msc electronic engineering (control) courses phases

MSc Electronic Engineering (Control)

Courses Phases Probability and Stochastic Processes Courses Code ENG 611 MD

Course Description

2 credit hours.

Course Objective To teach the students for applying probability and stochastic process techniques in design and analysis of Electronic systems.

Expected learning Outcomes

The students will able to apply the concepts of probability and stochastic process for analyzing the performance of Electronic systems.

Course Contents Unit I: Random Variables and their Probability Distributions Random Variables: Probability distribution function - probability density function - conditional probability - statistical independence - Bayes formula; Moments of random variables: Expected value and moments - mean and variance of random variable – coefficients of variation - skewness and kurtosis - moments - covariance and correlation coefficient - mean and variance of sum and product of two random variables - conditional mean and variance - application of conditional mean and variance. Unit II: Discrete Random Variables and their Distributions Moment Generation Function: Characteristics function - cumulants - probability generating function - binomial distribution - negative binomial distribution - hypergeometric distribution -multinominal - Poisson distributions - relationship between various discrete type distributions. Unit III: Continuous Random Variables and their Distributions Continuous Distributions: Normal - log normal - multivariate normal - gamma - exponential - chi square - Weibull - Rayleigh distributions - relationship between continuous distributions. Unit IV: Transformation of Random Variables Transformation of Single and Several Random Variables: Function of random variables - sum - differences - product and ratio of two random variables - transformation through characteristic functions. Unit V: Stochastic Processes Introduction: Classification of stochastic process - stationary process (SSS and WSS) - ergodic process - independent increment process - Markov process - counting process - narrowband process - normal process - Wiener Levy process

- Poisson - Bernoulli – shot noise process - autocorrelation function. Teaching Methods Lectures, tutorials, assignments and practical assignments. Assessment Methods 70% Exam; 30% Coursework.

Reference Books 1. Michel K. Ochi, “Applied Probability and Stochastic Processes”, John Wiley

and Sons, 2008. 2. Paboulis A., “Probability, Random Variables and Stochastic Processes”, Tata

McGraw Hill, 1984. 3. Kishor S. Trivedi, “Probability and Statistics with Reliability Queuing and

Computer Science Application”, John Wiley and Sons, 2002. Hyperlinks: 1. http://users.ece.utexas.edu/~gustavo/ee381j.html 2. http://www2.math.uu.se/research/telecom/software.html 3. http://www.ifp.illinois.edu/~hajek/Papers/randomprocesses.html. 4. http://www.rle.mit.edu/rgallager/notes.html.

Computer Interface Design Courses Code CON 612 MD

Course Description

3 credit hours.

Course Objective

This course provides the principles of data acquisition and hardware interfacing.

Expected Learning Outcomes

On successful completion of the module, students will have : An understanding of the principles of data acquisition and hardware interfacing to a

personal computer ; An understanding of visual programming and user interface design and

implementation;

Course Contents

Support in the use of the project PC-based hardware and software , including sensors , transducers , analogue I/O , digital I/O , event handling , data manipulation , data visualization and the design of user interfaces . Parallel interface , LPT , serial interface , RS 232 , network adapters ,Priority interrupt Controller (PIC), Programmable Timer (PTM), Direct Memory Access Controller (DMA) , programming interface devises are also discussed.

Teaching Methods

There will be 30 hours of lectures and 45 hours of lab

Assessment Methods

Examination 100 % Test 30 % Assignments 20 % Lab 50%

Reference Books

Transducers and interfacing , Bannister BR & whitehead DG , van Nostrand Reinhold ,

Soft Computing and Intelligent Systems Design Courses Code CON 613 MD

Course Description

3 credit hours.

Course Objective

Evolutionary computing has been used in engineering, particularly in optimization, to solve computationally hard problems. With experience, genetic algorithms can be applied as a general purpose method across disciplines. The course introduces the concept of genetic algorithms, shows how and why these algorithms work, and discussed some of the recent software tools such as MIT's GALib (Genetic Algorithm Optimization Toolbox (GAOT) under Matlab). The course proceeds to include other adaptive methods, such as simulated annealing, tabu search, and neural networks, especially as they relate to genetic algorithms.


On successful completion of the module, students should:-

Acquire understanding and knowledge of soft computing models and algorithms and so they will be able to design program systems using these techniques to solve various real world problems.

Appreciate the importance of tolerance of imprecision and uncertainty for design of robust and low cost intelligent systems.

