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Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Atlanta, Georgia, USA
November 4-5, 2011
Volume 10
Driving Force
Market D namics
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ASME DISRICT F, EARLY CAREER
TECHNICAL JOURNAL
Volume 10
Presented at
ASME District F, Early Career Technical Conference
Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Atlanta, Georgia
November 4-5, 2011
Sponsored by
The ASME Old Guard
Georgia Institute of Technology
HOLTEC International
Unified Brands
ASME District F
ASME Atlanta SectionASME Birmingham Section
ASME Mississippi Section
Technical Committee
Dr. J. Donnell
Dr. P. Durbetaki
Dr. S. Jeter
Chair of Coordinating CommitteeDr. K. R. Rao
For more information about ECTC 2011 visit: http://districts.asme.org/DISTRICTF/ECTC/
THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS SOUTHEAST DISTRICT F
THREE PARK AVENUE, NEW YORK, NY 10016
iii
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Copyright 2011 by ASME Southeast District F, Three Park Avenue,New York, NY 10016
All rights reserved. Printed in the United States of America. Except as permitted under the
United States Copyright Act of 1976, no part of this publication may be reproduced or
distributed in any form or any means, or stored in a data base or retrieval system, without
the prior written permission of the publisher.
ASME shall not be responsible for statements or opinions advanced in papers or printed in
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ASME District F encompasses the states of Alabama, Delaware, District of Columbia,
Florida, Georgia, Maryland, Mississippi, North Carolina, South Carolina, Tennessee and
Virginia. For more information go tohttp://districts.asme.org/DistrictF.htm.
ISBN 978-1-4507-9223-3
ASME 2011 Early Career Technical Journal - Vol. 10 iv
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FOREWORD
Journal of ASME District F Early Career Technical ConferenceNovember 4 5, 2011
One of the primary objectives of American Society of Mechanical Engineers (ASME) is the dissemination of
technical information. Pursuant to this goal the ASME Southeastern Region (Region XI) initiated the
Graduate Student Technical Conferences (GSTC) in 1991 when Dr. P. Durbetaki was the Vice President of
Region XI; it was held in the School of Mechanical Engineering, Georgia Institute of Technology, Atlanta,GA. The Regional Technical Conference (RTC) was initiated in 2001 by the Regional Vice President Dr. K.
R. Rao and with the help of the Region XI Operating Board.
The first RTC was held on April 6, 2002, in Jackson, Mississippi with the participation of students across all
five states (Florida, Georgia, Alabama, Tennessee and Mississippi) of the former ASME Region XI. The
quality of the reviewed papers, published in the four volumes of the Regional Technical Journal from 2002through 2006, called for this Conference to be renamed in 2006 as the Early Career Technical Conference
(ECTC) indicating the changing demands of the times. On October 6 and 7, 2006 ECTC was held, in
Jackson, MS with a robust support of Entergy Operations. Inc. In 2007, for the first time, ECTC was held ina University, Florida International University (FIU), in Miami, Florida. ECTC 2008 was also held at FIU,
Miami, Florida with invitations being extended to all of the Universities invited for ECTC 2007. Dr. Yong
Tao was the Chair, Technical Committee and Editor for ECTC 2007 and Dr. Sabri Tosunoglu for ECTC
2008. The generous financial support of ASME Old Guard funded the registration and even offset a part ofthe travel expenses of the paper presenters attending these conferences.
The success of the preceding ASME District F Early Career Technical Conferences prompted the organizersto look beyond the frontiers of the District. For the current ECTC 2010 invitations were sent to all of the
universities affiliated with all of the ASME Districts A to J, that were even beyond USA. It is gratifying to
mention that we reached the cap of 35 paper presenter entries that we targeted. Of these we have 14submittals from outside USA. Thus, the theme for this Conference ECTC Opens the Window to Outside
USA has been amply demonstrated! The Department of Mechanical Engineering of the University of
Alabama at Tuscaloosa, Alabama hosted the ECTC 2009. The Editor of the ECTC 2009 Journal establishedan elaborate review process similar to the process used by ASME Technical Divisions. Papers weredistributed to Associate Editors of the Editorial Board based on their areas of expertise, and they obtained
reviews for each paper by expert reviewers in the field. The Editorial Board made a significant effort to
ensure that the review process for each paper followed the criteria and deadlines. Because of this rigorousrequirement, the papers submitted were accepted as either for presentation only at the conference, or for both
presentation and journal publication. Authors whose papers were rated acceptable for publication in the
Technical Journal were required to make corrections and enhancements, based on reviewers comments.
The Woodruff School of Mechanical Engineering of the Georgia Institute of Technology in Atlanta, Georgia became
the host for the ECTC 2011. The review and selection process initiated with ECTC 2009 and continued with ECTC
2010, was followed for the ECTC 2011 Conference.
This ECTC 2011 Journal is a compilation of twenty-three (23) reviewed and accepted technical papers from USA,England and Asia.
K. R. Rao, PhD, PE., Fellow ASME, FIE, CEChairECTC Coordinating Committee
P. Durbetaki, PhD, Life Fellow ASMECo-ChairECTC Coordinating Committee
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ACKNOWLEDGEMENTS
ASME District F Early Career Technical JournalNovember, 2011
Many individuals contributed to the success of this Early Career Technical Conference and this
ECTC 2011 Journal.
The Coordinating Committee express gratitude and appreciation to Dr. K R. Rao, Chair, Dr. P.
Durbetaki, Co-chair of the Conference Coordinating Committee, and members of the Conference
Committee Dr. Bill Wepfer, Dr. Sheldon Jeter, Dr. Jeffrey Donnell, Michael D. Stewart, Dr.
Yong Tao and John Mulvihill for their tireless and dedicated efforts in organizing theconference, directing the review process and overseeing the presentations. Their hard work has
made this journal possible.
ECTC 2011 Journal Editorial Board comprises of theEditors, Dr. J. Donnell, Dr. P. Durbetaki
and Dr. S. Jeter. Like the previous years ECTC 2009 and ECTC 2010, the ECTC 2011 had
separate tiers of Associate Editors from Faculty and from Professionals. We thank all themembers of the Editorial Board and invited reviewers, who spent countless hours of their own
time reviewing and critiquing the papers to ensure a quality publication. The advice and
guidance of Dr. K. R. Rao in the preliminary set up of the web site, establishment of the finalweb site, revisions of the web site as needed, as well as with reviews and editorial matters is
hereby acknowledged and appreciated. Dr. Yong Tao is acknowledged and appreciated as web
master for ECTC 2011; his help is very much recognized.
ASME District F and the Coordinating Committee thank ASME staff, Christina Perakis, JessicaAlbert, Deidra L. Hackley, and Nicole Alston for the administrative support to the success of the
conference and the publication of this journal.
Finally, we all owe a great debt of gratitude to our principal sponsors Woodruff School of
Mechanical Engineering of the Georgia Institute of Technology, the ASME Old Guard,HOLTEC International, Unified Brands, ASME District F, ASME Mississippi Section, ASME
Atlanta Section and ASME Birmingham Section, and Faculty Support from the Faculty of
Georgia Tech, ME.
-- 2011 ASME ECTC Coordinating Committee --
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The ASME Early Career Technical Conference Committee and
Technical Journal Editorial Board Express their Appreciation
to the Following Sponsors for SupportingECTC 2011
WWooooddrruuffffSScchhoooollooff
MMeecchhaanniiccaallEEnnggiinneeeerriinngg
Georgia Institute
ofTechnology
ASME DISTRICT F
ASME ATLANTA SECTION
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Table of Contents
INSIDE COVER iii
FOREWORD v
ACKNOWLEDGEMENTS vi
SPONSORS vii
1. MOHAMMED ALIand ESSAM A. IBRAHIM 1Computational Investigation of Particle Settling Effects on Inhaled Submicron Bioaerosol Deposition in the Human Lung
2. THOMAS M. STONE, SEUNG-KYUM CHOI, PhD andHEMANTH AMARCHINTA 6Dynamic Modeling and Parameter Estimation for Impact Analysis and Robust Packaging Design
3.
