Course Catalogue

This course provides students with basic knowledge on combinational and sequential circuit design. The course includes a lab component to help students get hands-on experience with the theoretical concepts they take in the course. Topics include: number systems, application of Boolean algebra, modular design of combinational logic and basic design of sequential circuits using state diagrams.

This course provides students with advanced knowledge on synchronous sequential machines and basic knowledge in programmable logic devices. The course includes a lab component to help students get hands-on experience with the theoretical concepts they take in the course. Topics include: registers and counters, state machines, design of datapath and control circuits, hardware description languages (HDL) and the synthesis of logic circuits on programmable logic devices such as FPGA and CPLD.

This course provides students with basic knowledge in computer architecture hardware as well as the assembly language to program the processor. Topics include: history and factors affecting the advances in processor design; basic computer organization, performance evaluation and metrics; assembly language of MIPS processor, instruction formats, instruction sets and their design; integer and floating-point representations and arithmetic operations; datapath design, control design, pipelining and their effect on performance; memory hierarchy organization and its effect on performance; I/O systems.

Programming embedded systems with assembly and C as well as how to configure and use different processor modules, such as: reset, oscillator interrupts, timers, Analog/Digital and serial communications. This course introduces the embedded system design concepts. Students will investigate the architecture of a selected microcontroller family and its instruction set. The course includes a lab component to help students get hands-on experience with the theoretical concepts they take in the course.

Mathematical description and classification of various signals and systems: introduction to mathematical software packages (e.g. MATLAB), continuous linear time-invariant systems, convolution and correlation, Fourier series and transforms, Laplace transform, applications to communication systems: signal modulation, signal bandwidth, channel bandwidth. The aim of the course is to provide an understanding of signals and systems; the ability to sketch and analyze signals, express them in terms of other basic signals and perform basic time-domain operations on them; perform convolution and understand LTI concepts; apply the basic definitions of the Fourier series, Fourier transform and Laplace transform; explain modulation and demodulation of AM, PM and FM systems and use MATLAB for the simulation of signals and systems.

Introduction to communication systems; protocol architecture and reference models; transmission impairments; transmission media: guided and unguided media, wireless propagation; digital signaling: NRZ, AMI, Biphase; analog signaling: ASK, FSK, PSK, multi-level signaling; error detection: 2D Parity, Checksum, CRC; forward error correction; data link control: framing, flow-control, ARQ protocols; multiplexing: FDM, TDM; digital carrier systems: PCM, E1, SONET/SDH; xDSL systems; duplexing techniques: FDD, TDD. This course introduces the student to various technologies and algorithms used in the physical and the data link layer of computer networks.

History and overview; sampling theorem, aliasing; sampled signals, periodic signals, non-periodic signals; impulse response and convolution; digital spectra analysis; discrete Fourier transform, fast Fourier transform; z-transform; digital filters, FIR and IIR filter design; windowing; effect of finite word length in digital signal processors; application in audio and image processing. The course aims at providing an understanding of digital signal processing, the ability to apply Sampling theory, DFT/FFT, and z-Transform, the ability to distinguish between, analyze, and design different types of digital filters including FIR and IIR filters, and the application of digital signal processing methods to audio and image signals.

Professional ethics in the context of computer engineering: IEEE code of ethics, writing ethics, legal and ethical issues in software development; research skills: research report organization and formatting, referencing and bibliography management; guidelines for good research papers and effective oral presentations. The main goal of this course is to improve students' research and report writing skills in preparation for their graduation projects. Students conduct a small research project and write a formal report along with an oral presentation.

Introduction to random variables, parameter estimation, stochastic processes, random signal processing and applications to systems. Topics to be covered include experiments, models, and probabilities; discrete and continuous random variables; pairs of random variables; random vectors; sums of random variables; parameter estimation using the sample mean; stochastic processes; and random signal processing.

Fundamentals of computer design, power, cost, performance, instruction set principles, instruction and arithmetic pipelines, dynamic and speculative execution, precise exception, memory hierarchy, multilevel caches, virtual memory, multicores, multiprocessors, new trends in computer architecture. This course provides students with advanced knowledge in computer architecture hardware such as multicore and multilevel memory hierarchy as well as virtual memory concepts.