Apply these techniques in engineering case studies, including robotics, traffic light systems, and reservoir properties prediction

Course Contents

Artificial Intelligence: Intelligence, Artificial intelligence, intelligent systems. Knowledge representation: Reasoning, issue and acquisition: propositional calculus, predicate calculus, Rule-based knowledge representation, Truth Maintenance system.

Basic neuron models: McCulloch-Pitts model and the generalized one distance or similarity based neuron model, radial basis function model, etc.

Basic neural network models: multilayer perceptron, distance or similarity based neural networks, associative memory and self-organizing feature map, radial basis function based multilayer perceptron, neural network decision trees, etc.

Fuzzy Systems: introduction, foundation of fuzzy systems, fuzzy relations, arithmetic operations of fuzzy numbers, linguistic descriptions and their analytical forms, defuzzification methods, fuzzy logic in control and decision-making applications

Basic learning algorithms: the delta learning rule, the back propagation algorithm, self-organization learning, the r4-rule, etc.

Genetic Algorithms and Evolutionary Programming: introduction, genetic algorithms, procedures of genetic algorithms, the working of genetic algorithms, evolutionary programming, genetic-algorithm-based machine learning classifier system.

An Overview of Combinatorial Optimization Swarm Intelligent Systems: introduction, importance of the ant colony

paradigm, ant colony systems, development of the ant colony systems, application of ant colony. Intelligence, the working of ant colony systems: Probabilistic Transition rule, Pheromone Updating, Types of ant colony models. Particle Swarm intelligent systems Applications:

pattern recognition, function approximation, information visualization.

Teaching Methods

Lectures, tutorials, assignments and practical assignments.

Assessment Methods

70% Exam; 30% Coursework.

Reference Books

Soft Computing and Intelligent Systems Design, Theory, Tools and Applications, F. Karray, C. De Silva, Prentice Hall, 2004.

Parallel Processing Courses Code CON 614MD

Course Description

3 credit hours.

Course Objective

This course will introduce students to the fundamentals of explicitly parallel programming. This includes the types of explicit parallelism, the general models used in parallelization, as well as practical usage.


On successful completion of the module, students will have: A Knowledge of the Shared memory programming with Open MP A Knowledge of the Shared memory programming with pthreads , and A Knowledge of the Distributed memory programming with MPI

Course Contents

This course will cover a range of topics involved in designing and programming parallel architectures . The course will focus on the most common type of parallel machines : shared and distributed memory multi –processor systems. As time permits, the course will also cover other parallel machines and programming paradigms including data-flow, vector processing, and multi-threaded architectures.

Teaching Methods

There will be 30 hours of Lab and tutorial with total of 45 credit hours

Assessment Methods

Examination 100 % Tests 30 % Assignments 20 % Lab 50 %

Reference Books

Introduction to parallel Computing , second edition by Grama , Gupta Karypis & Kumar

Real Time Embedded System Design Courses Code CON 615 MD

Course Description

3 credit hours.

Course Objective

This module aims to provide students with a more in-depth understanding of the theory and applications of real time embedded systems. The course introduces the concept of real time embedded systems


On successful completion of the module, students should:- Know the theory and applications of real time embedded systems. Know design concepts of real time embedded systems. Apply different techniques of real time embedded system in engineering case studies

Course Contents

Architecture of Embedded System, Hardware Architecture, Software Architecture, RTOS, Architecture of Kernel, Features/ Characteristics of RTOS. Task Scheduling, Signals, Events, Queues, Mail Boxes, Semaphores, Creation of Threads and Inter Thread Communication, Memory Management

Detailed study of PIC18 Family Microcontroller Architecture, Pin Description, File Structure, Status Register, PIC data formats, Directives, RISC Architecture in PIC, SFR, PIC18 Hardware Connections, PIC 18 Timers, PIC 18 Serial Port, PIC 18 Interrupts. Features of ATMEL, ARM, AVR Microcontrollers.

PIC 18 Instruction set, Programming using C / Assembly: Data types, time delays, I/O Programming, Data Conversion, Timer/Counter, Serial Port, Interrupt programming, ADC,DAC, Sensor Interfacing.

Clock-Driven Scheduling: Notation and Assumptions, Static, Timer Driven Scheduler, General structure of Cyclic Schedules, Cyclic Executives, Improving the Average Response Time of periodic Jobs, Scheduling Sporadic Jobs, Practical Consideration and Generalizations, Algorithms for Constructing Static Schedules, Pros and Cons of Clock-Driven Scheduling.