POONAM SAVSANIand VIMAL SAVSANI 14
Motion Tracking Through Machine Vision
4. ERIN E. McDONALD, GAIL D. JEFFERSON, PhD, ANDREW J. WHELTON, PhD andTINH NGUYEN, PhD 22Significance of MWCNT and TiO2Nanofillers on Low-Density Polyethylene Thermal & Mechanical Properties:Applicability for Pipes Used In Water & Energy Systems
5. AEREL J. RANKIN, SVETLANA V. POROSEVA andROB O. HOVSAPIAN 27Power Curve Data Analysis for Rim Driven Wind Turbine
6.
TEZESWI P. TADEPALLI andP. RAJU MANTENA 33Blast Response of Sandwich Composite Structural Panels
7. RODWARD L HEWLIN, Jr. andJOHN P. KIZITO 39Evaluation of the Effect of Simplified and Patient-Specific Arterial Geometry on Hemodynamic Flow in Stenosed CarotidBifurcation Arteries
8. IBRAHEEM R. MUHAMMADandJOHN P. KIZITO 45Evaluation of Pulse Jet Mixing Using a Scalar Quantity and Shear Stress
9. ABDUL SHAKOOR, ARSHAD MEHMOOD, KHIZAR AZAMand RIAZ AKBER SAYYED 53Manufacturing Knowledge Sharing: The Utilization of Knowledge Base Technologies in PLM
10. ADEEL KHALID 60Multidisciplinary Design Optimization of Aerospace Vehicle: Single Engine Rotorcraft
11. RICHARD OPOKU andJOHN P. KIZITO 68Optimal Liquid Film Thickness of an Evaporating Film Down an Inclined Plane for Maximum Heat Flux in Spray Cooling
Applications
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12.
YACOB M. ARGAW andJOHN P. KIZITO 73Similarity Solution for Flow Over a Wedge with Phase Change
13.
RAMBOD RAYEGAN andYONG X. TAO 78Solar Power Generation: Methods and Comparisons
14.
WUQAYAN ALWUQAYAN andSABRI TOSUNOGLU 87Fuzzy-Logic Health Monitoring Robots for the Elderly
15.
KWABENA A. KONADU and SUN YI 91Design of a Controllers for a Laser Beam Stabilizer Using PID and Observed-Based State Feedback Control
16.
CAITLIN PLUNKET, AUSTIN YUILL, DAVID BRANSCOMB andCHAD RODEKOHR, PhD 99Development and Testing of a Diamond Braided Eye Splice
17.
REETA WATTAL andSUNIL PANDEY 105Effect of Welding Parameters on Metallurgical Transformations of Aluminium Alloy 7005
18.
JAIME MUDRICH, ANDRES PACHECO, LEONARDO AMPIE andSABRI TOSUNOGLU 113Development of a Modular Companion Robot for the Elderly
19. NASTASSJA DASQUEandFREDERICK FERGUSON 120The Design and Validation of Waveriders Derived from Axisymmetric Flowfields
20. JOSE MATOS andSABRI TOSUNOGLU 126Peristaltic Crawler for Pipeline Unplugging
21.
TRENICKA ROLLE andRAMANA PIDAPARTI 136A Study of Tissue Strains Induced in Airways Due to Mechanical Ventilation
22.
FRANCIS S. FERNANDEZ, BADER ALE, ALFONSO PARRA andSABRI TOSUNOGLU 142Ocean Wave Energy Generator
23.
MANDEEP SHARMA, MATTHEW LOUSTEAU andINGMAR SCHOEGL 149An Experimental Study of Small-Sized Conical Spouted Beds
AUTHOR INDEX 157
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Clearly, it implies that terminal settling velocity is high for
large particles (Cc 1, and , g, are constant for a givenbioaerosol). Therefore mathematically, the GS depositioneffects on larger particles should be higher.
Figure 1 illustrates how the GS deposition becomes
significant for larger (either large aerodynamic diameter or
became large due to coagulation) particles and low inclinedangle () airways of the human lung. It also shows thatgravitational force on particle exceeds the influence of
Brownian diffusion force (FDiffusion), which is dominant for
smaller particles.
So far, direct in-vitro or in-vivo experiments to determine
the GS deposition effects in human respiratory systems arehardly available. Published literatures only include deposition
fractions (out of total inhaled aerosols) in the distal airways and
alveolar duct regions, where the GS and diffusion may occurprimarily. Based on a comprehensive literature survey on lung
deposition mechanisms it is found that the GS deposition in the
tracheobronchial (TB) and distal airways, and the alveolar ducts
are estimated via analytical equations derived from the
sedimentation losses in flows between parallel plates or circulartubes/channels [5-7].
Computational fluid dynamics (CFD) simulations for the
GS deposition started in the early 1990s, focusing on distal
airways and alveolar ducts. One such simulation study for theasymmetric single bifurcating airway representing generations
15 and 16 showed that the GS on a 10 m particle was verymuch dependent on gravity angle for determining particle
distribution and localizes doses [8]. Using a 2-D symmetric six
generation CFD model, Darquenne and Prisk (2003) reported
that gravity is an important deposition mechanism for 0.5 and 1
m particles in the human acinus [9]. Using a 3-D CFD
simulation for the larger (dp 5 m) particle in the bronchial
airways (generations 6-9) Kleinstreuer et al. (2007) reportedthat, though the inertial impaction is still dominant depositionmechanism in these airway regions, the GS may become
significant with certain gravity angles ( 40o). Two other
studies employing 3-D simulation on alveolar ducts
(generations 18-22) concluded that the total deposition of
aerosol particles can be a function of the gravity angle, and theratio of the terminal setting velocity to mean lumen flow
velocity [10,11]. In summary, most of the CFD simulations
reported the dominance of GS while studying distal airways
and alveolar ducts. The effects of GS on the bioaerosoldeposition in the TB airways have not been thoroughly
investigated.
The aim of this study is to understand the GS of evaluatinginhaled bioaerosols in determining the extent to which GSaffects their transport and deposition characteristics in the upper
TB region. In order to examine this issue, the following
objectives were investigated.
1) Simulation of the effects of gravitational force, FGS, on
submicron particles while flowing through the trachea and main
bronchus. Flow conditions were turbulent through laminar
because the Reynolds number of trachea and main bronchi
ranges from 2214 to 1634 for an inhalation rate of 28.3 L/min2) Solving gravitational force equations of fluid dynamics by
employing object-oriented programming code C++. 3)
Calculation of the initial particle profiles, particle deposition
factors, inlet and wall mass flow rates by using MATLAB. 4)Discretization of all transport equations were done to at least
second order accurate in space. 5) Model results were analyzed
and compared with data reported in the literature.
MATERIALS AND METHODSAirway Geometries
As alluded to in the Introduction section, the GS effects on
particles of various sizes were simulated for the TB regionsFigure 2 illustrates the trachea and main bronchus, two
consecutive lung airway generations (generations 0-1 or G0 and
G1).
The geometric dimensions of the TB model are close to
those given by Weibel (1963) for adults with lung volume 3500
mL. The simulation parameters are listed in the following
Table 1.
Table 1.Summary of the simulation parameters of the in-silicotracheobronchial model.
Simulation Parameter TracheaMain
Bronchus
Lung generation (index) 0 1
Number of airway 1 2
Airway diameter, m 0.018 0.0122
Airway length, m 0.12 0.048
Inhalation flow rate, Q, l/min 28.3 14.15
Bioaerosol flow velocity, m/s 1.86 2.02
Reynolds number,Re 2214 1634
Figure 2. Illustrations of the human lungstrachea and main bronchus (not to scale).
Trachea
Main
Bronchus
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Figure 3 shows 3-D views of a representative single
bifurcation TB regions. The inlet conditions of bioaerosol flow
and particle transport at G0 were selected from outlet in the
upstream Larynx.
Transport Equations
The Lagrangian particle transport (LPT) equation has beenwidely used to simulate the effects of various forces on aerosol
particle trajectories in the respiratory airway [12,13]. Others
estimate that a complete LPT to follow all individual particles
and air parcels as they travel through the entire lungs 24generations would require 10
17 floating point operations and
several terabytes of RAM [14]. In order to overcome this
limitation, simplifying assumptions have been made in the
various simulation studies [15-17].
