EMBEDDED SYSTEMS FOR
MECHATRONICS
MASTER PROGRAMME
2017
MODULE HANDBOOK
Version 12
Table of Contents
Mathematics for Controls & Signals (MOD1-01) ....................................................... 7
Distributed and Parallel Systems (MOD1-02) ............................................................ 9
Embedded Software Engineering (MOD1-03) ......................................................... 11
Requirements Engineering (MOD1-04) .................................................................... 14
Introduction to Embedded Systems Design (MOD1-05) ....................................... 16
Mechatronic Systems Engineering (MOD2-01) ....................................................... 18
Microelectronics & HW/SW-Co-Design (MOD2-02) ................................................ 20
R&D Project Management (MOD2-03) ...................................................................... 22
Signals & Control Systems 1 (MOD2-04) ................................................................. 25
Research Project (Thesis) (MOD3-03) ...................................................................... 27
Master Thesis + Colloquium (MOD4-04) .................................................................. 29
Applied Embedded Systems (MOD-E01) ................................................................. 32
Biomedical Systems (MOD-E02) ............................................................................... 34
SW Architectures for Embedded and Mechatronic Systems (MOD-E03) ........... 36
Signals and Systems for Automated Driving (MOD-E04) ...................................... 38
Internet of Things (MOD-E05) ................................................................................... 41
Computer Vision (MOD-E06) ..................................................................................... 43
Signals & Control Systems 2 (MOD-E07) ................................................................ 45
Formal Methods in Mechatronics (MOD-E08) ......................................................... 47
System on Chip Design (MOD-E09) ......................................................................... 49
Automotive Systems (MOD-E10) .............................................................................. 51
Research Seminar (S) ................................................................................................ 53
Study Programme overview
1st semester (winter semester)
module
examin-
ation
mod-nr-/
exam-nr
student workload
ECTS
points contact hours
self-study
(hrs) SWS1 hours
Mathematics for Signals & Controls MOD1-01 10110/11 4 60 120 6
Distributed and Parallel Systems MOD1-02 10120/21 4 60 120 6
Embedded Software Engineering MOD1-03 10130/31 4 60 120 6
Requirements Engineering MOD1-04 10140/41 4 60 120 6
Introduction to Embedded Systems
Design MOD1-05 10150/51 4 60 120 6
Total 5 20 300 600 30
2nd semester (summer semester)
module
examin-
ation
mod-nr/
exam-nr
student workload ECTS
points contact hours
self-study
(hrs) SWS1 hours
Mechatronic Systems Engineering MOD2-01 10210/11 4 60 120 6
Microelectronics & HW/SW Co-
Design MOD2-02 10220/21 4 60 120 6
R&D Project Management MOD2-03 10230/31 4 60 120 6
Signals and Control Systems 1 MOD2-04 10240/41 4 60 120 6
Elective 1 * MOD2-05 10250 4 60 120 6
Total 5 20 300 600 30
3rd semester (winter semester)
module examin-
ation
mod-nr/
exam-nr
student workload ECTS
points contact hours
self-study
(hrs) SWS1 hours
Elective 2 * MOD3-01 10310 4 60 120 6
Elective 3 * MOD3-02 10320 4 60 120 6
Research Project (Thesis) MOD3-03 10330/31 0 0 540 18
Total 3 8 120 780 30
4th semester (summer semester)
module
examin-
ation
mod-nr/
exam-nr
student workload
ECTS
points contact hours
self-study
(hrs) SWS1 hours
Master Thesis and Colloquium P 103 0 0 900 30
Total 5 20 300 600 30
1 SWS= weekly hours per semester
*cf. Attachment 2
Attachment 2: Catalogue of Compulsory Elective Modules
Catalogue of Compulsory Module (Electives 1, 2 and 3)*
Module module examin-ation
mod-nr/
exam-nr
student workload
ECTS-Punkte
contact hours self-study (hrs)
SWS1 hours
Applied Embedded Systems MOD-E01
10401 4 60 120 6
Biomedical Systems MOD-E02
10402 4 60 120 6
SW Architectures for Embedded and Mechatronic Systems
MOD-E03 10403 4 60 120 6
Signals and Systems for Automated Driving *** MOD-E04
10404 4 60 120 6
Internet of Things MOD-E05
10405 4 60 120 6
Computer Vision *** MOD-E06
10406 4 60 120 6
Signals & Control Systems 2 *** MOD-E07
10407 4 60 120 6
Formal Methods in Mechatronics MOD-E08
10408 4 60 120 6
System on Chip Design MOD-E09
10409 4 60 120 6
Automotive Systems MOD-E10
10410 4 60 120 6
Research Seminar S 10411 180 6
Module(s) from cooperating institutions 10421
Module(s) from study courses of the home institution**
10431
* From the Catalogue of Compulsory Electives a minimum of 3 modules must be completed with an examination
(MOD2-05, MOD3-01 and MOD3-02). More than 18 credit points may be obtained which will be marked in the
certificate.
** If compulsory elective modules of the Ruhr Master School (RMS) are part of the course programmes of Dortmund
University of Applied Sciences and Arts (Fachhochschule Dortmund), students must complete the examinations
within their own course programme.
Upon application, modules of the course programmes participating in the RMS may be elected.
*** At least 1 of the following Modules must be taken as an Elective: MOD-E04, MOD-E06, or MOD-E07.
M O D U L E S
Mathematics for Controls & Signals (MOD1-01) .................................................... 7
Distributed and Parallel Systems (MOD1-02) ......................................................... 9
Embedded Software Engineering (MOD1-03) ....................................................... 11
Requirements Engineering (MOD1-04) ................................................................. 14
Introduction to Embedded Systems Design (MOD1-05)……………………………16
Mechatronic Systems Engineering (MOD2-01) .................................................... 18
Microelectronics & HW/SW-Co-Design (MOD2-02) .............................................. 20
R&D Project Management (MOD2-03) ................................................................... 22
Signals & Control Systems 1 (MOD2-04) .............................................................. 25
Research Project (Thesis) (MOD3-03) ................................................................... 27
Master Thesis + Colloquium (MOD4-04) ............................................................... 29
Mathematics for Controls & Signals (MOD1-01) Code Number
10110/11
Workload
180 h
Credits
6
Semester
Sem. 1
Frequency
annually
Duration
1 Semester
1 Course Title
Mathematics for Controls &
Signals
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group Size
25 students
2 Course Description
This course introduces the necessary mathematical concepts for signal processing and control engineering. It starts with a tailored review of real and complex analysis. A major focus is on different kind of integral transforms that are of essential use in subsequent courses. A huge amount of physical phenomena can be described by sets of linear differential equations and thus the latter are dealt with in this course. Linear algebra plays a prominent role in case of systems with several states and/or multiple input and output. Usually, sensor signals are corrupted by noise or other sources of uncertainty. To be able to deal with those, probability theory is introduced. Matlab and Octave are used as examples for state of the art tools for numerical mathematics and as a preparation for following courses.
3 Course Structure
1. Real and complex analysis 2. Fourier, Laplace and Z transform 3. Differential equations 4. Linear algebra 5. Probability theory 6. Introduction into Matlab/Octave 7. Numerical mathematics
4 Case Studies
None – courses contain small labs
5 Parameters
Course characteristics: compulsory
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: none
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Written Exam at the end of the course
Teaching staff: Prof. Dr. Andreas Becker, (Prof. Dr. Thomas Felderhoff)
6 Learning outcomes
6.1 Knowledge
Knows basic theorems of complex analysis and linear algebra
Knows relevant theoretical foundations of signal processing and control engineering
Knows the most important concepts of probability theory
6.2 Skills
Can make use of analysis and linear algebra to describe physical phenomena
Can make use of different domains for the description of signals
Can apply probabilistic concepts
Can make use of tools for numerical mathematics
6.3 Competence – attitude
Can discuss mathematical prerequisites of mechatronic systems with experts
Understands experts for mathematics and translates between different domains
7 Teaching and training methods
Lectures & Exercises
Labs with Matlab/Octave
E -learning modules on higher mathematics, tool tutorials
8 Course mapping
Input for:
MOD2-04 – Signals & Control Systems 1
MOD-E02 – Biomedical Systems
MOD-E04 – Signals and Systems for Automated Driving
MOD-E05 – Computer Vision
MOD-E011 – Signals & Control Systems 2
9 References James, Modern Engineering Mathematics, Pearson Education, 2015
Stroud, Engineering Mathematics, Macmillan Education, 2013
Oppenheim, Willsky, Nawab, Signals and Systems, Pearson Education, 2013
Distributed and Parallel Systems (MOD1-02) Code Number
10120/21
Workload
180 h
Credits
6
Semester
Sem. 1
Frequency
annually
Duration
1 Semester
1 Course Title
Distributed and Parallel
Systems
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group Size
25 students
2 Course Description
Distributed systems are groups of networked computers and/or embedded systems, which have a common goal for their work. The terms distributed computing and parallel computing have a lot of overlap and frequently the term concurrent computing is used in this field. There is no clear distinction between them. This course is a prerequisite for the deeper understanding of multicore and manycore systems. It builds the theoretical core knowledge about cyber physical systems (CPS) and about the current state of research in the field of embedded distributed systems.
