Master of Science in Electrical and Computer Engineering

This graduate level course focuses on technical communication related to the stylistic conventions of writing in science and engineering disciplines, with a focus on clarity, conciseness, and coherence as well as integration of relevant graphics. Students will employ process writing to produce genre specific writing familiar to their academic disciplines, including research-related documents designed for specific audiences. This course also trains students to deliver effective and appealing professional and scientific presentations, with attention to best practices in the use of technical English and oral communication.

This course will cover algorithm design techniques at a graduate level. Topics include advanced design and analysis techniques, sorting and order statistics, advaced data structures, graph algorithms, network flows, sorting networks, linear programming, NP-Completeness, approximation algorithms, string matching, randomization, and online algorithms.

This course provides an overview of select topics in computer architecture and embedded systems. Students will develop an appreciation for the role of hardware in computer operations, an understanding of embedded systems, and will be able to design, write, and debug embedded programs using a state-of-the-art integrated development environment. Additional concepts will include interfacing with external hardware, as well as principles of good software/hardware co-development.

The main goal of this course is to provide a mathematical introduction to the theory of stochastic processes. Topics covered in this module include:

1. Overview of Probability and Random Variables: axioms of probability theory; random vectors and random sequences
2. Introduction of Stochastic Processes: Definitions; Markov and Martingale processes; stationarity and its properties; ergodicity
3. Power Spectra and Orthogonal Expansions: Autocorrelation function and power spectral density; power spectrum analysis for linear time-invariant systems; Fourier series and Karhunen–Loève expansions of processes
4. Bandlimited and Discrete-time Processes: Properties of bandlimited processes; sampling theorem for stochastic processes; i.i.d. white noise, AR, MA and ARMA models
5. Introduction to Estimation Theory: Minimum mean-squared error (MMSE) and linear MMSE criteria; Orthogonality principle, causal and non-causal Wiener filters and their applications

The mathematical sub-discipline of differential equations is foundational in the study of applied mathematics for engineers. Differential equations arise in a variety of contexts, some more theoretical and some of practicalinterest. The qualitative and quantitative study of differential equations incorporates an elegant mixture of linear algebra and advanced calculus. For this reason, it is expected that the student has already completed courses in (i) linear algebra; (ii) multivariable calculus; and (iii) introductory differential equations.

The course will cover advanced topics of first order and second order ODEs like initial and boundary value problem and stability of solutions and phase portrait of them. A classification will be provided of the various existing types of methods for exploring solutions in connection with the various types of scientific andtechnological mathematical models that utilize them. The content of the course includes power series techniques as well as the Laplace transformation method for acquiring solutions of ODEs.

Furthermore, PDEs will be studied to model problems involving functions of several variables, and are either solved by hand, or used to create a relevant computer model. PDEs categories will be connected with a wide variety of interesting and important phenomena such as sound, heat, electrostatics, electrodynamics, fluid dynamics, elasticity, or quantum mechanics. All these seemingly distinct physical phenomena can be formalized similarly in terms of PDEs. The content of the course will cover techniques that refer to Elliptic, Parabolic Partial Differential Equations and Hyperbolic Partial Differential Equations as well as Initial and Boundary Conditions. The course will be delivered with lectures that will deliver the necessary theoretical background. However, all the lectures and the tutorials will encapsulate many examples with a software capable to perform symbolical calculations in order to capture the interest and motivate engineers.

This course addresses the primary need for graduate students to undertake formal training that will help them in understanding how to conduct their research. The course will develop students’ understanding of research planning and engender skills enhancement for reading, interpreting, writing about and presenting key ideas. The course will also instill an understanding of a variety of research methods and ethics, as well as implementation of appropriate strategies in lecture and workshop settings.

This course enables students to gain and apply basic research knowledge and skills to select their research projects in Electrical and Computer Engineering. The course develops the following knowledge and skills:
1. Identifying the area of research interest;
2. Surveying of state of the art for the area of interest;
3. Identifying the research topic and associated issues;
4. Understanding of the social, cultural, global and environmental issues associated with chosen research topic;
5. Writing a research paper based on the literature survey under the supervision of a faculty member;
Presentation of a seminar and being assessed by a panel.

