimizing the mixing coefficients of the mixing water masses such that the observed patternsare well approximated. This is the fundamental idea of Optimum Multiparameter (OMP) analysis. Here aparametric variant of the OMP model is presented (called POMP). It is based on the same basic assumptionsbut by parameterizing the unknown mixing fraction fields, the total number of unknowns to be estimated isgreatly reduced. This results in a more robust and hence more interpretable model outcome. Considerableeffort was also directed to uncertainty estimation, because without a reliable uncertainty estimate noquantitative interpretation is possible. A final attempt to improve the model concerns the characterisation ofthe source waters. This would be a step forward in OMP-like analysis because it now represents the mostsubjective step in the procedure. The possibilities offered by this parametric model are explored by applyingit to several datasets from the Southern Ocean. Direct application of the method delivers the spacialcontribution of the mixing water masses, which can be seen as a snap shot of the circulation process.Therefore it represents a tool in a better understanding of the mixing, circulation and transformation of watermasses. It is a data-based approach, so results are location and time dependent. Using Southern Oceandatabases, mixing distributions of the main source water masses were reconstructed. Apart from this morephysical application, the method also provides the possibility to identify variables whose distribution is NOTonly the result of mixing. This exercise was executed for the dissolved Barium tracer to reveal and confirmits potential as a biogeochemical tracer.

c607 Faster ESD device characterization with wafer-level HBMScholtz, M., D. Trémouilles, D. Linten, Y. Rolain, R. Pintelon, M. Sawada, T. Nakaei,T. Hasebe, G. GroesenekenProceedings International Conference on Microwave Electronic Teststructures(ICTMS2007), 19-22nd March, Tokyo (Japan).

c608 Simulation methodology and experimental verification for the analysis ofsubstrate noise on LC-VCO's S. Bronckers, C. Soens, G. Van der Plas, G. Vandersteen, Y. Rolain DATE 2007, Design, Automation and Test in Europe, Nice, France, 16-20 April, 2007This paper presents a methodology for the analysis and prediction of the impact of wideband substrate noiseon a LC-Voltage Controlled Oscillator (LC-VCO) from DC up to Local Frequency (LO). The impact of substratenoise is modeled a priori in a high-ohmic 0.18µm 1P6M CMOS technology and then verified on silicon on a900MHz LC-VCO. Below a frequency of 10MHz, the impact is dominated by the on-chip resistance of the VCOground, while above 10MHz the bond wires, parasitics of the on-chip inductor and the PCB decouplingcapacitors determine the behavior of the perturbation.

c609 Optimizing Analog Filter Designs for Minimum Nonlinear Distortions UsingMultisine ExcitationsJ. Lataire, G. Vandersteen, R. PintelonDATE 2007, Design, Automation and Test in Europe, Nice, France, 16-20 April, 2007Nonlinear distortions in submicron analog circuits are gaining importance, especially when power constraintsare imposed and when operating in moderate inversion. This paper proposes a method to optimize the designof analog filters for minimum noise and nonlinear distortions. For this purpose a technique is presented forquantifying these nonlinearities, such that their influence can be compared with that of the system noise.Having quantified the nonidealities, an optimization can be carried out which involves the tuning of designparameters.

116 Bibliography

c610 Nonlinearity Analysis of Analog/RF Circuits Using Combined Multisine andVolterra AnalysisBORREMANS Jonathan, DE LOCHT Ludwig, WAMBACQ Piet, ROLAIN YvesDATE 2007, Design, Automation and Test in Europe, Nice, France, 16-20 April, 2007Modern integrated radio systems require highly linear analog/RF circuits. Two-tone simulations arecommonly used to study a circuit's nonlinear behavior. Very often, however, this approach suffers limitedinsight. To gain insight into nonlinear behavior, we use a multisine analysis methodology to locate the

c611 Measurement and modeling of the sensitivity of LC-VCO’s to substratenoise perturbationsBRONCKERS Stephane, VANDERSTEEN Gerd, SOENS Charlotte, VAN DER PLAS G.,ROLAIN YvesIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007On-chip oscillators are very sensitive to substrate perturbations. Both low-frequency and high-frequencydistortions can ruin the required spectral purity of the oscillator. This paper presents the measurement andmodeling results of the sensitivity of a 900MHz LCVoltage Controlled Oscillator (LC-VCO) to substrateperturbations in a frequency range from DC to 900MHz. Accurate measurement of the sensitivity requirevarious measurement setups to investigate different impact fenomena. These measurements impliesspectral analysis, linear vectorial network analyzer measurements and time domain measurements. Theimpact of substrate noise is modeled in a high Ohmic 0.18um 1P6M CMOS technology. Below 10MHz, theimpact is dominated by the on-chip resistance of the VCO ground. Above 10MHz, the bond wires, parasiticsof the on-chip inductor and the off-chip decoupling capacitors determine the sensitivity to substrateperturbations.

c612 Identification of a crystal detector using a block structured nonlinearfeedback modelJohan Schoukens, Liesbeth Gommé, Wendy Van Moer and Yves RolainIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007This paper studies the identification of a block structured nonlinear Wiener-Hammerstein system that iscaptured in the feed forward or the feedback path of a closed loop system. First nonparametric initialestimates are generated for the three dynamic blocks, modelled by their frequency response function, andthe static nonlinear system, described using cubic splines. Next a parametric model is identified. Random orperiodic excitations can be used in the experiments. The method is illustrated on the identification of anAgilent HP420C crystal detector.

c613 Frequency domain errors-in-variables estimation of linear dynamicsystems using data from overlapping sub-recordsKurt Barbé , Johan Schoukens en Rik PintelonIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007In this paper, we study the consistency of a Frequency domain Error-In-Variables estimator using dataextracted from overlapping sub-records. We show that consistant models can be found using periodicexcitations. Only two periods of the steady state response are needed to extract all required information fromthe data. This is done using overlapping sub-records. Moreover, the system identification procedure used fordata extracted from independent experiments is shown to be valid for data extracted from overlapping sub-records. This allows the user to reduce the measurement time considerably without changing theidentification procedure.

c614 Enhanced Time Base Jitter Compensation of Sine WavesFrans Verbeyst, Yves Rolain, Johan Schoukens and Rik PintelonIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007The goal of this paper is to estimate the amplitude of a sine wave in the presence of time base jitter, timebase drift, time base distortion and additive noise. This work is motivated by a comparative study of theamplitude distortion estimated using a nose-to-nose and electro-optic sampling based calibration of ahighfrequency sampling oscilloscope. It uses the exact expression of the variance of a sine wave in thepresence of normally distributed additive and jitter noise, instead of a Taylor approximation of thisexpression.

c615 Measuring the Response of a Voltage Controlled Oscillator using the Large-Signal Network AnalyserYves Rolain, Wendy Van Moer, Rik Pintelon and Johan SchoukensIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007In this paper, a method is proposed that allows for a direct measurement of the dynamic voltage-tofrequency

c616 System identification approach applied to drift estimationFrans Verbeyst, Rik Pintelon, Yves Rolain, Johan Schoukens and Tracy ClementIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007


A system identification approach is applied to estimate the time base drift introduced by a high-frequencysampling

c617 Validation of a crystal detector model for the calibration of the LargeSignal Network AnalyzerLiesbeth Gommé, Johan Schoukens, Yves Rolain and Wendy Van MoerIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007If the Large Signal Network Analyser [1] is used to measure the waveforms present at the device ports, twoadditional calibration steps, a power and a phase calibration, are needed besides the classical linear SOLT-calibration. The aim of this paper is to estimate and validate a parametric black-box model for a crystaldetector. This detector is used as a reference element to calibrate the phase distortion of a LSNA operatingunder narrow band modulated excitation.

c618 Some practical applications of a nonlinear block structure identificationprocedureLieve Lauwers, Johan Schoukens, Rik Pintelon, Wendy Van Moer and LiesbethGomméIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007In this paper, a structure identification method based upon the best linear approximation which waspresented in earlier work is applied to three different physical systems: the silverbox, a crystal detector, andan RF amplifier. The approach of this method consists in applying a Gaussian-like input signal, and to varyin a first experiment the root mean square (rms) value of this signal while maintaining the colouring of thepower spectrum. Next, in a second experiment the colouring of the power spectrum is varied while keepingthe rms value constant. Based on the resulting behaviour of the best linear approximation, a distinction canbe made between candidate nonlinear block structures for approximating the real system.

c619 Adaptive smoothing of Frequency Response Functions Measurements inDigital Spectral AnalysersJerome Antoni and Johan SchoukensIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007We claim in this paper that, in many applications, the non-parametric measurement of frequency responsefunctions can be greatly improved by adaptive smoothing in the frequency domain. Adaptive smoothing aimsat finding the optimal balance between bias and variance errors at any frequency, depending on whetheraccuracy or precision is locally of concern. Our approach is based on a generalisation of the weightedoverlapped segment averaging procedure, such as currently used in digital spectral analysers, but whereadditional cross-correlations between segments are allowed for. The number of significant cross-correlationsto be accounted for, at a given frequency, automatically and optimally specifies the “degree of smoothness”

c620 Application of Blind Identification to Calibration of SensorsLaurent Vanbeylen, Rik Pintelon and Johan SchoukensIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007This paper handles the identification of nonlinear discrete-time Wiener systems from output measurementsonly (blind identification). Assuming that the unobserved input is white Gaussian noise, that the staticnonlinearity is invertible, and that the output is observed without errors, a Gaussian maximum likelihoodestimator is constructed. A two-step procedure for generating high quality starting values is presented aswell. Finally, the proposed scheme is applied to both a simulation example and a laboratory experiment,illustrating the potential usefulness of the method for nonlinear calibration applications.

c621 Detection of Unmodeled Nonlinearities Using Correlation MethodsMartin Enqvist, Johan Schoukens and Rik PintelonIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007This paper concerns the validation of linear models with respect to unmodeled nonlinearities. Two versionsof a method for nonlinearity detection based on estimators of higher order crosscorrelations are describedand evaluated. Unlike many existing approaches, the proposed method seems to be applicable to a widerange of systems and input signals. It can also distinguish between even and odd nonlinearities.

c622 Measuring the best linear approximation of a nonlinear system. Anasymptotic frameworkTadeusz Pawel Dobrowiecki and Johan SchoukensIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007This paper studies the approximation, in least square sense, of a nonlinear system with a linear system,when the measurements are made with periodic signals with a high number of harmonics. The dependencyof the asymptotic (period length tends to infinity) behavior of the best linear approximation on the localselection scheme for the excited frequencies is analyzed in detail. Under some weak conditions, error boundsand a full equivalence with random noise excitations is established.

