Download Low Power System Design

PhD
Faculty of Engineering & Sciences
Bahria University
The PhD programs in Engineering Sciences at Bahria University are offered in following
disciplines
1.
2.
3.
4.
Computer science (CS)
Computer engineering (CE)
Electrical engineering (EE)
Software engineering (SE)
Road Map
The proposed road map of the PhD program is presented in the following. After passing the 18
credit hours course work, preferably in the first two semesters, the candidate will have to pass
comprehensive exam. After qualifying the comprehensive exam, candidate will have to defend
the synopsis and will be offered Supervised Research (PhD Thesis) of 36 credit hours.
SEMESTER I
Course Code
Subject
Credits
EEN-801
Research Methods in PhD Studies
3
Elective-I
3
Elective-II
3
Total credit hours for the 1stsemester
9
SEMESTER II
Course Code
Subject
Credits
Elective-III
3
Elective-IV
3
Elective-V
3
Total credit hours for the 2ndsemester
9
SEMESTER III
Course Code
Subject
Credits
EEN-901
Comprehensive Exam
0
EEN-902
Supervised Research (PhD Thesis) including defense and acceptance
of research proposal
9
Total credit hours for the 3rdsemester
9
SEMESTER IV
Course Code
Subject
Credits
EEN-902
Supervised Research (PhD Thesis) including design and
implementation of the proposed solution
Total credit hours for the 4thsemester
9
9
SEMESTER V
Course Code
Subject
Credits
EEN-902
Supervised Research (PhD Thesis) including analysis of the results
and thesis write-up
Total credit hours for the 5thsemester
9
9
SEMESTER VI
Course Code
Subject
Credits
EEN-902
Supervised Research (PhD Thesis) - Submission of the final thesis for
evaluation.
9
Total credit hours for the 6thsemester
9
Total Credit hours for PhD Program
54
PhD Course List (Faculty of Engineering & Sciences)
PhD students, as a part of their course work, are allowed to enroll in 700 or plus level courses (not in
the PhD course list given below), if offered in MS programs at BU with the approval of FDRC.
S. No.
Course
Code
Title of the Course
1
EEN-710
MOS VLSI Circuit Design
2
EEN-711
Real Time DSP Design and Applications
3
EEN-712
Advanced Digital Communications
4
EEN-801
Research Methods in PhD Studies
5
EEN-802
Power management in wired and wireless systems
6
EEN-803
Low Power System Design
7
EEN-804
Advance System Modeling and Simulation
8
EEN-807
Special Topics in distributed systems
9
EEN-808
Power awareness in distributed systems
10
EEN-813
Power System Stability and Dynamics
11
EEN-814
Power System Transients
12
EEN-815
HVDC and Flexible AC Transmission
13
EEN-816
Rural Electrification and Distributed Generation
14
EEN-817
Artificial Intelligence techniques in Power systems
15
EEN-818
Power System Deregulation
16
EEN-819
Advanced Computer Architecture
17
EEN-820
Advanced Embedded Systems
Credit
Hours
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
18
EEN-821
Advanced Digital Signal Processing
19
EEN-822
Advanced Digital System Design
20
EEN-823
ASIC Design Methodology
21
SEN-805
Power Aware Computing
22
SEN-809
Advanced Artificial Intelligence
23
SEN-810
Advanced Neural Networks
24
SEN-811
Data Ware housing and Mining
25
SEN-812
Machine Learning
26
SEN-710
Formal Methods and Specifications
27
SEN-719
Human Aspects in Software Engineering
28
MAT-853
Advanced Engineering Mathematics
29
*MGT-801
Logic and Research
30
*MGT-802
Advanced Qualitative Research Methods
31
*MGT-806
Advanced Quantitative Research Methods
32
*MGT-803
Critical Review of Literature
33
CSC-750
Computer Vision
34
CSC-715
Pattern Recognition
35
CSC-815
Agent-Based Modeling
36
CSC-816
Bio Medical Image Analysis
* Only one course may be allowed with the approval of FDRC.
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Course Outlines
MOS VLSI CIRCUIT DESIGN
Course Code:
EEN-710
Credit Hours:
3
Pre requisite:
None
Objectives:
This is a graduate level course covering the design and analysis
of low power and high performance digital CMOS integrated
circuits. Examples of such circuits that feature large-Digital,
small-Analog architectures include microprocessors, FPGAs and
DSP and multimedia SoC modules. The course covers the
traditional CMOS inverter in depth. Other topics include
interconnects, layout, simulation techniques, hierarchical
design, timing issues, EDA tools, complex macro architectures,
arithmetic building blocks and memory structures. Intensive
project work is included using Mentor Graphic IC design tools.
Fabrication of modern CMOS circuits is covered along with a
survey of the industry at the beginning of the course.
Course Outline:

 Introduction, course administrivia, outline, CMOS
fabrication technology (video, trends, basic
processes).
MOSFET device physics, second order effects,
capacitance, layout related effects as it relates to
the design of a static CMOS inverter
 CMOS inverter operation and analysis (continued),
design metrics, area power delay, analog vs.
digital, why CMOS?
 General discussion on digital design, synthesis,
P&R tools, simulators, EDA history and trends,
custom/automated layout of custom digital and
large digital ASICs and SOCs, introduction to FPGA
and DSP architectures
 Introduction to SPICE/ELDO for circuit simulation,
BSIM models etc
 Interconnects
 Logic families
 Sequential circuit element design (flip flop metrics






Resources:
and architectures)
Top level simulation and implementation
strategies for large chips, top level chip issues,
package considerations
Timing issues in digital circuits, PLLs
Arithmetic building blocks (adders: transistor level
implementations)
Arithmetic building blocks (multipliers, shifters and
rotators)
Memory structures
Review and other remaining or specialized topics
1. Jan M. Rabaey, Digital Integrated Circuits, A Design
Perspective (2nd ed)
2. Peter van Zant: Microchip Fabrication (4th ed)
Real Time DSP Design and Applications
Course Code:
EEN-711
Credit Hours:
3
Pre requisite:
None
Objectives:
This course introduces real time application and design to the
students.
Course Outline:
Resources:
 Introduction to the Digital signal processors
o Design challenges and attributes
o Processor technologies
o Review of the DSP concepts
 FPGA and Micro processors Basics
 Discrete time signal processing
 Architecture of the Microprocessor
 Peripheral Components, Real Time Implementation
issues
 Architecture of the DSP
 Fixed and Floating point DSP architecture Designing
 Efficient Computation of the Discrete Fourier
transform
 Implementation of the DFT using Convolution
synchronization, interrupts
 Real time Communications
 Fourier analysis of stationary random signals Real
time data bases
 Designing and implementation of the Hilbert
transform
 Designing and implementation of the Filters
Hardware Accelerators
 Designing approach on DSK TMSc6713 System-onchip
 Case Studies (Applications of DSK)
3. Kuo. S M, Gan W S,” Digital Signal Processor; Architectures,
Implementations and Applications” Prentice Hall
publications
4. Digital signal processing- application, algorithms and
application, John G, Proakis et al
Advanced Digital Communications
Course Code:
EEN-712
Credit Hours:
3
Pre requisite:
None
Objectives:
This course introduces advance topics in digital communication
Course Outline:

Resources:
 Introduction to Digital Communications
 Random and Deterministic Signals
 Bandpass Modulation and Demodulation
o Bandpass Equivalent of baseband signal
o GramSchmidth Procedure
o Matched Filter
 Digital Modulation
o Memory less modulation
o Multidimensional signaling
o Power spectrum of the Digitally modulated
signal
o Waveform and vector channel model
 Waveform and vector AWGN channels 
o Error of maximum likelihood detection
o Optimal Detection
o Error probability for power-limited
signaling
 Synchronization 
o Carrier and symbol synchronization
o Carrier phase estimation
o Symbol timing estimations
 Multichannel and multicarrier systems 
 Fading channel and impacts on Communications 
 Channel Coding 
o Linear Block codes
o Trellis and graph based codes
o State Encoding
o Hamming codes
o LFSR, Gold sequence, Kasami sequences
1. John. G. Proakis and Masoud Salehi, Digital
communications, 5th Edition, McGraw-Hill International
editions,2008
2. B. Sklar, Digital communication, Fundamental and
applications, Prentice Hall,2006
3. R.G Gallager, “ Principles of Digital communications”.
Cambridge University Press, 2008
Research Methods in PhD Studies
Course Code:
EEN-801
Credit Hours:
3
Pre requisite:
None
Objectives:
This course introduces students to a number of research
methods useful for academic and professional investigations of
information practices, texts and technologies. By examining
the applications, strengths and major criticisms of
methodologies drawn from both the qualitative and
quantitative traditions, this course permits an understanding
of the various decisions and steps involved in crafting (and
executing) a research methodology, as well as a critically
informed
assessment
of
published
research.
The course offers an overview of the different approaches,
considerations and challenges involved in social research. In
addition to reviewing core human research methods such as
interviews, ethnographies, surveys and experiments, we will
explore methods used in critical analysis of texts and
technologies (discourse/content/design analysis, historical
case studies), with an emphasis on the digital (e.g. virtual
worlds, videogames, and online ethnographies). We will also
discuss mixed method approaches, case studies, participatory
and user-centered research, as well as research involving
minors.
Course Outline:









The Research Process,
Choosing Your Supervisor (s),
Reviewing Literature,
Formulating a Research Problem,
Identification of Research Parameters,
Constructing Research Question and Hypothesis,
The Research Design, Selecting a Study Design,
Problem Formulation and Modeling,
Establishing the Validity and Reliability of a Research
Bench/ Simulator,
 Sampling,




Resources:
Research Proposal,
Ethical and Confidentiality Issues in the research,
Processing Data and coding,
Writing a Research Report/ Thesis/ Dissertation/
Research Paper
1. Dr Ranjit Kumar, "Research Methodology: A Step-by-Step
Guide for Beginners", Sage Publications Ltd; Second Edition
2009, ISBN-10: 141291194X , ISBN-13: 978-1412911948
2. Gina Wisker, “Postgraduate Research Handbook: Succeed
with your MA, MPhil, EdD and PhD (Palgrave Study
Guides)”, Palgrave Macmillan; 2nd edition (December 26,
2007), ISBN-10: 0230521304 , ISBN-13: 978-0230521308
3. R. Panneerselvam, "Research Methodology", Prentice Hall
of India, 2005, ISBN: 81-203-2452-8
4. Dr. A. K. Phophalia, "Modern research methodology - New
trends and techniques", Paradise Publishers, India, 2010,
ISBN: 9789380033009
5. Loraine Blaxter,Christina Hughes And Ma, "How to
Research", ViVa Books, India, 1999, ISBN: 81-7649-089-X
6. Anil Kumar (Edtr.), "Encyclopedia of Research Methodology
Vol. I to Vol. IV", Alfa Publications, India, 2009, ISBN:
9788190784337
Power Management in wired and wireless systems
Course Code:
EEN-802
Credit Hours:
3
Pre requisite:
None
Objectives:
This is an introductory course on the fundamentals of electrical
power system in general. First a general introduction to the
elements of the power system and the participants. Simple
calculations are introduced on transmission lines, which lead
to the use of a power system simulator, a tool used to assess
load
flow, short circuit and transient stability of power systems.
Additional examples are introduced regarding transformers,
protection systems, circuit breakers, coordination studies,
conductor sizing, and other typical engineering assignments
from the real world on power systems.
Course Outline:
 Introduction to power management
 Power trends
 Mobile devices and applications
 Cellular handset: deep drive
 Hierarchical view of energy conservation: Issues and
challenges, power versus energy types, hierarchy of
Energy Conservation Techniques, Lower power
process and transistor technology, lower power
packing technique
Power
Design
Technique,
Design
 Lower
Methodology and Tools: low power architectural and
subsystem technique, Low power SoC design
methodology , tools and standards, advance power
management, advanced configuration and power
interface, the demand for application driven power
management
 Batteries and Displays: Battery technology and
chemistry selection, low power display technique
 Power Management Integrated Circuits: Voltage
Regulator, PMICs plus audio
 System Level Approach to Energy Conservation: Low
power system framework, low power software,
Technology specific energy efficient algorithms, ARM
intelligent energy manager, National Semiconductor
Power Wise Technology,
 Future trends in Power Management
Resources:

Findlay Shearer, “Power Management in Mobile
Devices”
 Schaums Outline, Power Systems
 Glover, Power Systems
Low Power System Design
Course Code:
EEN-803
Credit Hours:
3
Pre requisite:
None
Objectives:
Power consumption is one of the critical design factors in
modern VLSI design. The rapid increase in both power and
performance requirements are especially true in applications
such as wireless communication, notebook, and portable
multi-medium devices. As technology down scaling, heat
dissipation and packaging cost also demand low power IC. This
course will cover from fundamental of power consumption to
system-level design. The course emphasizes the balance
between theory and hand-on practices.
Course Contents:
Resources:
Topics covered include:

Introduction to low-power system design

Low-power digital IC basics

Gate-level & RTL low-power implementation

Dynamic power management

Power-aware Verification

System-level power optimization

Power analysis and estimation
1. A. Bellaouar and M. Elmasry, “Low-power digital VLSI
design: Circuits and Systems,” Kluwer, 1995
2. “Digital Integrated Circuits: A Design Perspective,” 2nd ed.,
J. Rabaey, A. Chandrakasan, B. Nikolic, Prentice Hall, 2003.
ISBN: 0-13-120764-4.
3. “Advanced Digital Design with Verilog HDL,” Michael D.
Ciletti, Prentice Hall, 2003. ISBN: 0-13-089161-4.
4.
“Low-Power CMOS VLSI circuit design,” by Kaushik Roy and
Sharat C. Prasad, John Wiley & Sons, INC, 2000. ISBN:0-47111488-X.
5. “Low-Power CMOS design,” edited by Anatha
Chandrakasan and Robert Brodersen, IEEE Presss, 1996.
ISBN: 0-780-33429-9.
6. “Low-Power Electronics Design,” edited by Christian Piguet,
CRC Press, 2004. ISBN: 0-8493-1941-2.
7. Michael Keating, David Flynn, Robert Aitken, Alan Gibbons,
Kaijian Shi, “Low Power Methodology Manual for Systemon-Chip Design,” Springer, 2007.
Advance System Modeling and Simulation
Course Code:
EEN-804
Credit Hours:
3
Pre requisite:
None
Objectives:
The course will cover both analytical methods (Markov Models
and Queuing Networks) and simulation techniques (Monte
Carlo Techniques and Event Driven Simulation) applied in
performance modeling of communication systems and
networks.
Course Outline:
Topics covered include:
 The Essentials of Probability
Resources:

Monte Carlo Techniques

Discrete Event Stochastic

Markov Models with

Queuing Models
1. Discrete-Event System Simulation, J. Banks and B. Nelson,
Prentice-Hall, 5th Edition, 2010.
2. Simulation, S.M. Ross, Academic Press, 4th edition, 2006.
3. Probability and Statistics with Reliability, Queuing and
Computer Science Applications, K. Trivedi, Wiley, 2nd
edition, 2002.
Special Topics in Distributed Systems
Course Code:
EEN-807
Credit Hours:
3
Pre requisite:
None
Objectives:
The course will cover both analytical methods (Markov Models
and Queuing Networks) and simulation techniques (Monte
Carlo Techniques and Event Driven Simulation) applied in
performance modeling of communication systems and
networks.
Course Outline:
Topics covered include:
Resources:

Introduction to Distributed Systems, Introduction
to Erlang

System Architecture, Communication Replication
& Consistency, Distributed Shared Memory

Synchronization & Coordination

Fault Tolerance

Middleware

Naming, Distributed File Systems

Security

Parallel Programming and Cloud Computing

Distributed Systems in Practice
1. George Coulouris, Jean Dollimore & Tim Kindberg:
Distributed Systems: Concepts and Design, 5th ed,
2011, Addison-Wesley.
2. Andrew S. Tanenbaum & Maarten van Steen:
Distributed Systems: Principles and Paradigms, 2nd ed,
2007, Pearson Prentice Hall.
3. Pradeep K. Sinha: Distributed Operating Systems, 1997,
IEEE Press.
4. Doreen L. Galli: Distributed Operating Systems, 1999,
Prentice Hall.
5. Mukesh Singhal & Niranjan G. Shivaratri: Advanced
Concepts in Operating Systems, 1994, McGraw-Hill.
Power Awareness in Distributed Systems
Course Code:
EEN-808
Credit Hours:
3
Pre requisite:
None
Objectives:
Power systems are complex networks of generators and loads
interconnected via transmission lines and various types of
equipment and apparatus (transformers, switchgear, etc). An
overview of modern power systems meeting present and
future challenges involves understanding the fast changing
structure of this system, the behavior of its components under
steady state,
dynamic and transient conditions, in order to be able to
evaluate the response of this complex system to variation of
loads, and to determine how this system can be controlled to
supply the loads reliably while it is economical and safe to the
environment.
Course Outline:
Topics covered include:
Resources:

Review of the basic concepts used in power
system analysis: phasors,complex power, three
phase systems and per-unit.

Iintroduction of equivalent circuit models for
power system components including transformers,
generators, transmission lines and loads

Application of network matrices techniques and
power flow analysis to study the steady-state and
dynamic behavior of power systems

Power system fault calculations including:
symmetrical components, symmetrical faults, and
unsymmetrical faults; surge propagation during
transients in power system

Power system stability by introduction of swing
equation, and a multi-machine system; power
system protection principles; power system
control and economic dispatch.
1. J.D. Glover, and M.S Sarma, T.J. Overbye, Power System
Analysis and Design, 5th Edition (SI), Cengage Learning,
2012.
2. B.M. Weedy, and B. Cory, Electric Power Systems, 4th
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
edition, Wiley, 1998.
ELEC4612 Power System Analysis - Course Outline 2012 p.6/6
N. Mohan, First Course on Power Systems, Minneapolis,
2006.
T.R. Bosela, Electrical Power System Technology,
Prentice-Hall, 1997.
J. Eaton, and E. Cohen, Electric Power Transmission
Systems, 2nd ed., Prentice-Hall.
M.E. El-Hawary, Electrical Power System Design and
Analysis, Prentice-Hall, 1983.
T. Gonen, Electric Power Distribution System
Engineering, McGraw-Hill, 1986.
P. Hasse, Overvoltage Protection in Low Voltage
Systems, Peter Peregrinus, 1992.
F. Kussy, and J. Warren, Design Fundamentals for Low
Voltage Distribution and Control,
Marcel Dekker, 1987.
J.C. Whitaker, AC Power Systems Handbook, CRC Press,
1991.
Stevenson, W D: Elements of Power System Analysis,
4th edition, McGraw-Hill, 1982
Greenwood, A: Electrical Transients in Power Systems.
John Wiley.
Wood, A & Wollenberg, B: Power Generation Operation
& Control, Wiley,1984
Power System Stability and Dynamics
Course Code: EEN-813
Credit Hours: 3
Pre requisite: None
Objectives:
The objective of the course is to introduce the students to modeling the
dynamics of power systems for stability studies, and provide them with the basic
concepts and fundementals of power systems contro; and stability. Students will
be able to analyze and solve voltage and frequency stability and control problems
using classical and modern control theory tools, as well as graphical and
simulation tools.
Contents:
Topics covered include:

Introduction to stability theory and modeling of dynamic system 

State space and s-domain modeling of power systems 

General concepts of state estimation 

Synchronous machines and state space models 

System response to small disturbances 

System response to large disturbances 

Analysis of linearized dynamics 

Steady state stability of multi machine systems 

Linearized models and simulation of multi-machine systems 

Voltage stability and stability criteria 

Frequency stability 

Classical models based control of power systems 

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
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
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Resources:

State feedback and optimal control of power systems 

Voltage and frequency automatic control 
P.M. Anderson and A.A. Fouad, Power system control and stability, 2nd edition,
Wiley-IEEE Press.
Power System Transients
Course Code:
EEN-814
Credit Hours:
3
Pre requisite:
None
Objectives:
This course explores the topic of transient problems on electric
utility and industrial power systems. The purpose is to teach
students the fundamentals and to enable them to recognize
and solve transient problems in power networks and
components. Topics include: a review of the Laplace transform
and dc circuit transients, ac switching transients, transients in
three-phase circuits, transients waves on transmission lines,
system modeling, computer analysis methods, lightning, and
insulation coordination.
Course Outline:

Electrical transients

Principle of superposition

The Laplace Transform

Solving differential equations

Closing transients
 Removing short-circuits and the transient recovery
voltage

RCL circuits
 Resistance switching and damping

Capacitor switching
 Magnetizing inrush and Ferroresonance
 Three-phase reactor switching and capacitor
switching
 Symmetrical components for solving three-phase
switching transients
 Electromagnetic induction, magnetic flux, and
currents
Resources:

Transient electromagnetic phenomena

Transmission lines, the wave equation, and line
terminations

Traveling wave attenuation and distortion

Power system components and frequency response

Frequency-dependent parameters

Lightning and the power system

Computation of lightning events

Lightning protection using shielding and surge
arresters

Transient voltages and grounding practices
1. Power System Analysis, 2nd Edition by Allan
Greenwood Wiley-Interscience
2. Software Tool:Electro-Magnetic Transients with DC
Analysis
HVDC and Flexible AC Transmission
Course Code:
EEN-815
Credit Hours:
3
Pre requisite:
None
Objectives:
This course covers two very important applications of power
electronics in the modern power system – High Voltage DC
transmission
and Flexible AC power transmission.
High voltage DC transmission has been used worldwide to
transmit bulk power over long distances. Recently the VSCHVDC has been introduced for the applications in the
connection of relatively weak grids. Modular multilevel
converters based HVDC (MMC-HVDC) is the latest development
in the HVDC transmission technology. MMC-HVDC will be the
backbone for the proposed offshore DC super-grids.
Flexible AC transmission technology refers to the application of
power electronics in the power system which would allow the
control series or shunt compensation techniques or their
combination. Power electronic switches may also be used to
switch passive devices like the shunt capacitors or the reactors.
Course Outline:
The course contents include:
 Classic HVDC transmission (LCC HVDC) for the bulk
power transmission over long distances
 Introduction,
operation
and
control
of
VSC-HVDC transmission for the connection of
relatively weak grids, and grid connection of
renewable energy sources
 Introduction, operation, control and evolution of
Modular Multi-level converters and MMC-HVDC
 Introduction, operation, control and theory of
reactive power compensation in power systems
 Power converters and FACTS devices 


