Master of Science in Computer Engineering

Prerequisite: Grad Standing

This course covers solution of linear equations, Eigenvalue eigenvector decomposition, Special functions, Complex analysis, Fourier analysis, Laplace transform, Introduction to partial differential equations. The course deals with various examples from engineering disciplines.

Prerequisite: Grad Standing

This course covers students learning activity on how to apply the engineering research process and methods of inquiry to solve engineering problems; doing literature review in the areas of interest, this involves critiquing current research work. Students learn legal and ethical issues related to protecting and exploiting research, more specifically, intellectual Property rights. They will also learn how to communicate findings in specific engineering formats to specialist audiences. Students will learn basic project management and teamwork skills in addition to research ethics. Course project will allow the students to apply research methodology components on research problems of their choice. Students are expected to present and defend their research proposals.

Prerequisite: Grad Standing

The course deals with formulation, solution and implementation of optimization models such as linear programming, dynamic programming, integer programming, quadratic programming, convex programming, geometric programming and unconstrained optimization for analyzing complex systems problems in industry.

Prerequisite: Grad Standing

Students are required to attend seminars given by faculty members, visitors, and fellow graduate students. Each student is also required to present a seminar outlying the research topic of the master thesis.

Prerequisite: Grad Standing

The student has to undertake and complete research topic under the supervision of a faculty member. The thesis work should provide the student with an in-depth understanding of a research problem in computer engineering. It is expected that the student, under the guidance of the supervisor, will be able to conduct research somewhat independently, and may also be able to provide solution to that problem.

Prerequisite: Grad Standing

Prerequisite: Grad. Standing

Elements of modeling and simulation, Simulation techniques, Review of probability theory basics, Discrete-event simulations, Design of simulation models, Generation of pseudo random numbers, Testing Random Number Generators (RNG), Random variates generation, Commonly used distributions in modeling & simulation, Statistical analysis of the output of simulations, Verification and Validation, Simulation languages and Packages, Applications of modeling and simulation to computer science and engineering. A term paper and a final project are required from each student.

Prerequisite: Grad. Standing

Prerequisite: Grad. Standing

This course covers the fundamentals of computer design, Instruction set design principal Pipelining, Instruction-Level Parallelism, Dynamic Scheduling Multi-processor, Thread-level Parallelism, Memory-Hierarchy Design, Virtual memory, Buses, I/O and RAIDs.

Prerequisite: Grad. Standing

This course covers Parallel algorithms, Multi-processing, Process level multiprocessors, interconnection, and processing elements, Task partitioning & allocation, Inter-process communication, Message passing protocols, Performance evaluation measures, Scalability and maintainability, Proto-types & commercial distributed systems, Cloud Computing & Virtualization concepts, Cloud architecture & Components, Cloud infrastructure, Cloud Services, Cloud Controllers, User interface & Cloud Dashboard, OS images, Cloud Data Storage and Management.

Prerequisite: Grad. Standing

The course reviews the main components of the VHDL, introduces the reconfigurable architecture such as FPGAs, explains how to use the IP cores to implement the reconfigurable Computing applications. In addition to reconfigurable case studies.

Prerequisite: Grad. Standing

The course covers the architecture of real time embedded systems, design and construction of instruction set of embedded system, selected case study: Design software/hardware of MIPS processor, and the task scheduling algorithms.

Prerequisite: Grad. Standing

This course includes introduction, background and biological inspiration. The course covers survey of fundamental methods of artificial neural networks: single and multi-layer networks; perceptions and back propagation. The course deals with associative memory and statistical networks, supervised and unsupervised learning, merits and limitations of neural networks, and applications.

Prerequisite: Grad. Standing

Advanced and emerging topics are selected from the area of Computer Applications. Contents of the course will be provided one semester before it is offered.

Prerequisite: Grad. Standing

This is a first, graduate-level course in computer and communication networks. The course focuses on network mechanisms, such as error-control, routing, subnetting, congestion control, and resource allocation. The course covers also aspects of network protocols and technologies, such as Ethernet, WiFi, IP, TCP and UDP. Fundamental concepts of network delay and delay-bandwidth-product calculations will be covered. In addition, the general concept of packet switch architectures will also be covered.