Priority-Driven Scheduling of Periodic Tasks: Static Assumption, Fixed-Priority versus Dynamic-Priority Algorithms, Maximum Schedulable Utilization, Optimality of the RM and DM Algorithms, A Schedulability Test for Fixed- Priority Tasks with Short Response Times, Schedulability Test for Fixed-Priority Tasks with Arbitrary Response Times, Sufficient Schedulability Conditions for the RM and DM Algorithms.

Scheduling Aperiodic and Sporadic Jobs in Priority-Driven Systems: Assumption and Approaches, Deferrable Servers, Sporadic Servers, Constant Utilization, Total Bandwidth, and Weighted Fair Queuing Servers, Scheduling of Sporadic Jobs, Real-time Performance for Jobs with Soft Timing Constraints.

Teaching Methods


Assessment Methods


Reference Books

Real Time Embedded System Design, 2nd edition, K. Robert, 2010

Scientific English Courses Code ENG 616 MD

Course Description 0 credit hours. Course Contents Writing skills: Scientific writing overview, Elements of a paragraph,

Practice of paragraph form, How to write a summary, Practice of summary

writing, Introductions and conclusions, Structuring essays/articles, Note

taking, Proof reading, common errors, peer review, Describing processes

and conditions

Reading skills: Reading paragraphs, identifying elements of a paragraph,

Reading articles, identifying structure, Recognising and following

arguments, Proof reading, constructive criticism, Skimming and scanning

for essential information

Communication (speaking, listening): Discussion of articles, Debate skills

(structure and development of argument), Group presentation skills,

Individual presentation skills, Watching scientific/medical documentaries,

Development of creative thinking, Asking and responding to questions,

Group discussion of contemporary issues, Expressing opinions, reservations,

agreement and disagreement, Asking for clarification/further information Teaching Methods Lectures, assignments.

Assessment Methods 70% Exam; 30% Coursework. Reference Books

Optimal and Robust Control Courses Code CON 621 MD

Course Description

3 credit hours.

Course Objective

Basic concepts of optimal control systems are emphasized. Properties and Application of the Optimal Regulator and State Estimator Design. Different control methods will be covered such as Linear Quadratic Gaussian (LQG) Control. Frequency Shaping. Robust Control Systems and System sensitivity along with the system Uncertainty and Robustness. Performance robustness of Control Systems will be covered.


On successful completion of the module, students will have an understanding of: 1. The basic techniques used to describe linear and nonlinear control system 2. Frequency response analyze for robust control system 3. Stability analysis 4. Performance robustness of control system 5. Ability in using MATLAB as a tool for analyzing and processing different control system

Course

Contents Optimal Control Systems and Performance Indices. Optimal Control of linear systems with Quadratic Performance Index. Optimal State Regulator Design through Matrix Ricatti equation. Properties and Application of the Optimal Regulator. Linear Quadratic Gaussian (LQG) Control. State Estimator Design. System Design using State Estimators. Loop Transfer Recovery (LTR). Frequency Shaping. Robust Control Systems and System sensitivity. Uncertainty and Robustness. Structured and Unstructured uncertainty. Internal Stability. Kharitnov's methodology. Stability robustness and Performance robustness of Control Systems. Mu-Synthesis. Robust Tracking. H2 and H-infinity Control. H-Infinity Loop Shaping. Gap Metric. Linear Matrix Inequalities (LMI). Quantitative Feedback Theory (QFT).

Teaching Methods


Assessment Methods


Reference Books

Roaust Control System – Third Edition .M.BAKI ISBN 0-204-11523

Advanced Digital Signal Processing Courses Code ENG 622 MD

Course Description

3 credit hours.

Course Objective

Basic concepts of discrete linear shift-invariant systems are emphasized, including sampling, quantization, and reconstruction of analog signals. Extensive coverage of the z-transform, discrete Fourier transform, and fast Fourier transform is given. An overview of digital filter design includes discussion of impulse invariance, bilinear transform, and window functions. Filter structures, finite length register effects, round off noise, and limit cycles in discrete-time digital systems are also covered.


On successful completion of the module, students will have an understanding of: 1. The basic techniques used to describe continuous and discrete time signals; 2. Time-domain and frequency-domain descriptions of signals; 3. The transform techniques used to convert between time- and frequency-domain descriptions; 4. The specification and design of digital filters; 5. Ability in using MATLAB as a tool for analyzing and processing signals and for designing Digital Filters.