3 Course Structure
1. Architectures for distributes systems (in principle) 2. Communication
a. Synchronous, Asynchronous b. Peer-to-Peer, Broadcast, Multicast c. Protocols
3. Time and States a. States and Timestamps b. Clocks
4. Coordination and Agreement a. Transactions and Concurrency Control b. Deadlocks c. Replication and Fault Tolerance
5. Scheduling/Partitioning/Distribution (Multicore/Manycore) 6. Cyber physical systems (CPS) 7. Dependable Systems 8. Programming Paradigms and Methods
4 Case Studies
CS01: AMALTHEA tool chain – Scheduling & Partitioning tools (e.g. TA tools)
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: computer science & programming
Skills trained in this course: theoretical and methodological skills
Assessment of the course: Written Exam at the end of the course (50%) and individual
homework (50%): paper/report about a recent topic from CPS research
Teaching staff: Prof. Dr. Burkhard Igel, (Prof. Dr. Erik Kamsties)
6 Learning outcomes
6.1 Knowledge
Knows theory of distributed and parallel systems
Knows critical issues concerning reliable distributed systems
Knows recent research about partitioning and scheduling for cyber physical systems
6.2 Skills
Can assess the feasibility of distributed CPS
Can implement algorithms for distributed embedded systems
Can model the behavior of distributed CPS
Can apply state of the art tools and can develop new tools for distribution
6.3 Competence - attitude
Can setup tooling and design flows
Can discuss distribution issues with computer scientists
Understands the potential of concurrency in CPS
7 Teaching and training methods
Lectures & Exercises, AMALTHEA and TA tool labs
e-learning modules on theoretical informatics, tool tutorials
8 Course mapping
Input for:
MOD2-01- Mechatronic Systems Engineering
MOD2-02 – Microelectronics & HW/SW Codesign
MOD-E04 – SW Architectures for Embedded and Mechatronic Systems
MOD-E07 – Model Based and Model Driven Design
9 References
G. Coulouris, J. Dollimore, T. Kindberg, G.Blair: Distributed Systems: Concepts and Design (5th
ed.), Addison Wesley, May 2011
Hermann Kopetz, Real-Time Systems: Design Principles for Distributed Embedded Applications
(Real-Time Systems Series), Springer, April 2011
P. Linington, Z. Milosevic, A. Tanaka, A. Vallecillo. Building Enterprise Systems with ODP: An
Introduction to Open Distributed Processing, Chapman & Hall/CRC, September 2011
P. Koopmann. Better Embedded System Software, Drumnadrochit Education, 2010
Research Papers: Lamport, Chandy & Lamport
Other recent research papers
Embedded Software Engineering (MOD1-03) Code Number
10130/31
Workload
180 h
Credits
6
Semester
Sem. 1
Frequency
annually
Duration
1 Semester
1 Course Title
Embedded Software
Engineering
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Embedded software engineering is a multidisciplinary approach for developing Solutions to complex engineering problems. The continuing increase in system complexity is demanding integrated engineering practices combining software engineering, control engineering, mechanical engineering, and electrical engineering. Therefore, modeling embedded systems often results in a mix of models from a multitude of disciplines. An integrated modelling approach is provided by SysML as an extension of the Unified Modeling Languague (UML ), version 2, which has become the de facto standard software modeling language. SysML is a robust language that addresses many of the embedded software engineering needs, while enabling the embedded software engineering community to leverage the broad base of experience and tool vendors that support UML. Embedded systems are often safety-critical applications where correct operation is vital to ensure the safety of the public and environment. Furthermore, these systems have to fulfill real-time requirements and they have to cope with restricted resources Finally, we focus on several development processes of embedded systems and their underlying tools. In addition to the lecture exercises are organized to give an insight how to use state of the art approaches and tools. Within small projects the students can contribute the gained knowledge by using these introduced tools and concepts.
3 Course Structure
1. Characteristics of Embedded (and real-time) Systems
2. Motivation for Embedded Software Engineering
3. Modeling of Embedded Systems
4. Overview and Architecture of SysML
a. SysML: Requirements and Use Cases
b. SysML: Basic Concepts
c. SysML: Modeling Structure with Blocks
d. SysML: Modeling Constraints with Parametrics
e. SysML: Modeling Control Flow-Based Behavior with Activities
f. SysML: Modeling Message-Based Behavior with Interactions
g. SysML: Modeling Event-Based Behavior with State Machines
h. SysML Tools in General and Enterprise Architect
5. Development Processes of Embedded Software Systems
6. SW Quality Management, Software-Test
7. Development Tools (e.g. Enterprise Architect, IBM Rational Rhapsody)
4 Case Studies
CS01: AMALTHEA tool chain – modeling tools
CS05: M2M System – modeling with Enterprise Architect, IBM Rational Tools
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: computer science & programming
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Written Exam at the end of the course (50%) and group work
as homework (50%) with Enterprise Architect or IBM Rhapsody use case and
demonstration/presentation
Teaching staff: Prof. Dr. Stefan Henkler, (Prof. Dr. Martin Hirsch)
6 Learning outcomes
6.1 Knowledge
Students know the characteristics of embedded (and real-time) systems
Students know the most important SysML diagrams.
Students know the syntax and semantic of the most important SysML diagrams.
Students know modeling tools for embedded software systems.
Students know processes and methods of embedded software engineering.
6.2 Skills
Students can choose SysML-Diagrams to model specific software aspects.
Students can model structural aspects by means of block diagrams.
Students can model constraints by means of parametric diagrams.
Students can model control flow-based behavior by means of activity diagrams.
Students can model message-based behavior by means of interaction diagrams.
Students can model event-based behavior by means of state machines.
Student can tailor processes and methods to specific project needs.
Students can evaluate and use tools for embedded Software engineering.
6.3 Competence - attitude
Students develop an attitude to embedded software engineering according to modeling
and processes.
Students show a quality attitude according to embedded software engineering modeling.
Students understand the main challenges of complex embedded software projects.
Students understand the importance of modeling complex embedded software systems
Students can improve their effectiveness and efficiency by using dedicated methods and
tools to support engineering processes.
Students understand the differences between software and embedded software systems
projects and act accordingly
7 Teaching and training methods
Lectures introducing concepts, methods and tools
Group work to train concepts and methods, to develop skills and to work on case
studies
Home work to add contributions on a case study as group work
Presentations to communicate results
Presentation and discussion of an industry case by a partner company (itemis or smart
mechatronics)
8 Course mapping
Input for:
MOD2-01- Mechatronic Systems Engineering
MOD2-02 – Microelectronics & HW/SW Codesign
MOD-E04 – SW Architectures for Embedded and Mechatronic Systems
MOD-E03 – Automotive Systems
MOD-E07 – Model Based and Model Driven Design
Connects to:
MOD1-02- Distributed and Parallel Systems
9 References
Alt, O.: Modellbasierte Systementwicklung mit SysML: in der Praxis, Carl Hanser Verlag GmbH
& Co. KG, März 2012, ISBN: 978-3446430662
Friedenthal, S.; Moore, A.; Steiner, R.: A Practical Guide to SysML: The Systems Modeling
Language, Morgan Kaufmann, 2nd Edition, Oktober 2011, ISBN: 978-0123852069
Oshana, R.: Software Engineering for Embedded Systems: Methods, Practical Techniques,
and Applications (Expert Guide), Newnes, Mai 2013, ISBN: 978-0124159174
Requirements Engineering (MOD1-04) Code Number
10140/41
Workload
180 h
Credits
6
Semester
Sem. 1
Frequency
annually
Duration
1 Semester
1 Course Title
Requirements Engineering
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Requirements engineering (RE) is the very first activity in software, systems, and service development. This course builds on software engineering skills from 1st semester (UML, SysML). Deriving a comprehensive set of requirements is a mandatory and critical task in the early phase of the systems engineering design flow. Requirements are the starting point and main angle for design, verification & validation, and for the test and integration of systems. Configuration and change request management are connected with RE. Defining requirements and dealing with requirements in a structured way is still a major area for research on tools and methodologies – especially for large and complex mechatronic systems. In this module, students will get specific knowledge about the state of the art and the main future challenges in RE.
3 Course Structure
1. Introduction (What is a requirement?, problem vs. solution)
2. Frameworks (e.g. Jackson's WRSPM Modell)
3. Requirements Engineering Process (stakeholder, activities)
4. System and system context
5. Elicitation of requirements (techniques and supporting activities, Kano model)
6. Textual requirements documents
7. Requirements modeling (e.g. goal-oriented modeling, requirements patterns)
8. Non-functional requirements
9. Validation of requirements
10. Requirements Management (attributes, prioritization, traceability, change management,
RE tools, CMMI, ReqIF exchange format)
11. Software product lines and variability management
4 Case Studies
CS01: AMALTHEA tool chain – application of product line management tool and ReqIF
support
CS02: HVAC Control System Demonstrator – setup in IBM Rational DOORS
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: none
Skills trained in this course: practical, methodological, and personal skills
Assessment of the course: Paper/essay on literature review about recent research as
individual homework (50%) and group work as homework (50%): DOORS demonstration
and presentation of example
Teaching staff: Prof. Dr. Erik Kamsties, (n.n.)