In order to perform the LPT with a manageable number ofcomputational points, several assumptions were made. These
assumptions include (1) the aerosol particles are spherical in
shape, non-reactive, and stable (i.e., nonevaporating or
noncondensing); (2) Particles and air in the aerosol streamtravel at the same velocity since the submicron particles are
airborne within less than 0.001 second, i.e., negligible
relaxation time; (3) Inhaled aerosols are incompressible and
isentropic, i.e., no effects of thermophoretic anddiffusiophoretic forces; and (4) The human lung is a sequenceof branching circular pipes whose diameters are given in the
idealized Weibel (1963) lung morphometry.
The LPT can be expressed in the following general form.
)1()(and tudt
dx
FFFFdt
dum Braebp
=
+++=
In the above equation, u(t) is the particle velocity and mpis
the particle mass. The forces acting on individual particles
include body forces, Fb (e.g., inertia, gravitation) due to the
particles position in the aerosol streamline, surface forces (Fe)
(e.g., electrostatic space and image forces) due to interactionwith the surrounding particles and airway walls, adhesive
forcesFa(e.g., Van der Waals and capillary forces, interceptive
force), and Brownian diffusive forces, FBr due to irregularwiggling (Brownian) motion of aerosol particles and random
variations in the relentless bombardment of air molecules
against the particles.
Gravitational force on a spherical particle in the respiratory
airway can be determined by the following form of Newtonslaw.
)2(6
)( 3gdF
pgp
g
=
Since the particle density, p, is much larger than density o
fluid (air or gas), g, the equation (2) can be used with close
approximation of the gravitational force by assuming g = 0Moreover, the body force,Fb, is combined effects of inertia and
gravitation several considerations need to be addressed. Forexample, when determining the bioaerosol particle deposition
in the lung due to the GS only, Pich (1972) suggested ignoring
impaction or inertial forces. Finlay (2001) proposedconsidering Froude number (Fr) when taking into account the
importance of inertia force vs. gravitational force. Fr =
U/(gD)0.5,where Uis the mean velocity of the aerosolized fluid
flow, andDis the airway tube diameter. This expression clearlyshows that the GS deposition has impacts on overall particle
deposition in addition to the impaction in the TB airways with
largeFr values.
Numerical Methods
In order to solve governing mass and momentum
conservation equations, user-enhanced finite-volume-based
program, the CFD package Fluent 12.1 from Ansys, Inc wasemployed. The mesh topologies of the TB airway model were
determined by refining the meshes until the grid independence
of the flow field and the particle deposition fraction solutions
were achieved. All the computations were performed on an
Intel Core i7-860 8GB DDR3 workstation. The solutions of theflow field were assumed to be converged when dimensionless
mass and momentum residual ratios were
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Figure 5. The gravitational settling deposition ofsubmicron bioaerosol particle in the
tracheobronchial airways. Comparison betweensimulation results and Pich (1972) mathematical
model results.
0
2
4
6
8
10
12
14
16
0.1 0.2 0.4 0.6 0.8 1.0 2.0 4.0 6.0 8.0 10.0
Pich, 1972
Simulation
0
2
4
6
8
10
12
14
16
0.1 0.2 0.4 0.6 0.8 1.0 2.0 4.0 6.0 8.0 10.0
Pich, 1972
Simulation
DepositionEfficiency,
%
Aerodynamic Diameter, m
Figure 4. Velocity (cm/s) vector profiles ofinhaled bioaerosols in the trachea and main
bronchus regions of the human lung.
where
cos4
3
D
L
U
vsettling=
and being inclination angle measured relative to the
horizontal axis (i.e., = 0ofor horizontal tubular airway).
RESULTS AND DISCUSSIONComputational models on bioaerosol particle deposition
calculate the mean deposition in each lung generation using an
idealized equation for stable laminar airflows. However, the
airflows at bifurcations are very complicated [18], and far frombeing stable and laminar. A diseased lungs geometry and
ventilation differ from a healthy lung. Such differences affect
bioaerosol flow profiles through the lung airway. In order tosimulate the realism, this study adopted an approach of
modeling the affected flow profiles influence on gravitational
settling force on particles which ultimately effect inhaled
bioaerosol deposition.
In this computational model the inhalation cycle was
considered, and the deposition efficiency was defined in asimplified manner as the number of particles deposited in a
region of the lung divided by the number of particles enteringthe region. The model did not account for deposition due to
impaction, interception, Brownian diffusion, and electrostatic
force effects besides the GS as it will make the model run NP-
hard.
Most of the results found in the literature refer to aerosol
consisting of a collection of modispersed particles. In practicethis is hardly the case, and the bioaerosol particles are not only
having an aerodynamic size distributions but also electrostatic
charge distributions. Therefore, this study accountedpolydispersed aerodynamic size and electrostatic charge
distributions of aerosol particles.
Figure 4 depicts the profiles of velocity vector of theparticles inside TB regions at a steady inhalation flow rate o
28.3 L/min. Velocity characteristics are expressed as the
change of colors from blue (12 cm/s) to red (850 cm/s). It can
be seen that particle velocity reaches two log of magnitudefrom inlet point of trachea to near the bifurcation. The increase
of velocity can be explained by the combined effects of
impaction and gravity force. Larger particles tend to deposit in
the bifurcations due to the resulting impacts of both forces.The variation in velocity magnitude in the junction regions
will result in significantly higher residence time of particles in
the main bronchus (see Figure 4). Therefore, the particles in
this region will display more depositing tendency due the GSforce. In addition to the GS, literature reported that the greates
deposition by inertial impaction typically occurs at or near the
first carina, the dividing point or "hot spots" of trachea
bifurcation [4]. Our simulation results also showed thisphenomenon (sudden rise in velocity) for bioaerosols while
approached to the first carina.
The effect of gravitational force magnitude in particles is
presented in Figure 5. The simulated deposition efficiencies dueto the GS in the TB airway as a function of particle size(aerodynamic diameter) and inlet velocity match the correlation
(Eq. 3) very well. Particle deposition efficiency increases with
an increasing size or decreasing velocity because bothparameters not only cause increasing gravity but also longe
residence time. As the particles size increased from 1 micron to
10 micron, gravitational force becomes the more dominant
mechanism. It can be seen that the deposition almost increased
fifteen fold for particularly for larger (dp 10 m) particles
compared to the smaller (dp< 2 m) particles.
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In summary, the good agreements between simulation
findings and theoretical predictions instill confidence that the
present in-silico simulation model is sufficiently accurate to
analyze bioaerosol flow, particle transport and deposition in
human lung airways.In our previous study it was shown that the accounted
charged particles were carrying both positive and negative
charges [19]. Bipolarly charged particles experience attractionamong each other due to Coulombic attraction forces, which
allowed smaller particles to grow larger and therefore tend to
deposit more efficiently due to the GS (because of formation to
a larger mass). The agglomeration due to electromagnetic
forces is consistent with the physical phenomenon ofpersuasion of electromagnetic forces between oppositely
charged particles [12].
A primary implication of this investigation is thatsedimentation or gravitational force on particles may be more
significant in the regional deposition of submicron bioaerosol
particles than previously considered. The Lagrangian particle
transport model directly accounts for inertia and diffusion
forces which may effect the GS deposition. Major limitationsof this study include the assumptions of simplified inlet andoutlet conditions, the use of idealized lung airway geometries,
steady flow, and the symmetric bifurcations. Each of these
factors may affected physical realism of the model predictionsin relation to actual bioaerosol particle deposition reported in
the in-vitro studies.
CONCLUSIONSA flexible lung simulation incorporating gravity effects on
bioaerosol particles has been developed and realized. At this
point, several conclusions can be made based upon the data
generated from this study. Gravitational force on submicron
bioaerosol particles increases exponentially in the order of threewith the increase of their aerodynamic diameter. The GS has 15
times strong deposition effects on a 10 micron particle than that
of a one micron particle. Compared to the Brownian diffusion,
gravitational settling deposition becomes dominant in the lungfor coagulated bipolarly charged particles.
ACKNOWLEDGEMENTThis work was supported by the Mississippi-INBRE
(P20RR016476) funded by the National Center for Research
Resources, National Institutes of Health.
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Aerosol Sci., 38, pp. 228-245.[18] Jan D. L., Sharpiro, A. H., and Kamm, R., 1989, SomeFeatures of Oscillatory Flow in Model Bifurcation, J. Appl.
Physiol., 67, pp. 147-159.