Course Contents

INTRODUCTION TO SIGNALS AND SYSTEMS 1 Introduction to MATLAB functions for signals and systems. Introduction to signals and systems. Time-domain models. Frequency-domain models. Periodic signals and the Fourier Series. Non-Periodic Signals and the Fourier Transform. The Laplace Transform and its application INTRODUCTION TO SIGNALS AND SYSTEMS 2 Simple Continuous Time systems. Convolution. Impulse Response. Filtering of Continuous Time Signals. Sampling and Discrete-Time signals. The Sampling Theorem. Aliasing. Analogue-to-Digital (ADC) and Digital-to-Analogue (DAC) Converters. Discrete Time Systems DIGITAL FILTERS AND DIGITAL FILTER DESIGN The Discrete Fourier Transform, The Fast Fourier Transform. The z-transform. Pole-Zero diagrams, Stability issues. The Transfer Function. Introduction to Digital Filters. FIR Filters design, implementation and applications. Windowing. IIR Filters: design, implementation and applications. The bi-linear z-transform. MATLAB tools for filter design. DSP performance and limitations issues.

Teaching Methods


Assessment Methods


Reference Books

Digital Signal Processing - A Practical Approach, Second Edition. Emmanuel C. Ifeachor, Barrie W. Jervis, Prentice Hall, ISBN 0-201-59619-9

Digital Signal Processing - Principles, Algorithms and Applications, Fourth Edition. John G. Proakis, Dimitris G. Manolakis, Pearson Prentice Hall, ISBN 0-13-187374-1.

Advanced Control System Courses Code CON 623 MD

Course Description

3 credit hours.

Course Objective

Basic concepts of feedback control techniques. Digital control system will be covered. Z- Transform and digital approximation of classical controllers. State space methods should be covered


On successful completion of the module, students will have an understanding of: 1. The basic techniques control system 2. State space description 3. Digital control system 4. Z- Transform and digital approximation 5. Ability in using MATLAB as a tool for analyzing and processing different control system

Course Contents

Review of classical feedback control techniques and performance specifications; state-space fundamentals; introduction to digital control; discrete system analysis and Z-transform; sampling of continuous-time signals and samples data systems; discrete equivalents; design using transfer technique; digital approximation of classical controllers; role of digital computer in systems design; design using state space methods; case study; design of a typical control system using either transfer techniques or state space method.

Teaching Methods


Assessment Methods


Reference Books

1. Gene F. Franklin, J David powell, Michael L workman, “Digital control of dynamic

systems”. Addison longman Inc 1988. 2. B.C. Kuo “Digital control systems” prentice hall 1996

Computer Control of Industrial Processes Courses Code CON 624 MD

Course Description

3 credit hours.

Course Objective

Basic concepts of computer interfacing techniques. SISO and MIMO system are covered. DMA and peripherals interface. DDC controllers and PID controllers should be studied. PLC and different application along with the distributed control.


On successful completion of the module, students will have an understanding of: 1. SISO and MIMO systems 2.computer interfacing 3. Programmable logic controller 4. Digital implementation of PID controller 5. Distributed control

Course Contents

Introduction to computer Control: Brief History, Advantages, different types and application areas, Direct Digital Control and Supervisory Control, SCADA, Embedded Controller. Example Processes: Standard SISO process, first order with delay Standard sensors and actuators. Simple MIMO process. Batch process, which require sequential control. Architecture of a Computer Control System: Generic architecture. Use of Context diagram and DFD for architectural description. Specific Examples Interfacing : Digital data transfer from peripherals, polling, interrupt, DMA. Interfacing considerations for field Input-output. Programmable Logic Controllers: Features functionalities and Architecture, Examples of programming and applications DDC controllers: Features functionalities and Architecture, Examples. DDC algorithms. Digital Implementation of Two term (PI, PD) and three term (PID) controller. Implementation of digital compensators. Model Based Control and their digital implementations. Distributed Control: Architecture, advantages. Communication for distributed control. Field Bus. Application Examples.

Teaching Methods


Assessment Methods


Reference Books

Inelustricul control using computer .L. samull . 2nd Edition .2011

Research Methodology Courses Code ENG 625 MD

Course Description 2 credit hours. Course Contents Course Contents: Definition, purpose and need for researching. Phases

of research conduction: Connectional phase, Design and planning phase,

The empirical phase, The analytical phase, The dissemination phase.

Principles of preparing and submitting research proposals. Types of

studies: Observational studies, Experimental studies, Questionnaire

designing, explaining the research triangle concept.

Teaching Methods Lectures, assignments Assessment Methods 70% Exam; 30% Coursework.