118 Bibliography

c623 On the Equivalence between some Block Oriented Nonlinear Models andthe Nonlinear Polynomial State Space ModelJohan Paduart, Johan Schoukens and Liesbeth GomméIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007In order to determine how rich the class of nonlinear polynomial state space models is, a comparison withsome standard block-oriented nonlinear models is made. The pros and cons of both modelling approachesare discussed, and illustrated on two real measurement examples.

c624 A Multisine based Calibration for Broadband MeasurementsWendy Van Moer and Yves RolainIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007Classical calibration techniques use single sinewave measurements to maximize the signal-to-noise ratio. Inlarge-signal characterisation, the large number of instrument states to calibrate results in unacceptably longcalibration times. The proposed approach uses multisine excitation signals during the calibration, in order toreduce the calibration time, while having only a marginal influence on the measured device response.

c625 Extending the Best Linear Approximation for Frequency TranslatingSystems: The Best Mixer ApproximationKoen Vandermot, Wendy Van Moer, Yves Rolain and Rik PintelonIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007True physical modelling of a mixer is a tedious task. In this paper the true behavior of a mixer isapproximated by a so called Best (in a mean squares sense) Mixer Approximation (BMA) for the class ofGaussian excitation signals with a given power spectrum (rms value and coloring). This class of signalsincludes random phase multisines and special modulated signals, such as W-CDMA, OFDM and QAM signals.

c626 Transfer Function Estimation of Digital Subscriber Lines with Single EndedLine TestingCarine Neus, Patrick Boets and Leo Van BiesenIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007In order to identify if a subscriber loop is suitable for a certain Digital Subscriber Line (DSL) service, thetransfer function of the loop has to be estimated. Several measurement techniques exist, however Single-Ended Line Testing (SELT) is often preferred by the telecom operators because all necessary measurementscan be done at the central office. The drawback is that the transfer function cannot be measured directly,but can only be calculated through the estimation of the loop make-up. In contrast to the classical approachwhere the SELT data is interpreted in the time domain, this paper presents a new approach by doing theidentification in the frequency domain. After explaining the drawbacks of the time domain approach, thepaper explains how these are avoided by using a frequency domain identification algorithm. Measurementresults show the new algorithm leads to a good estimation of the loop make-up and thus of the transferfunction.

c627 Stable Approximations of Unstable ModelsT. D’haene, R. Pintelon, P. GuillaumeIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007This paper considers the following problem: starting from noisy data a model for the transfer function isestimated. For a finite number of data points, the model is not guaranteed to be stable, even when the truelinear system is known to be stable. [3] describes an iterative procedure for calculating a stableapproximation of an unstable model. This paper presents an improved algorithm resulting in significantlysmaller approximation errors. In addition stable minimax solutions can also be generated.

c628 Asymptotic Uncertainty of Transfer Function Estimates Using Non-Parametric Noise ModelsR. Pintelon, and M. HongIMTC/2007, Proceedings of the IEEE Instrumentation and Measurement TechnologyConference, Warsaw, Poland, May 1-3, 2007Identification of parametric transfer function models from noisy input/output observations is an importanttask in many engineering applications. Besides the parametric model, the estimation algorithm used shouldalso provide accurate confidence bounds. In addition it is important to know whether the proposedestimation algorithm has the lowest possible uncertainty within the class of consistent estimators. This paperhandles these issues for the frequency domain Gaussian maximum likelihood estimator of rational transferfunction models.

c629 Extraction of a quantitative reaction mechanism from linear sweepvoltammograms obtained on a rotating disk electrodeTourwé, E., T. Breugelmans, R. Pintelon, and A. HubinElectrocor 2007, Second International Conference on the Simulation ofElectrochemical Processes, May 9-11, Myrtle Beach (USA)A new methodology to quantitatively determine the mass and charge transfer parameters of an


electrochemical reaction is applied successfully in this work for the study of the reduction of ferricyanide toferrocyanide at a platinum electrode, rotating at 1000 rpm.

c630 On the P+ guard ring sizing strategy to shield against substrate noiseBRONCKERS Stephane, VANDERSTEEN Gerd, VAN DER PLAS G., ROLAIN YVES2007 IEEE Radio Frequency Integrated Circuits Symposium (RFIC), Honolulu,Hawaii, June 3-5, 2007Substrate noise coupling remains a major problem for a System on a Chip (SoC) design. Coupling betweenvarious parts of the system through the substrate may cause malfunctioning of the system. Guard rings arefrequently used to shield the analog circuitry from the noisy digital circuits. In this paper measurements showthat the isolation does not increase linearly with the guard ring width. These experimental results reveal thatstarting from a guard ring width above 16µm, the isolation saturates with the guard ring width. It also showsthat the effectiveness of the guard ring strongly depends on its ground connection. The effectiveness of theproposed guard rings against substrate noise is demonstrated on a 5-7GHz LC Voltage Controlled Oscillator(VCO), designed in a CMOS 130nm technology.

c631 A Bondpad-Size Narrowband LNA for Digital CMOSBORREMANS Jonathan, WAMBACQ Piet, VANDENPLAS GERD, ROLAIN YVES, KUIJKMAARTEN2007 IEEE Radio Frequency Integrated Circuits Symposium Honolulu, Hawaii, June3-5, 2007The need for a high level of integration in wireless and multi-standard radios, as well as the expensive areain downscaled CMOS pushes towards low-area circuit solutions. Feedback-type inductorless LNAs are suchan example. This paper demonstrates a bondpad-size feedback type narrowband LNA using only one stackedinductor. The gain is 20.8 dB at 3.4 GHz with a noise figure of 2.2 dB. This solution is many times smallerthan a classical LNA configuration with several inductors, while obtaining similar performance. It is thus anappealing solution for low-area radio integration in digital CMOS.

c632 Design and analysis of inductors for 60 GHz applications in a digital CMOStechnologyKaren Scheir, Piet Wambacq, Yves Rolain, Gerd Vandersteen69th ARFTG Microwave Measurement Conference , June 3-8, 2007, Honolulu, HawaiiRFIC designers of on-chip transceivers for 60 GHz applications face the trade-off between lumped anddistributed design techniques, due to the on-chip wavelength of approximately 3 mm. This paperdemonstrates that the lumped approach is favorable for realizing 60 GHz inductive components in digitalCMOS technologies. Advantages in area consumption, Q-factor and the range of achievable componentvalues are shown using simulations and measurements. Simulations of lumped inductors using theelectromagnetic field solver HFSS are compared with measurements and different topologies for the lumpedinductor are investigated and compared. The measurement results reveal that a planar unshielded topologyyields the best inductor quality for 60 GHz circuits in a digital CMOS technology.

c633 A 5 kV HBM transformer-based ESD protected 5-6 GHz LNABORREMANS Jonathan, THIJS S., WAMBACQ Piet, LINTEN Dimitri, ROLAIN YVES,KUIJK MAARTEN2007 Symposium on VLSI Technology, Kyoto, Japan, June 12-14, 2007Integrated designs in deep-submicron CMOS require ESD protection for their I/O pins. Since CMOS scalingdrastically lowers the breakdown voltage of a MOS transistor, the available design window for ESD protectionis narrowing. An inductor-based ESD protection offers superb protection but is severely area consuming. Inthis paper we propose a transformer-based ESD protection for inductor-based LNAs. We demonstrate thatthe proposed technique offers excellent ESD protection and RF performance without the loss of area.

c634 Channel Capacity Estimation of Digital Subscriber Lines: a FrequencyDomain ApproachCarine Neus, Patrick Boets and Leo Van Biesen IEEE International Conference on Communications (ICC 2007), June 24-28, Glasgow(UK)In order to identify if a subscriber loop is suitable for a certain Digital Subscriber Line (DSL) service, thetransfer function of the loop has to be estimated. Several measurement techniques exist, however Single-Ended Line Testing (SELT) is often preferred by the telecom operators because all necessary measurementscan be done at the central office. The SELT data is typically interpreted in the time domain. This paperpresents a new approach by doing the identification in the frequency domain. It starts by explaining thedrawbacks of the time domain approach. The paper explains how these are avoided by using a frequencydomain identification algorithm. Measurement results show this is a viable alternative to the classical timedomain identification.

c635 Blind Maximum Likelihood Identification of Wiener SystemsL. Vanbeylen, R. Pintelon, J. SchoukensEuropean Conference on Control (ECC 2007), Kos, Greece, 2-5 July, 2007, pp. 4625-4632This paper handles the identification of discrete-time Wiener systems from output measurements only (blindidentification). Assuming that the unobserved input is white Gaussian noise, that the static nonlinearity isinvertible, and that the output is observed without errors, a Gaussian maximum likelihood estimator isconstructed. Its asymptotic properties are analyzed and the Cramér-Rao lower bound is calculated. A two-step procedure for generating high quality initial estimates is presented as well. The paper includes theillustration of the method on a simulation example.

