Graduate courses

 

 First Semester

  • GE 604 Advanced Mathematics
  • GE 606 Advanced Statistics
  • EC 681    Advanced Microprocessors

Second Semester

I- Computer Systems Industrial Applications Branch

 Third Semester

  • EC647     Distributed Real Time Systems
  • Elective   Course I
  • Elective    Course II

Fourth Semester

  • EC 698   Computer Systems Industrial Applications Seminar
  • Elective   Course III
  • EC 699     Project

II – Computer Systems Design  Branch  

 Third Semester

  •  EC682 Computer System Design
  • Elective Course I
  • Elective Course II

 Fourth Semester

  •  EC 697  Computer Systems Design Seminar
  • Elective  Course III
  • EC 699   Project

LIST OF ELECTIVE COURSES

I- Computer Systems Industrial Applications Branch Elective Courses:

  • EC 640 Adaptive Control.
  • EC 642 Mechatronics.
  • EC 643 Computer Control of Stochastic Systems.
  • EC 644 Robotics.
  • EC 645 Fuzzy Engineering.
  • EC 648 Adaptive Signal Processing and Image Processing.
  • EC 649 Robust and Large Scale Systems.
  • EC 661 Computers in Biomedicine.
  • EC 672 Neural Networks.
  • EC 673 Intelligent Control Systems.
  • EC691 Special Topics in Computer Systems Industrial
    Applications.

II- Computer Systems Design Branch Elective Courses

  • EC 610 Advanced Semiconductor Devices.
  • EC 611 Optoelectronic Devices.
  • EC 633 High speed Computer Network.
  • EC 635 Fiber Optical and Digital Communications.
  • EC 653 Parallel Computing.
  • EC654 Queuing Theory and Performance Modeling.
  • EC 655 Computer Vision.
  • EC 656 Advanced Internet Protocols and Applications
  • EC674 Neuro-Fuzzy and Soft Computing
  • EC683 VLSI Systems
  • EC 684 Design of Fault-Tolerant Digital Systems
  • EC637 Computer Communications
  • EC690 Special Topics in Computer Systems Design

COURSE CONTENTS

EC637 COMPUTER COMMUNICATION 3(3+2)

Terminology and concepts of computer communication design and operation Digital transmission techniques and transmission impairments. Types of communication terminal, Mod, Dem , Cod and Dec. Circuit switching and packet switching. OSI 7 layer model, physical layer, data link layer, network layer, transport and application layer. TCP/IP.
Course Prerequisite : EC651

EC633 HIGH SPEED COMPUTER NETWORKS 3( 3 + 2 )

Fiber optic technology. Fiber optic link design. Digital subscriber line technology. High speed local area and metropolitan area network (FDDI), Fast Ethernet, and Gigabit network. Asynchronous Transfer Mode (ATM) networks
Course Prerequisite : EC533

EC 654 QUEUING THEORY and PERFORMANCE MODELING 3(3+2)

Performance analysis of computer network. Analytical methods. Queue theory techniques. Discrete state markov chains. Simulation method. Protocol for channel access. Multiple access protocol. Point to point protocol
Course prerequisite : EC651, GS206

EC 656 ADVANCED INTERNET PROTOCOLS
AND APPLICATIONS 3(3+2)

Network programming. Software and Technical architectures that support internet application. TCP/IP and Socket programming . Network management SNMP model. RMON model. Network security . Cryptography. Symmetric-Key Algorithms. Public Key algorithms. Authentication protocols. E-Mail Security. WEB security. Secure socket layer . Virtual Private network
Course prerequisite : EC651

EC681 ADVANCED MICROPROCESSORS: 3( 3 + 2 )

Review of Processor Design, Advanced Microprocessor architectures. Bus standards, VME bus , ISA , PCI etc. Parallel arrays processor, SIMD, MIMD computers. Cache memory system, and MSEI model Distributed memory multi-computers. Distributed processing. Parallel processing, parallel algorithms. Distributed operating system. Interconnected topologies. Microcontrollers. Embedded systems.
Course prerequisite : EC483