6 Learning outcomes
6.1 Knowledge
Knows frameworks and models for RE
Knows relevant RE processes and interfaces to other processes
Knows concepts and recent research on product line and variability management
6.2 Skills
Can model requirements with RE tools
Can set up and integrate RE tools into tool chains and design flows
Can derive requirements in a structured and comprehensive way
6.3 Competence - attitude
Understands the importance of RE in the early project phase
Can set up and lead RE in a cross domain team
7 Teaching and training methods
Lectures introducing concepts, methods and tools
Group work to train concepts and methods, to develop skills and to work on case
studies
Literature review and Essay writing
Home work to add contributions on a case study as group work
Presentations to communicate and demonstrate homework
8 Course mapping
Input for:
MOD-E03 – Automotive Systems
MOD-E07 – Model Based and Model Driven Design
Requires:
MOD1-03 - Embedded Software Engineering
Connects to:
MOD2-01 – Mechatronic Systems Engineering
MOD2-03 – R&D Project Managemen
9 References
Pohl, K.; Requirements Engineering: Fundamentals, Principles, and Techniques, Springer 2010.
Robertson, S. and Robertson, J.; Mastering the Requirements Process: Getting Requirements Right, Addison-Wesley, 2012.
van Lamsweerde, A.; Requirements Engineering: From System Goals to UML Models to Software Specifications, John Wiley & Sons, 2009.
Introduction to Embedded Systems Design (MOD1-05) Code Number
10150/51
Workload
180 h
Credits
6
Semester
Sem. 1
Frequency
annually
Duration
1 Semester
1 Course Title
Introduction to Embedded
Systems Design
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
This module is tailored for new students with different levels of proficiency from their bachelor programmes. It is intended to close the gaps to the knowledge required for the master programme. Students select a minimum of 4 out of 7 compact courses on basic topics relevant for the further study programme. These compact courses will enable students with different backgrounds to get a smooth start into the master programme.
3 Course Structure
The programme offers a selection of about 7 compact courses. More compact courses might
be added according to the needs of the individual student group:
1. Compact Electronics Course 2. Compact Programming Course 3. Modeling of Embedded Systems (UML) 4. Applications of Embedded Systems 5. Tools and Techniques for Embedded Systems Design 6. Embedded Systems Lab Project 7. Engineering Communication 1 (German)
4 Case Studies
None – courses contain small labs
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: block courses
- Contact hours: 60 (4 x 15 h)
- Self-Study hours: 120 (including tutorials)
Course characteristics: compulsory, students have to choose a minimum of 4 out of 7
courses, based on assessment of their prior knowledge
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: none
Skills trained in this course: theoretical, methodological and practical skills
Assessment of the course: tests for each compact course, graded project work, compact
course results are summarized for overall module grade
Teaching staff: Prof. Dr. Rolf Schuster, professors + tutors for each compact course
6 Learning outcomes
6.1 Knowledge
Knows the foundations of each topic at least up to a bachelor level
6.2 Skills
Can apply the knowledge in the upcoming master courses
6.3 Competence - attitude
Can assess the gaps in own knowledge
Can use a variety of tools, online-courses, tutorials to close the gaps through self-study
7 Teaching and training methods
Lectures introducing concepts, methods and tools
Group work to train concepts and methods, to develop skills and to work on projects
Literature review and essay writing
Homework to contribute to projects as group work
Presentations to communicate and demonstrate homework / project work
8 Course mapping
Input for: All other courses
9 References
Peter Marwedel, Embedded System Design, Springer (2nd Edition, 2011)
Herbert Schildt, Java: A Beginner's Guide, McGraw-Hill Education (6th Edition, 2014)
Joshua Bloch, Effective Java: A Programming Language Guide, Addison-Wesley (2nd Edition,
2008)
P. Wilson, The Circuit Designer's Companion, Newnes (3rd Edition, 2012)
Mechatronic Systems Engineering (MOD2-01) Code Number
10210/11
Workload
180 h
Credits
6
Semester
Sem. 2
Frequency
annually
Duration
1 Semester
1 Course Title
Mechatronic Systems
Engineering
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Mechatronics Systems Engineering is both a challenge and a chance. A holistic and well elaborated engineering process for complex mechatronic system/cyber physical systems is a mandatory requirement for developing future intelligent products. Teaching this new school of engineering is the major goal of the whole master programme and an attractive offer for a university of applied sciences. This module introduces the holistic engineering methodology and offers the big picture for the other modules. The focus is on the early phase of mechatronic systems design since this phase offers the biggest leverage for better technical systems. Topics like cross domain engineering and systems integration are addressed, too. The content of the course is largely inspired from finding of the BMBF Spitzencluster “it’s OWL” and the new Fraunhofer Institute “Entwurfstechnik Mechatronik”. A continuous transfer of new findings into this course is intended.
3 Course Structure
1. Motivation: a. Examples for Mechatronic Systems b. Characteristics of Mechatronic Systems c. Challenges
2. Discipline-spanning development process 3. Systems Engineering (according to INCOSE SE handbook) 4. Conceptual Design of Mechatronic Systems
a. CONSENS 5. The Software Engineering Domain
a. MechatronicUML b. Behavior synthesis
6. Self-Optimization: Operator Controller Module (OCM) 7. Application to Use Case (Printing Industry, Rail Cab)
4 Case Studies
CS07: Rail Cab – modeling with CONSENS (Enterprise Architect)
CS07: Rail Cab – modeling with Mechatronic UML
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4 - Contact hours: 60 - Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: mechanics/physics, basics of embedded systems
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Written Exam at the end of the course (50%) and individual homework (50%): MechatronicUML model of an example
Teaching staff: Prof. Dr. Stefan Henkler, (Prof. Dr. Martin Hirsch)
6 Learning outcomes
6.1 Knowledge
Knows CONSENS, INCOSE SE handbook, MechatronicUML
Knows mechatronic systems engineering processes
Knows Enterprise Architect and other relevant tools 6.2 Skills
Can model mechatronic systems
Can apply methodology and state of the art tools on real use cases (e.g. printing machine)
Can select tools and define tool chains and design flows 6.3 Competence - attitude
Can structure the early phase of mechatronic systems design
Can lead cross domain design of mechatronic systems
Understands issues from different domains and can integrate solutions into a holistic design
7 Teaching and training methods
Lectures, Labs (with Enterprise Architect and other tools), homework
Access to tools and tool tutorials
Access to recent research papers
8 Course mapping
Input for:
MOD-E04 – SW Architectures for Embedded and Mechatronic Systems
MOD-E06 – Formal Methods in Mechatronics
MOD-E07 – Model Based and Model Driven Design Requires:
MOD2-04 - Control Theory and Systems
MOD1-03 - Embedded Software Engineering Connects to:
MOD1-04 – Requirements Engineering
MOD2-03 - R&D Project Management
9 References
Jürgen Gausemeier, Franz Rammig, Wilhelm Schäfer (Editors): Self-optimizing Mechatronic Systems: Design the Future. HNI-Verlagsschriftenreihe, Band 223, 2008
P.L. Tarr, A.L. Wolf (eds.): Engineering of Software. Springer-Verlag Berlin Heidelberg 2011
K. Pohl, H. Hönninger, R. Achatz, M. Broy (Eds.): Model-Based Engineering of Embedded Systems: The SPES 2020 Methodology, Springer, 2012
INCOSE: Guide to the Systems Engineering Body of Knowledge - G2SEBoK: http://g2sebok.incose.org/app/mss/menu/index.cfm
Microelectronics & HW/SW-Co-Design (MOD2-02) Code Number
10220/21
Workload
180 h
Credits
6
Semester
Sem. 2
Frequency
annually
Duration
1 Semester
1 Course Title
Microelectronics & HW/SW-
Co-Design
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Digital Systems are the main hardware platform for embedded systems and the target of embedded SW development. A good knowledge and overview of available HW platforms is required. Furthermore, a concurrent engineering process (HW/SW Codesign) is used to develop state of the art embedded systems. The coordination of (more agile) SW development and (more V-model) HW development is a challenge. Digital system development is applying complex tools and tool chains. The goal of this module is to enable to students to select, to assess, and to develop digital target platforms for embedded systems.