Students get detailed knowledge in the area of digital circuits design, especially with respect to their implementation into PLDs (FPGAs, CPLDs) and ASICs. Students get an overview of current technology of these integrated circuits and their off-the-shelf architectures. Students will be able to design and implement a digital system into an FPGA using HDL language.

Large-scale MOS design: MOS transistors, static and dynamic MOS gates, stick diagrams, programmable logic array design, MOS circuit fabrication, design rules, resistance and capacitance extraction, power and delay estimates, scaling, MOS combinational and sequential logic design, register and clocking schemes, memory, data-path, and control unit design. Elements of computer-aided circuit analysis and layout techniques. The goal of the course is to learn VLSI design techniques and methodologies, CAD tools, and create a foundation for further exploration of VLSI.

Topics include: standard and unconventional number representations, design of fast two-operand and multi-operand adders, high-speed multiplication and division algorithms, floating-point numbers, algorithms, and hardware algorithms. Also, implementation like pipelined, digit-serial and fault-tolerant arithmetic processors are introduced. The aim of the course is to provide an understanding of algorithms and structures used for efficient implementation of all basic arithmetic operations in VLSI circuits.

Introduction to computer networks; network architecture with respect to OSI and TCP/IP reference models; Ethernet, 802.11 technologies, Bluetooth, and cellular systems; frame switching and VLANs; bridges and spanning trees; basic network protocols: IPv4, ARP, DHCP, ICMP; interior routing protocols; transport layer protocols: UDP, TCP, and RTP. This course introduces students to the concepts and algorithms used in medium access control, network layer, and transport layer.

Symmetric and public key cryptography; digital signatures; cryptographic hash functions; authentication pitfalls; network and internet security: network access control and cloud security; transport-level security; wireless network security; electronic mail security, and IP security; system security: malicious software, intruders, and firewalls. This course introduces the student to principles and practices of computer and network security, identification and authentication, and protection of information against attacks.

Introduction to wireless and mobile networks; fundamental concepts in mobile wireless networks; characteristics of wireless links, RF propagation, path loss models; fixed assignment multiple access techniques: FDMA/TDMA/CDMA; the cellular concept: frequency reuse, cell architecture and handoff protocols; cellular technologies: 2G/GSM, 3G/UMTS/HSPA, 4G/LTE/LTE-A; wireless LANs: IEEE 802.11 (WiFi); mobility management: Mobile IP protocol.

A broad range of advanced internetworking topics; inter-domain routing (BGP); IPv6, and multicast routing; multiprotocol label switching (MPLS); quality of service; congestion control; application layer protocols (HTTP, FTP, DNS, SMTP, and BitTorrent). This course introduces students to advanced networking concepts and the fundamental design elements of large-scale distributed computer networks.

Internetworking hardware, bridging and switching technologies, virtual LANs; network design: the network development life cycle, network analysis and design methodology, enterprise network design model, backbone design concepts; network management: foundations, architectures, information model, organizational model, communication model, functional model; network management standards and protocols: SNMP, SNMPv2, SNMPv3, SMI, MIB, RMON.

Review of mathematical representation of systems (transfer functions) modeling and parameter identification, system analysis in time domain, system stability, steady state error, root locus, and compensator design using pole placement and root locus. Digital control and stability. Introduction to robotics and control of robotic systems. Students will be able to mathematically represent control systems, model dynamic systems, analyze and design analogue and digital control systems, and use MATLAB/SIMULINK for simulation and real-time implementation.

Topics include: artificial intelligence definitions, knowledge representation, deep neural networks, fuzzy logic, evolutionary optimization such as genetic algorithms and particle swarm optimization, applications to signals and systems. The course provides an understanding of artificial intelligence techniques, including knowledge representation, search heuristics, planning, learning and reasoning, with a focus on practical aspects such as image classification, speech recognition, and mobile robot navigation.

Historical development of robotics, robot arm kinematics, inverse kinematics, trajectory planning, dynamics and control, applications of mobile robots, autonomous mobile robots (navigation and localization), computer vision, vision-based control, Q-bot mobile robot, and other robotic devices for experiments, Internet and web robotics, future trends.

Review of sampling theorem, filtering, and noise management; speech and language fundamentals; speech perception and production; tools for digitally processing speech signals: windowing, pre-emphasis, and framing; linear predictive coding; applications of digital speech processing such as speech recognition and synthesis. Students will be able to apply Fourier transform and z-transform in speech signal processing, analyze speech in short-time domain, extract speech features using LPC and Mel-frequency spectral coefficients.