EC682 COMPUTER SYSTEMS DESIGN 3(3 + 2 )

Computer Architecture: Design methodology. CPU and ALU design. Hardware and micro programmed control. Interrupt and DMA I/O processors. VLSI, VHDL. Logic Circuit Synthesis and Optimization: Advanced design of logic circuits. Multilevel optimization of combinational circuits. Optimization of finite-state machines. Computer- aided design algorithms.
Course prerequisite : EC681

EC635 FIBER OTICAL AND DIGITAL COMMUNICTIONS 3(3+2)

Digital communication: digital transmission across discrete and analog channels. Huffman’s algorithms and entropy. Digital modulation methods. Fiber optical communications: Principles of fiber optical communications and networks. Point-to-point systems. Fiber propagation Erbium-doped amplifiers. Fiber transmission based on non-return-to-zero and wavelength-division-multiplexed networks Satellite communications: Antenna systems for satellites. Down and up links design. Influence of earth path on satcom. Satellite TV network distribution.
Course Prerequisite : EC681

EC610 ADVANCED SEMICONDUCTOR DEVICES 3(3+2)

Advanced numerical simulation techniques with emphasis on drift diffusion. Decoupled and coupled solution. Hydrodynamic models for device simulation. Self-consistent ensemble Monte Carlo method for device simulation. Operation of advanced bipolar and FET transistors. High electron mobility transistors. Deep submicorn MOSFETS.

Prerequisite : EC 681
EC 683 VLSI SYSTEMS 3 (3 + 2 )

VHDL : Modeling systems behavior in VHDL. Automated/ manual synthesis. Testing and design for testability. Top-down VLSI design methodology . CAD tools in the VLSI CMOS circuit and subsystem design. Design tools. Simulation and verification methods. Advanced VLSI Circuits : Architecture and circuit level design and analysis of integrated A/D and D/A interfaces in CMOS and BICMOS VLSI technology. CAD tools for analog design including simulation and synthesis.
Prerequisites : EC681

EC655 COMPUTER VISION 3(3+2)

Computational methods: recovery, representation and application of visual information, binary images, digital geometry, curve and surface fitting. Knowledge based: hardware and software techniques. Object representation and recognition. Model of computer vision.
Prerequisite : EC651

EC674 NEURO-FUZZY and SOFT COMPUTING 3(3 + 2)

Fuzzy set theory: fuzzy rules, fuzzy inference systems, derivative-based optimization Neural adaptive networks : off- line and on-line supervised learning networks, learning from reinforcement , neuro-fuzzy modeling, adaptive fuzzy modeling . Neuro-fuzzy control: expert control , sliding control fuzzy-Filtered Neural networks . ANFIS applications , genetic algorithms N.B . Lectures are enhanced by computer– based Laboratory sessions using MATLAB software.
Prerequisite : EC651

EC 611 OPTOLECTRONIC DEVICES 3(3+2)

Electronic properties of optical semiconductors, effect of temperature and pressure on bandgap, carrier scattering phenomena, density of carries in intrinsic and extrinsic semiconductors. Optical processes in Semiconductors. High speed lasers . strained Quantum Well Lasers. Quantum wire lasers, Quantum dot lasers current. Topics in semiconductor Lasers.

Course prerequisite : EC681
EC 653 PARAELL COMPUTING 3(3+2)

Analysis of Algorithms and program for parallel and pipeline computations; bounds on time and processors for various numerical algorithms. Processing data time and gate bounds for single arithmetic process pipeline and parallel costs, speedup and limitations. Data alignment networks, gate and time bounds for blocking networks. Parallel memory systems, conflict free access methods.