3 Course Structure
1. Microelectronic Components for Embedded Systems
a. DSP, Microcontroller b. FPGA c. ASIC, ASSP d. Memories e. Communication components (e.g. serial busses) f. PCB and standard circuits
2. Digital systems design methodologies and processes a. ESL concepts b. SystemC c. VHDL/Verilog d. Simulation and validation e. HW/SW partitioning f. Verification and test g. Synthesis (on FPGA)
3. Virtual Prototypes and HW/SW co-verification 4. Tools and Tool Chains 5. New Trends: Multicore/Manycore, SoC, 3D, MEMS
4 Case Studies
CS01: AMALTHEA tool chain – Use of Virtual Prototypes
CS03: CoreVA – Implementation of IP blocks and testbenches in SystemC and VHDL
CS04: Avionics Computer & Robots – Design and implementation on FPGA
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4 - Contact hours: 60 - Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: electronics, basics of embedded systems
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work as homework (50%): SystemC or VHDL implementation, mapping on FPGA, demonstration and presentation
Teaching staff: Prof. Dr. Peter Schulz, (Prof. Dr. Carsten Wolff)
6 Learning outcomes
6.1 Knowledge
Knows microelectronic components of embedded systems
Knows digital systems design methodology and processes
Knows tools and technologies for digital design
Knows concept of virtual prototype and its application in HW/SW Codesign 6.2 Skills
Can compose an embedded system out of microelectronic components
Can describe digital systems with SystemC or VHDL
Can run a digital simulation
Can assess synthesis and verification reports for simple designs
Can run test and debug sessions with FPGAs 6.3 Competence - attitude
Can set up HW/SW Codesign projects for embedded systems
Can choose and tailor the tool chain and methodology
Can present and demonstrate the design flow for a digital design project
7 Teaching and training methods
Lectures
Labs with: SystemC and VHDL simulation (Mentor), FPGA synthesis (Mentor or Synopsis) and FPGA implementation (Xilinx or Lattice). Access to tools and tool tutorials (Europractice tool chain)
8
Course mapping Input for:
MOD-E08 – SoC Design Requires:
MOD1-03 - Embedded Software Engineering Connects to:
MOD2-03 - R&D Project Management
9 References
Documentation of Europractice – Mentor Graphics Tools and Cadence Tools Neil H.E. Weste, David Money Harris: “Integrated Circuit Design”, Pearson, 2011 Clive “Max” Maxfield (Editor): “FPGAs World Class Designs”, Newnes / Elsevier, 2009 Jack Ganssle (Editor): “Embedded Systems World Class Designs”, Newnes / Elsevier, 2008 Peter J. Ashenden: “Digital Design – An Embedded Systems Approach Using VHDL“, Morgan Kaufmann / Elsevier, 2008 Peter J. Ashenden: “The Designer’s Guide to VHDL 2nd Edition”, Morgan Kaufmann / Academic Press, 2002 Schaumont, Patrick: A Practical Introduction to Hardware/Software Codesign. Springer 2010 Bailey, Brian, Martin, Grant: ESL Models and their Application: Electronic System Level Design and Verification in Practice. Springer 2010
R&D Project Management (MOD2-03) Code Number
10230/31
Workload
180 h
Credits
6
Semester
Sem. 2
Frequency
annually
Duration
1 Semester
1 Course Title
R&D Project Management
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
The course R&D project management is focusing on processes, methods and tools for the management of innovative research and development projects in engineering. R&D projects are characterized by creativity and a high degree of innovation and uncertainty. Advanced project management methodology has to deal with the uncertainty and has to foster creativity. Apart from this general problem, R&D project methodology has to be aligned with the engineering processes and with the different engineering domains. Topics like quality management, configuration management and specific tools for risk management are part of the methodology, too. The course enables students to understand and structure R&D projects and to choose appropriate tools and methods based on a proper analysis of the project characteristics. The students are able to tailor the methodology and they understand the remaining gaps in the methodology. They can develop new project management methods and tools to fill the gaps and they can do research to assess the effectiveness and efficiency of project management methodology in R&D. The course is based on one main project case study and several small cases for specific topics.
3 Course Structure
1. Characteristics of R&D projects 2. Project management processes:
a. planning, controlling (cost, time, quality) b. agile & lean c. V-model
3. Milestones and Reviews 4. Risk Management for R&D Projects 5. Configuration & Release Management 6. Change and Claim Management (incl. Patents) 7. Quality Management (incl. CMMI) 8. KPIs and Scorecards 9. Large R&D projects and Cross Domain Projects 10. Management of R&D organizations 11. Engineering Communication 2 (German)
4 Case Studies
CS01: AMALTHEA tool chain – setup of the ITEA2 research project
CS05: M2M System – management of a ZIM project
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4 - Contact hours: 60 - Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: none
Skills trained in this course: methodological and personal skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work as homework (50%): project kickoff/release report and presentation
Teaching staff: Prof. Dr. Carsten Wolff, (Dr. Oliver Hempel)
6 Learning outcomes
6.1 Knowledge
Students know the basic body of knowledge for project management
Students know processes, methods and tools for risk management for R&D projects (e.g. FMEA, @risk)
Students know processes, methods and tools for configuration management (esp. from SW engineering)
Students know processes, methods and tools for change and claim management
Students know processes, methods and tools for quality management according to ISO9001 and TS16949
Students understand the importance of Reviews in R&D projects
Students understand the main challenges of large R&D projects 6.2 Skills
Students can tailor processes and methods to the respective projects
Students can apply the respective project management methodology
Students can assess R&D projects and can extract relevant characteristics
Students can develop new methods according to gaps in the existing methodology
Students can do the complete planning and preparation of a real project case
Students can develop relevant KPIs and scorecards for measuring effectiveness and efficiency
6.3 Competence - attitude
Students develop an attitude to project management according to engineering standards
Students show a quality attitude according to engineering standards
Students manage projects based on structured and well defined processes and in depth analysis
Students can achieve high effectiveness and efficiency in running complex and innovative R&D projects
Students understand the differences between small and large projects and act accordingly
7 Teaching and training methods
Lectures introducing concepts, methods and tools
Group work to train concepts and methods, to develop skills and to work on case studies
Home work to add contributions on a case study as group work
Presentations to communicate results
Presentation and discussion of an industry case by a partner company
8 Course mapping
Input for:
MOD-E10 – Automotive Systems Requires:
MOD1-03 - Embedded Software Engineering Connects to:
MOD1-04 – Requirements Engineering
MOD2-01 – Mechatronic Systems Engineering
MOD2-02 – Microelectronics & HW/SW Codesign
9 References
PMBOK® - 4th edition, PMI® 2008. Kerzner, Harold: Project Management: A Systems Approach to Planning, Scheduling, and Controlling, 10th edition, New York 2009 ICB - IPMA Competence Baseline, Version 3, PMA/GPM-Eigenverlag 1999 INCOSE – SE handbook
Signals & Control Systems 1 (MOD2-04) Code Number
10240/41
Workload
180 h
Credits
6
Semester
Sem. 2
Frequency
annually
Duration
1 Semester
1 Course Title
Signals & Control Systems
1
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Control theory is one major part of the description of the dynamic behavior of mechatronic systems. Control systems are the connection between the mechanical/physical world and the control task performed by the embedded system. The goal of this module is to enable students to interact with control system experts and to integrate their results into embedded and mechatronic systems. Cross Domain Engineering requires a deeper understanding of control tasks and the underlying principles of control theory, especially for digital control systems. A holistic view on control system topics is taught. The curriculum limited to linear systems and the course structure follows the book Modern Control Systems by Bishop/Dorf. An additional goal is to teach the use and the development of advanced tools for control system design.
3 Course Structure
1. State Variable Models
2. State Feedback Control Systems
3. Robust Control Systems
4. Digital Control Systems
5. Applications of the above
6. Control Engineering with Matlab/Simulink
4 Case Studies
CS04: Avionics Computer & Robots – Control Algorithms
CS04: Avionics Computer & Robots – MATLAB/Simulink implementation for Arm Type
Robots
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: higher mathematics
Skills trained in this course: theoretical and methodological skills
Assessment of the course: Written Exam at the end of the course (50%) and group work
as homework (50%) with Matlab/Simulink use case and demonstration/presentation
Teaching staff: Prof. Dr. Andreas Becker, (Prof. Dr. Jörg Thiem)
6 Learning outcomes
6.1 Knowledge
Knows relevant theoretical foundations of control theory
Know mathematical background of controllers
Is aware of critical limitations of control systems
6.2 Skills
Can model control systems for mechatronic systems
Can implement digital control systems into embedded systems
Can apply state of the art tools and can develop tools for control system design
Can select embedded system platforms according to controller requirements
6.3 Competence - attitude
Can discuss control system design for mechatronic systems with experts
Can lead cross domain design of control systems
Understands control system experts and translates between different domains
7 Teaching and training methods
Lectures & Exercises, Matlab/Simulink labs
e-learning modules on mathematics and control theory, tool tutorials
8 Course mapping
Input for:
MOD-E05 – Computer Vision
MOD-E011 – Signals & Control Systems 2
9 References
P. Corke: Robotics, Vision and Control, Springer, 2013
R. Bishop, R. Dorf: Modern Control Systems, Pearson Education, 2010
Research Project (Thesis) (MOD3-03) Code Number
10330/31
Workload
540 h
Credits
18
Semester
Sem. 3
Frequency
annually
Duration
1 Semester
1 Course Title
Research Project (Thesis)
Contact hours
0 SWS / 0 h
Self-Study
540 h
Planned Group
Size
25 students
2 Course Description
The research project is intended to introduce students into scientific research work in a bigger context. Students will participate in one of the ongoing research projects. They will contribute with an own sub project. The starting point is the definition of the research questions they want to answer and the selection of the appropriate methodology. The students will plan and execute their project independently with regular review and consulting. They will summarize their finding in a research project thesis (project report). The research project will be a preparation for further work on the master thesis. The intention of the research project is to familiarize with the research methodology in a certain scientific field and to formulate the scientific state of the art and the research questions. The student proves the ability to execute own and independent research on master level and with a certain complexity.
3 Course Structure
Students will select a topic from one of the ongoing projects in CPS and Embedded Systems.
The will get individual consulting and feedback. During the semester the students will write a
project thesis and present it in a colloquium at the end of the semester.
Excellent results are intended to be published and presented (oral or poster) at a conference
(can be done in connection with the master thesis, too).