This course introduces students to recently developed and advanced techniques for solving modeling and control problems. The course presents theory and methodology for analysis and modeling of systems and signals, and methods for design and synthesis of feedback controllers. The emphasis of the course will be on mathematical foundations and numerical methods for system modeling from physical properties, identification, and advanced control of dynamical systems. In particular, it explores state space representation, dynamical modeling from Lagrange equations, model validation, model approximation, Kalman filtering, real-time system identification. The course also discovers control techniques such as state-feedback controller, output feedback controller, state observer, stabilizing controllers, automatic Tuning, gain scheduling, and auto-calibration.

The Master’s thesis constitutes a piece of applied research and in this context, the MSc student primary goal is to analyze, solve and present his/her research findings for an existing problem relevant to his/her field of study. This process should be based on existingscientific and engineering knowledge and follow the principles of responsible research conduct.

The primary focus of the research project is usually expressed in terms of aims and objectives. The aims should comprise aspirations and/or intentions defined in broad terms, which essentially describe what the student is hoping to achieve. These aims set out what the studentis targeting to deliver at the end of the project. Objectives, on the other hand, are specific statements that define measurable outcomes and comprise specific goals and steps that must be followed for achieving the student aims. Hence, this course intends to give students the opportunity to conduct the research thesis. The course develops the following knowledge and skills:

1. Developing and justifying the methods to conduct the research;
2. Formulation of the research hypothesis and carrying out research;
3. Perform simulation study or conduct practical work and obtaining the results/outputs;
4. Analysis the simulation/practical results;
5. Preparation of thesis manuscript and thesis submission and defense

Electrical energy system analysis is the core knowledge required for understanding, planning, and operating of power system. This unit introduces students to the core knowledge of electric energy systems analysis and modelling along with planning and reliability study. The course will cover the advanced concepts related to the operation of plant equipment and the regulations in energy industry. This is followed by a detailed study into modeling of electric energy lines, transformer concept, current, voltage, capacitance and impedance relations on a short, medium and long-distance transmission lines. Power-flow calculations for various symmetrical and unsymmetrical fault conditions will be discussed using the methods of Gauss-Seidel and Newton-Raphson. In addition, electric energy simulation software is used to practice a real simple electric energy network. This is a crucial course for electrical energy systems expertise and its understanding is therefore essential for a career in this field. In this course, the student will learn about electric energy flow analysis, short circuit analysis and their employment in electric energy systems. Other aspects covered include electric energy system stability and reliability, control, contingency analysis and economic operation.

The course aims to provide the student with the fundamentals of electrical industrial and commercial power systems: design, demand calculation, maintenance, load calculation, management and operation. In specific, it provides practical and essential knowledge for designing the electrical distribution infrastructure in large commercial buildings or industrial sites. Particular emphasis is on compliance with current practices and regulations within Eurasia. The course also touches on some aspects of utilization. Regulatory aspects; switchboard design; underground cabling systems; designing neutral system in industrial and commercial structure, surge protection; lightning rod system design and calculation; electrical lighting systems; industrial heating; energy efficiency and energy management; power factor correction; power quality and the effects of voltage and current harmonics; escalator and lift traffic analysis; transformer loading and overload in industrial and commercial sites; and introduction to the smart grid will be discussed in this course. This is an essential course for power engineers which are planning to work in industry or doing research in smart grid field in distribution system.

The general purpose of the module is to have the students exposed to modern DC-DC (Switched Capacitor and Flying Capacitor) and DC-AC (Neutral Point Clamped, Flying Capacitor, Cascade H-bridge) power converter topologies, their operation principles, methods of analysis and computer simulation.

This course is designed to familiarize the students with the fundamental topics in digital signal processing and it applications. The course starts from the basics of signals and systems and gradually develops to fundamentals in digital signals processing, random signal processing, and linear optimal filtering. A tentative list of topics includes:

• Basics of Discrete-Time Signals and Systems;
• Discrete-Time Structures;
• Digital Filter Design;
• Efficient implementation of DFT; 
• Multirate Signal Processing; 
• Random Processes& Spectral Estimation;
• Introduction to Linear Optimal Filtering (time permitting)

This graduate course consists of two distinct aspects of advanced electronic circuits and systems. The first part focuses on the low frequency aspects of circuits and systems. It aims to equip the students with the fundamentals of the lumped circuit abstraction. Topics covered include: resistive elements and networks; independent and dependent sources; switches and MOS transistors; amplifiers; energy storage elements; dynamics of first- and second-order networks; design in the time and frequency domains; and analog and digital circuits and applications. In the second part of this course, high frequency circuits and components will be covered. This segment of the course covers concepts of transmission line based circuits and antenna for multi-frequency and broadband applications. The targeted applications include consumer electronics and wireless technology.