[19] Ali, M., 2010, In-silico Simulation of ElectrostaticCharge Effects on Inhaled Aerosol Particle Deposition in the
Human Lung," ASME Early Career Technical J., 9(1), pp. 75-
79.
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ASME Early Career Technical Journa2011 ASME Early Career Technical Conference, ASME ECTC
November 4 5, Atlanta, Georgia USA
DYNAMIC MODELING AND PARAMETER ESTIMATION FOR IMPACT ANALYSISAND ROBUST PACKAGING DESIGN
Thomas Stone, Seung-Kyum Choi, PhDG. W. Woodruff School of Mechanical Engineering
Georgia Institute of TechnologyAtlanta, GA, USA
Hemanth Amarchinta, PhDBecton Dickinson and Company
Franklin Lakes, NJ, 07417
ABSTRACTOrganizations desire cost-effective methods for evaluating
the integrity of packaging designs. Packaging models enable
optimization of packaging designs such that product failure is
minimized during shipping and handling. This paper presents a
practical, cost-effective method for developing mass-spring-
damper (MSD) packaging models and estimating modelparameters for product packaging.
System model development requires the determination of
parameters for complex spring and damper models, which can
be difficult to find via standard measurement techniques. In
addition, these complex elements, along with other system
model features (e.g., wall friction, colliding components, etc.),
introduce nonlinearities into governing equations. Care must be
taken to choose appropriate spring/damper models and model
features, such that experimentation cost is reduced and accurate
impact analysis results are obtained.
For a particular packaging design example, three MSD
models are compared and contrasted with respect to (a) number
of unknown parameters, (b) model response versus
experimental response data, and (c) model response, namely,
contact force between individual packages. All of the models
were capable of simulating the trends in experimental data, and
maximum contact force values were within 10% of the
corresponding average value. The validated MSD system could
be used to optimize the packaging design.
PROBLEM INTRODUCTIONCurrent research investigates methods for modeling
systems by implementing mass-spring-damper (MSD)systems.
Different systems require varying complexity in spring/damper
models and system model features. Systems of interest include
laminates subject to impact [1], automobile/passenger
interaction [2], structures [3], produce subject to shipping andhandling [4], etc. Moreover, different systems require unique
measurement and experimentation techniques in order to find
model parameters. This research is related to the development
of an MSD system for packaging designs subject to shipping
and handling.
Significant cost is saved in shipping and handling by
inserting individually packaged products directly into shipping
cartons, without using impact absorption packaging material
The effect on profit margin is substantial when the cost of the
individual products is low. When individually packaged
products are directly inserted into the carton, the produc
packaging itself acts as the impact absorber for the producduring shipping. However, this packaging design can result in
product failuresparticularly in the case where a compromised
product package results in complete product failure (e.g.
packaged food, sterile medical products, etc.).
This research develops a generalized MSD representation
for a generic packaging design, and then applies the developed
methodology to a particular packaging example. Severa
complexities are handled, such as stiffness and damping
coefficient determination, spring/damper model development
product-packaging interactions, and packaging-carton
interactions, among other features.
PACKAGING MODEL ASSUMPTIONS ANDDEVELOPMENT
In this research, the generic packaged design consists of
individually packaged products inserted into a standard
shipping carton, (slight modifications can be made to accoun
for different carton shapes). Figure 1ashows a 3D lattice
representation of the individually packaged products, and
Figure 1b shows the corresponding 2D lattice of the products
and the carton. In this particular packaging design, there are 8
rows of product packages and 12 columns of product packages.
Each product package is treated as an individual mass, and
the interaction between the masses is modeled using spring and
damper modelsseeFigure 1c. In this initial model, each mass
has 2 degrees of freedom: horizontal and vertical. The model is
further simplified by considering only flat drops, where thepackaging is not dropped on an edge of the carton or a corner of
the carton. Thus, only one degree of freedom, vertical, must be
considered in the model.Also, when assuming a flat drop, each
row of masses can be consolidated into individual larger
masses. The mass combination is depicted in Figure 1d. The
springs and dampers in parallel can also be combined.
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MULTIPLE COLLISIONMODELINGThe interaction of the MSD components must be modeled
with sufficient intricacy in orderto accurately match actual
system behavior. Therefore, care must be taken to limit the
numberof required modeling parameters such that the final
model can be determined via limited experimentationand low
computational costs.
A critical feature of the system model is the multiple
collisions that occur between the product packages.The product
packaging, which enclosing the actual product,is the first
system component that interacts with any other system
component during impact, (contact phase 1). Then, once the
product packaging is sufficiently deformed, the actual productbegins to interact with other system components, (contact phase
2).
Due to the existence of colliding components, the package
system is modeled as an n-degree-of-freedom oscillator with
elastic stops. The vibro-impact characteristics of an MSD
model with elastic stops were investigated in Ref. [1], including
dynamics and stability during oscillation. The model shown in
Figure 2 is a slight modification of the model used in Ref. [1].
Figure 2. MSD model with elastics stops; both elasticstops for each mass are capable of disengaging
from the adjacent mass
(a) (b)
(c) (d) (e)
Figure 1. (a) 3D lattice representationof individually packaged products (shown as circles), (b) corresponding 2Dlattice composed of products and carton (carton length is into the page), (c) MSD system representation ofpackaged products (shown as masses), (d) MSD system with combined masses, and (e) MSD system with
combined springs and dampers
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In Figure 2, mn is the combined mass of the nth row of
product packages, kpand ksare combined stiffness coefficients,
cpand csare damping coefficients,gis the gap between product
packaging and the enclosed product, qn is the generalized
position coordinate for mass n, andfn is the generalized forcing
for mass n. As briefly mentioned, three modes of packaging
interaction are possible for the model, as shown inFigure 3.
Contact Phase:Contact phase 1 involves the exclusive
interaction of the packaging materials only. The packaging
stiffness and damping coefficients are represented as kpand cp,
respectively.
Contact Phase 2: After the gap, g, decreases to zero, contact
phase 2 is initiated. Contact phase 2 involves the interaction of
the packaging material along with the interaction of the
enclosed products. The product stiffness and damping
coefficients are represented as ksand cs, respectively.
No-contact Phase: If the packaging carton is dropped from a
sufficient height, the packaged products may rebound and lose
contact with each other, resulting in the no-contact phase. A
model of the no-contact phase is not necessary when
considering only the initial impact, but it is useful for parameter
estimation.
Of particular interest is the contact force between the
masses in the system. The contact force on the bottom of a
package,Fc, is
(1)
where p and s are the deformations of the product package
spring and product spring, respectively. and are thedeformation rates product package spring and product spring,
respectively. Positive values of deformation, , correspond to
spring compression. The gap, g, is approximated by acquiring
the distance of product package deflection via experimentation.
Equation (1) describes the interaction of the packages only
during contact phases.When the packages are in the no-contac
phase, the interaction of the package with the carton wall and
gravitational forces dictate the motion of the packagesno
depicted inFigure 2.The low weight of each product package
causes the wall interactions to be the prevalent contributor to
forcing during the no-contact phase. Consequently, the wal
interactions can be approximated by observing the response
during of the packages during the no-contact phase.The
equations of motion during this phase are defined by
(2)where is an empirically determined constant, and qn is the
generalized coordinate of the nthmass, andFwis the wall force
acting on the nthpackage.
Negative deformation, , values correspond to spring
tension. The product package cannot act as a spring in tension
so the dominate forcing is due to interactions between the
product package and the carton wall. Equation (2) assumes tha
the wall interaction force parameters are some proportion, , o
the product package spring and damper parameters. In addition
notice that the wall interaction forces depend on the
displacement and speed relative to the carton wall, and respectively. However, the effects of the no-contact phase are
only needed for the estimation of a few parameters. Thus, a
heuristic estimation approach is suitable.
Equations (1) and (2) are then applied to all of the masses
in order to develop a system of equations that defines the entire
MSD system. The number of equations in the system is equal to
the number of rows of product packages in the particular
packaging design. The equations of motion in general matrix
form is
(3)where M is the inertial matrix, q is the generalized coordinate
vector, Fc,bot is the contact force vector for the bottom of the
masses, Fc,top is the contact force vector for the top of the
masses, Fw is the wall interaction force vector, and f is the
generalized force vector.