4 Case Studies
None – topics will be selected from ongoing projects
5 Parameters
ECTS: 18
Hours of study in total: 540
Weekly hours per semester: only colloquium
- Contact hours: 40 (individual consulting and colloquium)
- Self-Study hours: 500
Course characteristics: compulsory
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: none
Skills trained in this course: theoretical, practical, methodological, and personal skills
Assessment of the course: project thesis about own research in an ongoing project as
individual homework + presentation in colloquium (100%)
Teaching staff: all professors
6 Learning outcomes
6.1 Knowledge
Knows state of the art in a certain scientific field
Knows open research questions in this field
Knows relevant literature
Knows methodology and tools to execute project
6.2 Skills
Can define and plan an own research project
Can apply appropriate research methodology
Can create own research findings
Can describe project execution, methodology and findings in a scientific report
6.3 Competence - attitude
Can run an own more complex scientific research project
Masters uncertainty and unknown topics in new area
Can present and defend results (in colloquium or at a conference)
7 Teaching and training methods
Project Work
Writing of a scientific report
Presentations to communicate and discuss the findings
E-learning course on scientific work and scientific writing
Individual review and feedback on papers and presentations
8 Course mapping
Input for:
MOD4-01 – Master Thesis + Colloquium
9 References
According to topic
Master Thesis + Colloquium (MOD4-04) Code Number
103
Workload
900 h
Credits
30
Semester
Sem. 4
Frequency
annually
Duration
1 Semester
1 Course Title
Master Thesis + Colloquium
Contact hours
0 SWS / 0 h
Self-Study
900 h
Planned Group
Size
25 students
2 Course Description
The master thesis is intended for the students to show their ability for scientific research work in a bigger context. Students will participate in one of the ongoing research projects. They will contribute with an own sub project and with own scientific results. The starting point is the definition of the research questions they want to answer and the selection of the appropriate methodology. The students will plan and execute their project independently with regular review and consulting. They will summarize their finding in a master thesis (scientific report). The intention of the master thesis is to apply the research methodology in a certain scientific field and to contribute own findings to that scientific field. The student proves the ability to execute own and independent research on master level and with a certain complexity. Furthermore, the master thesis proves the ability to summarize and publish the results according to scientific standards.
3 Course Structure
Students will select a topic from one of the ongoing projects in CPS and Embedded Systems.
The will get individual consulting and feedback. During the semester the students will write a
master thesis and present it in a colloquium at the end of the semester.
Excellent results are intended to be published and presented (oral or poster) at a conference.
4 Case Studies
None – topics will be selected from ongoing projects
5 Parameters
ECTS: 30
Hours of study in total: 900
Weekly hours per semester: only colloquium
- Contact hours: 60 (individual consulting and colloquium)
- Self-Study hours: 840
Course characteristics: compulsory
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: max. 1 module from semester 1 - 3 not finished.
Skills trained in this course: theoretical, practical, methodological, and personal skills
Assessment of the course: master thesis about own research in an ongoing project as
individual homework + presentation in colloquium
Teaching staff: all professors
6 Learning outcomes
6.1 Knowledge
Knows state of the art in a certain scientific field
Knows open research questions in this field
Knows relevant literature
Knows methodology and tools to execute project
Knows how to document new findings according to scientific standards
6.2 Skills
Can define and plan an own research project
Can apply appropriate research methodology
Can create own research findings
Can describe state of the art, methodology and findings in a scientific report
6.3 Competence - attitude
Can compare own findings with state of the art and do a critical discussion
Can run an own scientific research project and create new findings
Masters uncertainty and unknown topics in new area
Can present and defend results (in colloquium or at a conference)
7 Teaching and training methods
Project Work
Writing of a scientific report
Presentations to communicate and discuss the findings
E-learning course on scientific work and scientific writing
Individual review and feedback on papers and presentations
8 Course mapping
None – can be based on research project thesis
9 References
According to topic
E L E C T I V E S
Applied Embedded Systems (MOD-E01) .............................................................. 32
Biomedical Systems (MOD-E02)............................................................................ 34
SW Architectures for Embedded and Mechatronic Systems (MOD-E03) .......... 36
Signals and Systems for Automated Driving (MOD-E04) .................................... 38
Internet of Things (MOD-E05) ................................................................................ 41
Computer Vision (MOD-E06) .................................................................................. 43
Signals & Control Systems 2 (MOD-E07) .............................................................. 45
Formal Methods in Mechatronics (MOD-E08)....................................................... 47
System on Chip Design (MOD-E09) ...................................................................... 49
Automotive Systems (MOD-E10) ........................................................................... 51
Research Seminar (S) ............................................................................................. 53
Applied Embedded Systems (MOD-E01) Code Number
10401
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Applied Embedded
Systems 1
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Applied embedded systems such as embedded controllers for industrial (i.e. robotics) applications are surrounded from sensors and actuators. Together with other embedded systems they can be groups of networked computers, which have a common goal for their work. This course gives an overview about the recent state of the art in embedded and cyber physical systems. Each semester, a selected CPS application will be analyzed in depth. This can be from robotic, energy, mobile communications or industrial scenarios (industry 4.0). The student will learn how to explore and structure a certain application domain and how to map the acquired skills and knowledge to that particular domain. CPS applications will be selected from recent research projects.
3 Course Structure
1. Introduction to the application domain
2. Characteristics of CPS in the application domain
3. Architectures for application specific CPS
a. Standards
b. Platforms and Frameworks
c. Design methodology and processes
4. Domain specific languages (DSL) and applications
a. DSL engineering
b. Tools and Tool Chain Integration
5. Target Platforms and Code Generation
a. Code generation
b. Using real time operating systems (RTOS)
4 Case Studies
CS01: AMALTHEA tool chain – will be used for case study
A recent use case from a research project will be discussed
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: none
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work
as homework (50%): modeling and target mapping of an example with AMALTHEA
tools, demonstration and presentation
Teaching staff: Prof. Dr. Burkhard Igel, (Prof. Dr. Carsten Wolff)
6 Learning outcomes
6.1 Knowledge
Knows standards and platforms for specific domain
Knows target systems
Has acquired overview of target domain
6.2 Skills
Can describe relevant characteristics and challenges of application domain
Can model mechatronic systems for the domain
Can apply methodology and state of the art tools on real use cases
Can select tools and define tool chains and design flows
6.3 Competence - attitude
Can structure a real mechatronic systems design project
Can communicate and find solutions with domain experts
Understands issues from application domains and can integrate solutions into a holistic
design
7 Teaching and training methods
Lectures, Labs (with AMALTHEA tools), homework
Access to tools and tool tutorials
Access to recent research papers
8 Course mapping
Requires:
MOD1-02 – Distributed and Parallel Systems
MOD1-03 - Embedded Software Engineering
Connects to:
MOD-E02 – Biomedical Systems
MOD-E04 – SW Architectures for Embedded Systems
MOD-E03 – Automotive Systems
9 References
AMALTHEA documentation
Research papers of PIMES research group:
http://www.fh-dortmund.de/en/fb/3/forschung/pimes/Eigene_Veroeffentlichungen.php
Biomedical Systems (MOD-E02) Code Number
10402
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Biomedical Systems
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Biomedical Systems are a major application domain for mechatronic and embedded systems. Dortmund University of Applied Sciences and Arts has established a research centre on biomedical technology (BMT) in 2013. Research topics from this research centre and the research from pimes are defining the content of this module. Students will learn about the biomedical application domains and about embedded systems and signal processing in that domain. The course will be based on a recent research project.
3 Course Structure
1. Introduction on Biomedical Systems 2. Description of real biomedical data and modelling 3. Stochastic signals and statistical parameters 4. Linear time-variant signals 5. Methods for time-frequency-analysis (wavelets) 6. Applications on ECG-data 7. Applications on other selected data (EEG, motion analysis, foot pressure measuring) 8. Modeling and Implementation with Matlab/Simulink
4 Case Studies
CS06: Biomedical Systems - Artificial Hand: will be used for modeling with
Matlab/Simulink
CS06: Biomedical Systems - Long Term Analysis of Medical Signals: will be used for
modeling with Matlab/Simulink
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: higher mathematics, basics of embedded systems
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work
as homework (50%): modeling and target mapping of an example with Matlab/Simulink,
demonstration and presentation
Teaching staff: Prof. Dr. Thomas Felderhoff, (n.n.)