Covers quantitative models of imaging systems, spatial domain and frequency domain methods, digital filter design for image enhancement and restoration, edge detection, image denoising, image segmentation, image enhancement, image restoration, image compression, and image representation and description. Students will be able to classify image types, apply spatial and frequency domain filters, perform adaptive contrast enhancement, and explain the basics of image compression and decompression.

Computer engineering discipline and the role of computer engineers in modern society; overview of the engineering design process; project selection and needs identification; requirements specification; concept generation and evaluation; system design; behavior models: state diagrams, flowcharts, data flow diagrams, UML; testing and system reliability; ethical and legal issues; project management: project plan, Gantt charts, cost models, break-even analysis; professional skills: effective teamwork, effective written and oral communication.

The student undertakes a B.Sc. project in a related area of specialization with technical merit. This project spans two semesters and counts as three credits for the first semester. At the end of the semester the student submits a report describing the project and the parts completed in the first semester along with proposed work for the second semester. Projects are oriented toward providing experience in the establishment of objectives, criteria, synthesis, analysis, construction, testing, and evaluation; solution of open-ended problems; design methodology.

Continuation of the graduation project. The student completes the B.Sc. project in a related area of specialization and with technical merit. Projects are oriented toward providing experience in the establishment of objectives, criteria, synthesis, analysis, construction, testing, and evaluation; solution of open-ended problems; design methodology.

This course introduces the fundamentals of quantitative design and analysis, the basic and intermediate concepts of pipelining, the idea of instruction-level parallelism and its exploitation (speculation, limits to ILP), and the topics of multiprocessors (snoopy cache and directory-based) and thread-level parallelism, cache design, advanced memory hierarchy, and data-level parallelism (Vector, SIMD, GPU).

Topics at an advanced level including standard and unconventional number representations, the design of fast two-operand and multi-operand adders, high-speed multiplication and division algorithms, floating-point numbers and its algorithms, and advanced hardware algorithms. Implementation of some topics is also performed and demonstrated.

Topics include cyber-physical systems, design processes, embedded processors, sensors and actuators, inputs and outputs, multitasking, scheduling, discrete dynamics, the composition of state machines, concurrent models of computation, and quantitative analysis.

This course covers the topics of parallel computers, message-passing computing (MPI), embarrassingly parallel computations, partitioning and divide-and-conquer strategies, pipelined computations, synchronous computations, programming with shared-memory (OpenMP), distributed shared-memory systems, load balancing, scalable algorithmic techniques, and termination detection. Case studies including image processing and numerical algorithms are discussed.

This course provides in-depth coverage of the fundamental architecture and implementation techniques for modern processor chips. Topics include a comprehensive introduction, advanced pipelining, superscalar organization, instruction fetch, branch prediction, register renaming, dynamic scheduling, multithreading, and multi-core chips.

Introduction to computer networks covering: physical layer (transmission media, electromagnetic spectrum, signal encoding, modulation); data link layer (framing, error control, medium access control, Ethernet, switching, VLANs, wireless LANs); network layer (routing algorithms, distance vector and link state, IP protocol, addressing, sub-netting); transport layer (services, UDP, TCP, sockets, flow control, and congestion control); application layer (DNS, email, web, and HTTP).

Overview of mobile computing and wireless networks, wireless channel signal propagation characteristics, modulation and coding schemes (QPSK, DQPSK, n-QAM), multiple access techniques (CSMA/CA, PCF, CDMA, OFDM, OFDMA, FHSS, DSSS), cellular concepts (frequency-reusing, microcellular, picocell, femtocell), wireless networking standards (IEEE 802.15.1/4, 802.11, LTE, LTE-Advanced, Mobile WIMAX), radio and network resource management, QoS guarantees, and cognitive radio networks.

This course introduces the fundamental concepts of performance evaluation and analysis techniques for computer networks, queuing theory, Markov chains, separable queuing networks, priority queuing systems and queuing networks, and how to measure the performance of computer networks and LANs.

Overview of wireless sensor network protocols, deployment and coverage issues, hardware platforms and operating systems including motes and TinyOS; physical and data link layers including MAC issues, localization, self-organization, time synchronization, power management; network layer protocols including energy-aware routing, node discovery, data dissemination, LEACH, and query models; reliable transport protocols; sensor network programming (nesC); and security issues such as SPINS.