Resources:
Shunt compensating devices SVC, STATCOM- 
introduction,
operation
and
control
1. R. S. Ramshaw: Power Electronics and Semiconductor
Switches, 2nd Edition, Kluwer Academic Press, 1993.
2. M. H. Rashid: Power Electronics, Circuit, Devices and
Application, 2nd Edition, Prince Hall, 1993.
3. N. U. Mohan, T. M. Robbins and P. William: Power
Electronics, Converters, Application and Design, 2nd Edition,
John Wiley & Sons, 2002.
4. N. G. Hingorani and L. Gyugyi: Understanding of FACTS:
Concept and Technology of Flexible AC Transmission
System, Wiley-IEEE Press, December 1999.
Rural Electrification and Distributed Generation
Course Code:
EEN-816
Credit Hours:
3
Pre requisite:
None
Objectives:
Rural electrification, which is requires huge investment, is an
important concern in Asian electric power utilities. Distributed
generation is one new option being promoted to solve rural
electrification problems along with the some other problems of
urban distribution systems. The issues such as system capacity
investments, grid expansions, etc. also benefit through
distributed generation. This course is intended to provide the
knowledge on the importance and benefit of rural
electrification, availability of resources, distributed generation
technologies, technical and financial feasibility of applying
distributed generation to rural and urban areas.
Course Outline:
Topics covered include:

Introduction to Power system Economics 

Economic Dispatch and Optimal Power Flow 

Market Overview in Electric Power Systems. 

Short-Term Load Forecasting. 

Electricity Price Forecasting. 

Price-Based Unit Commitment. 

Arbitrage in Electricity Markets. 

Market Power Analysis Based on Game Theory. 













Resources:
1. Mohammad Shahidehpour, Hatim Yamin, Zuyi Li,
Market Operations in Electric Power Systems:
Forecasting, Scheduling, and Risk Management, April
2002, Wiley-IEEE Press
2. J. Wood and B. F. Wollenberg, Power generation,
operation and control, 2nd Edition, 1996, WileyInterscience.
Artificial Intelligence techniques in Power Systems
Course Code:
EEN-817
Credit Hours:
3
Pre requisite:
None
Objectives:
A reliable, continuous supply of electrical energy is essential for
the functioning of today's modern complex and advanced
society. Electricity is one of the prime factors for the growth
and determines the value of the society. Manual calculation,
technical analysis and conclusions initially adopted the power
system design, operation and control. As the power system
grew it became more complex due to the technical
advancements,
variety
and
dynamic
requirements.
Conventional Power System analysis become more difficult due
to
1. Complex versatile and large amounts of data that are used
in calculation, diagnosis and learning.
2. The increase in the computational time period and the
accuracy due to extensive system data handling. The
modern power system operates close to their limits due to
the increasing energy consumption and impediments of
various kinds, and the extension of existing electric
transmission networks. This situation requires a
significantly less conservative power system operation and
control regime which, in turn, is possible only by
monitoring the system states in much more detail than was
necessary previously using artificial intelligence techniques.
Course Outline:





Technology of
Intelligent Systems : Introduction,
Fuzzy Logic and Decision Trees, Artificial Neural
Networks (ANN), Robust Artificial Neural Network,
Expert Systems, Fuzzy Sets and Systems, Expert
reasoning and Approximate reasoning
Application of ANN to Short-term Load Forecasting,
An ANN approach to the Diagnosis of Transformer
Faults, Real-Time Frequency and Harmonic Evaluation
using ANN.
Application of Artificial Intelligence to Angle Stability
Studies: Introduction, Transient Stability,
Critical
Clearing Time(CCT), Methods of Fast Assessment of
CCT . Knowledge-Based System for Direct Stability
Analysis.
Application of Artificial Intelligence to
Voltage
Stability Assessment and Enhancement to Electrical
Power System : ANN-Based Voltage
Stability
Assessment, ANN-Based Voltage Enhancement, A
Knowledge-Based Support System for Voltage
Collapse Detection and Prevention (KBVCDP),
Implementation of KBVCDP
Evolutionary Computation: Introduction,
Genetic
Algorithms (GAS), Object –oriented Analysis of GAS,

Resources:
Object oriented GA Design, Evolutionary Programming
(EP), Object oriented analysis, Design and
implementation of EP.
An EP Approach to Reactive Power Planning, Optimal
Reactive Power Dispatch using EP, Application of EP
to Transmission Network Planning: Introduction,
Problem formulation, EP, Numerical Results,
1. Intelligent System Applications in Power Engineering by Loi
Lei Lai John Wiley Publication
2. Electrical Systems, Dynamics, and Stability with Artificial
Intelligence Application by James A. Momoh and Mohamed
E. El-Hawary Marcel Dekker, Inc Publication USA
3. Genetic Algorithms by David E. Goldberg, Pearson
Education
4. Introduction to Neural Systems by Jacek Zurada, Jaico
Publishing House
Power System Deregulation
Course Code:
EEN-818
Credit Hours:
3
Pre requisite:
Objectives:
The main course goal is to provide students with an overview
of economic matters involved in operating and controlling the
power generation and transmission of a large scale,
restructured, interconnected power system. At the conclusion
of the course students should be able to understand difference
between vertical integrated power system and deregulated
power system and how they operate.
Course outline
Topics covered include:
 Introduction to Power system Economics
 Economic Dispatch and Optimal Power Flow
 Market Overview in Electric Power Systems.
 Short-Term Load Forecasting.

Electricity Price Forecasting.
 Price-Based Unit Commitment.
 Arbitrage in Electricity Markets.
 Market Power Analysis Based on Game Theory.

Resources:
Mohammad Shahidehpour, Hatim Yamin, Zuyi Li,
Market Operations in Electric Power Systems:
Forecasting, Scheduling, and Risk Management, April
2002, Wiley-IEEE Press
 J. Wood and B. F. Wollenberg, Power generation,
operation and control, 2nd Edition, 1996, WileyInterscience.
Advanced Computer Architecture
Course Code:
EEN-819
Credit Hours:
3
Pre requisite:
None
Objectives:
Course Outline:
 Computer Organization review
 Instruction Set Design principles and MIPS
architecture
 Principles of Scalable Performance
o Speedup Performance laws











Resources:
o Scalability analysis and approaches
Pipelining 
o Basic pipelining
o Data and control Hazards
o Exceptions
o Branch Prediction
o Speculation
o Performance Evaluation
Instruction level Parallelism 
o Score Board Architecture
o Dynamic Scheduling
o Multiple instruction issue using superscalar
approach
o VLIW – software based ILP
Compilers and code optimization 
Caches 
o Cache basics
o Techniques to reduce miss rate
o Techniques to reduce miss penalty
Programming for memory performance 
Main memory organization 
Virtual Memory and paging 
Storage devices 
Parallel Computers 
o Multiprocessors
o Parallel Architectures and applications
o Synchronization Mechanisms
1. John L. Hennessy and David A. Patterson, “Computer
Architecture: A quantitative approach”, 4th edition 2006,
ISBN 9780123704900
2. D. Sima, T. Fountain, P. Kacsuk, “Advanced Computer
Architecture”,
Addison-Wesley,
1997
3. H.S. Stone, “High-performance Computer Architecture”,
3rd
edition,
Addison-Wesley,
1993
4. Patterson, D. A. and Hennessy, J. L., “Computer
Organization and Design: The Hardware/ Software
Interface”, Morgan Kaufmann, 1998
5. Kai Hwang, “Advanced Computer Architecture”, McGraw
Hill, 2008
Stallings,
“Computer
Organization
and
6. William
Architecture”, 5th Edition, Prentice Hall International Inc.,
2000
Advanced Embedded Systems
Course Code:
EEN-820
Credit Hours:
3
Pre requisite:
None
Objectives:
Advance embedded system introduces to the students the
application of embedded systems and prepare them for the
challenges of the practical world.
Course Outline:
 Embedded Systems Introduction
o Design challenges, Metrics
o Processor technologies

















