This course provides the theory, design, engineering, and implementation of computer networks. It is also aimed at preparing students to plan and implement an IP-based (IPv4 and IPv6) network using network devices such as switches and routers. The topics covered in the course include the foundation of networks such as OSI and TCP/IP network models, implementation principles, and design issues. Network services. Data transmission basics. Data-link protocols. Local area networks. Wide-area networks. Internet structures. TCP/IP protocol suite and application Layer protocols. IP addressing.

This course provides students with the principles of scientific and industrial development trends within the areas of engineering and industrial design. The course is designed with an emphasis on how research may be used for the benefit of industry and society by promoting innovation as well as scientific writing, reviewing, and presentation to an international audience. The topics covered include the foundation of research and concepts. Engineering research methodology. Research ethics and integrity. Literature search, review, and citation practices.

This course aims at providing students with the foundation and techniques required to interpret, analyze, verify, and troubleshoot microprocessor circuits and programs as well as low-level language programming of the microprocessors and different peripherals. The topics covered include microcomputer organization and operation, evaluation of microprocessor, ALU, register, instruction execution, bus operation, memory array design, and interfacing. Architecture: the architecture of 8086, addressing modes, assembler directives.

This course aims at providing students with the foundation and techniques required to create software solutions for problems related to distributed systems.

The objective of the course is to present an overview of artificial intelligence (AI) principles and approaches, to develop a basic understanding of the building blocks of AI, as presented in terms of intelligent agents: Search, Knowledge representation, inference, logic, and learning. Topics covered include the history of artificial intelligence, philosophical questions about nature of intelligence, ethical issues in artificial intelligence, nature of knowledge and knowledge-based systems, issues of the ordering of information, modeling the world.

This course aims to provide students with industrial training required to reinforce the technical knowledge gained through courses taken and to expose students to real-life experiences in the working environment.Students acquire different learning outcomes to demonstrate the following abilities: system design and development, testing and implementation, maintenance and installation, feasibility studies, data collection, processing and analysis, software development and documentation, apply ethical principles and commit to responsibilities and engineering practices.

This course introduces students to the theory of digital signal processing and design process for realization and implementation of digital filter (FIR and IIR) algorithms to solve practical problems.The topics covered include Concepts of analog and digital signal processing systems. Architecture of DSP systems and components. Discrete time systems and z-transform, system implementation structures, frequency response of systems. Theory and design principles, realization, and implementation of FIR and IIR digital filters. Filter coefficient quantization.

This course seeks to provide students with a fundamental understanding of economic concepts and principles applicable to engineering.Topics to be covered include an introduction to making economic decisions, supply, demand, and equilibrium in economics. Concept of engineering economics: economic efficiency, engineering efficiency, marginal costs and revenues, opportunity and sunk costs, break-even analysis, economic analysis involving material. Decision-making and value engineering: value engineering procedure, interest formula, and applications in time value of money.

This course introduces students to the concept of probability and statistics for engineering application. Topics covered in this course include probability functions axioms and rules, counting techniques, conditional probability, independence, and mutually exclusive events. Discrete Random Variable: Expectation and variance, Binomial distribution, Hypergeometric distribution, Poisson distribution, the relationship between Poisson and Binomial.

This course provides the fundamental concepts and principles underlying a computer architecture design and instruction set architectures. It highlights the lower end operations of a typical computer, and the way computers manage their resources during operation.The topics covered in this course include Von Neumann architecture, CPU, registers, MIPS assembly language, instruction types and addressing, memory, interrupts, and I/O, the system bus. Instructions: fetch and execute cycle, machine language instructions.