6 Learning outcomes
6.1 Knowledge
Knows standards and platforms for biomedical systems
Knows target systems
Has acquired overview of medical application domain
6.2 Skills
Can describe relevant characteristics and challenges of biomedical systems
Can model signal processing systems for biomedical use
Can apply methodology and state of the art tools on real use cases
Can select tools and define tool chains and design flows
6.3 Competence - attitude
Can structure a real signal processing project in medicine
Can communicate and find solutions with domain experts
Understands issues from biomedical application domain and can integrate solutions into
a holistic design
7 Teaching and training methods
Lectures, Labs (with Matlab/Simulink), homework
Access to tools and tool tutorials
Access to recent research papers
8 Course mapping
Requires:
MOD1-01 – Mathematics for Controls & Signals
MOD1-03 - Embedded Software Engineering
Connects to:
MOD2-04 - Signals & Control Systems 1
MOD-E01 – Applied Embedded Systems 1 & 2
MOD-E05 – Computer Vision
MOD-E04 – SW Architectures for Embedded Systems
9 References
Bruce, E.N.; Biomedical Signal Processing and Signal Modeling, Wiley-Interscience, 2000
Devasahayam, S.R.; Signals and Systems in Biomedical Engineering: Signal Processing and
Physiological Systems Modeling, Springer, 2012
Gacek, A. and Pedrycz, W.; ECG Signal Processing, Classification and Interpretation, Springer,
2012
Northrop, R.B.; Signals and Systems Analysis in Biomedical Engineering, CRC press, 2010
Rangayyan, R.M.; Biomedical Signal Analysis: A Case-Study Approach, Wiley-IEEE Press, 2001
Semmlow, J.; Signals and Systems for Bioengineers: A MATLAB-Based Introduction,
Academic Press, 2011
SW Architectures for Embedded and Mechatronic Systems (MOD-E03) Code Number
10403
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
SW Architectures for
Embedded and Mechatronic
Systems
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
The ongoing complexity increase in mechatronic solutions consequently leads to more complex embedded systems and embedded software. Therefore, advanced SW engineering methodology from large software development projects is consecutively applied in the embedded world, too. Software architectures help to structure, to manage and to maintain large embedded SW systems. They allow re-use, design patterns and component based development. In addition, specific topics like safety, SW quality, integration and testing are addressed by SW architectures and respective standards (e.g. AUTOSAR). In this module, students learn about the concepts and structure of SW architectures for embedded systems.
3 Course Structure
1. Characteristics of Embedded (and real-time) Systems 2. Motivation for Architectures for Embedded and Mechatronic Systems 3. Software Design Architecture for Embedded and Mechatronic Systems 4. Patterns for Embedded and Mechatronic Systems 5. Real-Time Building Blocks: Events and Triggers 6. Dependable Systems 7. Hardware's Interface to Embedded and Mechatronic Systems 8. Layered Hierarchy for Embedded and Mechatronic Systems Development 9. Software Performance Engineering for Embedded and Mechatronic Systems 10. Optimizing Embedded and Mechatronic Systems for Memory and for Power 11. Software Quality, Integration and Testing Techniques for Embedded and Mechatronic
Systems 12. Software Development Tools for Embedded and Mechatronic Systems 13. Multicore Software Development for Embedded and Mechatronic Systems 14. Safety-Critical Software Development for Embedded and Mechatronic Systems
4 Case Studies
CS01: AMALTHEA tool chain – front end will be used for modeling, Artop modeling tool
for AUTOSAR will be used
CS05: M2M System – architecture of the middleware will be used
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: programming, basics of embedded systems
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and individual
homework (50%): paper/essay on a recent research topic, presentation
Teaching staff: Prof. Dr. Stefan Henkler, (Prof. Dr. Martin Hirsch)
6 Learning outcomes
6.1 Knowledge
Knows concepts and structure of SW architectures for embedded systems
Knows standards and frameworks
Knows specific challenges (e.g. real time, functional safety)
6.2 Skills
Can define requirements and features for a specific problem
Can develop a SW architecture for a specific problem
Can model SW architectures with state of the art tools
Can apply SW architecture standards to structure a project
6.3 Competence - attitude
Ensures quality and safety for embedded SW
Can discuss and assess the advantages and disadvantages of different SW
architectures
Understands the main issues within research about SW architectures for embedded
systems
7 Teaching and training methods
Lectures, Labs (with AMALTHEA and Artop tools), homework
Access to tools and tool tutorials
Access to recent research papers
Presentation of an industry case by partner BHTC GmbH
8 Course mapping
Requires:
MOD1-02 – Distributed and Parallel Systems
MOD1-03 - Embedded Software Engineering
MOD2-01 – Mechatronic Systems Engineering
Connects to:
MOD-E01 – Applied Embedded Systems 1 & 2
MOD-E03 – Automotive Systems
9 References
Robert Oshana and Mark Kraeling, Software Engineering for Embedded Systems: Methods, Practical Techniques, and Applications, Expert Guide, 2013 Bruce Powel Douglass. Doing Hard Time: Developing Real-Time Systems with UML, Objects, Frameworks and Patterns. Addison-Wesley, May 1999 Bruce P. Douglass, Real-Time Design Patterns: Robust Scalable Architecture For Real-Time Systems, Addison-Wesley, 2009 F. Buschmann, R. Meunier, H. Rohnert, P. Sommerlad, and M. Stal. Pattern Oriented Software Architecture. John Wiley & Sons, Inc., 1996
Signals and Systems for Automated Driving (MOD-E04) Code Number
10404
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Signals and Systems for
Automated Driving
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Automated driving requires the use of a multitude of sensors, controllers and actuators installed on the vehicle. Additionally, vehicle to vehicle and vehicle to infrastructure communication will be necessary. This course gives an overview about technologies used for automated driving. It starts with an overview about current R&D trends and then covers several sensor technologies with a special focus upon radar. Students will learn basic principles of stochastic signal processing and its application to tracking and mapping. Motion models and vehicle control technologies will be discussed to gain further insight into requirements for sensors and algorithms. Additional focus of this course is on architectures and infrastructures for automated driving. This includes bus interfaces and SW architectures as well as the basic principles of systems engineering. ISO 26262 as well as legal frameworks and their application to automated driving will be discussed. In addition to the lecture, exercises and small projects give additional insight into the technologies and concepts introduced in this course.
3 Course Structure
1. Technology overview 2. Sensors
a. Radar b. Lidar c. Ultrasonic d. Camera
3. Radar signal processing a. Detection b. Target estimation
4. State estimation a. Vehicle motion models b. Random processes c. Tracking d. Target classification e. Mapping
5. Actuators & Vehicle Control a. Bicycle model b. Longitudinal control c. Brake and steering systems
6. Architectures a. Bus interfaces b. Car-to-X c. Safety domain controllers d. AUTOSAR
7. System Engineering a. Quality Process standards b. Process models c. Requirement engineering d. SPICE
8. ISO 26262 a. Basics b. Concept phase c. Product development
9. Legal frameworks a. Vienna convention b. Relevant norms and legislation
4 Case Studies CS08: Radar Systems for Automated Driving
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4 - Contact hours: 60 - Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: higher mathematics, programming, signal processing
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work as homework (50%)
Teaching staff: Prof. Dr. Andreas Becker
6 Learning outcomes
6.1 Knowledge
Knows common driver assistance components and architectures
Knows basic signal processing algorithms for radars
Knows state estimation algorithms
Knows basics of related system engineering 6.2 Skills
Can develop tracking algorithms
Can develop radar signal processing algorithms
Can analyze requirements for subsystems of automated driving 6.3 Competence – attitude
Understands the challenges in the development of automated driving and can discuss with experts from different domains
Can lead development of subsystems for automated driving
Can lead system level tests for automated driving
7 Teaching and training methods
Lectures, Labs (with Matlab/Simulink)
Access to tools and tool tutorials
Access to recent research papers
Company visit
8 Course mapping
Requires:
MOD1-01 - Mathematics for Controls & Signals Connects to:
MOD1-04 – Requirements Engineering
MOD2-01 – Mechatronic Systems Engineering (MOD2-01)
MOD-E03 – Automotive Systems
MOD-E05 – Computer Vision
9 References
Winner et al., Handbook of Driver Assistance Systems, Springer reference, 2016 Pebbles, Radar Principles, John Wiley & Sons, 1998
Bar-Shalom et al., Estimation with Applications to Tracking and Navigation, John Wiley & Sons, 2001 Maurer et al., Autmotive Systems Engineering, Springer 2013
Internet of Things (MOD-E05) Code Number
10405
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Internet of Things
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Internet of things (IoT) is a fundamental building block for digitization and the upcoming information society. This course provides insights into key IoT-technologies including embedded systems, networks and cloud computing. For the selection of use cases and technologies the course focuses on the area of Edge Computing. Within this area student will learn about latency analysis and optimization in distributed systems. Last not least, the course offers hands on experiences with IoT and Edge Computing technologies through focused team projects and homework assignments.
3 Course Structure
1. Introduction
2. Real-time Embedded Systems
3. Real-Time Networking
4. Cloud Computing
5. Edge Computing
4 Case Studies
CS11: Edge Sensor Fusion
CS12: Gabriel - Edge Computing Platform for Wearable Cognitive Assistance
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: Basics in embedded systems, networks and
programming
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work
as homework (50%)
Teaching staff: Prof. Dr. Rolf Schuster
6 Learning outcomes
6.1 Knowledge
Knows concepts and architectures of real-time embedded systems
Knows key aspects of real-time networking
Has acquired overview of cloud computing and selected cloud platforms
6.2 Skills
Can implement, deploy and test simple IoT-systems
Can set-up and utilize a cloud system
Can analyze the E2E latency in distributed systems
6.3 Competence - attitude
Can design a simple IoT system for a given set of requirements
Can structure an IoT development project regarding function and time
Can propose and implement measures to reduce latency in a distributed system
7 Teaching and training methods
Lectures, group project, homework
Access to tools and tool tutorials
Access to recent research papers
8 Course mapping
Requires:
MOD1-02 – Distributed and Parallel Systems
MOD1-03 – Embedded Software Engineering
MOD1-05 – Introduction to Embedded System Design
Connects to:
MOD2-01 – Mechatronic Systems Engineering
MOD-E04 – Signals and Systems for Automated Driving
MOD-E06 – Computer Vision
MOD-E01 – Applied Embedded Systems 1
MOD-E10 – Automotive Systems
9 References
Peter Marwedel: Embedded System Design, 2nd Edition, Springer, 2011
Andrew S. Tanenbaum, David J. Wetherall: Computer Networks, 5th Edition, Pearson Education,
2014
Thomas Erl, Zaigham Mahmood, Ricardo Puttini, Cloud Computing, Prentice Hall, 2013
Computer Vision (MOD-E06) Code Number
10406
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Computer Vision
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Computer Vision is both a basic technology and an application domain for mechatronic and embedded systems. It is used in automotive systems, robotics and biomedical systems. This module focus on the use in the biomedical application domain since Dortmund University of Applied Sciences and Arts has established a research centre on biomedical technology (BMT) in 2013. Research topics from this research centre and the research from pimes are defining the content of this module. The module introduces the basic algorithms and components for computer vision systems. In addition, students will learn about the application of that knowledge in the biomedical domain. The course will involve topics from a recent research project.