Resources:
o IC technology
o Programmable Logic Devices
o FPGA Programming Models
o HDL
o EDK Structure
o EDK Programming
Instruction Set Architecture Design
Real Time Task Scheduling
Scheduling Algorithms
Resource Sharing in RT Tasks
Scheduling RT Tasks in Multiprocessors
Case Studies of RTOS, posix, unix, windows
embedded, embed linux etc.
RTOS, processes, threads, context switching, interprocess communications, process synchronization,
interrupts
Real time Communications
Real time Communications: Routing and Rate
Control
Real time data bases
Overview of Embedded System Architecture, I/O
interface / Memory interface, Memory and
Caches, etc.
Embedded Computing Platforms
Program Design, Dataflow Graphs, Simulation,
Verification, Optimization, and Testing,
Hardware Accelerators
Device Drivers
System-on-chip
Case Studies (Applications of Embedded Systems)
1. Rajib Mall , “Real-time Systems Theory and Practice”,
Pearson Education, 2007
2. Wayne Wolf, “Computers as Components Principles of
Embedded Computer System Design”, Morgan Kaufmann /
Elsevier, 2005
Advanced Digital Signal Processing
Course Code:
EEN-821
Credit Hours:
3
Pre requisite:
None
Objectives:
This course introduces the digital filter design and its
applications to the students.
Course Outline:
 Review of DSP
( 2- 3 weeks for review )
o Discrete-time Signals
o Input-Output Relationships
o Discrete-Time Networks





Resources:
o Sampling of Signals
o Discrete Fourier Transform & FFT
Algorithms
o IIR, FIR Filters
Design of signal-processing system 
o Numerical Computation
o Conversion technologies and data
acquisition
o Implementation/description of algorithms
for DSP architectures
Advanced digital filter design 
o Wiener filter
o Parks-McClellan algorithm
Multi-rate DSP 
o Decimation, Interpolation
o Sampling rate conversion
o Applications
FFT applications 
o Advance FFT Algorithms
o Efficient algorithm design e.g. Wireless
Communication Transceivers
DSP applications 
o Modulation Schemes
o Speech Processing Applications
o Image Processing
1. Emmanuel Ifeachor and Barrie Jervis, “Digital Signal
Processing - A Practical Approach”, 2nd edition, Prentice
Hall (Pearson Education), 2002
2. John G. Proakis, “Digital Signal Processing”, 4th edition,
2006
3. John G. Proakis & Dimitris G. Manolakis , “Digital Signal
Processing: Principles, Algorithms and Applications”, 3rd
edition, Prentice Hall, 1996
4. Vinay K. Ingle, John G. Proakis, “Digital Signal Processing
using
Matlab”,
2006
5. Alan Oppenhein & Ronald Schafer, “Discrete-Time Signal
Processing”, 2nd edition, Prentice Hall, 1999
Advanced Digital System Design
Course Code:
EEN-822
Credit Hours:
3
Pre requisite:
None
Objectives:
The course introduces the students to the design and analysis
of the digital system.
Course Outline:
 Application-Specific Integrated Circuits
o ASICs: types, economics
o The ASIC design flow
o Validation methods
 System on Chip (SoC)
 Validation, Verification
o Simulation versus formal verification
o Combinational equivalence checking
o Sequential equivalence checking
o Model checking
 Simulation
 Hardware acceleration
 VHDL language, from specification to model
o Principles of Event Driven Simulation
o Practical Organization of Files and Projects
o Compilation Units
o Syntax
 Sequential VHDL
 Concurrent VHDL
o Standardized Packages
o Logic Synthesis
o Symphony EDA (a full featured VHDL
simulator)
 Application-specific instruction-set processor
(ASIP) design
 Field-programmable gate array (FPGA)
 Hardware Design Methodologies
 Case Study
 gEDA (GPL Electronic Design Automation)
 Programmable logic devices
o PLA, PAL, GAL, CPLD, FPGA
o Comparative Analysis
Resources:
1. Michael John Sebastian Smith, “Application specific
integrated circuits”, Addison-Wesley, 1997
2. Peter J. Ashenden, “Designer's guide to VHDL”, Morgan
Kaufmann, 2002
3. Mark Balch , “Complete digital design: a comprehensive
guide to digital electronics and computer system
architecture”, McGraw-Hill, 2003,
4. Wayne Hendrix Wolf, “Modern VLSI design: systems on chip
design” Prentice-Hall, 2002
5. Mark Zwolinski, “Digital system design with VHDL”,
Prentice-Hall, 2004
ASIC Design Methodology
Course Code:
EEN-823
Credit Hours:
3
Pre requisite:
None
Objectives:
The course gives an introduction to the design and analysis of
Application Specific Integrated Circuits (ASICs). The main focus
would be on, logic and physical synthesis, verification and
testing. Describe the different phases of the design flow for
digital ASICs, how non-functional design constraints affect the
design process, categorize different types of ASICs and explain
their technology, apply techniques to analyze the timing of the
final implementation.
Course Outline:








Resources:
The design and analysis of Application Specific
Integrated Circuits (ASICs)
Logic and physical synthesis
Verification and testing
Introduction to analog mixed signal Integrated circuit
design
The different phases of the design flow for digital ASICs
Non-functional design constraints
Categorize different types of ASICs and explain their
technology
Techniques to analyze the timing of the final
implementation.
1. M.J.S. Smith, “Application-Specific Integrated
Circuits”, Addison-Wesley, ISBN 0-201-50022-1, 1997.
2. HimanshuBhatnagar, “Advanced ASIC Chip Synthesis Using
Synopsys Design Compiler Physical Compiler and
PrimeTime”. 2001
3. N. H. E. Weste and D. Harris, “CMOS VLSI Design: A Circuits
and Systems Perspective”, 3rd Edition Addison-Wesley,
2004
4. J. Rabaey , A. Chandrakasan , B. Nikolic , “Digital Integrated
Circuits: A Design Perspective” 2nd Edition, Prentice Hall,
2003
5. W. Wolf, “Modern VLSI Design: System-on-Chip Design”,
3rd Edition, Prentice Hall, 2002
6. Thoman Kropf, Formal Hardware Verification, Springer
1999.
Power Aware Computing
Course Code:
SEN-805
Credit Hours:
3
Prerequisites:
Objectives:
This course basically describes the Flip Flops and Applications of Data Gating in
dynamic Flip Flops for High Speed. LowPower Sandwich/Spin Tunneling Memory
Devices and Micro Architecture Design and Control Speculation for Energy
Reduction are also discussed. From application point of view a Compiler
Targeting ASICs and FPGAs with Power and Performance.
Course outline:
 Comparative analysis of Flip Flops and Applications of Data Gating
in dynamic Flip Flops for High Speed, Low active and Low Leakage
Power Dissipation
 Low Power Sandwich/ Spin Tunneling Memory Device
 Power Efficient Issue Queue Design
 Micro Architecture Design and Control Speculation for Energy
Reduction
 Energy Exposed Instruction Sets
 Dynamic Management of Power
 Power Management Points in Power Aware Real Time Systems
 A Power Aware API for Embedded and Portable Systems
 A Compiler Targeting ASICs and FPGAs with Power and
Performance Optimization.
 Compiler Optimization for Low Power Systems
 Power Performance Tradeoffs in Second Level Memory Used by an
ARM like RISC Architecture.
 Application Level Power Awareness
 Challenges for Architectural Level Power Modeling
 Software Energy Profiling
Resources:
Power Aware Computingby Robert Graybill and Rami Melhem, 2010.
Advanced Artificial Intelligence
Course Code:
SEN-809
Credit Hours:
3
Prerequisites:
Objectives:
Course outline:
This course in Artificial Intelligence (AI)deals with the coverage of search,
knowledge representation and reasoning, machine learning (paradigms, models,
and algorithms), use of knowledge in learning, and AI applications. The emphasis
of the course is on recent developments in AI, especially contributions that
forged novel connections among diverse areas, or addressedproblems of
significant impact. The goal is to emphasizecertain thematic issues that recur in
AI systems and applications.