Reference Books

Elective Course 1 : Advanced Microprocessor and Microcontroller Design

Courses Code CON 626 MD

Course Description 2 credit hours. Course Objectives This course examines applications of the methods and concepts

microprocessors Expected Learning

Outcomes

On successful completion of the module, students will have :-

1. Knowledge of the practical use of microprocessors. 2. Knowledge of the data analysis representation and hardware

models 3. Knowledge of the design of integrated hardware /software

systems. Module Contents Examples emphasize the practical use of microprocessors . Analysis of

data representation , computation of efficient hardware algorithms , and hardware models are studied. Advance computer systems engineering – microprocessors , design of integrated hardware /software systems, studies of current techniques . A comprehensive design project is required part of the course.

Teaching Methods There will be 30 hours of lecture and 45 hours of lab Assessment Methods Examination 100 %

Tests 30 % Assignments 20 % Lab 50 %

Reference Books Single and Multiple Chip Microcomputer Interfacing , G.J.Lipovski prentice Hall , 198 , ISBN0- 13-811654-7.

Programming Microcontrollers in C , Ted van Sickle , Motorola , 1994 , ISBN 1-8788707-14-0

Elective Course 2 : Artificial Intelligence Applications Courses Code CON 626 MD Course Description 2 credit hours. Course Objectives The goal of this course is to understand important problems , challenges ,

concepts and techniques from the field of Artificial Intelligence .In order to achieve this , students learn how to analyze , design , and program intelligent agents of varying complexities . These agents gather information from their environment , convert it into a suitable internal representation (Which may be augmented with information provided by the designer), analyze their internal knowledge to determine suitable actions , and finally execute some actions .


On successful completion of the module, students should :- Know classical examples of artificial intelligence Know characteristics of programs that can be considered

“intelligent”

Understand the use heuristics in search problems and games Know a variety of ways to represent and retrieve knowledge and

information Know the fundamentals of artificial intelligence programming

techniques in a modern programming language Consider ideas and issues associated with social technical , and

ethical uses of machines that involve artificial intelligence Module Contents 1. Introduction to AI . Brief history

2. Search: uninformed and heuristic search , local search and optimization.

3. Constraint satisfaction problems 4. Game playing and adversarial search 5. Logical reasoning . Propositional Logic . First – order logic .

inference in first – order logic 6. Planning 7. Supervised learning methods . Neural networks. 8. Reasoning under uncertainty. Bayes rule. Belief networks. Using

beliefs to make decisions. Learning beliefs 9. Sequential decision making. Reinforcement learning. 10. Special topics : Robotics , Natural Language Processing , Game

Theory , Other AI applications 11. Wrap- up



Reference Books Artificial Intelligence : A Modern Approach (Second Edition) by Stuart Russell and peter Norvig ; Prentice Hall, 2003.ISBN 0-13-103805-2.

Elective Course 3 : Artificial Intelligence Applications Courses Code CON 626 MD Course Description 2 credit hours. Course Objectives This module aims to provide students with a more in – depth

understanding of the operation system of a typical computer system. Expected Learning

Outcomes

On completion of this module , students be able to :- Understand the need for operating systems , and be aware of their

overall structure. Be able to identify and explain issues relating to performance of

systems and user programs. Understand hardware support for high level languages , and be

aware of the relationship between compliers , compiled code and the operating system , and its effect on performance .

Be able to understand and modify existing operating system as necessary.

Course Contents 1. Review of operating systems principles , and the idea of the operating system a provider of a virtual machine .

2. Structures and types of operating systems; microkernels , layered systems , monolithic systems , relevance to distributed systems .

3. Basic Kernel components; concurrency; synchronization and communication; interrupt handling, scheduling, processes and threads.

4. Memory management and its relation to the hardware ; virtual memory , mechanisms and policies.

5. Program environment ; virtual machine layout ; link editing , link loading , object file formats , dynamic linking . editing , link loading , object file formats , dynamic linking . Relocation , Position independence . Static and dynamic data areas : stacks , heaps , sharing .

6. Filing systems ; structures , data metadata , performance , reliability and robustness . Related issues ; backup , arching , security .

7. Miscellaneous issues ; bootstrapping , diskless systems , multiprocessors.



Reference Books Operating Systems Design and Implementation . Andrew S. Tanenbaum and Albert S. Woodhull . Published , Prentice – Hall , 2006.

Introduction to Operating Systems : Behinf the Desktop . John English . Published by Palgrave Macmillan .

msc electronic engineering (control) courses phases

Documents