The existence of the elastic stop results in piecewise
ordinary differential equations. Moreover, the local solution of
an equation of motion for mass ndepends on the local solution
of the equation of motions for mass n+1 and mass n-1. Thus
traditional transient solution equations are not applicable
MATLAB is used to simultaneously solve all of the equationsof motion while considering elastic stops and initial conditions.
PROPOSED SPRING/DAMPER MODELSIn this section, various spring and damper models are
explored for use in the model shown in Figure 2. The spring
and damper must be modeled with sufficient intricacy such that
results are accurate; however, care must be taken to limit the
complexity such that the final model is useful. Equations (1
(a) (b)
Figure 3.Depiction of (a) contact phase 1 and (b)contact phase 2 for packaged product with arbitraryshape
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and (2) employthe Kelvin-Voigt model [5] for viscoelastic
materials. This is a simple model that results in piecewise linear
differential equations; computations of this model are relatively
straightforward. The softer spring and damper elements that
represent the phase 1 loading, kpand cp, can be assumed to be
linear elements.
However, the Kelvin-Voigt model may not adequately
describe the response of the spring and damper elements that
represent phase 2 loading,i.e., the phase 2 loading spring force
may not be linearly related to spring deformation, and the
associated damping force may not be linearly related to the
spring deformation rate. Preliminary inspection of packaging
design shows that these nonlinearities do exist. Choosing the
appropriate models will limit the complexity of the spring and
damper models and reduce the experimentation cost, while also
delivering sufficiently accurate results.
For the purpose of discussion, we will consider the partial
contact force, Fc, associated with the product spring and
damper elements, ksand cs. The total contact force, Fc, includes
the contact force contributed by both the soft and hard spring
and damper elements.
Nonlinear Viscoelastic Models
Hunt and Crossley presented one of the first nonlinear
viscoelastic contact force models[6].
(4)where is the deformation of the product packaging spring,and is the deformation rate of the product packagingspring. A similar formulation of Eq. (4) was found in Ref. [7].
The kpvalue can easily be determined by static experimentation
setups. However, researchers investigating Eq.(4) identify the
damping coefficient, cp, as a function of a coefficient of
restitution, elastic moduli, and/or Poisson ratiosassuming
spherical contact. For example, the damping coefficient can be
defined as [6]
(5)where e is the coefficient of restitution, and vi is the initial
velocity. The coefficient of restitution is often difficult to
determine for product packaging, and contact between product
packages is often not characterized as spherical contact. Also,
elastic moduli and Poisson ratios are not easily acquired for
product packaging.
Based on empirical observations, another nonlinear
viscoelastic model was introduced in Ref. [8]:
(6)The advantage of this model for this research is that the
damping coefficient is also determined by an empirical
relationship. The formulation of the damping parameter is
(7)where is an empirically determined constant, and m is theeffective mass.
Generalized Exponential ModelA slightly modified version of the contact force
formulation [9]is
(8)where a and b are empirically determined parameters. This
model provides freedom in determining an appropriate contac
force model for contacts involving non-spherical elements. In
the standard Hertz model, a is equal to 3/2, as seen in the
aforementioned models. However, non-spherical contact may
require different values of a.
GeneralizedPolynomialModelsThe vibration dynamics of a system were analyzed in Ref
[10], while considering the following spring and damper
contact force models:
(9) (10)where Fc,spring and F
c,damper are the spring contac
force,respectively. There are multiple stiffness coefficientsks,and ks,2and multiple damping coefficientscs,1and cs,2.
The formulations in Eqs. (9) and (10) can be generalized
according to
(11) (12)
where ks,iis the ithspring coefficient, and cs,jis the j
thdamping
coefficient. Note that the units of the ks and cs terms change
according to the values of i and j, respectively. Thismode
provides a great amount of freedom in empirically matching the
actual system. But the number of unknown parameters can be
as large as i+j.
Proportional Damping
A popular method of approximating the damping
coefficients is assuming that it is proportionally related to thestiffness and mass parameters. Equation (13) describes the
relationship between stiffness, mass, and damping matrices
when proportional damping is assumed.
(13)
where and are empirically determined constants. For the
case where dampers are parallel to spring elements, rather than
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being connected to ground, the damping coefficients are
exclusively proportional to the stiffness coefficients:
(14)
This relationship is useful when there are multiple, unique
spring and damper coefficients. In this case, only the spring
coefficients and one empirical constant, , must be determined
via experimentation.
This research compares and contrasts three spring/damper
models for the contact phase 2 elements: the Kelvin-Voigt
model, Eq. (6), and Eqs. (9) and (10). Contact phase 1 is
modeled using Kelvin-Voigt model for all three system models.
Table 1 shows the contact force expressions for each system
model.
EXPERIMENTAL METHODS AND PARAMETERESTIMATIONContact Phase 1 Experimentation
When a carton of product packages is dropped from a low
height (approximately 1 to 2 cm), no collisions occur between
the enclosed products; i.e., the kp and cp parameters have no
influence on the MSD model. Thus, drop tests from a lowheight are used to determine the kp and cs parameters for a
particular packaging design. In the experimental setup, a laser
doppler vibrometer (LDV) is focused on a reflective tape that is
applied to a product package.The LDV records the velocity of
an individual product on the n-1 row as the carton is dropped
from a low height.
Figure 4 and Figure 5 present graphs of the velocity
response of the n-1 row of product packages. Figure 4 shows
theoretical MSD responses for various kp values. The
appropriate kpvalue is chosen by matching the theoretical MSD
response to the experimental velocity response data. Notice that
increasing the packaging stiffness coefficient alters the
magnitude and frequency of the system response. Figure 5presents the same experimental data along with MSD responses
for various cp values. The cp value is also chosen by
comparisons to experimental data. Notice that the changing
stiffness parameter primarily affects the magnitude of the
response, and also has an effect on the response frequency. It
can also be noted, some noise has been removed from the
experimental data at around 0.05 sec.
Contact Phase 2 Experimentation
When a carton of product packages is dropped a large
distance, the contact phase 2 parameters are requiredks and csThese parameters correspond to the collision of the products
which calls for the inclusion of elastic stops in the MSD
model.The experimental setup using the LDV is not sufficien
to record data for high drop tests. Thus, accelerometers are
attached to individual packaged products within the carton. The
row of the measured packaged product is recorded.
Figure 6 presents the experimental response of measured
rows of product packages, along with responses calculated
using the three MSD system models. Of particular interest isthe maximum acceleration of the package, and the impulse
time. Variations in the accelerometer reading after impact
(0.004 sec to 0.014 sec) are caused by the vibration of the
accelerometer components, and do not reflect the actua
response of the packaging. Contact phase 2 model parameters
are modified such that the theoretical MSD response matches
the experimental acceleration data. The data in Figure 6
supports the claim that each of the contact force models can be
Figure 5. Velocity response of packaging subject to
0.5 in drop; data shown for n-1 row;ksis held constantat 28,000. N/m
Figure 4. Velocity response of packaging subject to0.5 in drop; data shown for n-1 row; csis held
constant at 84.0 N-s/mTable 1. Contact force models
Contact Phase 2 ModelNumber ofUnknowns
Model 1 2Model 2 2Model 3
4
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Figure 6. Acceleration response of MSD system subject to 30 in drop; data for 7th
product package row shown
used to accurately model the actual system. However, the data
also shows that contact phase 2 may need to be divided into a
loading phase and an unloading phase. The model and
theoretical data exhibit different trends in the unloading phase.Fortunately, the loading phase and maximum acceleration are
the key features when considering impact analysis.
The estimated model parameters are presented in Table 2.
The same contact phase 1 model was used for all three system
models. Thus, the associate parameter values are the same
throughout. Model 3 has four unknown parameters: ks,1, ks,2,
cs,1, and cs,2.
CONTACT FORCE RESULTSThe composition of the contact forcespring force,
damping force, and total forceexperienced by the bottom of
an individual package on row two is shown in Figure 7. The
damping force is the primary contributor to the maximum
contact force. Model 1 is used to produce this data; other
models show the same trend.
The contact force for individual product packages onvarious rows is shown inFigure 8.Notice that the packages on
the bottom row experience a significantly higher contact force
than the other products. Thus, it is important to understand the
interaction of the packaging carton and product packages. Even
though the contact force is highest on the bottom, an increased
surface area may contribute to lower rate of failure for
packages on the bottom row.