120 Bibliography

c636 Combining GPS and GSM Cell-ID positioning for Proactive Location-basedServicesNico Deblauwe, Peter RuppelMobiquitous 2007, Proceedings of the 2007 Fourth Annual International Conferenceon Mobile and Ubiquitous Systems: Networking & Services, August 6-10,Philadelphia, USAMobile terminals with built-in GPS receivers are becoming more and more available, thus the publicdeployment of location-based services (LBS) becomes feasible. Upcoming LBS are no longer only reactivebut getting more and more proactive, enabling the users to subscribe for certain events and get notifiedwhen e.g. a friend approaches or a point of interest comes within proximity. However, power consumptionfor continuous tracking is still a mayor issue with mobile terminals. In this paper we define this problem andpropose solutions for an energyefficient combination of GPS and GSM Cell-ID positioning for mobileterminals. We introduce several strategies for extending the lifetime of the battery and show how thesestrategies can be integrated into existing middleware solutions. Simulations based on a realistic proactivemulti-user context confirm the approach

c637 Wireless Broadband Communications in Rural and Remote Areas - aWiMAX Solution?Patrick Boets, Leo Van Biesen, Sven De Backer46th FITCE Congress, Warsaw, Poland, August 30 - September 1, 2007, pp. 150-156In this contribution, the communication aspects for delivering broadband services to remote communitiesand rural regions are investigated. A short description of the targeted rural areas in underdeveloped regionsis given. Next, it has been investigated which telecommunication technologies, such as satellite, 3G cellularsystems, wireless LAN or proprietary systems, can support those services. Hereto a technology comparisonhas been made. Finally, the very promising communication standard WIMAX is studied as a technology forbusiness models to deploy successfully broadband services into rural and marginal communities. It will beshown that using UHF frequencies WiMAX based systems will be able to play the key role in ensuringbroadband communications in rural and remote regions.

c638 Public Transport LBS: Combining GPS and GSM Positioning TechniquesNico Deblauwe and Leo Van Biesen46th FITCE Congress, Warsaw, Poland, August 30 - September 1, 2007, pp. 79-84Location-based Services (LBS) are being hyped again. The first generation of applications was not fullycapable of doing justice to the user's demands, mainly to technical limitations. Portable Information Devices(PID) have improved greatly over the latest years, allowing the second generation of LBS to fulfill theexpectations. This paper presents the case of an LBS application for public transport, focusing on thepositioning component. As modern PIDs are equipped with several technologies, a choice needs to be made,taking into account the limited battery power of the device and strigent accuracy demands of the application.This paper examines the possibilities for GPS, GSM Cell-ID, and RSS-based positioning for both the UserApplication and Fleet Management. Accuracy measures on the individual technologies are given, andinteraction schemes are presented.

c639 The effects of in-home wiring for VDSL transmissionWim Foubert, Patrick Boets, Leo Van Biesen15th IMEKO TC4 International Symposium on Novelties in Electrical Measurementsand Instrumentation, September , 19 - 21, 2007, Iasi, RomaniaIn this paper, it is shown how the downstream en upstream capacity of a VDSL connection will be affectedif the in-home splitter is not installed according to the client installation instructions. The established datarate will be investigated for several real-life in-home cabling topologies.

c640 Scaling friendly design methodology for inductively-degenerated RF Low-Noise AmplifiersG. Vandersteen, L. Bos, P. DobrovolnyEuropean Microwave Week 2007, 8-12 October 2007, Munich, GermanyVarious design methodologies for common-source low noise amplifiers (LNAs) in Si CMOS technologies wereproposed in the past. These start from long-channel assumptions to derive analytic design equations. Thispaper compares the various existing LNA design methodologies and verifies the long-channel assumptionsusing a commercial .13µm CMOS technology. After demonstrating that the design assumptions are no longervalid, a new methodology is proposed which enables the LNA design in a systematic way, without thedrawback that it is relying on a particular transistor model for computing the input impedance and the noisefigure. This makes the proposed technique robust to transistor model changes in future technology nodes.

c641 An Angle Of Arrival Location Estimation Technique for Existing GSMNetworksNico Deblauwe, Leo Van Biesen IEEE International Conference on Signal Processing and Communications (ICSPC2007), Dubai (United Arab Emirates), 24-27 November 2007In this paper, the information contained by the measurement report of a GSM, being the received signalstrengths of up to seven base station antennas, is used to locate a mobile. First the restrictions of using areal network situation are derived, after which a method to take into account the antenna patterns isproposed, for which we formulate an extended path loss model. This is subsequently used to determine theangle of arrival of a terminal, by using the differences of the measured signal strengths. Simulations provideinformation on the achievable accuracy; a field test confirms the applicability. The proposed algorithm canbe implemented as network or terminal-based method and it outperforms the accuracy of Enhanced Cell-ID.


4. ABSTRACTS (SINCE 2007)

a161 A multi proxy model for the reconstruction of the environmental history using bivalve shell compositionBauwens M., De Ridder F., Barbe K. and Dehairs F.Anual calmars meeting, Brussels, 31 January 2007Every plankton species is characterized by his own chemical signature and his own ecological niche. Thismeans that a chemical composition of a total plankton fraction is linked to precise environmental conditions.If we suppose a direct correlation between plankton composition and shell composition, successive planktonblooms could give a perfect explanation for the non linear patterns that are observed in high resolutionprofiles of trace elements in bivalve shells. In this poster we propose a n-dimentional polygonal model todescribe environmental variations.

a162 Some practical applications of a nonlinear block structure identification procedureL. Lauwers, J. Schoukens, R. Pintelon26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007A structure identification method based upon the Best Linear Approximation is applied to three differentphysical systems: the silverbox, a crystal detector and an RF amplifier.

a163 Crystal detector as calibration standard for Large Signal AnalysisLiesbeth Gommé, Johan Schoukens, Yves Rolain, Wendy Van Moer26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007The aim of this paper is to estimate and validate a parametric black-box model for a crystal detector. Thisdetector is used as a reference element to calibrate the phase distortion of a LSNA, [1], operating undernarrow band modulated excitation.

a164 On the Equivalence between some Block Oriented Nonlinear Models and the Nonlinear Polynomial State Space ModelJohan Paduart, Johan Schoukens, Liesbeth Gommé26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007A comparison is made between block-oriented nonlinear models and nonlinear polynomial state spacemodels in order to determine which classes of nonlinear systems can be modeled using the nonlinear statespace approach. Some standard block-oriented models are discussed and the results are applied tomeasurements from two physical setups.

a165 Time averaging in solid substratesVeerle Beelaerts, Fjo De Ridder, Rik Pintelon en Johan Schoukens26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007Nature provides us with several proxy recordings of Earth’s climate (sources of climate information fromnatural archives), i.e. preserved in rock and in sediment layers on the ocean floor [1]; even in someorganisms like sponges and bivalves [2], which store environmental information during their life. Processingthe collected substrates, to obtain the proxies which are incorporated involves sampling solid substrate.Whether sampling means drilling a hole or counting a proxy per square meter, the sample always has acertain dimension (the dimension of the drill hole or of the count area). In most cases the dimension of thesample is neglected. This is allowed when the width of the sample is small compared to the variation ofinterest. Nevertheless, if the sample width spans a considerable part of this variation, the amplitude of thesignal will be averaged out and will result in an underestimation. In this work a method is presented topropose a correction for this underestimation, based on advanced signal processing tools and standardfiltering techniques.

a166 Frequency Domain Identification of Linear Slowly Time-Varying SystemsJohn Lataire, Rik Pintelon26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007The framework of LTI (Linear Time Invariant) systems has shown to provide good approximating models fora large amount of real-life dynamic systems. However, the assumption of time invariance is not alwayssatisfied for some applications: systems with a varying set point (e.g. flight flutter analysis) or systems withvarying parameters (e.g. pit corrosion or metal deposition). In this work we model linear, slowly andaperiodically timevarying (LSTV) systems via linear discrete-time models with linear time-varyingcoefficients. Contrary to previous work (e.g. [1]), the identification is performed in the frequency domain.This has the advantages that a nonparametric noise model can be used and that the frequency band ofinterest is well-defined.

122 Bibliography

a167 Stable Approximations of Unstable ModelsT. D’haene, R. Pintelon, P. Guillaume 26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007In this paper the stability enforcement algorithm of [1] is improved at several points. The originalstabilisation algorithm [1] consists of a two-step method. In the first step a (high) degree model withoutstability constraints is estimated that passes the validation tests (analysis cost function and whiteness testof the residuals). This step suppresses in an optimal way the noise without introducing systematic errors. Inthe second step the constraint will be added: The (high) degree model will be approximated by a stable one.For this the weighted difference between the unconstrained model and the stable model is minimized. Thebig advantage of the two step procedure is that it provides models with uncertainty and bias errors, whichis not the case when stability is imposed during the noise removal.

a168 Transmission line modelling exploiting 'common-mode' signalsWim Foubert, Patrick Boets, Leo Van Biesen26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007Digital Subscriber Line (DSL) technology provides broadband service over ‘twisted pair’ copper wires of theexisting telephone network. The inherent structure of this network causes crosstalk, which significantlyreduces the achievable bit rate for each user. The exploitation of ‘common-mode’ signals could compensatefor the performance reduction and lead to a significant increase in capacity.

a169 Extending the Best Linear Approximation for Frequency Translating SystemsKoen Vandermot, Wendy Van Moer, Yves Rolain and Rik Pintelon26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007A mixer is frequently used in all kinds of telecommunication applications [1]. Besides the ideal frequencytranslation, a real mixer produces a lot of additional linear and nonlinear disturbances: e.g. isolation effects,intermodulation products,... To obtain a simple model for this important system function, the FrequencyResponse Function (FRF) is no longer useful because the output at one frequency is no longer only dependenton the input at the same frequency. To generate a simple model for a real mixer, we will follow the lines ofthe Best Linear Approximation

a170 Estimating the power spectrum and sample variance of the fourier coeffi-cients using overlapping sub-recordsK. Barbé, J. Schoukens, R. Pintelon 26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007Welch’s method [3] for estimating the power spectrum based on averaging modified periodograms has beenwidely used. Such an estimate is based on random data and is therefore a random variable. To compute theuncertainty of the estimate one needs the probability density function (pdf) of the random variable. In [2]the pdf for estimating the power spectrum based on data drawn from overlapping sub-records wasinvestigated, an expression for the pdf in terms of generalized hypergeometric functions was derived. In [1]the pdf was derived in terms of Laguerre polynomials. Further, in [1] an unbiased estimate for the varianceof the Fourier coefficients, at frequency bin k, of a periodic signal u[n] corrupted with additive filteredGaussian noise was constructed, whereU(wk) denotes the fourier coefficient at frequency bin k, P the numberof measured periods of the signal u, r the fraction of overlap, and with

a171 Experimental stability analysis of nonlinear feedback systemsL. Vanbeylen, J. Schoukens 26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007It is well known that nonlinear modelling is a very hard task. First, the model selection is problematic. Butespecially the validation of nonlinear models is time consuming, extremely difficult, and hence expensive;there is no guarantee that the model is good everywhere, and consequently there is a large risk for remainingmodel errors. When a nonlinear plant has to be controlled, the best one can do is to use the available plantmodel to design the controller, and hope that the remaining model errors will not drive the real life feedback-controlled nonlinear system (RLFCNS) into an unstable mode. Indeed, even if the controlled model performswell (stably), nothing can be guaranteed about the stability of the RLFCNS; there still remains a risk that theRLFCNS behaves unstably, due to the remaining model errors. As supported by simulation studies, it appearsthat under certain circumstances (properties of the input signal, e.g. the rms-level), certain nonlinearfeedback loops behave stably, while under other circumstances, the same loop behaves occasionallyunstably, or even highly unstably, while the analysis of the control loop on the basis of the model guaranteedthe stability. The current problem has only been investigated in [1]; that relies on the framework of the bestlinear approximations, and on the small gain theorem. In this contribution, using extreme value statistics,we aim to get more information about the instability of a nonlinear feedback loop, starting from experimentaldata on the physical feedback system, without building an additional model.