Course Prerequisite : EC651

EC 684 DESIGN OF FAULT-TOLERANT DIGITAL SYSTEMS 3(3+2)

Fault-tolerant computing, demonstration of error detection and recovery. Hardware and software models. Fault –tolerant techniques, coding, check pointing recovery. Reliable networked systems. Security. Case studies of reliable system design.

Course Prerequisite : EC681
EC646 MODERN DIGITAL CONTROL SYSTEMS 3( 3 + 2 )

Optimization : use of numerical optimization and search methods for a wide range of engineering problems, genetic algorithms. Dynamic programming. State space analysis and design techniques of discrete-time systems. Linear quadratic problem LQG. Minimum principle . H control design. Wiener and Kalman filtering . Adaptation and tracking algorithms . Nonlinear control : analysis and design of non-linear control systems. Stability using Liapunov, tracking problems singular perturbations. Analysis and optimization of controlled stochastic systems, self-tuning regulators, controlled Markov chain.
Course prerequisite : EC446

EC641 INDUSTRIAL I/O INTERFACING AND APPLICATIONS 3(3 + 2 )

Data acquisition systems: solid- state sensors and sensing systems. Data acquisition circuits, micro actuators and integrated Microsystems. Power electronics: theory and applications of major power electronics devices, electromechanical drivers and their applications. Industrial instrumentations: Instrumentation amplifiers, active filter design, signal acquisition and conditioning, data conversion systems signal recovery and distribution Linear electronic devices: differential amplifiers, operational amplifier applications, devices for data conversions

Course prerequisite : EC441 + EC681

EC647 DISTRIBUTED REAL-TIME SYSTEMS 3(3 + 2 )
Embedded Control systems: Design, implementation and validation of hard and soft real-time embedded control systems. Practical implementation of a modern digital controllers. Real-time operating systems. Distributed Real-Time Systems: The building blocks of a distributed real-Time control systems, performance measures and validation. Current real-time communication systems and timing constraints. Design and implementation of distributed real-time control systems. Micro controllers : Architecture and programming, I/O hardware, real-time programming. Electronic systems that include micro controllers to perform specific dedicated applications are now days in applications use.
Course Prerequisite : EC 646

EC 644 ROBOTICS 3(3+2)

Robot Dynamics: Dynamics of flexible and rigid robots, linear parameterization, globally convergent algorithm , singular perturbations, time delay problems. Multiple and redundant robots, computational approaches to robot motion planning, C-space of a single, rigid object, obstacles in C-space. Artificial potential fields. Grasp and task-level planning. Trajectory planning. Position and force control.

Robot Control: Lagranian and Hamiltonian formulation. Feedback linearization Design via Lyapunov’s second method. Singular perturbations and integral manifolds. Robustness of adaptive control.

Course prerequisite : EC646
EC640 ADAPTIVE CONTROL 3( 3+2 )

Model reference adaptive control (MRAC): Lyapunov stability and bounded ness, parameter estimation and identification. Convergence, stability and robustness properties of adaptive control. Structured unmodelled dynamics in MRAC. Least-square adaptation. Design of adaptive control laws. Discrete MRAC Self-tuning Control: Direct and indirect self-tuning regulators Adaptive-predictive control, auto-tuning, gain-scheduling .Stochastic adaptive control.

Course Prerequisite : EC646

EC649 ROBUST AND LARGE SCALE SYSTEMS 3 (3+2)

Multivariable Control Systems: Controllability and operability. Irreducible realization, generalized stability criterion. Generalized stability criterion. Design requirement, sensitivity, interaction and integrity. Design with state observer. Decoupled control. Design trough dyadic transformation. State , output feedback and model following control. Large Scale Systems : Decomposition methods in non-linear programming. Dynamic optimization for large scale non-linear systems. Stochastic control for large scale systems. Robust decentralized control.