Figure 7. Theoretical contact force responsecomponents for Model 1; data for 2
ndproduct
package row shown
Table 2. Parameters estimations
kp(N/m)
cp(N-s/m)
ks cs
Model 1 28000. 84 1130. 81
Model 2 28000. 84 35000. 2835
Model 3 28000. 84 1400. 1260.
1.12x109
1.12x108
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The results of the 3 proposed models are presented in
Figure 9.The transient contact force experienced by the bottom
of a product package on the second row is shown. All three
models show similar trends, and the maximum contact force for
the second row does not vary from the corresponding average
contact force by more than 10%.Table 3 includes data for the
second row of packages, as well as the bottom row of packages.
The bottom row of packages experiences a significantly higher
contact force, and shows more variation between models.
However, further investigation is required in order to interpret
bottom row contact force. As aforementioned, an increased
surface area may contribute to lower rate of failure for
packages on the bottom row.
CONCLUSIONS AND FUTURE WORKPreliminary results show that all three models produce
similar results. Variation in the maximum contact force for the
second row is less that 10%, relative to the average value. The
bottom row contact force values have more variationup to
18.5%; the interaction of the bottom row of syringes with the
carton may require a more complex spring/damper model
Since all three system models exhibit comparable performance
the simple Kelvin-Voigt modelModel 1should be chosen inorder to minimize the number of model parameters required
while maintaining sufficient model accuracy, for this particular
packaging design. The increased number of unknowns in
Model 3 did not significantly improve the ability to match the
system model to experimental data. For future testing, pressure
sensors can be used to measure contact pressure values
whereby, contact force measurements can be derived. The
significance of the model parameters will be determined. The
implemented MSD model can be integrated intoa conventiona
design process, such as the reliability-based design
optimization.
REFERENCES[1] Yang, M., and Qiao, P., 2005, "Nonlinear impact analysis o
fully backed composite sandwich structures," Composites
Science and Technology, 65(3-4), pp. 551-562.
[2] Liang, C.-C., and Chiang, C.-F., 2006, "A study on
biodynamic models of seated human subjects exposed to
vertical vibration," International Journal of Industria
Ergonomics, 36(10), pp. 869-890.
[3] Muthukumar, S., and DesRoches, R., 2006, "A Hertz
contact model with non-linear damping for pounding
simulation," Earthquake Engineering & Structural Dynamics
35(7), pp. 811-828.
[4] Van Zeebroeck, M., Lombaert, G., Dintwa, E., Ramon, H.
Degrande, G., and Tijskens, E., 2008, "The simulation of the
impact damage to fruit during the passage of a truck over aspeed bump by means of the discrete element method,"
Biosystems Engineering, 101(1), pp. 58-68.
[5] Carcione, J. M., Poletto, F., and Gei, D., 2004, "3-D wave
simulation in anelastic media using the KelvinVoig
constitutive equation," Journal of Computational Physics
196(1), pp. 282-297.
Table 3. Theoretical maximum contact force values
Max Contact Force,Fc(N)
Variation of Max Fcfrom Average Fc(%)
BottomRow
SecondRow
BottomRow
SecondRow
Model 1 55.6 29.0 -10.8 0.928
Model 2 57.5 26.0 -7.71 -9.51
Model 3 73.9 31.2 18.5 8.58
Figure 9. Theoretical contact force response for threemodels; data for 2
ndproduct package row shown;
contact force is experienced on bottom side ofpackages
Figure 8. Theoretical contact force response forModel 1; data for first four product package rows
shown; contact force is experienced on bottom sideof packages
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[6] Hunt, K. H., and Crossley, F. R. E., 1975, "Coefficient of
Restitution Interpreted as Damping in Vibroimpact," Journal of
Applied Mechanics, 42(2), pp. 440-445.
[7] Kuwabara, G., and Kono, K., 1987, "Restitution Coefficient
in a Collision between Two Spheres," Japanese Journal of
Applied Physics, 26, p. 1230.
[8] Tsuji, Y., Tanaka, T., and Ishida, T., 1992, "Lagrangian
numerical simulation of plug flow of cohesionless particles in a
horizontal pipe," Powder Technology, 71(3), pp. 239-250.
[9] Cochran, A. J., 2002, "Development and use of one-
dimensional models of a golf ball," Journal of Sports Sciences,
pp. 635-641.
[10] Zhu, S. J., Zheng, Y. F., and Fu, Y. M., 2004, "Analysis of
non-linear dynamics of a two-degree-of-freedom vibration
system with non-linear damping and non-linear spring," Journal
of Sound and Vibration, 271(1-2), pp. 15-24.
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ASME Early Career Technical Journa2011 ASME Early Career Technical Conference, ASME ECTC
November 4 5, Atlanta, Georgia USA
MOTION TRACKING THROUGH MACHINE VISION
Poonam Savsani andVimal Savsani
B. H. Gardi College of Engineering and TechnologyRajkot, Gujarat, INDIA
ABSTRACT
The advance of technology makes video acquisition
devices better and less costly; thereby, increasing the number of
applications that can effectively utilize digital video. Compared
to still images, video sequences provide more information
about how objects and scenarios change over time. This work
deals with the tracking and following of a single object in asequence of frames and the co-ordinate of the object is
determined. The object tracking video is recorded with the help
of web cam and then recorded video is converted into AVI
format with 20 fps using Roborealm software. This AVI format
video is processed in developed Matlab code for tracking the
object. The object is tracked by plotting a rectangular bounding
box around it in each frame. Co-ordinate of the centroid of that
bounding box is determined for the further evaluation. An
algorithm is developed for improving the image quality,
segmentation and feature extraction. Segmentation is
performed to detect the object after reducing the noise from that
scene.
1. INTRODUCTION
It has always been a dream to have a robot with the same
abilities as a human being. Ten years ago, people could only
imagine the things robots are capable of today, but the road to
an autonomous robot that can act as a human, is still very long.
The fact that these robots have to be autonomous means that
they have to do everything without interference of humans. One
of the keys to success is a good vision system, so the question
is: how to make a robot see like a human? The only way for
now to approach human sight is with the use of a camera, so
that is what will be further developed in the future.
The main objective of the present work is to develop theobject tracking algorithm that will provide a communication
path to hardware system. For this purpose a matlab code is
developed which takes AVI video. Video is recorded by using
web camera and it is formatted into the required format by
using Roborealm software. The whole object tracking algorithm
is divided in two parts:
1. Roborealm software
a) Removing noise from the imageb) Extracting green color from the imagec) Recording the video in AVI format
2. Matlab codea) Segmentation of the recorded videob) Feature extractionc) Tracking the moving object
1.1 Related work
Cohen and Medioni [2] address the fragmentation
problem by requiring temporal coherence of blobs. They do no
handle groups of objects. Gabriel et al [3] review other multi
object tracking methods. There has been some work on tracking
groups of objects [1]. Gennari and Hager [4] propose a group-
tracking algorithm where objects and fragments are not
distinguished from groups. Yiwei Wang et al [5] proposes an
algorithm to track moving objects in video sequence. The
algorithm first separates the moving objects from the
background in each frame. Then, four sets of variables are
computed based on the positions, the sizes, the grayscale
distributions and the presence of textures of the objects. A rule-based method is developed to track the objects between frames
based on the values of the variables. S. Sulaima et al [6
proposed system employed background modeling and image
differential techniques to isolate the motion objects from its
background. Additionally, morphology processes are performed
to enhance the object pixels that have been extracted so that a
more accurate output image that comprised of the detected
object can be determined. Bing Leng and Qionghai Dai [7]
address the problem of extracting video objects from head-
shoulder video sequences. A method based on accumulative
frame difference is proposed. Biswajit Bose et al [8] propose a
framework for detecting and tracking multiple interacting
objects, while explicitly handling the dual problems o
fragmentation (an object may be broken into several blobs) and
grouping (multiple objects may appear as a single blob). They
use foreground blobs obtained by background subtraction from
a stationary camera as measurements.
2. OBJECT TRACKING
Object tracking is the process of locating and following
the moving object in sequence of video frames. A web camera
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is used as input sensors to record the video. The recorded video
may have some noise. For tracking the object pre-processing,
segmentation, feature extraction and object detection is
necessary.