a172 Analysis and modelling of analog discrete-time systems using multisine excitationsLynn Bos, Gerd Vandersteen, Yves Rolain26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007


Analog discrete-time systems are becoming more important in deep-submicron CMOS technologies. Thesesystems are based on switched-capacitor (SC) technologies where the sampling switches, driven by aperiodic clock, make the overall system periodic time-varying (PTV). The PTV behaviour results into thefrequency folding of frequency components higher than the Nyquist frequency. The nonlinear distortion ofnonlinear time-invariant systems can be quantified and qualified using a random phase multisine (MS) withan user defined amplitude spectrum. This enables the measurement of the even and odd nonlinearities (NL)on the respectively even and non-excited odd lines in the output spectrum. These nonexcited lines are theso-called detection lines . Random odd MS can’t be used straightaway for PTV systems. The non-exciteddetection lines of a random odd MS do not only contain the nonlinear contributes, but also the aliasingcomponents due to the frequency folding introduced by the PTV behaviour of the system. This paperintroduces an analysis with a specially designed MS for PTV systems in Section II which enables theseparation of the different linear and nonlinear contributions. The concept of the best linear approximationis extended towards PTV systems in Section III. This makes it possible to determine the conversion matrixof the best linear PTV approximation of the circuit. The technique is illustrated using simulations on a 5thorder SC filter.

a173 Special Session: A round-robin benchmark for the Large Signal Network AnalyzerY. Rolain, W. Van Moer, C. Gaquière, D. Ducateau, M. Fernandez Barciela, D.Scheurs, M. Vanden Bossche and F. Verbeyst26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007The importance of traceability of nonlinear measurements increases as the techniques start to be used inindustrial applications. An intercomparison between a number of Large Signal Network Analysis (LSNA)architectures and setups is proposed here as a first step towards standardisation for nonlinear microwavemeasurements

a174 A Frequency Domain Identification Algorithm for Single-Ended Line MeasurementsCarine Neus, Patrick Boets, Leo Van Biesen26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007In order to identify whether a subscriber loop is suitable for a certain Digital Subscriber Line (DSL) service,the transfer function of the loop has to be estimated. Several measurement techniques exist, howeverSingle-Ended Line Testing (SELT) is often preferred by the telecom operators because all necessarymeasurements are done at the central office, in contrast to Dual-Ended Line Testing (DELT), where atechnician needs to be dispatched to the customer’s site

a175 A positioning technique for GSM, using the signal strengths of one antenna siteNico Deblauwe, Leo Van Biesen26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007Mobile location estimation gained recently much attention, both scientifically and commercially. Based uponthe usage expectations and profit projections of LBS (Location Based Services), there is a high plausibilitythat future mobile communication systems, 4th generation (4G) and onwards, will include high precisionpositioning requirements already in their design phase [1]. Until then, for the current 2G and 3G networks,a positioning solution needs to be found. To provide an overview in the numerous studies that have beenexamining the possibilities for mobile location estimation, we would advocate a division into four categories:1) GPS-aided, 2) proximity sensing, and lateration or angulation 3) with additional hardware in the networkor 4) by using the received signal strengths. In this abstract, we take a closer look at a method that fits inthe latter category.

a176 Variance calculation of covariance-driven stochastic subspace identifica-tion estimatesE. Reynders, R. Pintelon, G. De Roeck26th Benelux Meeting on Systems and Control, Center Parcs “De Vossemeren”,Lommel, Belgium, March 13-15, 2007Stochastic Subspace Identification (SSI) [3] is today recognized as one of the most accurate and robustmethods for the identification of linear systems from output-only measurement data. SSI yields estimatesfor the system matrices A and C of the stochastic state-space description of the linear system. However, oneof the drawbacks of this method is that, till know, no information about the accuracy of the identified systemparameters was available from the algorithm.

a177 Block-oriented nonlinear models vs. nonlinear state space modelsJohan PaduartPoster presented at kickoff meeting of IAP network “DYSCO” (Dynamical Systems,Control, and Optimization), Chateau Ferme de Profondval, Belgium, 16 April 2007As a first step into determining which class of nonlinear systems can be described using nonlinear state spacemodels, we establish a link between well known block-oriented nonlinear models and the nonlinear statespace setting. It appears that Wiener, Hammerstein and Wiener-Hammerstein systems can be representedexactly using a nonlinear state space model. For the Nonlinear Feedback system, the equivalency holds onlywhen there is no direct coupling between input and output of the forward linear block. This analysis can beuseful when one wants to identify a nonlinear state space model and incorporate prior knowledge about thesystem, in order to reduce the number of parameters. Both the block-oriented and the nonlinear state space

124 Bibliography

approach offer some advantages. Block-oriented models demonstrate more physical insight into the systemthan the nonlinear state space model. On the other hand, the nonlinear state space model is easier toinitialize, and no a-priori knowledge is needed to build a model, although it can be taken into account whenit is available.

a178 Experimental stability analysis of nonlinear feedback systemsLaurent Vanbeylen, Johan SchoukensPoster presented at kickoff meeting of IAP network “DYSCO” (Dynamical Systems,Control, and Optimization), Chateau Ferme de Profondval, Belgium, 16 April 2007It is well known that nonlinear modelling is a very hard task. First, the model selection is problematic. Butespecially the validation of nonlinear models is time consuming, extremely difficult, and hence expensive;there is no guarantee that the model is good everywhere, and consequently there is a large risk for remainingmodelling errors. Due to these modelling errors, even if the model can be shown to be stable, one cannotbe perfectly sure that the nonlinear system is stable. As supported by simulation studies, it appears thatunder certain circumstances (properties of the input signal, e.g. the rms-level), certain nonlinear feedbackloops behave stably, while under other circumstances, the same loop behaves occasionally unstably, or evenhighly unstably, while the analysis of the control loop on the basis of the model guaranteed the stability. Thissuggests that some input dependent probability of instability should be introduced. The purpose of themethod is to give the user guidance, when he suspects potential instability problems due to nonlinearities.

a179 The Best Mixer ApproximationKoen Vandermot, Wendy Van Moer and Yves RolainPoster presented at kickoff meeting of IAP network “DYSCO” (Dynamical Systems,Control, and Optimization), Chateau Ferme de Profondval, Belgium, 16 April 2007In engineering applications, it is often necessary to assess how close an existing system comes to itsidealized design goal under real-world excitation conditions. When applied to linear time-invariant systems,this approach leads to the best linear approximation (BLA). For systems where the design goal is inherentlynonlinear, the BLA is of little help. This work proposes an extension of the BLA towards 2-input systems thatbehave as a static multiplier surrounded by linear dynamic blocks. In a communication context, such a deviceis called a mixer and is used for frequency translation operation. The new approximation, that measures thedeviation of a real device with this idealized model is then called the best mixer approximation (BMA). TheBMA is first introduced theoretically, and is then extracted from measurements performed on a real deviceoperating in the GHz frequencies.

a180 Averaging in solid substratesVeerle BeelaertsPoster presented at kickoff meeting of IAP network “DYSCO” (Dynamical Systems,Control, and Optimization), Chateau Ferme de Profondval, Belgium, 16 April 2007Proxies are sources of environmental information stored in natural archives. Processing the collectedsubstrates to obtain the proxies, always involves sampling solid substrate. These sample always have acertain volume, this means that one sample will give a mean value for all the variation in that volume. Upuntil now, the volume of the sample was neglected. This is only allowed when the width of the sample isnegligible with respect to the variation that needs to be reconstructed. However, when the width of thesample concerns a considerable part of the variation, the signal will be averaged and thus systematicallyunderestimated. In this poster a correction method for this underestimation is proposed

a181 EIS for the reliable and quantitatively evaluation of corrosion protection by organic coatingsTourwé, E., R. Pintelon, and A. HubinProceedings of the 7th International Symposium on Electrochemical ImpedanceSpectroscopy, June 3-8, Argelès-sur-Mer (France), p. 258

a182 On the P+ guard ring sizing strategy to shield against substrate noiseBRONCKERS Stephane, VANDERSTEEN Gerd, VAN DER PLAS G., ROLAIN YVESOral presentation at the meeting of the Chameleon RF project (STREP-projectsponsored by the European Commission under the IST-Programme) in Lisboa,Portugal, 9-10 October 2007Substrate noise coupling remains a major problem for a System on a Chip (SoC) design. Coupling betweenvarious parts of the system through the substrate may cause malfunctioning of the system. Guard rings arefrequently used to shield the analog circuitry from the noisy digital circuits. In this paper measurementsshow that the isolation does not increase linearly with the guard ring width. These experimental resultsreveal that starting from a guard ring width above 16um, the isolation saturates with the guard ring width.It also shows that the effectiveness of the guard ring strongly depends on its ground connection. Theeffectiveness of the proposed guard rings against substrate noise is demonstrated on a 5-7GHz LC VoltageControlled Oscillator (VCO), designed in a CMOS 130nm technology.


5. WORKSHOPS (SINCE 2007)

w74 Macromodeling of transfer functions with higher-order polesD. Deschrijver, T. Dhaene, Y. Rolain11th IEEE Workshop on Signal Propagation on Interconnects (SPI 2007), Genova(Italy), 13-16 May 2007.

w75 Macromodeling of transfer functions with higher-order polesD. Deschrijver, T. Dhaene, Y. Rolain11th IEEE Workshop on Signal Propagation on Interconnects (SPI 2007), Genova(Italy), 13-16 May 2007.

w76 Experimental stability analysis of nonlinear feedback systemsLaurent Vanbeylen, Johan SchoukensPoster presented at 16th ERNSI Workshop on System Identification, Venice, Italy,October 1-3, 2007 It is well known that nonlinear modelling is a very hard task. First, the model selection is problematic. Butespecially the validation of nonlinear models is time consuming, extremely difficult, and hence expensive;there is no guarantee that the model is good everywhere, and consequently there is a large risk for remainingmodelling errors. Due to these modelling errors, even if the model can be shown to be stable, one cannotbe perfectly sure that the nonlinear system is stable. As supported by simulation studies, it appears thatunder certain circumstances (properties of the input signal, e.g. the rms-level), certain nonlinear feedbackloops behave stably, while under other circumstances, the same loop behaves occasionally unstably, or evenhighly unstably, while the analysis of the control loop on the basis of the model guaranteed the stability. Thissuggests that some input dependent probability of instability should be introduced. The purpose of themethod is to give the user guidance, when he suspects potential instability problems due to nonlinearities.

