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.

Pre-requisite:
MATH 151

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

Pre-requisite:
CEN 211

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. 

Pre-requisite:
CEN 212

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.

Pre-requisite:
CEN 316

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.

Pre-requisite:
MATH 204

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. Specifically, it covers all aspects of point-to-point communication links such as transmission media, signal encoding, error detection, correction and data link control protocols. This provides the foundation for subsequent courses on computer networks.

Pre-requisite:
CEN 351

The course aims at providing an understanding of digital signal processing, the ability to understand and apply Sampling theory and periodicity, the application of basic Fourier Transform (Discrete Fourier Transform DFT and Fast Fourier Transform FFT) and z-Transform, the ability to distinguish between, analyze, and design different types of digital filters including Finite Impulse Response FIR and Infinite Impulse Response IIR filters, the application of the digital signal processing methods to simple applications including audio and image signals.

Pre-requisite:
CEN 351

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 paper; Guidelines for effective oral presentation.

The main goal of this course is to improve students’ research and report writing skills in preparation for their graduation projects. It also exposes students to current research topics in computer engineering. Students conduct a small research project about a relevant subject and write a formal report it along with an oral presentation. In addition, the course teaches students professional ethics, writing ethics and relevant ethical codes.

Pre-requisite:

ENGL 110

Introduction to random variables, parameter estimation, stochastic processes, random signal processing and applications to systems.

This goal of this course is to teach students about the different statistical models that can help them in their future studies and research in networking and signal processing. Topic to be covered include experiments, models, and probabilities, Discrete Random Variables, 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.

Pre-requisite:

CEN 351

MATH 244

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.

Pre-requisite:

 CEN 316

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

This course provides detailed knowledge in the area of implementing digital circuits into PLD components such as FPGA and CPLD.

Pre-requisite:

 CEN 212

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, Computer Aided Design (CAD) tools and how to use them, and create a foundation for further exploration of VLSI.

Pre-requisites:
CEN 212
EE 310

Students will be introduced to several computer arithmetic topics at an advanced level. 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 (addition, subtraction, multiplication, division, modular reduction, and exponentiation). The focus is on implementation of these operations in VLSI circuits with application to general-purpose computing, cryptography, coding, and digital signal processing.

Pre-requisite:
CEN 316

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 computer networks. It covers the concepts and algorithms used in medium access control of the link layer. In addition, it provides various concepts, technologies, and algorithms used in network and transport layers.

Pre-requisite:
CEN 341

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, educate students about concepts and techniques for access to computer systems and network resources. Identification and authentication. Protection of information against intentional and unintentional attacks and threats. Cryptography and encryption of data. Encryption algorithms and their information theory foundations. Computer hardware and software for data encryption.

Pre-requisite:
CEN 441

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, Performance of Fixed assignment techniques, Erlang-B model; The cellular concept: frequency reuse, cell architecture and handoff protocols; Cellular Technologies: 2G/GSM, 3G/UMTS/HSPA, 4G/LTE/LTE-A; Wireless LANs: multiple access techniques for LANs, IEEE 802.11 (WiFi); Mobility management: Mobile IP protocol.

This course aim at recognizing the principles of wireless and mobile networks, challenges facing wireless network design and the latest technologies in the field.

Pre-requisite:
CEN 441

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 exposes them to the fundamental design elements of large-scale distributed computer networks. It covers inter-domain routing and label switching along with Quality of service techniques. In addition, this course covers concepts, technologies, and protocols in the application layer.

Pre-requisite:
CEN 441

Internetworking: 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 of network management, management architectures, information model, organizational model, communication model, functional model; Network Management Standards, Network Management Protocols, Abstract Syntax Notation One (ASN.1), Simple Network Management Protocol (SNMP), SNMPv2 and SNMPv3, Structure of Management Information (SMI), Management Information Base (MIB), Remote Monitoring RMON 1 and 2.

The objective of this course is to introduce the fundamental principles of internetworking design, network analysis and design methodologies network management concepts, architectures, and protocols.

Pre-requisite:
CEN 441

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. Control of robotic systems.

Through problem solving and laboratory practice, the aim of this course is to provide a foundation in digital control system theory. After successfully completing the course, students are able to: 1. Mathematically represent the control systems, transfer functions, state space models, system stability, and design compensator. 2. Model time-delayed first and second order dynamic systems. 3. Analyze, design, and synthesize analogue and digital control systems using transform techniques (root locus) and pole placement. 4. Use MATLAB and SIMULINK in the analysis, design, simulation, and real-time implementation of discrete-time control for robotic systems.

Pre-requisite:
CEN 352

MATH 244

Students will be introduced to several artificial intelligence topics at an advanced level. 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 main goal of the course is to provide an understanding of artificial intelligence techniques, including knowledge representation, search heuristics, planning, learning and reasoning. The focus is on practical aspects of artificial intelligence, designing and implementing appropriate solutions of computer engineering problems like image classification, speech recognition and mobile robot navigation using neural networks, fuzzy logic and evolutionary optimizatio

Pre-requisite:
CEN 453

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.

Pre-requisite:
CEN 453

Review of sampling theorem, filtering, and noise management; speech and language fundamentals; speech perception and production; tools for digitally processing speech signal: windowing, pre-emphasis, and framing; linear predictive coding; applications of digital speech processing such as speech recognition and synthesis.

The main purpose of this course is to make the students able to perform Fourier transform and z-transform in speech signal processing; identify and explain phonemes, classes of phonemes, symbols, semantic, and syllables of a language, especially for Arabic and English; illustrate hearing and auditory perception theory; analyze speech in short-time domain, especially using zero crossing and autocorrelation; perform homomorphic analysis of speech signal; extract speech features using linear predictive coding and Mel-frequency spectral coefficients; apply digital speech processing techniques to speech recognition and speech synthesis tasks

Pre-requisite:
CEN 352

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.

The aims of the course are: Identify the basic components of an image processing system. Recognize the basics of the human visual system as they relate to image processing. Classify image types as binary images, gray-scale images, color and multi-spectral images. Describe image geometry, convolution masks, image algebra and basic spatial filters. Describe the 2-D Fourier including implied symmetry, phase, circular convolution, and filtering. Apply lowpass, high pass, and bandpass filtering; including ideal and non-ideal filters. Classify the categories of image processing applications into restoration, enhancement and compression. Perform adaptive contrast enhancement to digital images. Explain the concepts of image sharpening and smoothing in both spatial domain and spectral domain. Design Wiener and power spectrum equalization filters for image restoration. Explain the basics of image compression and decompression.

Pre-requisite:
CEN 352

Pre-requisites:
CEN 316

ENGL 110

Pre-requisite:
CEN 492

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, and optimization), 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 lectures on 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 intolerance); Fault Tolerance; Replication, Redundancy, and Diversity.

Solutions to analysis and synthesis problems that may be 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 & 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.

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 will be developed to help guide the creation of high performance complex optical networks

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. IP Route Lookup, Packet Classification, Packet Scheduling, Buffer Management, Basics of Packet Switching, Input-buffered Switches, Output-buffered Switches, Shared-memory Switches, Crosspoint-buffered Switches, Close-network Switches, Multi-Stage Buffered Switches, Two-Stage Load-Balanced Switches, Optical Packet Switches, and ASIC for IP Routers.

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, etc.), overview of other machine learning techniques and data mining.

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 (& video) compression and coding, speech and speaker recognition, audio and video watermarking and steganography, and speech & video enhancement, inferring 3D properties from, object detection & recognition, multiview geometry, and structure from motion.