2.1 Pre-processing
The pre-processing performs some steps to improve the
image quality. Several methods are explained below.
2.1.1 Mean Filter
Mean filtering is a simple, intuitive and easy to
implement method of smoothing images, i.e., reducing the
amount of intensity variation between one pixel and the next. It
is often used to reduce noise in images. The idea of mean
filtering is simply to replace each pixel value in an image with
the mean (`average') value of its neighbors, including itself.
This has the effect of eliminating pixel values which are
unrepresentative of their surroundings. For every pixel the
mean from the box elements are calculated and the pixel values
are stored in to the central element. Let Sxy represents the set
of coordinates in a rectangular subimage window of size mn,
centred at a point (x,y). the arithmetic mean filter computes the
average value of the corrupted image g(x,y) in area defined by
Sxy. In other words,
f(x, y) =1
mn g(s, t)(s,t)Sxy
This operation can be implemented using spatial filter
of size mn in which all coefficients have value 1/mn. Let us
consider an example with a 3x3 matrix,
1 2 94 3 85 6 7
Pixel value= 1/9(1+2+3+4+5+6+7+8+9)
in the above matrix, central element is '3' and after calculating
the pixel value, the value of 3 is replaced by the pixel value.
The calculation time for mean filter is very less compared to all
other. By using this filter smoothing is done. [9] [10] the two
main problems with mean filtering, which are:
A single pixel with a very unrepresentative value cansignificantly affect the mean value of all the pixels in
its neighborhood.
When the filter neighborhood straddles an edge, thefilter will interpolate new values for pixels on the edge
and so will blur that edge. This may be a problem if
sharp edges are required in the output.
Both of these problems are tackled by the median filter, which
is often a better filter for reducing noise than the mean filter
but it takes longer to compute.
2.1.2 Gaussian Smoothing
The Gaussian smoothing operator is used to `blurimages and remove detail and noise. In this sense it is similar to
the mean filter, but it uses a different function that represents
the shape of a Gaussian (`bell-shaped') hump. A box is scanned
over the whole image and the pixel value calculated from the
standard deviation of Gaussian is stored in the central element
The size of the box may vary from 1 to 7 means 1x1 to 7x7
elements [11].
(,) = 122+2
Where x and y denotes the positions of elements in the
box. The effect of Gaussian smoothing is to blur an image, in asimilar fashion to the mean filter. The degree of smoothing is
determined by the standard deviation of the Gaussian. The
Gaussian outputs a `weighted average' of each pixel's
neighborhood, with the average weighted more towards the
value of the central pixels. This is in contrast to the mean filter's
uniformly weighted average. Because of this, a Gaussian
provides gentler smoothing and preserves edges better than a
similarly sized mean filter. One of the principle justifications
for using the Gaussian as a smoothing filter is due to its
frequency response. Most convolution-based smoothing filters
act as low pass filters. This means that their effect is to remove
high spatial frequency components from an image.
2.1.3 Median Filter
The median filter is a classical noise removal filter
Noise is removed by calculating the median from all its box
elements and stores the value to the central element. The values
of the median of the intensity levels in the neighborhood of that
pixel:
f(x, y) = median(s,t)Sxyg{(s, t)}
The value of the pixel at (x,y) is included in the
computation of the median. If we consider an example of 3x3
matrix
1 2 94 3 85 6 7
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The median filter sorts the elements in a given matrix and
median value is assigned to the central pixel. Sorted elements 1,
2, 3, 4, 5, 6, 7, 8, 9 and median 5 will assign to the central
element. Similar box scan is performed over the whole image
and reduces noise. Execution time is more compared to mean
filter, since the algorithm involves with sorting techniques [12].
Instead of simply replacing the pixel value with the mean of
neighboring pixel values, it replaces it with the median of those
values. The median filter has two main advantages over the
mean filter:
The median is a more robust average than the meanand so a single very unrepresentative pixel in a
neighborhood will not affect the median value
significantly.
Since the median value must actually be the value ofone of the pixels in the neighborhood, the median filter
does not create new unrealistic pixel values when the
filter straddles an edge. For this reason the median
filter is much better at preserving sharp edges than the
mean filter.
2.1.4 Segmentation
Segmentation is the process of dividing digital image
into multiple regions. Segmentation shows the objects and
boundaries in an image. Each Pixel in the region has some
similar characteristics like color, intensity, etc. The purpose of
image segmentation is to partition an image into meaningful
regions with respect to a particular application. Few methods
for segmenting the images are explained below.
2.1.5 Histogram Based Segmentation
Histograms are the basis for numerous spatial domain
processing techniques. One of the simple ways of doing
segmentation is using histogram. Histogram modeling
techniques provide a sophisticated method for modifying the
dynamic range and contrast of an image by altering that image
such that its intensity histogram has a desired shape. Histogram
equalization employs a monotonic, non-linear mapping which
re-assigns the intensity values of pixels in the input image such
that the output image contains a uniform distribution of
intensities. This technique is used in image segmentation
processes. Histogram is computed for all the image pixels. The
peaks in histogram are produced by the intensity values that areproduced after applying the threshold and clustering. The pixel
value is used to locate the regions in the image. Based on
histogram values and threshold we can classify the low
intensity values as object and the high values are background
image (most of the cases)[13]. In figure 1 (a) shows source
image while (b) shows the segmented image after application of
histogram. Assume for a moment that intensity levels are
continuous quantities normalized to the range [0, 1], and let
denote the probability density function Pr(r), (PDF) of the
intensity levels in a given image, where the subscript is used for
differentiating between the PDFs of the input and output
images.
.
(a) Without applying histogram (b) with equalize histogram
Figure 1. Effect of histogram
Suppose that we perform the following transformation on the
input levels to obtain output (processed) intensity levels, s,
=() = ()0
where w is a dummy variable of integration. It can be shown
(Gonzalez and Woods [2002]) that the probability density
function of the output levels is uniform; that is,
() = {10 10
In other words, the preceding transformation generates
images whose intensity levels is equally likely, and, in addition
cover the entire range [0, 1]. The net result of this intensity-
level equalization process is an image with increased dynamic
range, which will tend to have higher contrast.
2.1.6 Frame Difference
Frame difference calculates the difference between 2
frames at every pixel position and store the absolute difference
It is used to visualize the moving objects in a sequence of
frames. It takes very less memory for performing the
calculation.[14]. The frame method basically employs the
image subtraction operator. The image subtraction operator
takes two images as input and produces as output a third image
whose pixel values are simply those of the first image minus
the corresponding pixel values from the second image. The
subtraction of two images is performed straightforwardly in a
single pass. The output pixel values are given by:
Q(i, j) = P1(i, j) P2(i, j)
Let us consider an example, if we take a sequence of frames
the present frame and the next frame are taken into
consideration at every calculation and the frames are shifted
(after calculation the next frame becomes present frame and the
frame that comes in sequence becomes next frame). Figure 2
shows the frame difference between 2 frames.
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Figure 2. Frame difference between two frames
2.1.7 Feature Extraction
Feature Extraction plays a vital role to detect the moving
objects in sequence of frames. Every object has a specific
feature like color or shape. In a sequence of frames, any one of
the feature is used to detect the objects in the frame and that
feature is used to detect the object. Based on color and edge
feature can be extracted,
2.1.8 Edges
Edges are formed where there is a sharp change in the
intensity of images. If there is an object, the pixel positions of
the object boundary are stored and in the next sequence offrames this position is verified. Edge pixels are pixels at which
the intensity of an image changes abruptly and edges are sets of
connected edge pixels. Edge detector is local image processing
methods designed to detect edge pixels. Corner based algorithm
uses the pixel position of edges for defining and tracking of
objects. [15]. Over view of the mathematics behind the edge
detection is given below. The tool of choice for finding edge
strength and direction at location (x,y) of an image, f is the
gradient and defined as follows:
= =
The magnitude of vector is:
=() =2 +212= [(/)2 + (/)2]1/2
Is the value of the rate of change in the direction of the gradient
vactor. Here Gx, Gy and vector are images of the same size as
the original, created when x and y are allowed to vary along all
pixel location in f. the direction of the gradient vector is given
by the angle,
(,) = tan1 Measured with respect to the x-axis.