Basics of intelligent agents

Uniformed search including breadth-first, depth-first, iterative
deepening, bidirectional etc.

Informed search including best-first, A*, iterative deepening A*,
Heuristics
Resources:

Understanding of game playing

understanding of propositional logic and inference procedures

First order logic and inference procedures

Introduction to neural networks

Different types of neural networks

Understanding of machine learning algorithms

Different clustering, classification and association algorithms
1. Artificial Intelligence: The Basics by K. Warwick , 2011
2. Artificial Intelligence: A Modern Approach (3rd Edition) by Stuart Russell
and Peter Norvig, 2009
3. A.I. Artificial Intelligence [Blu-ray] Starring Haley Joel Osment, Jude Law,
Frances O'Connor, et al. , 2011
Advanced Neural Networks
Course Code:
Credit Hours:
Prerequisites:
Objectives:
Course outline:
SEN-810
3
Artificial Intelligence
This course presents an overview of the theory and applications of artificial
neural network and fuzzy systems to computer science and software engineering
applications. The objective of this course is on the understanding of various
neural network and fuzzy systems models and the applications of these models
to solve computing/software engineering problems.
Introduction Contexts for and Motivation Neural Networks: Artificial Intelligence
Artificial Neural Network overview.
Supervised Learning: Single-Layer Networks , Perceptrons , Adalines Supervised
Learning: Multi-Layer Networks.
Multi-Layer Perceptrons (MLPs) , Backpropagation , Conjugate Gradient method ,
Levenberg-Marquardt (LM) method , Madalines , Radial-Basis Networks ,
Cascade-Correlation Networks , Polynomial Networks , Recurrent Networks
(Time series , Backpropagation through time , Finite Impulse Response (FIR) MLP
), Temporal Differences method (TD).
Unsupervised Learning
Simple Competitive Networks: Winner-take-all | Hamming network , Learning
Vector Quantization (LVQ), Counterpropagation Networks (CPN) , Adaptive
Resonance Theory (ART) , Kohonen Self-Organizing Maps (SOMs) , Principal
Component Analysis networks (PCA)
Associative Models
Linear Associative Memory (LAM) , Hopfield Networks , Brain-State-in-a-Box ,
BSB) , Boltzmann Machines and Simulated Annealing , Bi-Directional Associative
Memory (BAM)
Optimization Problems
Resources:
Neural Network Approaches, Evolutionary Programming , Fuzzy logic and its
connection to NNs
1. Neural Networks: A Comprehensive Foundation, Simon Haykin, Prentice Hall,
Upper Saddle River, NJ, SECOND EDITION, 1999
2. Artificial neural networks: an introduction, by Kevin L. Priddy, Paul E.
Keller-Technology & Engineering-2005
3. Neural networks: methodology and applications, by G. Dreyfus-computers-,
2005
Data Warehousing and Mining
Course Code:
SEN-811
Credit Hours:
3
Prerequisites:
Database Management System
Data Structures and Algorithms
Objectives:
By the end of this course students will be familiar with concepts of Data
Warehousing including: Strategic need of data warehousing, Building blocks of a
data warehouse, Data warehouse project management, Business requirements
of a data warehouse, Architectural components of a data warehouse, Data
warehouse metadata management, Dimensionality Modeling, ETL & Data
quality, Online Analytical Processing, as well as the following areas of data
mining: Motivation for data mining, Data Preprocessing, Data mining primitives
and query languages, Architectures of data mining systems, Major Data Mining
Tasks, Cluster Analysis , Statistical measures in large databases, Classifications
and Predictions, Anomaly Detection
Course outline:
Introduction to Data Warehouse, Planning and Requirements, Data
Warehouse Architecture, Data Warehouse Infrastructure, Dimensional
Modeling, Metadata, Extraction, Transformation and Loading, Online
Analytical Processing, Data Preparation Techniques: outlier and missing
data analysis, Data Reduction Techniques, Introduction to Data Mining,
Modeling and Principal Feature Extraction, Clustering, Hierarchical
Clustering, Partitional Clustering, Classification , Decision Tree
Classification, Bayesian Classification, Nearest Neighbor Classification.
Resources:
1. Data Warehousing Fundamentals for IT Professionals, Paulraj Pooniah,
Wiley, 2nd Edition, 2010.
2. Data Mining Concepts & Techniques, Jaiwei Han, Micheline Kamber, 2nd
Edition, 2005.
3. Tutorial on Data Mining, Eamonn Keogh
Machine Learning
Course Code:
SEN-812
Credit Hours:
3
Prerequisites:
Computer Programming
Statistics
Objectives:
This course is an overview of concepts and techniques in machine
learning, beginning with topics such as classification and linear regression
and ending up with more recent topics such as boosting, support vector
machines, hidden Markov models, and Bayesian networks. The course will
give the student the basic ideas behind modern machine learning
methods.
Course outline:
Introduction to Machine Learning, Concept learning, Decision tree
learning, Linear models for regression, Linear models for classification,
Artificial neural networks, Kernel methods, Sparse kernel machines,
Mixture models and the EM algorithm, Evaluation, Combining multiple
learners, Support vector machines, Bayesian networks
Resources:
1. C. M. Bishop, Pattern recognition and machine learning, Springer,
2006.
2. Tom M. Mitchell, Machine Learning. McGraw-Hill, 1997.
Formal Methods and Specifications
Course Code:
SEN-710
Credit Hours:
3+0
Prerequisites:
None
Objectives:
As more complex computational systems are used within critical
applications, it is becoming essential that these systems are formally
specified. Such specifications are used to give a precise and unambiguous
description of the required system. While this is clearly important in
critical systems such as industrial process management and air/space craft
control, it is also becoming essential when applications involving Ecommerce and mobile code are developed. In addition, as computational
systems become more complex in general, formal specification can allow
us to define the key characteristics of systems in a clear way and so help
the development process. Formal specifications provide the basis for
verification of properties of systems.
Course outline:
Introduction to formal methods and specification, State-Based Formal
Methods;Transformational
systems,
traditional
approaches,
Z
specification, formal development cycle, Refinement, Temporal
Specification : reactive systems; syntax and semantics of temporal logic,
temporal specification of reactive systems (safety, aliveness, fairness),
Model Checking : generating finite models;analysis of a simple model
checking algorithm, symbolic model checking, overview of reduction
methods, "on the fly" model checking, Spin and Promela, Case study and
practical verification of properties.
Resources:
1. Using Z: Specification, Refinement and Proof, J. Woodcock and J.
Davies Prentice Hall, 1996.
2. Model Checking, E. M. Clarke, O. Grumberg, and D. Peled MIT
Press, 2000.
Course Code:
Credit Hours:
Prerequisites:
Objectives:
Course outline:
Resources:
Human Aspects in Software Engineering
SEN-719
3
Software engineering
Extensive human involvement in software development has made humans
an important stakeholder in software process. Furthermore, due to
emerging paradigm of end user development where end users also take
up the role of software enhancement, need for understanding human
aspects has become more critical. The goal of this course is to provide an
introduction to the fundamental human aspects in software development
process. Students will also be introduced to end user software
engineering paradigm and underlying issues.
Software Evolution, Program Comprehension, Development Team
Processes, Software development tools and environments, Quantitative &
qualitative evaluation of software engineering research
Software