Prerequisite: Grad. Standing

This course covers theory and practice of cryptographic techniques used in computer security. Topics include Encryption (secret-key and public-key) and privacy, Secure authentication, Cloud Security and Electronic commerce, Key management, Cryptographic hashing and data integrity (Digital signatures), Internet voting and Trust systems, Blockchains and Zero-knowledge protocols.

Prerequisite: Grad. Standing

This course covers advanced topics in Computer Networks. The contents will vary depending on the selected topics in the computer network research field.

Prerequisite: Grad. Standing

This course covers basic integrated circuit design & process technology, Design of simple analog & digital IC components in Bipolar & MOS technology. Modeling & simulation of integrated circuits, SPICE simulation, Fundamentals of Photo-lithography, Basic integrated circuit layout techniques, Applications & types of IC chips.

Prerequisite: Grad. Standing

The course serves as an advanced course for electronics students in analog integrated circuits (IC) design. The course will focus on conventional and modern analog building blocks for analog signal processing in BJT and MOS technology both in continuous time and discrete time applications. The course include analog multipliers, the op-amp applications in active filters, op-amp non-idealities, Nonlinearity cancellation of the MOS transistors, MOS-C Continuous time filters, Switched-C Circuits, and High frequency analog blocks ( ex: Current Conveyors and current feedback amplifiers).

Prerequisite: Grad. Standing

Advanced and emerging topics are selected from the area of Microelectronics and VLSI Contents of the course will be provided one semester before it is offered.

Prerequisite: Grad. Standing

This course covers security aspects of electronic systems and wireless networks. Topics include e-commerce, e-government, e-services, biometrics-based security, wireless networks security, Virtual Private Networks (VPNs), intrusion detection systems, and computer network security risk management. A refresher of fundamentals of e-security concepts including e-security tools, like Public key cryptosystems, and trust management systems. A term paper and a final project are required from each student.

Prerequisite: Grad. Standing

Prerequisite: Grad. Standing

Prerequisite: Grad. Standing

The student is expected to carry out an independent study on a current issue in a selected area of Computer Engineering. This study is to be supervised by a faculty member and requires the approval of the department. The student is required to produce a formal report, which will be evaluated by his instructor.

Prerequisite: Grad. Standing

This course covers concepts, design, implementation of computational intelligence involving integration of different methodologies: intelligent database management systems, rule-based systems, neural-type systems and fuzzy systems for heuristic problem solving, diagnostics, risk analysis and decision support; decision trees, reasoning techniques.

Prerequisite: Grad. Standing

This course deals with the modeling and control of open-chain serial manipulator and their basic applications. Topics include an overview of robotic systems, serial manipulator, forward kinematic, inverse kinematics, Jacobian and forward velocity kinematics, inverse velocity kinematics, motion control and trajectory design.

Prerequisite: Grad. Standing

The course includes: the convergence of wide-area wireless networking and mobile telephony to support ubiquitous access to information, anywhere, anyplace, and anytime. Topics include Mobile-IP, Ad-hoc networks, Local connectivity, 3G-wireless networks, Approaches to building mobile applications (e.g., mobile client/server, thin client, proxy architectures, and disconnected operation) and mobile e-commerce.

Prerequisite: Grad. Standing

This course covers image formation, image representation and display, image processing (smoothing, enhancement, edge detection, filtering), convolution, Gaussian masks, scale, space and edge detection, Feature extraction, Hough transforms, stereoscopic vision and perspective projection, motion, active contour models.

Prerequisite: Grad. Standing

This course covers the fundamental of communication system engineering. It provides an overview of probability and random processes, autocorrelation, spectral density and noise in linear systems. Additionally, it covers PAM and PCM systems, detection of binary and M-ary signals in Gaussian noise, matched filter and correlator receivers. Moreover, the course deals with error performance for binary and M-ary systems, pulse shaping, band pass modulation and demodulation techniques, channel capacity and other selected topics in digital communications.

Prerequisite: Grad. Standing

This course focuses on the fundamental limits on data compression, and on transmission over communication channels. The course topics include the information measures of entropy and mutual information, source coding theory, data compression, Huffman coding, Lempel-Ziv codes, arithmetic codes, the rate distortion theory, the channel capacity theory, and Gaussian channels.