Course Prerequisite ; EC646
EC645 FUZZY ENGINEERING 3 (3+2)

Fuzzy logic modeling : Fuzzy sets and fuzzy logic. Fuzzy rule-based control system design. Fuzzy function approximation. Fuzzy control : Fuzzy control and chaos. Brake controller. Hybrid ellipsoidal function learning. Gap control test. Fuzzy and recursion, fuzzy signal processing , additive fuzzy systems. Fuzzy image coding. Adaptive fuzzy frequency hopping for spread spectrum. Fuzzy signal detection in impulse noise. Fuzzy hardware Adaptive VLSI additive fuzzy systems. Optimal additive fuzzy systems.
Course Prerequisite : EC646

EC643 COMUTER CONTROL OF STOCHASTIC SYSTEMS 3(3 +2)

Stochastic processes : probability and random processes, probability axioms, sigma algorithms, random vector, quadratic mean calculus, wide-sense stationary processes. Markov chains, reversibility and queuing approximations. Stochastic optimal control: optimal prediction and filtering for discrete-time systems. Estimation and control of linear stochastic optimal control laws ( H infinity ). Prediction error and instrumental variable methods Recursive identification, Kalman filtering .
Course prerequisite : EC646 + GS606

EC648 ADAPTIVE SIGNAL PROCESSING AND
IMAGE PROCEESING 3(3+2)

Adaptive filter : Filter structures, FIR, IIR and filter banks. Adaptation and tracking algorithms. RLS and Kalman-based adaptation. LMS and RLS. Analysis of adaptation speed and convergence. Frequency domain adaptation. System Identification: signal processing and estimation practical application of methods to several industrial cases studies. Signal detection in discrete-time and multi-user detection. Image processing: Image compression, enhancement and restoration, 3D motion analysis. Markov random fields. Morphology. Optimal image processing. Video segmentation and key frame extraction. Image estimation from partial data.
Course Prerequisite : EC 646

EC642 MECHATRONICS 3(3+2)

Electromechanical sensors and drivers: variable speed drive systems. Actuators , sensors and power electronics for motor control. Analysis of electro mechanics. Modeling and control: Models and circuits of electrical drives, drive system models for electromechanical systems. Applied non-linear system methods for drives. Variable-structure control applications, feedback linearization .
Course prerequisite : EC641

EC661 COMPUTERS IN BIOMEDICINE 3(3+2)

Medical electronics: Basic principles of biomedical instrumentation and techniques. Measuring instruments for bio-signals e.g. ECG, EMG and EEG. Biotelemetry of biological signals, biosensors. Neonatal monitoring Computers in biological science: Expert systems. Algorithms for automated analysis of bioelectrical signals like electrocardiograms. Assembly of 3-D images. Applications of microprocessors in rehabilitation aids. Robotics in medicine. Artificial neural network models .
Course prerequisite : EC641

EC672 NEURAL NETWORKS 3(3+2)

Classifying Patterns. Decision function, background learning. Clustering patterns, self-organizing feature map. Pattern association, the discrete Hopfield network. A sample of recurrent networks. Probabilistic neural networks. Links to artificial intelligence. Synthesizing symbols with neural networks. Recursive autoassociative memory.
Course Prerequisite : EC651

EC673 INTELEGENT CONTROL SYSTEMS 3(3+2)

Elements of Neural Networks, Thresholds, Activation function, Multi-layer Perceptron , Neural Networks for Identification. Digital filter design using Neural Network . Predictor Model training. Fuzzy membership function. Fuzzy control design. Sliding Mode Fuzzy Control. Adaptive Neuro-Fuzzy . Inference Systems.
Course Prerequisite :EC 646 + EC 651

EC651 ADVANCED SOFTWARE SYSTEMS 3(3+2)

Distributed operating systems, intercrosses communications services, process migration, recovery, reliability, and fault tolerance services. Ubiquitous computing systems. Versatile Parameterizable Blocks (VPBs). A Strategically programmable system. Navigation software. Rules of software technology in transportation systems.
Iterative algorithms and their applications in computer engineering.

Course prerequisite GS604