Development Environments, The Nature of Software Engineering (SE),
Software as a Product, Software-Human Interaction, Learning Processes in
SE, Program Comprehension, Code Inspections, and Refactoring,
Abstraction and Other Heuristics of Software Development, Meta Design,
System Design and Tailor ability, Technology Appropriation, End User
Software Engineering, Psychological issues in end user software
engineering, Integrated software engineering approach to end user
development.
1. End-User Computing, Development, and Software Engineering:
New Challenges: New Challenges Ashish Dwivedi and Steve Clarke,
2012
2. Human Aspects of Software Engineering James Tomayko, Orit
Hazzan 2004
3. End User Development Henry Lieberman, Fabio Paterno, and
Volker Wulf, Springer 2006
Course Code:
Credit Hours:
Prerequisites:
Objectives:
Course outline:
Advanced Engineering Mathematics
MAT-853
3
There are three main objectives of this course. First, an introduction the
concepts of partial differential equations and complex variables and some basic
techniques for analyzing these problems. Second, by studying the application of
PDE's to physics, engineering, and biology, the student will begin to acquire
intuition and expertise about how to use these equations to model scientific
processes. Finally, by utilizing numerous numerical techniques, the student will
begin to visualize, hence better understand, what a PDE is and how it can be
used to study the Natural Sciences.
Review of main topics of B.S.Engineering Mathematics: Multivariable calculus,
ordinary differential equations, linear algebra, numerical methods, mathematical
statistics. Partial Differential Equations: Classical PDE’s and boundary value
problems, separation of variables, boundary value problems in cylindrical and
spherical polar coordinates. Non-homogeneous boundary value
problems.
Mathematical Modeling:
Stochastic Techniques, Generation
functions,
Convolutions, Compound distributions, Introductory Stochastic processes,
Simulation, Introductory simulations, Generation of random numbers.
Probabilistic modeling: Markovian Models, Exponential distributions, Poisson
processes. Complex Analysis: Taylor and Laurent series, zeros, singularities and
residues, Integration of analytic functions, conformal mapping. Linear Algebra:
Unitary, Normal and Hermition matrices, Decomposition of matrices into
triangular factors, Eigen values and Eigen Vectors, Diagonalization, powers of
matrices, method of least squares.
Resources: 1. E. Kreyszig, Advanced Engineering Mathematics, 9th Edition, John Wiley and
Sons, New York.
2. Nguyen V.M. Man, Mathematical Modeling and Simulation.
3. E.C. Zachmanoglou and D.W. Thoe, Introduction to Partial
Differential Equations with Applications, Dover, 1986.
4. Dennis G.Zill And Warren S. Wright, Advanced Engineering
Mathematics Jones and Bartlett, 2011
5. G. Strang, Linear Algebra and its Applications, 4th Edition,
Wellesley-Cambridge Press, 2009.
Logic and Research
Course Code:
Credit Hours:
Prerequisites:
Objectives:
MGT-801
3
-
Course outline:
The course will discuss in detail the field of logic, with reference to its history,
nature, types and composition. The course would also discuss the interface
between logic and construction of theory and see how theory can be utilized in
the process of research. Of pivotal significance for this course would be a
discussion of the relationship between language and research with reference to
the product of research as well as the interaction between the researcher and
the researched. It would also look in detail at the domain of philosophy of
science. It will also comprise a discussion of issues of significance in behavioral &
management research to include concepts, laws, explanations, causation,
measurement and models. The course would also strive to look at discussion of
how values can affect behavioral and management science research and various
means of dealing with the same.
This course will look at the domain of logic in terms of its structure, dynamics
and intellectual debates within it.The course also aims at enabling the students
to understand the articulation of the principles of logic with the process of
research and knowledge production within behavioral sciences with a particular
focus on Management research.The course would also discuss the interface
between logic and construction of theory and see how theory can be utilized in
the process of research.
Resources:
1. The Logic of Social Research by Arthur L. Stinchcombe, 2005
2. Constructing Social Theories by Arthur L. Stinchcombe
Course Code:
Credit Hours:
Prerequisites:
Objectives:
Course outline:
Advanced Qualitative Research Methods
MGT-802
3
This course aims at offering an introduction to qualitative research methods.
Participants will learn about the usefulness of qualitative research methods, the
philosophical and theoretical underpinnings of this type of research, the various
approaches and schools of thought, as well as about particular research
methods. Finally, the course will also place qualitative approaches and methods
within the broader research design, i.e., in the case of engineers, often as a
complement to quantitative research. But most of all, the course will help the
participants to make progress in the formulation of their problem statement,
their research design, qualitative data collection, and analysis of qualitative data.
 Bases of classification of research
 Qualitative research vis-à-vis quantitative research
 Major dimensions of differences between quantitative research
and qualitative research
 Explain and apply different approaches to qualitative research
including the following:
- grounded theory, ethnography, phenomenology, narrative
inquiry, and case study
- Select and use different tools of data collection such
as:Interviewing,Focus Group, Observation methods, and Visual
methods.
Resources:
1. Qualitative Research Methods for the Social Sciences, 7th Edition
by Bruce L. BergDec 13, 2008
2. Qualitative Research & Evaluation Methods by Michael Quinn
PattonOct 2001
3. Qualitative Research: A Guide to Design and Implementation by
Sharan B. Merriam , Apr 6, 2009
Course Code:
Credit Hours:
Prerequisites:
Objectives:
Course outline:
Advanced Quantitative Research Methods
MGT-806
3
The purpose of this course is to introduce some important fundamental concepts
of quantitative research especially to novice researchers. It comprises types of
research, definitions of quantitative research, different types and assumptions of
quantitative research, when to use and not to use quantitative methods,
advantages, common approaches and samples of quantitative research, and
common misconceptions. Besides, a set of criteria for evaluating quantitative
research proposal is provided. The main focus is on the assumptions underlying
the quantitative research and some of the misconceptions that many researchers
have when they are conducting a research study.
Introduction to Research, Quantitative Research,Interpreting the Natural World
Testable and Untestable, Logic and Arguments, Types of Research, The
Hypothesis, Introduction to Testing Observations, Theory and Hypothesis,
Formulating a Technical Hypothesis, Relationships Between Variables,
Conceptual, Operational, Measurable, Observation of Human Systems,
Research Plan , P-value and the Null, Problems in Observation, Descriptive
Statistics, Overview of Descriptive Statistics, Standard Deviation and Variance,
Types of Variables, The Experiment, Sources of Variability, ANOVA, The Survey,
Introduction to the Survey, Survey Design, Asking Questions, The Index ,The
Scale, Survey Stats Overview,Sampling, Designing a Study, The Task of
Writing,Statistical Testing Overview ,Graphing Data.
Resources:
1. Quantitative Research: An Introduction by Benjamin Mis, 2012.
2. Quantitative and Statistical Research Methods: From Hypothesis to