w77 Frequency Domain Identification of Linear Slowly Time-Varying SystemsJohn Lataire, Rik PintelonPoster presented at 16th ERNSI Workshop on System Identification, Venice, Italy,October 1-3, 2007The framework of LTI (Linear Time Invariant) systems has shown to provide good approximating models fora large amount of real-life dynamic systems. However, the assumption of time invariance is not alwayssatisfied for some applications: systems with a varying set point (e.g. flight flutter analysis) or systems withvarying parameters (e.g. pit corrosion or metal deposition). In this work we model linear, slowly andaperiodically timevarying (LSTV) systems via linear discrete-time models with linear time-varyingcoefficients. Contrary to previous work (e.g. [1]), the identification is performed in the frequency domain.This has the advantages that a nonparametric noise model can be used and that the frequency band ofinterest is well-defined.

w78 Improved Non-parametric Power Spectrum Estimation using Circular OverlapKurt Barbé, Johan Schoukens and Rik PintelonPoster presented at 16th ERNSI Workshop on System Identification, Venice, Italy,October 1-3, 2007This poster studies non-parametric power spectrum estimation with circular overlap. Although circularoverlap imposes a discontinuity on the stochastic process under investigation; it can be shown that for a(Gaussian) ergodic weakly stationary process the power spectrum estimator with circular overlap isasymptotically unbiased. Furthermore the Mean-Squared-Error of the Power Spectrum Estimator via CircularOverlap is smaller than the Power Spectrum Estimator of Welch.

w79 Generating initial estimates for a Wiener-Hammerstein system via partial fraction expansion of the Best Linear ApproximationLieve Lauwers, Johan SchoukensPoster presented at 16th ERNSI Workshop on System Identification, Venice, Italy,October 1-3, 2007A method is proposed to initialize the linear dynamic blocks of a Wiener-Hammerstein model. The idea is toreplace these blocks by the Best Linear Approximation of the system under test which contains all the polesof both linear blocks. The goal is then to select the true poles in each block by solving a Total Least Squaresproblem. The proposed method is applied to measurements from an electronic Wiener-Hammerstein circuit.

w80 Analysis of time-domain maximum likelihood method and sample maximum likelihood method for errors-in-variablesHong, M., T. Söderström, J. Schoukens, and R. Pintelon 16th ERNSI Workshop on System Identification, Venice, Italy, October 1-3, 2007The time domain maximum likelihood (TML) method and the sample maximum Likelihood (SML) method aretwo approaches for identifying errors-in-variables models. Both methods may give the optimal estimationaccuracy (achieve Cramér-Rao lower bound) but in different senses. In the TML method, an importantassumption is that the noise-free input signal is modeled as a stationary process with rational spectrum. ForSML, the noise-free input needs to be periodic. It is interesting to know which of these assumptions contain

126 Bibliography

more information to boost the estimation performance. In this paper, the estimation accuracy of the twomethods is analyzed statistically. Numerical comparisons between the two estimates are also done underdifferent signal-to-noise ratios (SNRs). The results suggest that TML and SML have similar estimationaccuracy at moderate or high SNR

w81 The incorporation of trace elements and stable isotopes in Mytilus edulis: Black box versus white box modelingMaite Bauwens, Fabrice Servaes, Johan Schoukens and Frank DehairsBivalve workshop, Brest 4-5 October 2007

w82 The elimination of averaging-errors in proxy records.Veerle Beelaerts, Fjo De Ridder, Rik Pintelon and Frank DehairsBivalve workshop, Brest 4-5 October 2007

w83 Using ANOVA in a Round-Robin ComparisonKurt Barbé, Wendy Van Moer and Yves RolainEuropean Microwave Week 2007, 8-12 October 2007, Munich, Germany

w84 Measurement of the Best Linear Approximation of nonlinear systemsYves Rolain, Wendy Van Moer, Johan Schoukens and Rik Pintelon37th European Microwave Conference (EuMC), 8-12 October 2007, Munich,Germany

w85 A Multisine based Calibration for Broadband Nonlinear MeasurementsWendy Van Moer and Yves RolainEuropean Microwave Week 2007, 8-12 October 2007, Munich, Germany

w86 Initializing the linear blocks of a Wiener-Hammerstein system? Use the Best Linear ApproximationLieve Lauwers, Johan Schoukens, Rik PintelonIAP VI/4, DYSCO Workshop, Royal Military Academy, Brussels, 22 November 2007A method is proposed to initialize the linear dynamic blocks of a Wiener-Hammerstein model. The idea is tobuild these blocks from the poles and zeros of the Best Linear Approximation of the system under test, whichcan easily be extracted from the data. This approach results in an easy to solve problem (linear in theparameters) from which initial estimates for the linear dynamics can be obtained. The proposed method isapplied to measurements from an electronic Wiener-Hammerstein circuit.

w87 Estimating the Input/Output Measurement Noise of Linear, Slowly Time-varying SystemsJohn Lataire and Rik PintelonIAP VI/4, DYSCO Workshop, Royal Military Academy, Brussels, 22 November 2007This poster proposes a methodology for estimating the power spectrum of the disturbing noise associatedwith input/output measurements of linear, slowly time-varying systems. Multisine excitations are shown tobe very helpful for this task. The method circumvents repeated measurements, which are, for time-varyingsystems, usually difficult to execute. An estimation of the disturbing noise is mandatory when identifyingdynamic systems within an errors-in-variables framework. The discussed noise model is intended for systemidentification performed in the frequency domain, in which case a non-parametric noise model is easilyimplemented. The noise model is estimated independently of the parameters of the time-varying systemunder consideration.

w88 RF pulse train generator for a dense frequency grid phase calibrationLiesbeth Gommé, Yves RolainIAP VI/4, DYSCO Workshop, Royal Military Academy, Brussels, 22 November 2007The aim of this paper is to introduce an RF pulse train generator to provide a reference signal for the phasecalibration of the Large Signal Network Analyser (LSNA) under modulated excitations.

w89 Introducing the Power-Scalable Best Mixer ApproximationKoen Vandermot IAP VI/4, DYSCO Workshop, Royal Military Academy, Brussels, 22 November 2007A simple way is proposed to estimate an approximate behaviour for a mixer. The Best Mixer Approximation(BMA) is the best approximation in least square sense for a given class of signals, namely the class of theGaussian signals with a fixed power spectrum. As a consequence, the BMA depends on the power of the inputsignal. The aim of this paper is to estimate one model that predicts the BMA for any input power within ana priori specified range of powers based on measurements.

w90 Experimental stability analysis of nonlinear feedback systemsLaurent Vanbeylen and Johan SchoukensIAP VI/4, DYSCO Workshop, Royal Military Academy, Brussels, 22 November 2007In this poster, the unstable behaviour of certain nonlinear feedback systems (e.g. nonlinear controlapplications) is investigated. Although the systems are unstable from the classical point of view, using verysimple nonlinear block structured nonlinear feedback systems, it is shown that depending on the statisticalproperties of the input the system may be "close" or "far" from instability in some sense. Viewing thenonlinear feedback structure as a black box system, an extreme-value-statististics based method is


proposed that allows to find an experimental measure of this risk of an unstable behaviour, starting from afinite input-output data record. It is evaluated by means of numerical Monte-Carlo experiments.

w91 Improved Non-parametric Power Spectrum Estimation using Circular Overlap.Kurt Barbé, Johan Schoukens and Rik PintelonIAP VI/4, DYSCO Workshop, Royal Military Academy, Brussels, 22 November 2007This poster studies non-parametric power spectrum estimation with circular overlap. Although circularoverlap imposes a discontinuity on the stochastic process under investigation; it can be shown that for a(Gaussian) ergodic weakly stationary process the power spectrum estimator with circular overlap isasymptotically unbiased. Also the variance decreases due to the overlap.

6. SCIENTIFIC LECTURES (SINCE 2007)

sl25 Averaging in solid substratesVeerle Beelaerts, Fjo De Ridder, Rik Pintelon, Johan Schoukens en Frank DehairsPresentation of poster at the first annual PALEOSALT meeting in Barcelona, June 4-5, 2007Proxies are sources of environmental information stored in natural archives. Processing the collectedsubstrates to obtain the proxies, always involves sampling solid substrate. These sample always have acertain volume, this means that one sample will give a mean value for all the variation in that volume. Upuntil now, the volume of the sample was neglected. This is only allowed when the width of the sample isnegligible with respect to the variation that needs to be reconstructed. However, when the width of thesample concerns a considerable part of the variation, the signal will be averaged and thus systematicallyunderestimated. In this poster a correction method for this underestimation is proposed

sl26 A non-linear multi-proxy approach for the reconstruction of the paleo-environmentMaite Bauwens, Fjo De Ridder and Frank DehairsPresentation of paper at the first annual PALEOSALT meeting in Barcelona, June 4-5, 2007

sl27 Characteristics of Location-based Services, and Multi-technology PositioningNico DeblauweMicroseminar, Linköping University, Dept. of Control and Communications, 10September 2007

7. PATENTS

1 TDR Based Transfer Function Estimation of Local LoopTom Bostoen, Patrick Boets, Leo Van Biesen, Thierry Pollet and Mohamed ZekriEuropean Patent Office, Application No./Patent No. 01400832.0-1246

2 Method and Apparatus for Identification of an Access Network by Means of1-Port MeasurementsTom Bostoen, Thierry Pollet, Patrick Boets, Mohamed Zekri and Leo Van BiesenEuropean Patent Application, Application No. EP 1 248 383 A1

3 Method and Apparatus for Identification of an Access Network by Means of1-Port MeasurementsTom Bostoen, Thierry Pollet, Patrick Boets, Mohamed Zekri and Leo Van BiesenUnited States Patent Application Publication, Pub. No. US 2002/0186760 A1

4 Method for Matching an Adaptive Hybrid to a LineTom Bostoen, Patrick Boets, Leo Van Biesen and Thierry Pollet

128 Bibliography

European Patent Office, Application No./Patent No. 03292891.3

5 Interpretation system for interpreting reflectometry informationT. Vermeiren, Tom Bostoen, Leo Van Biesen, Frank Louage, Patrick BoetsEuropean Patent Application, Application No. EP 1 385 724 A1

6 Interpretation system for interpreting reflectometry informationT. Vermeiren, Tom Bostoen, Leo Van Biesen, Frank Louage, Patrick BoetsUnited States Patent Application Publication, Pub. No. US 2004/0019451 A1

7 Localisation of Customer Premises in a Local Loop Based on ReflectometryMeasurementsTom Bostoen, Thierry Pollet, Patrick Boets, Leo Van BiesenUnited States Patent Application Publication, Pub. No. US 2004/0022368 A1