3 Course Structure
1. Introduction 2. Position and Orientation 3. Light and Color 4. Image Creation 5. Image Processing 6. Feature Extraction 7. Multiple Images 8. Advanced Topics and Applications
4 Case Studies
CS10: Avionics Computer & Robots – Vision-Based Control
CS06: Biomedical Systems - Medical Imaging Techniques: Algorithms and
implementation
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: higher mathematics, basics of embedded systems
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work
as homework (50%): modeling and target mapping of an example with Matlab/Simulink,
demonstration and presentation
Teaching staff: Prof. Dr. Jörg Thiem, (Dr. Roland Brockers)
6 Learning outcomes
6.1 Knowledge
Knows standards and platforms for computer vision
Knows cameras, components, target systems
Has acquired overview of algorithms and methods
6.2 Skills
Can model signal processing path for computer vision
Can apply methodology and state of the art tools for computer vision
Can adapt and modify/parameterize relevant algortihms
6.3 Competence - attitude
Can structure a real computer vision project
Can integrate cameras and vision modules into mechatronic systems
Can analyze mechatronic systems and derive requirements for computer vision
7 Teaching and training methods
Lectures, Labs (with Matlab/Simulink), homework
Access to tools and tool tutorials
Access to recent research papers
8 Course mapping
Requires:
MOD1-01 – Mathematics for Controls & Signals
MOD1-03 - Embedded Software Engineering
MOD2-02 – Microelectronics & HW/SW-Codesgin
MOD2-04 – Signals & Control Systems 1
Connects to:
MOD-E01 – Applied Embedded Systems 1 & 2
MOD-E02 – Biomedical Systems
MOD-E04 – SW Architectures for Embedded Systems
MOD-E03 – Automotive Systems
9 References
P. Corke: Robotics, Vision and Control, Springer, 2013
R. Szeliski: Computer Vision: Algorithms and Applications, Springer, 2011
E. Gopi: Digital Signal Processing for Medical Imaging Using Matlab, Springer, 2013
Signals & Control Systems 2 (MOD-E07) Code Number
10407
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Signals & Control Systems
2
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Control theory is one major part of the description of the dynamic behavior of mechatronic
systems. Control systems are the connection between the mechanical/physical world and the
control task performed by the embedded system.
This module extends the concepts from Signals & Control Systems 1 (MOD2-04) to systems
with states that are not directly measurable and/or noise corrupted. For this purpose, observer
structures, estimation and adaptive signal processing concepts are reviewed. Emphasis is put
on digital control and signal processing to path the way to embedded processing.
Based on those concepts, the linear quadratic controller is dealt with as one example to deal
with noisy measurement and control signals. Furthermore, in order to incorporate control
constraints, modern control strategies like model predictive control are studied.
The goal of this module is to enable students to interact with control system experts and to
integrate their results into embedded and mechatronic systems under consideration of real-
world constraints.
3 Course Structure
1. State Variable Feedback Control Systems
2. Optimal control
3. Robust Control Systems
4. Digital control
5. Adaptive Signal Processing
6. State estimation
7. Linear Quadratic Gaussian Control
8. Model Predictive Control
9. Applications of the above
10. Control Engineering with Matlab/Simulink
4 Case Studies
CS04: Avionics Computer & Robots – Control Algorithms
CS04: Avionics Computer & Robots – MATLAB/Simulink implementation for Arm Type
Robots
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: compulsory
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: higher mathematics
Skills trained in this course: theoretical and methodological skills
Assessment of the course: Written Exam at the end of the course (50%) and group work
as homework (50%) with Matlab/Simulink use case and demonstration/presentation
Teaching staff: Prof. Dr. Andreas Becker, (Prof. Dr. Jörg Thiem)
6 Learning outcomes
6.1 Knowledge
Knows relevant theoretical foundations of state variable compensators
Knows concepts of optimal and robust control
Knows approaches of adaptive signal processing and state estimation
Knows concepts of predictive control
6.2 Skills
Can model complex control systems for mechatronic systems
Can estimate states that are not measurable
Can apply modern concepts like model predictive control
Can select embedded system platforms according to controller requirements
6.3 Competence - attitude
Can discuss control system design and signal processing for mechatronic systems with
experts
Understands control system experts and translates between different domains
Can lead cross domain design of control systems
7 Teaching and training methods
Lectures & Exercises
Matlab/Simulink labs
Tool tutorials
8 Course mapping
Requires:
MOD2-04 – Signals & Control Systems 1
Connects to:
MOD-E04 – Signals and Systems for Automated Driving
MOD-E05 – Computer Vision
9 References
Stergiopoulos, Advanced Signal Processing, CRC Press, 2009
Kouvaritakis, Cannon, Model Predictive Control, Springer, 2015
P. Corke: Robotics, Vision and Control, Springer, 2013
R. Bishop, R. Dorf: Modern Control Systems, Pearson Education, 2010
Kay, S.; Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory, Prentice
Hall,1993
Formal Methods in Mechatronics (MOD-E08) Code Number
10408
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Formal Methods in
Mechatronics
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Software has become the driving force in the development of self-optimizing mechatronic
systems. Such systems include hard-realtime coordination, which is realized by software, at
the network level between distributed components as well as controllers which are more and
more implemented by software. The communication goes beyond the use of system and
environmental data from controllers. If necessary, complex status information about
appropriate protocols and communication channels are exchanged, which themselves can
massively influence the underlying behavior of the individual components. This development
leads to extremely complex hybrid (discrete / continuous) software. In addition, self-optimizing
mechatronic systems are often used in safety-critical environments. This enforces the use of
formal verification techniques to ensure the correctness of specified properties.
In the course concepts and methods for the modelling and verification of these mechatronic
systems are introduced and formally described. In order to enable an efficient verification for
such mechatronic systems, techniques like abstraction, decomposition as well as rule-based
modelling are introduced. Here, these non orthogonal techniques are skillfully combined. One
aim is to handle all models specified by all different domains. The presented approach for the
model-based verification of mechatronic systems is massively characterized by the integration
of efficient verification techniques for the different domains, based on their domain specific
model-based knowledge.
3 Course Structure
1. Motivation: a. What are Formal Methods? b. Why should we use Formal Methods? c. When in the overall development process should we use Formal Methods?
2. Model Checking 3. Theorem Proving 4. Testing 5. Formal Verification in practice: The MechatronicUML Approach 6. Recent Research: literature review 7. AMALTHEA Methodology and Tool Chain
4 Case Studies
CS01: AMALTHEA tool chain – will be used to integrate formal verification tools
CS02: HVAC control system demonstrator – will be used as example
CS07: Rail Cab – will be used as an example
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: programming
Skills trained in this course: theoretical and methodological skills
Assessment of the course: Written Exam at the end of the course (50%) and group work
as homework (50%): verification of an example, demonstration and presentation
Teaching staff: Prof. Dr. Martin Hirsch, (Prof. Dr. Stefan Henkler)
6 Learning outcomes
6.1 Knowledge
Knows methodology of formal verification
Knows relevant theoretical background
Knows specific requirements
6.2 Skills
Can methods on use case
Can model verification artefacts (e.g. properties)
Can use MechatronicUML approach and tools
6.3 Competence - attitude
Can research on state of the art and theoretical background
Can demonstrate and discuss results in group
Can structure scientific field and get overview
7 Teaching and training methods
Lectures, Labs (with MechatronicUML), homework
Access to recent research papers
Literature review and discussion of results
8 Course mapping
Requires:
MOD1-02 – Distributed and Parallel Systems
MOD1-03 - Embedded Software Engineering
MOD2-01 – Mechatronic Systems Engineering Connects to:
MOD-E04 – SW Architectures for Embedded Systems
9 References
Spivey: The Z Reference Manual (http://spivey.oriel.ox.ac.uk/mike/zrm/zrm.pdf)
E. Clarke et al.: Model Checking, MIT Press
T. Fischer, J. Niere, L. Torunski, and A. Zündorf: Story Diagrams: A new Graph Rewrite Language based on the Unified Modeling Language. In Proc. of the 6th International Workshop on Theory and Application of Graph Transformation (TAGT), Paderborn, Germany, 1998
W. Reisig: Petrinetze: Modellierungstechnik, Analysemethoden, Fallstudien. Vieweg+Teubner, 2010
J. Bengtsson, W. Yi: Timed Automata: Semantics, Algorithms and Tools. In Lecture Notes on Concurrency and Petri Nets. W. Reisig and G. Rozenberg (eds.), LNCS 3098, Springer-Verlag, 2004
T. Eckart, C. Heinzemann, S. Henkler, M. Hirsch, C. Priesterjahn, W. Schäfer: Modeling and verifying dynamic communication structures based on graph transformations. Computer Science - Research and Development 28(1): S. 3-22, Feb. 2013
M. Hirsch: Modell-basierte Verifikation von vernetzten mechatronischen Systemen. Dissertation, Logos Verlag, 2008
System on Chip Design (MOD-E09) Code Number
10409
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
System on Chip Design
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
This course introduces Systems on Chip with a strong focus on Multi- and Many-core Systems on Chip (SoC) The course deals both with the technology and the building blocks of SoCs and with the design process and tool chain. Complex SoCs are the basic hardware platform for embedded systems. Their development is a major area for research about tools, methodologies and development processes. ASIC development projects and tool chains are complex in size, technology and project structure. Students learn about the architecture and capabilities of SoCs and about the design flow.