Results , William E. Martin and Krista D. Bridgmon, 2012.
3. Research Design: Qualitative, Quantitative, and Mixed Methods
Approaches by John W. Creswell , 2008.
Critical Review of Literature
Course Code:
Credit Hours:
Prerequisites:
Objectives:
MGT-803
3
This course deals with the various methods for searching and reviewing of
the literature. Developing an argument, its analysis and building the
argument of discovery are also discussed in this course. The course also
aims to describe Tools for structuring a research thesis and how to cite
references, and prepare bibliography in different styles.
Course outline:
The Literature Review in Research: Why?
For Whom?
How? The
Literature Review Process – Getting Started, Reviewing and the Research
Imagination,Search the Literature, Developing an Argument:
Argumentation and Analysis,Survey the Literature: Building the Argument
of Discovery, Critique of the Literature and Structure of Criticism, Mapping
and Analyzing Ideas, Structuring the Literature Review, Writing the
Literature Review and the Writing Process, Being Critical in Writing the
Literature Review, and Academic Writing,
Managing Information and
Keeping the Records, The Research Proposal and its Format, Tools for
Structuring a Research Thesis and How to Cite References, and Prepare
Bibliography in APA Style.
Resources:
1. Writing Literature Reviews Fourth EDITION by Jose L. Galvan, 2009
2. The Literature Review: Six Steps
to Success by Lawrence A.
Machi and Brenda T. McEvoy, 2008
3. Preparing Literature Reviews: Qualitative and
Quantitative
Approaches by M. Ling Pan, 2008
Computer Vision
Course Code:
CSC-750
Credit Hours:
3
Prerequisites:
Digital Image Processing
Objectives:
B y the end of this course, the students would have developed an understanding
of the problems in simulating human perception into machines. Students will
have a thorough understanding of the state of the art computer vision methods,
algorithms and results. The students will also be able to apply the tools and
techniques learned to solve practical vision related problems.
Course outline:
Introduction to Computer Vision and related areas along with applications,
Image formation and representation: imaging geometry, digitization, cameras
and projections, rigid and affine transformations, Filtering: convolution,
smoothing,. Segmentation: region splitting and merging; quadtree structures for
segmentation; Feature detection: edge detection, corner detection, line and
curve detection, SIFT and HOG descriptors, shape context descriptors. Model
fitting: Hough transform, line fitting, ellipse and conic sections fitting, algebraic
and Euclidean distance measures. Camera calibration: camera models; intrinsic
and extrinsic parameters; affine, and perspective camera models. Epipolar
geometry: introduction to projective geometry; epipolar constraints; the
essential and fundamental matrices; Motion analysis: the motion field of rigid
objects; motion parallax; optical flow, the image brightness constancy equation,
affine flow; differential techniques; feature-based techniques; Motion tracking:
the Kalman filter; Object recognition and shape representation.
Resources:
1. Computer Vision: Algorithms and Applications, R. Szeliski, Springer, 2011.
2. Computer Vision: A Modern Approach, D. Forsyth and J. Ponce, Prentice Hall,
2nd ed., 2011.
3. Computer Vision: A Modern Approach, By David Forsyth, Jean Ponce,
Prentice Hall, 2003.
4. Computer Vision, By Linda G. Shapiro, George C. Stockman, Prentice Hall,
2001.
5. Handbook of Mathematical Models in Computer Vision, By Nikos Paragios,
Yunmei Chen, Olivier Faugeras, Birkhäuser, 2006
Pattern Recognition
Course Code:
CSC-715
Credit Hours:
3
Prerequisites:
1. Probability and Statistics
2. Linear Algebra
Objectives:
The goal of this course is to provide an introduction to the fundamental
concepts of machine learning and pattern recognition with examples from
several application areas. The students will be acquainted with real world
regression and classification problems and the models and classifiers to
solve these problems. Students will also be introduced to dimensionality
reduction and feature selection concepts. Additionally, students will be
exposed to various clustering techniques. A key objective to this course is
for the students to also acquire hands-on experience related to
classification and clustering tasks.
Course outline:
Introduction to Pattern recognition and Machine learning, Matrices and
vectors: Toeplitz and Vendermonde matrices, classification and regression,
Bayesian Decision theory, Normal Density and decision functions for
normal distribution, Maximum likelihood estimation, Dimensionality
reduction – Component analysis, feature selection, Hidden Markov
Models and Artificial neural networks, Non-parametric methods,
Unsupervised learning and clustering: Clustering techniques.
Resources:
1. Pattern Classification, Duda, Hart and Stork, Second Edition, Wiley, 2001.
2. Pattern recognition and Machine Learning, Christopher M. Bishop,
Springer, 2007.
3. Introduction to Machine Learning, Ethem Alpaydin, MIT Press, 2004.
4. The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani and
Jerome Friedman, Springer, 2009.
5. Pattern Recognition, S. Theodoridis & K. Koutroumbas, Academic Press,
2008.
Agent Based Modeling
Course Code:
Credit Hours:
Prerequisites:
Objectives:
CSC-815
3
After taking this course, the participants:
 will have an understanding of the agent system terminology and
development process of agent-based systems.
 will have learned techniques to design agent-based system.
 will know how to modify architecture of the current software
systems and re-structure them to be agent-based.
Course outline:
1. Introduce the basic concepts of agent-based modeling;
2. When and why agent-based models are used;
3. Methodologies for agent-based modeling, analysis and design.
4. Agent-based Unified Modeling Language (AUML)
5. Agent communication and knowledge sharing.
6. Agent-based System Architecture and Organization.
7. FIPA: Foundation for Intelligent Physical Agents.
8. Agents and web services
9. Mobile Agents
10. Simulation of agent Based Modeling
11. Standards for Agents and Agents based Systems
Resources:
1. The Agent Modeling Language--AML: A Comprehensive Approach
to Modeling Multi-agent Systems by Radovan Cervenka and Ivan
Trencansky 2007
2. Multiagent Systems : A Modern Approach to Distributed Artificial
Intelligence, Gerhard Weiss, MIT Press, 1999.
3. Readings in Agents, M.N. Huhns and M.P. Singh, Morgan
Kaufmann Publishers. 1998.
4. Heterogeneous Agent Systems, V. S. Subrahmanian, Piero Bonatti,
Jurgen Dix, Thomas Eiter and Fatma Ozcan. 2000
5. Constructing Intelligent Agents Using Java: Professional
Developer's Guide, (432 pages) Joseph P. Bigus, Jennifer Bigus,
(2nd Edition) John Wiley and Sons. 2001.
6. Agent-Oriented Methodologies, Brian Henderson-Sellers, Paolo
Giorgini. 2005
Bio Medical Image Analysis
Course Code:
Credit Hours:
Prerequisites:
CSC-816
3
Objectives:
The objective of the course is to learn how to “process” signals to obtain
medical images for each modality (based on its physics, mathematical
modeling and instrumentation) but not digital signal processing (DSP) of
medical imaging.
Computer Vision/Digital Image Processing
Course outline:
Resources:
 Course overview
 Introduction of medical imaging
 Signals and system
 Imaging quality
 Physics of radiography
 Projection radiography
 Computed tomography (CT)
 Physics of nuclear medicine
 Planer scintigraphy
 Emission computed tomography (SPECT, PET)
 Physics of ultrasound
 Ultrasonic imaging systems
 Electrical impedance tomography (Guest lecture by Prof. A. Adler)
 Physics of magnetic resonance
 Magnetic resonance imaging (MRI)
 Project presentation
 Course review and wrap up
Medical Imaging Signals and Systems, by J.L. Prince and J.M. Links,
Pearson Prentice Hall, 2006