8 Localisation of Customer Premises in a Local Loop Based on ReflectometryMeasurementsTom Bostoen, Thierry Pollet, Patrick Boets, Leo Van BiesenEuropean Patent Application, Application No. EP 1 388 953 A1

9 Signal Pre Processing for Estimating attributes of transmission LineTom Bostoen, Thierry Pollet, Patrick Boets, Leo Van BiesenEuropean Patent Application, Application No. EP 1 411 361 A1

10 Signal Pre Processing for Estimating attributes of transmission LineTom Bostoen, Thierry Pollet, Patrick Boets, Leo Van BiesenUnited States Patent Application Publication, Pub. No. US 2004/0080323 A1

11 A method for determining bit error ratesVandersteen Gerd, Verbeeck Jozef, Rolain Yves, Schoukens Johan, Wambacq Piet,Donnay StephaneVUB/IMEC US60171854/US 2001 0044915 - US6678844, granted 13 January 2004

12 A method for determining signals in mixed signal systems Wambacq Piet, Vandersteen Gerd, Rolain Yves, Dobrovolny Petr VUB/IMEC US60138642 - EP1059594, granted 13 October 2004

8. DOCTORAL DISSERTATIONS

d1 Etude de la Production et des Conditions de Propagation d'Ondes de Choc Créés par un Plasma de DéchargeJean RenneboogDoctoral Dissertation, Université Libre de Bruxelles, 1967Promoter: P. Baudoux (ULB)

d2 Bijdrage tot het Verwekken en Meten van Nauwkeurig Bepaalde FazeverschuivingenAlain BarelDoctoral Dissertation, Vrije Universiteit Brussel, April 1976Judging-committee: G. Maggetto (VUB), Verlinden (VUB), Hoffman (RUG), C.Eugène (UCL)Promoter: J. Renneboog (VUB)

d3 Maximum Informatie Extractie door middel van een Optimaal Frequentie Domein ExperimentGuy VilainDoctoral Dissertation, Vrije Universiteit Brussel, March 1983Judging-committee: G. Maggetto (VUB), A. Barel (VUB), J. Cornelis (VUB), C. Eugène (UCL), Kerkhof, J.Vereecken (VUB), G. Vansteenkiste (VUB)Promoter: J. Renneboog (VUB)


d4 Foutdetectie op Electrische Lijnen met behulp van een Digitale Behandeling van het ReflectogramLeo Van BiesenDoctoral Dissertation, Vrije Universiteit Brussel, April 1983Judging-committee: G. Maggetto (VUB), A. Barel (VUB), Baert (RUG), C. Eugène (UCL), Goossens,Kirschvinck, J. Tiberghien (VUB)Promoter: J. Renneboog (VUB)

d5 Parameterestimatie in Lineaire en Niet-Lineaire Systemen met Behulp van Digitale Tijdsdomein MetingenJohan SchoukensDoctoral Dissertation, Vrije Universiteit Brussel, February 1985Judging-committee: G. Maggetto (VUB), A. Barel (VUB), P. Eykhoff (TU Eindhoven), C. Eugène (UCL),Hoffman (RUG), Spriet (RUG), O. Steenhaut (VUB), G. Vansteenkiste (VUB) Promoter: J. Renneboog (VUB)

d6 Active Microstrip AntennasRussell DearnlyDoctoral Dissertation, Vrije Universiteit Brussel, June 1987Judging-committee: G. Maggetto (VUB), L.P. Ligthart (TU Delft), O. Steenhaut (VUB), G. Szymanski (Tech.University of Poznan), J. Tiberghien (VUB), A. Van De Capelle (KUL)Promoters: J. Renneboog (VUB), A. Barel (VUB)

d7 Analysis and Application of a Maximum Likelihood Estimator for linear SystemsRik PintelonDoctoral Dissertation, Vrije Universiteit Brussel, January 1988Judging-committee: G. Maggetto (VUB), A. Barel (VUB), P. Eykhoff (TU Eindhoven), A. van den Bos (TUDelft), J. Vandewalle (KUL)Promoters: J. Renneboog (VUB), J. Schoukens (VUB)

d8 Time Division Multiplexing in Optical Fiber NetworksDanny SevenhansDoctoral Dissertation, Vrije Universiteit Brussel, June 1988Judging-committee: G. Maggetto (VUB), P. Kool (VUB), E. Stijns (VUB), R. Blondel (Université de Mons), P.Bulteel (Atea), C. Eugène (UCL), Baert (RUG)Promoters: J. Renneboog (VUB), A. Barel (VUB)

d9 Design of Optimal Input Signals with Minimal Crest FactorEdwin Van der OuderaaDoctoral Dissertation, Vrije Universiteit Brussel, Januari 1989Judging-committee: G. Maggetto (VUB), F. Delbaen (VUB) , P. Eykhoff (TU Eindhoven), R. Pintelon (VUB),J. Renneboog (VUB), A. van den Bos (TU Delft), J. Vandewalle (KUL)Promoter: J. Schoukens (VUB)

d10 Channel Multiple Access Protocols for a Hydrological Multihop Packet Radio NetworkThomas J. Odhiambo AfulloDoctoral Dissertation, Vrije Universiteit Brussel, June 1989Judging-committee: G. Maggetto (VUB), L. Van Biesen (VUB), J. Tiberghien (VUB), P. Van Binst (VUB), A.Van Der Beken (VUB)Promoter: A. Barel (VUB)

d11 Steady-State Analysis of Strongly Nonlinear CircuitsEli Van Den EijndeDoctoral dissertation, Vrije Universiteit Brussel, December 1989Judging-committee: G. Maggetto (VUB), R. Pintelon (VUB), J. Renneboog (VUB), A. Barel (VUB), R. Van Geen(VUB), P. Eykhoff (TU Eindhoven), J. Vandewalle (KUL), R. Pollard (T.U. Leeds)Promoter: J. Schoukens (VUB)

d12 Knowledge-Based Spectral EstimationJames Ambani KulubiDoctoral Dissertation, Vrije Universiteit Brussel, December 1989Judging-committee: G. Maggetto (VUB), R. Pintelon (VUB), A. Barel (VUB), W. Verhelst (VUB), O. Steenhaut(VUB), Hoffman (RUG)Promoter: L. Van Biesen (VUB)

d13 Radar Cross Section Reduction using Multiple - Layer Strip GratingsGert Van Der PlasDoctoral Dissertation, Vrije Universiteit Brussel, February 1990 Judging-committee: G. Maggetto (VUB), R. Van Loon (VUB), A. Van de Capelle (KUL), D. De Zutter (RUG),

130 Bibliography

P. Delogne (UCL)Promoters: A. Barel (VUB), E. Schweicher (KMS)

d14 Automated Diagnosis for Arbitrary Digital CircuitsPatrick BakxDoctoral dissertation, Vrije Universiteit Brussel, May, 1990 Judging-committee: G. Maggetto (VUB), M. Goossens (VUB), A. Barel (VUB), M. Verlinden (VUB), V.Jonckers (VUB), P. Vandeloo (UIA)Promoter: L. Van Biesen (VUB)

d15 Measuring Nonlinear Systems - A Black Box Approach for Instrument ImplementationMarc Vanden BosscheDoctoral dissertation, Vrije Universiteit Brussel, May, 1990Judging-committee: G. Maggetto (VUB),R. Pollard (T.U. Leeds), P. Eykhoff (TU Eindhoven), D. De Zutter(RUG), Rik Pintelon (VUB), D. Ritting (Hewlett Packard - USA)Promoters: A. Barel (VUB), J. Schoukens (VUB)

d16 Identification of Multi-Input Multi-Output Sytems using Frequency-Domain ModelsPatrick GuillaumeDoctoral dissertation, Vrije Universiteit Brussel, June, 1992Judging-committee: G. Maggetto (VUB), A. Barel (VUB), M. Van Overmeire (VUB), P. Eykhoff (TUEindhoven), M. Gevers (UCL), J. Vandewalle (KUL)Promoters: J. Schoukens (VUB), R. Pintelon (VUB)

d17 Identification of Linear Systems from Time- or Frequency-Domain Measure-ments: Part I: Parameter Estimation in Regression Models Applied to the Identification of Continuous-Time and Discrete-Time Systems Part II: Parameter Estimation of Superimposed Cisoids Applied to the Identification of Distributed SystemsHugo Van hammeDoctoral dissertation, Vrije Universiteit Brussel, June, 1992Judging-committee: G. Maggetto (VUB), A. Barel (VUB), F. Delbaen (VUB), M. Vanden Bossche (HewlettPackard Belgium), A. van den Bos (TU Delft), B. De Moor (KUL), L. Ljung (University of Linköping)Promoters: R. Pintelon (VUB), J. Schoukens (VUB)

d18 Identification of Linear Systems from Amplitude Information onlyYves RolainDoctoral dissertation, Vrije Universiteit Brussel, March, 1993Judging-committee: G. Maggetto (VUB), A. Barel (VUB), F. Delbaen (VUB), P. Eykhoff (TU Eindhoven), A.van den Bos (TU Delft), K. Godfrey (Univ. of Warwick, UK), J. Vandewalle (KUL)Promoters: J. Schoukens (VUB), R. Pintelon (VUB)

d19 The Use of the Method of Moments in Designing NMR AntennasGuido AnnaertDoctoral dissertation, Vrije Universiteit Brussel, December, 1993Judging-committee: G. Maggetto (VUB), R. Van Loon (VUB), R. Luypaert (VUB-AZ), R. Turner, C. De Wagter(RUG), P. Van Hecke (AGFA-GEVAERT NV.), M. Lumori (VECO)Promoters: A. Barel (VUB), M. Osteaux (VUB-AZ)

d20 Identification of Parametric Plane Wave Propagation Models for Underwa-ter Acoustic Reflection and Transmission ExperimentsLuc PeirlinckxDoctoral dissertation, Vrije Universiteit Brussel, June 1994Judging-committee: G. Maggetto (VUB), A. Barel (VUB), L. Bjørnø (TU Denmark), P. De Wilde (VUB), H.Leroy (KULAK), J. Van Campenhout (UG), J.P. Sessarego (CNRS-LMA, France)Promoters: L. Van Biesen (VUB), R. Pintelon (VUB)

d21 Radar Cross Section Calculations of Three-Dimensional Objects, Modelled by CADIsabelle De LeeneerDoctoral Dissertation, Vrije Universiteit Brussel, March 1995Judging-committee: G. Maggetto (VUB), V. Stein, D. De Zutter (UG), A. Van De Capelle (KUL), R. Van Loon(VUB)Promoters: A. Barel (VUB), E. Schweicher (KMS)

d22 Design and Realization of Low Crest Factor Broadband Microwave Excita-tion SignalsTom Van den BroeckDoctoral Dissertation, Vrije Universiteit Brussel, September 1995