Overview of linear time-invariant systems, discrete time signals, discrete Fourier transforms (DFT), fast Fourier transforms (FFT), and Z-transforms. Also introduces the design concepts of finite impulse response (FIR) filter, infinite impulse response (IIR) filter, and adaptive filters. The applications of audio and image processing are also covered.

This course covers different topics in digital image processing, including subjects of symmetric and public-key cryptography, authentication protocols, network security protocols, transport-layer security, IP security, wireless LAN security, email security, network attacks and threats, botnets, malware, intrusion detection systems, and firewalls.

Overview of sampling theorem and filtering, speech and language fundamentals, and speech perception and production. Introduction to tools for digitally processing speech signals such as windowing, pre-emphasis, and framing. The course also covers the principle of linear predictive coding and applications for digital speech processing, including speech recognition and synthesis.

Introduction and definition of mobile robots; characteristics from kinematics and dynamics perspectives; actuators and sensors; localization of mobile robots (Odometry and Kalman filter); path planning; intelligent motion control; robotic vision; and robotic navigation.

This course is designed to enable students to study various specialized topics of interest in the field of computer architecture. The contents are determined by the instructor and the department. The course intends to offer cutting-edge topics in the field.

Topics include discrete-event simulation approaches, simulation cycle, probability and statistics in simulation, mathematical and statistical models, validation and verification of simulation models, random number generation, building valid and credible simulation models, modeling of data simulation, and analyzing output data. The student is also introduced to simulation software, distributed and parallel simulation, and simulation of well-known systems such as queues.

Topics include: random variables, moments, conditional distributions and moments, generating functions, functions of random variables, joint distributions and moments; random process models: concepts, definitions, discrete-time and continuous-time models, time averages, and ergodic principle; theory of renewal, renewal processes, alternating renewal theorem, regenerative process, Markov chains and processes; and applications of random processes in control, reliability, networks, and communication systems.

This course covers techniques for the design and analysis of algorithms. Topics include worst and average case analysis, asymptotic order of growth, binary-search trees, hash functions, graph algorithms, BFS, DFS, greedy algorithm, minimum spanning tree, shortest path algorithms, divide and conquer, and mergesort. Also introduces dynamic programming, network flow, NP-completeness and computational intractability, and approximation algorithms.

Introduction to knowledge-based intelligent systems, rule-based expert systems, fuzzy expert systems, connectionist neural networks, learning and adaptation, support vector machine, and evolutionary algorithms (genetic algorithm, particle swarm optimization). Applications to signals and systems including speech processing, control, image processing, and communication.

This course covers different topics in network security, including symmetric and public-key cryptography, authentication protocols, network security protocols, transport-layer security, IP security, wireless LAN security, email security, network attacks and threats, botnets, malware, intrusion detection systems, and firewalls.

This compulsory course aims at improving the student's research and reading skills: how to search, read and analyze a research article, and how to draw relevant conclusions. The course also improves communication skills including how to deliver an oral presentation, write a scientific report, cite references, and consider ethical issues in academic research.

A graduate-level directed study course focused on a particular topic in computer engineering. Students must formulate a complete proposal with a faculty member, accurately describing the course content, learning goals, intended method, extent of supervision, and method of evaluation. This course cannot be taken as a substitute for another course with the same content.

Preparation of the thesis proposal outlining the research topic, related work, objectives, and methodology.

The student conducts research work on a selected topic related to computer engineering. The subject and focus of the proposed research must be novel and contemporary. During this course, the student prepares a thesis to be defended in the presence of a committee. The thesis must comprehensively introduce the selected topic, survey related works, include problem statements and formulations, propose system models and evaluation methods, present complete analysis of results, and provide conclusions and future works.

Random variables, moments, conditional distributions and moments, functions of random variables, joint distributions and moments; random process models: basic concepts, properties; stationary random processes: covariance and spectrum; response of linear systems to random inputs: discrete-time and continuous-time models; time averages and ergodic principle; sampling principle and interpolation. Selected applications in control, networks, and communication systems.

Solving logic design problems with CAD tools for VLSI circuits. Analysis and design of exact and heuristic algorithms for logic synthesis. Topics: representation and optimization of combinational logic functions (encoding problems, binary decision diagrams), representation and optimization of multiple level networks (algebraic and Boolean methods, don't-care set computation, timing verification), modeling and optimization of sequential functions and networks (retiming), semi-custom libraries and library binding.