3 Course Structure
1. Main building blocks of SoCs a. IP-cores (processors, communication, memories, sources for IP-cores) b. on-chip communication (topologies, wishbone) c. system definition d. ESL: electronic specification language e. on-chip vs. off-chip memory f. debugging methodologies
2. Multicore and Manycore architectures a. ASIP and Networks on Chip (NoC)
3. ASIC design flow a. Design entry (VHDL) b. Pre-silicon verification c. Synthesis & technology libraries d. Layout and signal integrity e. Timing closure f. Power routing, clocks and resets g. Semiconductor test & production
4 Case Studies
CS03: CoreVA – ASIC implementation
Europractice tools chain (Cadence and Mentor Graphics) and technology library
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: programming, electronics
Skills trained in this course: practical and methodological skills
Assessment of the course: Written Exam at the end of the course (50%) and group work
as homework (50%): implementation of a CoreVA based design, demonstration and
presentation
Teaching staff: Prof. Dr. Peter Schulz, (Prof. Dr. Carsten Wolff)
6 Learning outcomes
6.1 Knowledge
Knows basic components of SoCs
Knows modern multicore/manycore architectures and ongoing research
Knows SoC design tools and tool chains
6.2 Skills
Can develop an SoC from building blocks
Can move a simple design through the whole tool chain
Can select technology, constraints and layout
6.3 Competence - attitude
Understands ASIC design flow
Can consult on SoC selection and decision about SoC design
Masters set up and configuration of complex ASIC design tool chains
7 Teaching and training methods
Lectures, Labs (with Europractice tools), homework
Access to tool chains and tool tutorials
Access to recent research papers
Visit at Bielefeld university (CITEC) and Intel Mobile Communications GmbH
8 Course mapping
Requires:
MOD1-02 – Distributed and Parallel Systems
MOD1-03 - Embedded Software Engineering
MOD2-02 – Microelectronics & HW/SW-Codesign
Connects to:
MOD-E04 – SW Architectures for Embedded Systems
MOD-E06 – Formal Methods in Mechatronics
9 References
Neil H.E. Weste, David Money Harris: “Integrated Circuit Design”, Pearson, 2011
Clive “Max” Maxfield (Editor): “FPGAs World Class Designs”, Newnes / Elsevier, 2009
Jack Ganssle (Editor): “Embedded Systems World Class Designs”, Newnes / Elsevier, 2008
Peter J. Ashenden: “Digital Design – An Embedded Systems Approach Using VHDL“, Morgan Kaufmann / Elsevier, 2008
Peter J. Ashenden: “The Designer’s Guide to VHDL 2nd Edition”, Morgan Kaufmann / Academic Press, 2002
Peter J. Ashenden: “The System Designer’s Guide to VHDL-AMS”, Morgan Kaufmann / Elsevier, 2003
Automotive Systems (MOD-E10) Code Number
10410
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Automotive Systems
Contact hours
4 SWS / 60 h
Self-Study
120 h
Planned Group
Size
25 students
2 Course Description
Automotive systems are a major application domain for mechatronic and embedded systems. Due to the complexity and the specific requirements (e.g. safety) the domain specific engineering is well elaborated and leading edge in the embedded systems industry. The research centre pimes deals with various automotive partners and research projects. This course gives an overview about the recent state of the art in automotive systems and transfers recent findings into teaching. The student will learn how to explore and structure a certain automotive application and how to map the acquired skills and knowledge to that particular domain. Furthermore, the students will learn about domain specific standards, processes and frameworks.
3 Course Structure
1. Automotive Standards: e.g. AUTOSAR, Quality Standards, Automotive Spice
2. Automotive development processes
3. Tools in Automotive Engineering (ML/SL, Doors, Enterprise Architect)
4. Automotive Supply Chain
5. Automotive Software Development
6. Functional Safety
7. Testing and Verification
8. Product Qualification
9. Application Examples
10. AMALTHEA Methodology and Tool Chain
4 Case Studies
CS01: AMALTHEA tool chain – will be used for the whole design flow
CS02: HVAC control system demonstrator – will be used for modeling with
Matlab/Simulink
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: 4
- Contact hours: 60
- Self-Study hours: 120
Course characteristics: elective
Course frequency: every year - winter semester
Maximal capacity: 25 students
Course admittance prerequisites: programming, basics of embedded systems
Skills trained in this course: theoretical, practical and methodological skills
Assessment of the course: Oral Exam at the end of the course (50%) and group work
as homework (50%): set up of an automotive system development project, modeling and
target mapping of an example with AMALTHEA tools, demonstration and presentation
Teaching staff: Prof. Dr. Carsten Wolff, (Prof. Dr. Erik Kamsties)
6 Learning outcomes
6.1 Knowledge
Knows standards and platforms for automotive systems
Knows target systems
Knows specific requirements (e.g. safety)
Has acquired overview of automotive application domain
6.2 Skills
Can develop automotive software with the AMALTHEA tool chain
Can model an automotive system according to standards
Can select tools and define tool chains and design flows
6.3 Competence - attitude
Can structure a real automotive system development project
Can communicate and find solutions with automotive experts
Ensures quality and safety of applications
7 Teaching and training methods
Lectures, Labs (with AMALTHEA tools and Matlab/Simulink), homework
Access to tools and tool tutorials
Access to recent research papers
Company visit at one of the partner companies (Bosch, BHTC)
8 Course mapping
Requires:
All semester 1 & 2 courses
Connects to:
MOD-E01 – Applied Embedded Systems 1 & 2
MOD-E06 – Computer Vision
MOD-E03 – SW Architectures for Embedded Systems
9 References
Klaus Hoermann, Markus Mueller, Lars Dittmann, Joerg Zimmer: Automotive SPICE in Practice.
Rocky Nook Inc., US, 2008
Joerg Schaeuffele, Thomas Zurawka: Automotive Software Engineering, Bertrams, 2005
Markus Maurer, Hermann Winner (Eds.): Automotive Systems Engineering, Springer, 2013
Research Seminar (S) Code Number
10411
Workload
180 h
Credits
6
Semester
Frequency
annually
Duration
1 Semester
1 Course Title
Research Seminar
Contact hours
0 SWS / 0 h
Self-Study
180 h
Planned Group
Size
25 students
2 Course Description
The research seminar is intended to introduce students into scientific writing, literature review and into discussion of research questions in a scientific auditory. Students will write a scientific report or essay on a recent research topic from one of the ongoing projects. The seminar will be a preparation for further work on the research project thesis and the master thesis. The intention of the seminar is to explore a certain scientific field and to formulate the scientific state of the art and the open research questions. A motivation for students will be the possibility to publish and present excellent papers at a small conference. Instead of the seminar and the homework, the students can attend a third elective module.
3 Course Structure
Students will select a topic from one of the ongoing projects in CPS and Embedded Systems.
The will get individual consulting and feedback. During the semester the students will write a
paper/report and present it in a colloquium at the end of the semester.
Excellent papers will be published and presented (oral or poster) at the Dortmund International
Research Conference at FH Dortmund.
4 Case Studies
None – topics will be selected from ongoing projects
5 Parameters
ECTS: 6
Hours of study in total: 180
Weekly hours per semester: only introduction course and colloquium
- Contact hours: 20 (individual consulting and colloquium)
- Self-Study hours: 160
Course characteristics: compulsory
Course frequency: every year - summer semester
Maximal capacity: 25 students
Course admittance prerequisites: none
Skills trained in this course: theoretical, methodological, and personal skills
Assessment of the course: Paper/essay on literature review about recent research as
individual homework + presentation in colloquium (100%)
Teaching staff: all professors
6 Learning outcomes
6.1 Knowledge
Knows state of the art in a certain scientific field
Knows open research questions in this field
Knows relevant literature
6.2 Skills
Can analyze scientific literature based on a comprehensive review
Can write a paper/report according to scientific standards
Can synthesize findings in own words
6.3 Competence - attitude
Can run an own small scientific research project
Can present and defend results at a conference
7 Teaching and training methods
Literature review and Essay writing
Presentations to communicate and discuss the findings
E-learning course on scientific work and scientific writing
Individual review and feedback on papers and presentations
8 Course mapping
Input for:
MOD3-02 – Research Project Thesis
MOD4-01 – Master Thesis + Colloquium
9 References
German and European Research Agendas, recent research papers
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