Judging-committee: G. Maggetto (VUB), J. Tiberghien (VUB), L. Martens (UG), R. Pollard (University ofLeeds), M. Vanden Bossche (Hewlett Packard Belgium)Promoters: A. Barel (VUB), J. Schoukens (VUB)

d23 Accurate Experimental Modelling of Bounded Wave Propagation in Viscoe-lastic MaterialsDayu ZhouDoctoral Dissertation, Vrije Universiteit Brussel, October 1995Judging-committee: G. Maggetto (VUB), P. Guillaume (VUB), L. Bjørnø (TU Denmark), H. Leroy (KULAK), M.Lumori (VECO), J.P. Sessarego (CNRS-LMA, France), I. Veretennicoff (VUB).Promoters: L. Van Biesen (VUB), L. Peirlinckx (VUB)

d24 Calibration of a Measurement System for High Frequency Nonlinear DevicesJan VerspechtDoctoral Dissertation, Vrije Universiteit Brussel, November 1995Judging-committee: G. Maggetto (VUB), J. Schoukens (VUB), A. Cardon (VUB), M. Vanden Bossche (HewlettPackard, Belgium), L. Martens (RUG), B. Nauwelaers (KUL), U. Lott, A. Roddie, R. Pintelon (VUB), I.Veretennicoff (VUB)Promoter: A. Barel (VUB)

d25 Performance with dielectric resonators at microwave frequencies for stud-ying the pairing state in high-Tc supraconductorsAndrei MourachkineDoctoral Dissertation, Vrije Universiteit Brussel, January 1996Judging-committee: W. Van Rensbergen (VUB), G. Van Tendeloo (VUB), J. Drowart (VUB), N. Klein (IFF,Julich), V. Gasumyants (St. Petersburg)Promoters: A. Barel (VUB), S. Tavernier (VUB), R. Deltour (ULB)

d26 Identification of Linear and Nonlinear Systems in an Errors-in-Variables Least Squares and Total Least Squares FrameworkGerd VandersteenDoctoral dissertation, Vrije Universiteit Brussel, April 1997Judging-committee: G. Maggetto (VUB), A. Barel (VUB), M. Gevers (UCL), L. Ljung (University of Linköping),A. van den Bos (TU Delft), J. Vandewalle (KUL)Promoters: R. Pintelon (VUB), J. Schoukens (VUB)

d27 Frequency Domain Identification of Transmission Lines from Time Domain MeasurementsPatrick BoetsDoctoral dissertation, Vrije Universiteit Brussel, June 1997Judging-committee: G. Maggetto (VUB), A. Cardon (VUB), A. Barel (VUB), M. Goossens (VUB), R. Pintelon(VUB), D. Baert (RUG), C. Eugène (UCL), J. Capon (Belgacom), J. Verspecht (Hewlett Packard, Belgium)Promoter: L. Van Biesen (VUB)

d28 Nonparametric Identification of Nonlinear Mechanical SystemsStefaan DuymDoctoral dissertation, Vrije Universiteit Brussel, January 1998Judging-committee: G. Maggetto (VUB), R. Pintelon (VUB), A. Barel (VUB), J. Vandewalle (KUL), J. Swevers(KUL), K. Worden (Univ. of Sheffield)Promoters: J. Schoukens (VUB), M. Van Overmeire (VUB)

d29 Design of Digital Chebyshev Filters in the Complex DomainRudi VuerinckxDoctoral dissertation, Vrije Universiteit Brussel, February 1998Judging-committee: G. Maggetto (VUB), A. Barel (VUB), F. Grenez (ULB), I. Kollár (TU Budapest), McClellan(Georgia Institute of Technology), R. Pintelon (VUB), W. Verhelst (VUB)Promoters: J. Schoukens (VUB), Y. Rolain (VUB)

d30 Caching in Dataflow-Based Instrumentation & Measurement EnvironmentsEli SteenputDoctoral dissertation, Vrije Universiteit Brussel, October 1999Judging-committee: G. Maggetto (VUB), A. Barel (VUB), M. Goossens (VUB), E. Dierickx (VUB), H. Spoelder(Vrije Universiteit Amsterdam), E. Petriu (SITE-Ottawa)Promoters: Y. Rolain (VUB), J. Schoukens (VUB)

d31 Standstill Frequency Response Measurement and Identification Methods for Synchronous MachinesJef VerbeeckDoctoral dissertation, Vrije Universiteit Brussel, January 2000Judging-committee: G. Maggetto (VUB), J. Vereecken (VUB), A. Barel (VUB), J. Deuse (Tractebel), I. Kamwa(Institut de Recherche d’Hydro-Québec), J.C. Maun (ULB), J. Schoukens (VUB)

132 Bibliography

Promoters: R. Pintelon (VUB), Ph. Lataire (VUB)

d32 Nonlinear Identification with Neural Networks and Fuzzy LogicJürgen Van GorpDoctoral dissertation, Vrije Universiteit Brussel, August 2000Judging-committee: G. Maggetto (VUB), A. Barel (VUB), G. Horváth (Budapest University of Technology andEconomics), P. Kool (VUB), Y. Rolain (VUB), J. Sjöberg (Chalmers University of Technology, Göteborg), J.Suykens (KUL)Promoters: J. Schoukens (VUB), R. Pintelon (VUB)

d33 Patient and staff dosimetry in diagnostic radiologyJessica Pages PulidoDoctoral dissertation, Vrije Universiteit Brussel, September 2000Judging-committee: A. Hermanne (AZ-VUB), J. Vereecken (VUB), P. Van den Winkel (VUB-cyclotron), M.Osteaux (AZ-VUB), H. Thierens (Universiteit Gent), M.A.O. Thijssen (St. Radboud Ziekenhuis Nijmegen), H.Mol (AZ-VUB), M. Sonck (VUB-cyclotron)Promoters: R. Van Loon (VUB)

d34 Experimental Study of the Wave Propagation Through Sediments and the Characterization of its Acoustical Properties by Means of High-Frequency AcousticsSteve VandenplasDoctoral Dissertation, Vrije Universiteit Brussel, May 2001Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), A. Barel (VUB), L. Bjørnø (Technical Universityof Denmark), J.P. Sessarego (CNRS-Marseille), O. Leroy (KULAK), L. Peirlinckx (Phonetics Topographics)Promoter: L. Van Biesen (VUB)

d35 High Spatial Resolution Experimental Modal AnalysisSteve VanlanduitDoctoral Dissertation, Vrije Universiteit Brussel, May 2001Judging-commitee: G. Maggetto (VUB), R. Arruda (Univ. Est. de Campinas, Brazil), A. Barel (VUB), R.Pintelon (VUB), J. Swevers (PMA - KULeuven), M. Van Overmeire (VUB), H. Van der Auweraer (LMSInternational))Promoters: J. Schoukens (VUB), P. Guillaume (VUB)

d36 Development of New Measuring and Modelling Techniques for RFICs and their Nonlinear BehaviourWendy Van MoerDoctoral Dissertation, Vrije Universiteit Brussel, June 2001Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), R. Pollard (Univ. of Leeds, UK), D. VanHoenacker (Unv. Catholique de Louvain), J. Schoukens (VUB)Promoters: Yves Rolain (VUB), Alain Barel (VUB)

d37 Spectral and Kinetic Analysis of Radiation Induced Optical Attenuation in Silica: Towards Instrinsic Fibre Optic Dosimetry?Borgermans PaulDoctoral Dissertation, Vrije Universiteit Brussel, September 2001Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), J. Schoukens (VUB), I. Veretennicoff (VUB), B.Neerdael (SCK-CEN), M. Decréton (SCK-CEN), David Griscom (Naval Research Laboratory, USA))Promoter: Alain Barel (VUB)

d38 Multi-Carrier Modulation with Reduced Peak to Average Power RatiZekri MohamedDoctoral Dissertation, Vrije Universiteit Brussel, February 2002Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), A. Barel (VUB), J. Tiberghien (VUB), P. Boets(VUB), G. Vanhoutte (Belgacom), S. Popescu (Polytechnic Univ. of Bucharest)Promoter: L. Van Biesen

d39 Parametric modeling and estimation of ultrasonic bounded beam propaga-tion in viscoelastic mediaBey Temsamani AbdellatifDoctoral Dissertation, Vrije Universiteit Brussel, February 2002Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), A. Barel (VUB), D. Van Hemelrijck (VUB), L.Peirlinckx (Phonetic-Topographics, Ieper), O. Leroy (KULAK), Leif Bjørnø (Technical University of Denmark,Lyngby (DK)), Jean-Pierre Sessarego (Laboratoirre d'Acoustique et de Mecanique, CNRS, Marseille (F))Promoter: L. Van Biesen

d40 Measurement and modelling of the noise behaviour of high-frequency non-linear active systemsGeens AlainDoctoral Dissertation, Vrije Universiteit Brussel, May 2002Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), R. Pintelon (VUB), R. Pollard (University of