The principles and tradeoffs in the design of parallel architectures. Emphasis is on naming, latency, bandwidth, and synchronization in parallel machines. Parallel programming models, multiprocessor systems, multi-computer clusters; case studies on shared memory, message passing, data flow, and data parallel machines; architectural studies and techniques for programming parallel computers.

Physical fault errors and failures; fault analysis and diagnosis in digital circuits; fault modeling; testing techniques; test generation and fault simulation; design for testability; built-in-self-test; reliability concepts; fault prevention; fault tolerance; replication, redundancy, and diversity.

Solutions to analysis and synthesis problems formulated as flow problems in capacity constrained or cost constrained networks. Tools for network flow theory using graph theoretic methods. Applications to communication and transmission problems.

Principles of network design, network design algorithms, centralized network design, static and dynamic routing algorithms, application of minimum spanning tree and shortest path algorithms to problems in network design, distributed network design, case studies.

Underlying principles of personal communications and cellular systems; radio signal propagation and signal impairments; noise and interference-limited communications; multiple access, radio resources management, and mobility management; building blocks of personal networks; essential functions of cellular systems; examination of the leading and standard systems.

Error, flow and congestion control protocols, multiplexing and multiple-access, switching, routing. Selected case studies on access networks, packet networks, broadcasting networks, satellite and terrestrial radio networks.

Components, topologies and architecture of optical networks. Basics of the physical layer, higher network layers and protocols (Media Access Control, Network and Transport Layers) as integral parts of network design. Performance metrics, analysis and optimization techniques to guide the creation of high performance complex optical networks.

Sensor network and Ad-Hoc networks: characteristics and applications, routing and performance analysis; deployment; energy-efficiency; wireless communications; data-centric operation; capacity and lifetime; reliability, fault-tolerance and security; standards of sensor networks.

Overview of wireless networks with multimedia services; physical layer; wireless networks medium-access control (MAC) protocols (TDMA, FDMA, CDMA, ALOHA-based, CSMA/CA); wireless and multimedia network standards: 802.11, 802.11e, 802.11n, 802.15, and 802.16; QoS; wireless and multimedia networks new trends and applications.

Basics, theories, architectures, and technologies for high-performance high-speed large-scale routers and switches. Fundamental concepts and technologies of packet forwarding, classification, and switching in IP routers, Ethernet switches, and optical switches. Topics include IP route lookup, packet classification, packet scheduling, buffer management, input/output/shared-memory switches, crosspoint-buffered switches, multi-stage buffered switches, two-stage load-balanced switches, optical packet switches, and ASIC for IP routers.

Common digital signal processing techniques for the analysis of speech and video signals. Topics include: signal analysis using short-time Fourier transform, linear prediction, and spectrum processing; applications in speech and video compression and coding, speech and speaker recognition, audio and video watermarking and steganography, speech and video enhancement, object detection and recognition, multiview geometry, and structure from motion.

Decision theory, parameter estimation, density estimation, non-parametric techniques, supervised learning, linear discriminant functions, clustering, unsupervised learning, artificial neural networks, feature extraction, support vector machines, and pattern recognition applications (e.g., face recognition, fingerprint recognition, automatic target recognition), overview of other machine learning techniques and data mining.

This course covers digital signal processing techniques applied to the analysis of signals in nanoscale robotic systems. Topics include signal analysis using short-time Fourier transform, linear prediction, and spectrum processing; applications in speech and video compression and coding, recognition, watermarking and steganography, enhancement, object detection and recognition, multiview geometry, and structure from motion.

In this course, students are able to study advanced topics in Computer Engineering. The content and syllabi of this course is designed by a member of staff and approved by the department council.

The student selects a research topic and conducts a literature review in relation to the topic.

This course deals with some of the common digital signal processing techniques for the analysis of speech and video signals. Topics include: signal analysis using short-time Fourier transform, linear prediction, and spectrum processing; applications in speech and video compression and coding, speech and speaker recognition, audio and video watermarking and steganography, speech and video enhancement, object detection and recognition, multiview geometry, and structure from motion.

Prepare a preliminary report on the thesis topic, related work, objectives, and methodology.

Write a PhD thesis and defend it in front of a jury.