If the segmentation is performed using frame difference
the residual image is visualized with rectangular bounding box
with the dimensions of the object produced from residua
image. For a given image, a scan is performed where the
intensity value of the image is more than limit (depends on the
assigned value, for accurate assign maximum). In this Features
is extracted by color and here the intensity value describes the
color. The pixel values from the first hit of the intensity values
from top, bottom, left and right are stored. By using this
dimension values a rectangular bounding box is plotted within
the limits of the values produced.
2.1.9 Object Detection
Extraction of objects using the features is known as
object detection. Every object has a specific feature based on its
dimensions. Applying feature extraction algorithm, the object in
each frame can be pointed out.
2.1.10 Optical Flow
Optical flow is one way to detect moving objects in a
sequence of frames. Optical flow or optic flow is the pattern o
apparent motion of objects, surfaces, and edges in a visua
scene caused by the relative motion between an observer (an
eye or a camera) and the scene. Optical flow is the distribution
of apparent velocities of movement of brightness patterns in an
image. In this, the vector position of pixels is calculated and
compared in sequence of frames for the pixel position
Typically the motion is represented as vector position of pixels.
2.1.11 Block Matching
Block matching algorithm is a standard technique fordetermining the moving object in video. Blocks are formed in a
region without overlapping on the other region. The block
matching algorithm is a standard technique for encoding
motion in video sequences. It aims at detecting the motion
between two images in a block-wise sense. The blocks are
usually defined by dividing the image frame into non-
overlapping square parts. Each block from the current frame is
matched into a block in the destination frame by shifting the
current block over a predefined neighborhood of pixels in the
destination frame. At each shift, the sum of the distances
between the gray values of the two blocks is computed. The
shift which gives the smallest total distance is considered the
best match. In the ideal case, two matching blocks have theircorresponding pixels exactly equal. This is rarely true because
moving objects change their shape in respect to the observer's
point of view, the light reacted from objects' surface also
changes, and finally in the real world there is always noise
Furthermore, from semantic point view, in scenes containing
motion there are occlusions among the objects, as well as
disappearing of objects and appearing of new ones. Despite the
problems of pixel by pixel correspondence, it is fast to compute
and is used extensively for finding matching regions [16].
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2.1.12 Tracking
The process of locating the moving object in sequence
of frames is known as tracking. Object tracking is a key
computer vision topic, which aims at detecting the position of a
moving object from a video sequence. This tracking can be
performed by using the feature extraction of objects and
detecting the objects in sequence of frames. By using the
position values of object in every frame, the position of the
moving object can be calculated.
3. ALGORITHM
This section explains the sequential approach to track the
moving object and to calculate the centroid of the object.
Following steps are followed to track the object,
1 Pre-processing: to improve the image quality and getimage in required form.
2 Segmentation: to separate multiple regions in image3 Feature Extraction: to analyze the region on image4 Tracking : Analyzing the position
3.1.1 Pre-processing
To improve the image quality and to get the desired feature
in the image pre-processing is performed. So in pre-processing
three sequences are followed,
1 Color extraction2 Noise removal3 Write video to AVI format
All this three steps are evaluated by using Roborealm software.
3.1.2 Color Extraction
The Matlab code is developed to track the green
colored object; hence RGB filter is used to extract the green
color from the image. The RGB Filter uses RGB values to
focus the attention towards the primary RGB colors. Depending
on the color selected this filter will diminish all pixels that are
not of the selected colors.
For example, if green is chosen:
G = ((G-B)+(G-R))
R = 0
B = 0
G is then normalized with respect to the maximum green value.
Based on the above formula it can be seen that white pixels
result in a zero value whereas pure primary colors (G=255
R=0, B=0) G doubles its value. To filter green color by using
Roborealm software color>RGB filter dialog box is executed
and proper intensity and hue are tuned.
(a) source image (b) filtered image
Figure 3. Color extraction with RGB filter
Figure 3 (a) shows the source file and (b) shows the image after
applying RGB filter with intensity 158 and hue 60.
3.1.3 Noise Removal
Noise is removed from the image by using median
filter. The median filter is used to remove noise from an image
by replacing pixels with the middle pixel value selected from a
certain window size. In Roborealm for the application of the
median filter window size need to be selected. This window
size defines the size of the matrix. Depending on the window
size smoothness of the image is obtained.
(a) (b)
Figure 4. Noise removal with median 3 filter
(a) Source image (b) image after filtering with median 3
Figure 4 (a) shows the source image and (b) shows the image
after application of median 3 filter.
3.1.4 Write Video to AVI Format
Matlab takes AVI format video as input hence processed
video need to be written in AVI format. This can be achieved by
using the write AVI function of Roborealm software. The write
AVI function allows you to save the processed video to an AV
video file. This file can then be played back using any AVI
player (with the appropriate decompressor) when needed
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without running RoboRealm. To use the write AVI function the
following terms should be specified:
1 Which image to save2 Specify the file to save the recording to.3 Specify the time to stop recording.4 Specify the frame per seconds.5 Specify the type of compression.6 Quality of recording.7 Command variable/manual start-stop
For the present work, image is saved in Matlab and for the
recording 20 frames per second are chosen as it require less
memory. Video is recorded in an uncompressing mode. To start
and stop the video recording manual button is used instead any
command variable.
3.1.5 Segmentation
Segmentation is the process of analyzing multiple
regions in a frame. Frame difference algorithm is used to
separate the moving objects in frame. Advantage of using frame
difference over the others is less memory usage and two frames
are needed for calculation and gives good results when single
moving object is to be tracked. To perform the segmentation
operation, frame difference algorithm is implemented as it takes
less processing time. Frame difference algorithm performs
separation of two sequential frames and it is explained in detail
above. Algorithm for the segmentation is explained as follow:
1. Read the input images
2. For (present position=initial position: final position)
(a) Difference between the pixels values at present
position of two images is
calculated
(b) Calculate the absolute value
(c) Store the difference in new image at same pixel
position that is at present position
When the algorithm is implemented, the starting address of the
temporary images (both present and previous images) is takenand the image after the frame difference is stored in the
memory allocated for the output image.
3.1.5 Feature Extraction
Every object has a specific feature which is used to
visualize the object and used for tracking. After performing the
segmentation, a rectangular bounding box is plotted with the
dimensions of the object produced in the residual image
Section 2.3.2 explains a clear view on bounding box. Algorithm
for the bounding box is as followed
1. Read the image difference
2. for (present position=initial value: final value) of Y
resolution
3. For (present position=initial value: final value) of X
resolution
(a) Calculate the sharp change in intensity of image
from top and bottom
(b) Store the values in an array
4. Height of the bounding box is = bottom value- top value
5. For (present position=initial value: final value) of X
resolution
6. For (present position=initial value: final value) of Y
resolution
(a) Calculate the sharp change in intensity of image
from left and right
(b) Store the values in an array
7. Width of the bound box = right value - left value8. Using the dimensions, draw boundary to the imageInitial value: the starting position of the pixel in an image. Final
value: the ending position of the pixel in an image.
= 2
= 2
9. Add the height value with the top value and store it in a
variable like mid top
10. Add the width value to the left value and store it in avariable like mid left
11. Assign the max intensity to the pixel at pixel value at (mid
top, mid left)
When the algorithm is implemented for the image
dimensions of the image are produced and a rectangular
boundary is plotted. By using this bounding box dimensions
we plot the centroid of the box and used for tracking of objects
and determining the velocity of object.
3.1.6 Tracking
The science of motion tracking is fascinating becauseof its highly interdisciplinary nature and wide range o
applications. The aim of our object tracker module is to
generate the trajectory of an object over time by locating its
position in every frame of the video. The possible object region
in every frame is obtained by means of object detection, and
then tracking is done corresponds the object across frames. In
tracking, the object approach is represented using the Primitive
Geometric Shape Appearance Model [1], [3] (i.e. the object is
represented as a rectangle). The object is tracked by considering
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the positional information of the moving object as an input
from the object detection. This information is then used to
extract a square image template (whenever required) from the
last acquired frame. The module keeps on searching it in the
frames captured from that point. Whenever found it displays a
red overlaid rectangle over the detected object. As explained
above object tracking follows few steps. Figure-6 illustrated the
flow of the developed object tracking code. Centroid of
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