Leeds, UK), D. Van Hoenacker (UCL), J.C. Pedro (Universidade de Aveiro, Portugal), A. Barel (VUB)Promoter: Y. Rolain

d41 Model Based Calibration of D/A ConvertersVargha BalázsDoctoral Dissertation, Vrije Universiteit Brussel, June 2002Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), A. Barel (VUB), B. Bell (NIST, USA), I. Kollar(TUB, Hungary), Y. Rolain (VUB), G. Vandersteen (VUB-IMEC)Promoters: Johan Schoukens, István Zoltan (TUB)

d42 Frequency Response Function Measurements in the Presence of Non-Linear DistortionsKenneth VanhoenackerDoctoral Dissertation, Vrije Universiteit Brussel, June 2003Judging-commitee: G. Maggetto (VUB), J. Vereecken (VUB), A. Barel (VUB), P. Guillaume (VUB), H. Sol(VUB), J. Swevers (KUL), H. Van der Auweraer (LMS International)Promoter: Johan Schoukens

d43 Identification of Nonlinear Systems using Interpolated Volterra ModelsJ’zsef G. NémethDoctoral Dissertation, Vrije Universiteit Brussel, June 2003Judging-commitee: A. Barel (VUB), T. Dobrowiecki (Budapest University of Technology and Economics), M.P.Kennedy (University College Cork), R. Pintelon (VUB)Promoters: Johan Schoukens, István Kollár (TUB)

d44 Identification of block-oriented nonlinear modelsPhilippe CramaDoctoral Dissertation, Vrije Universiteit Brussel, June 2004Judging-commitee: G. Maggetto (VYB), J. Vereecken (VUB), P. Guillaume (VUB), L. Ljung (LinköpingUniversitet, Sweden), M. Verhaegen (TUD, The Netherlands), J. Vandewalle (KUL), A. Barel (VUB), R.Pintelon(VUB)Promoters: Johan Schoukens, Y. Rolain

d45 Identification of the Time Base in Environmental ArchivesFjo De RidderDoctoral Dissertation, Vrije Universiteit Brussel, December 2004Judging-commitee: W. Baeyens (VUB), A. Barel (VUB), A. Berger (UCL), Ph. Lataire (VUB), G. Munhoven(Université de Liège), D. Paillard (Centre d’Etudes de Saclay, Orme des Merisiers, France), J. Schoukens(VUB), J. Vandewalle (KUL), J. Vereecken (VUB)Promoters: R. Pintelon, Frank Dehairs

d46 Optimisatie van patiënt dosissen, gekoppeld aan beeldkwaliteit, in de vas-culaire radiologieLara StruelensDoctoral Dissertation, Vrije Universiteit Brussel, January 2005Judging-commitee: Promoter: R. Van Loon, co-promotors: H. Bosmans (KUL), F. Vanhavere (SCK-CEN)

d47 Modelling, Designing and Developing a Multidisciplinary Geodatabase GIS with the Implementation of RDBMS in conjuction with CAD and different GIS applications for the development of Coastal/Marine EnvironmentTesfazghi Ghebre EgziabeherDoctoral Dissertation, Vrije Universiteit Brussel, September 2005Judging-commitee: E. Vandijck (VUB), F. Canters (VUB), A. Barel (VUB), S. Wartel (Royal Belgian Instituteof Natural Sciences), L. Peirlinckx (Phonetics Topographics, Belgium)Promoters: L. Van Biesen and Marc Van Molle (Geography dept., fac. of Sciences)

d48 Modeling of Substrate Noise Impact on CMOS VCOS on a Lightly-Doped SubstrateCharlotte SoensDoctoral Dissertation, Vrije Universiteit Brussel, February 2006Promoters: M. Kuijk, Y. Rolain, P. Wambacq,Judging-commitee: A. Barel, G. Gielen, Ph. Lataire, G. Maggetto, M. Tiebout, G. Van der Plas, J. Vereecken,P. Wambacq

d49 Evaluation of deep-sub-quarter micron CMOS technology: low noise ampli-fiers, oscillators and ESD reliabilityDimitri LintenDoctoral Dissertation, Vrije Universiteit Brussel, February 2006Promoters: Y. Rolain, P. Wambacq and M. KuijkJudging-commitee: G. Maggetto, J. Vereecken, A. Barel, M.I. Natarajan (Mentor IMEC), I. Smedes (PhilipsSemiconductors), M. Tiebout (Infineon)

134 Bibliography

d50 Verification and Correction of Test Signals with a Spectrum AnalyzerDaan RabijnsDoctoral Dissertation, Vrije Universiteit Brussel, March 2006Promoters: G. Vandersteen, J. SchoukensJudging-commitee: P. Lataire, J. Vereecken, I. Kollar (Budapest Univ. of Techn. & Economics), D. DeGroot(CCNi Measurement Service), P. Guillaume, W. Van Moer

d51 Impact and Mitigation of Analog Impairments in Multiple Antenna Wireless CommunicationsJian LiuDoctoral Dissertation, Vrije Universiteit Brussel, May 2006Promoters: A. Barel, J. Stiens, G. VandersteenJudging-commitee: P. Lataire, J. Vereecken, L. Van der Perre (IMEC), V. Öwall (Lund University, Sweden),W. Van Moer

d52 Development and evaluation of a numerical method for the identification of a physical system described by a partial differential equation: a case studyKathleen De BelderDoctoral Dissertation, Vrije Universiteit Brussel, May 2006Promoters: R. Pintelon, J. SchoukensJudging-commitee: Ph. Lataire, J. Vereecken, J. Swevers (KUL), P. Guillaume, H. Van der Auweraer (LMSInternational), H. Sol, P. Roose (Cytec)

d53 Contributions to Large-Signal Network AnalysisFrans VerbeystDoctoral Dissertation, Vrije Universiteit Brussel, September 2006Promoter: Y. RolainJudging-commitee: J. De Ruyck, Jean Vereecken, Alain Barel, Don DeGroot (CCNi Measurement Services,Andrews University, Michigan, USA), Rik Pintelon, Roger Pollard (University of Leeds, UK), Johan Schoukens,Steve Vnlanduit

d54 Contribution to severe weather and multimodel ensemble forecasting in BelgiumDavid DehenauwDoctoral Dissertation, Vrije Universiteit Brussel, November 2006Promoters: A. Barel, H. DecleirJudging-commitee: Ph. Lataire, J. Vereecken, J. Schoukens, R. Willem, H. Malcoprs (KMI), A. Tijm (Kon. Ned.Metrologisch Inst.), F. De Meyer (KMI)

d55 A system identification view on two aquatic topics: phytoplankton dynam-ics and water mass mixingAnouk de BrauwereDoctoral Dissertation, Vrije Universiteit Brussel, April 2007Promoters: Willy Baeyens, Johan SchoukensJudging-commitee: Robert Finsy, Frank Dehairs, Rik Pintelon, An Smeyers-Verbeke, Joos Vandewalle (KUL),Eric Deleersnijder (UCL), Karline Soetaert (NIOO-KNAW), Johannes Karstensen (University of Kiel)

d56 Ultra-Wideband transceiver for low-power low data rate applicationsJulien RyckaertDoctoral Dissertation, Vrije Universiteit Brussel, May 2007Promoters: Yves Rolain, Piet Wambacq (IMEC)Judging-commitee: Annick Hubin, Rik Pintelon, Gerd Vandersteen, J. Rabaey (University of Berkley, USA),

M. Tiebout (Infeneon Germany), Christof Debaes, C. Desset (IMEC)

d57 Measuring, modeling and realization of high-frequency amplifiersLudwig De LochtDoctoral Dissertation, Vrije Universiteit Brussel, November 2007Promoters: Yves Rolain,Gerd VandersteenJudging-commitee: Annick Hubin, Rik Pintelon, Wendy Van Moer, Danielle VAnhoenacker (UCL), AndreaFerrero (Politechnico di Torino), Christof Debaes (VUB), Marc Vanden Bossche (NMDG Engineering)

d58 Body Area Communications: Channel characterization and ultra-wideband system-level approach for low powerAndrew FortDoctoral Dissertation, Vrije Universiteit Brussel, November 2007Promoters: Leo Van Biesen, Piet Wambacq (IMEC)Judging-commitee: Annick Hubin, R. Pintelon, G. Vandersteen, Y. Hao (Univ. of London), C. Desset (IMEC)


d59 Algorithms for identifying guaranteed stable and passive models from noisy dataTom D’HaeneDoctoral Dissertation, Vrije Universiteit Brussel, January 2008Promoter: Rik PintelonJudging-commitee: Gert Desmet, Jean Vereecken, Patrick Guillaume, Paul Van Dooren (UCL), Tom Dhaene(UGent), Martine Olivi (INRIA), Gerd Vandersteen

d60 Identification of Nonlinear Systems Using Polynomial Nonlinear State Space ModelsJohan PaduartDoctoral Dissertation, Vrije Universiteit Brussel, January 2008Promoters: Johan Schoukens, Rik PintelonJudging-commitee: Annick Hubin, Jean Vereecken, Steve Vanlanduit, Lennart Ljung (Linköping University),

Jan Swevers (KUL), Yves Rolain

9. THESIS TOT HET BEHALEN VAN HET AGGREGAAT VAN HET HOGER ONDERWIJS

ag3 Sytem Identification. A Frequency Domain Modeling ApproachJohan SchoukensGeaggregeerde van het hoger onderwijs, Vrije Universiteit Brussel, 1991Judging-committee: G. Maggetto (VUB), G. Baron (VUB), G. Vansteenkiste (VUB), P. Eykhoff (TUEindhoven), A. van den Bos (TU Delft), J. Vandewalle (KUL), M. Gevers (UCL)Promoters: J. Renneboog (VUB), A. Barel (VUB)

ag4 Frequency Domain Identification of Linear Time Invariant SystemsRik PintelonGeaggregeerde van het hoger onderwijs, Vrije Universiteit Brussel, 1994Judging-committee: G. Maggetto (VUB), A. Cardon (VUB), G. Baron (VUB), P. Eykhoff (TU Eindhoven), M.Gevers (UCL), A. van den Bos (TU Delft), J. Vandewalle (KUL), G. Van Steenkiste (RUG)Promoter: A. Barel (VUB)

136 Bibliography


CHAPTER 5. Location of the university (VUB) and the dept. ELEC

Getting to the department ELEC of the “Vrije Universiteit Brussel”

A. Arrival by car:

Take the “Ring” and exit at the crossing with the motorway E411 direction centre of

Brussels. At the end of the motorway, take the viaduct (go straight on), and at the second

traffic lights, turn on right (“Triomflaan”), the VUB is situated on the left side of this road,

starting from entrance 6 (see next map).

138 Location

B. From the Brussels National airport at Zaventem:

From the airport, every 20 minutes the rail shuttle quickly takes you to the North Station in

the centre of Brussels. At the North Station (“Bruxelles Nord”), you take the train to

Etterbeek (direction Etterbeek or Louvain La Neuve) and get off the train in Etterbeek,

which is 10 min. walking distance from the VUB. You only pay € 3.60 for a first class ticket,

and € 2.80 for a second class ticket (a taxi or cab from airport to the University is about €

50.00).

More information and timetables of the Belgian railways: surf to:

http://www.b-rail.be/E/index.html


C. Arrival by train:

Change in “Bruxelles Nord” and take the train to Etterbeek (direction Etterbeek or Louvain

La Neuve):

see http://www.b-rail.be/E/index.html

D. Arrival by subway (€ 1.50/ticket):

Take line 1 direction “Hermann-Debroux” and get off at “Petillon”, which is also 10 min.

walking distance from the VUB.

More information about Brussels subway:

surf to http://www.stib.irisnet.be/NL/31000N.htm

The dept. ELEC is located in building K, 6th floor.

140 Location

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