The objective of this course is to provide the mathematical framework for an understanding of adaptive statistical signal processing. The basic tools of vector spaces, optimization theory, and discrete-time stochastic processes are reviewed and applied to the methods of Wiener filtering and least-squares filtering. Various types of adaptive filters will be introduced and their properties will be studied, specifically convergence, tracking, robustness and computational complexity. Applications will mainly be addressed through student projects.

Pattern recognition and machine learning provide the technical basis of data mining. Data mining per se is the extraction of implicit unknown and potentially useful information from data. The aim of this module is to introduce fundamental concepts used in design and implementation of pattern recognition and machine learning techniques. After a brief review of probability theory, we will cover important and classical families of classification and regression rules, error estimation, and dimensionality reduction techniques. Our discussion on classical methods is then followed by a set of modern techniques for performing classification and regression on very large sparse data matrices.
The topics include:
Classification Rules: Bayes Decision Rule, Classification, Parametric Classification Rules, Nonparametric Rules, Perceptron, Support vector machines, Neural networks, Decision Trees, Discrete Classification
Error Estimation: Hold-out, Resubstitution, Cross-validation, Bootstrap, Dimensionality Reduction: Filter-based feature selection, Wrapper-based feature selection and various search strategies, Multiple Linear regression, Ridge Regression and LASSO, Deep Learning

Since the first transistor was invented in 1947, the field of semiconductors has been rapidly developing, leading to revolutionary changes in modernization of human lives. In the 21st century, we are living in the technological world with semiconductor-based instruments everywhere. Understanding the modern semiconductor devices and applications in systems is no doubt an important study area for the electrical and computer engineering students,preparing themto catch up the cutting-edge technologies in the field of semiconductors for their future career inindustries or academics. Thelevel of this course assumes that the student hashad introductory semiconductor physics. The contents are designed for the final year undergraduate students and graduate students, who will work /are involving in semiconductor related topics in scientific research.This course will cover the topics of modern semiconductor devices including photodetectors and imaging; photovoltaics; solid-state lighting and laser diodes. Students will learn the fundamental knowledge of these semiconductors devices, state-of-the-art device engineering, processing technologies and practical applications in systems. This course will link the theoretical principles to real-life issues by providing examples and research articlesto initiate the students’ critical thinking and allow students to get familiar with the latest progresses of these modern semiconductor devices and systems such as development trends, strengths, limitations and potential solutions to the challenges etc.

The Biosensors course will provide an interdisciplinary view of biosensors, focusing on the technological aspects, as well as on implementation and applications, with elements of electronics, photonics, surface chemistry, and biology. The course will present electronic biosensors, optical biosensors, biorecognition and functionalization, and applications of biosensors.

The aim of this course is to provide students with essential knowledge of renewable energy generation and energy conversion, as well as the identification of renewable energy sites and resources. Solar, wind, photovoltaic, geothermal, wave, tidal, micro-hydro generation are discussed and analyzed in this course. In particular, it provides essential fundamental knowledge for designing wind and solar/photovoltaic energy systems. The course also investigates the suitability of renewable resources as an alternative for conventional energy sources to meet upcoming energy demand, emphasizing the importance of sustainable energies. Additionally, the course introduces DC/AC conversion systems and equipment, their design, fabrication, calculation, and operation principles. Students will also learn the fundamentals of thermal energy systems, as well as the design, size selection, and circuit connection considerations for battery energy storage systems. The course also presents grid-connected and stand-alone systems and covers DC and AC dispatch. In addition, the course provides the design and operation of renewable energy systems. To improve the students’ practical capability, they will have the opportunity to practice with different solar and wind energy systems models, and the implementation factors of these systems will be described for them in detail. The course assigns particular emphasis on compliance with current practices and regulations within Eurasia region.

The objective of this course is to provide the fundamental mathematical knowledge needed to recognize and solve convex optimization problems that arise in engineering. The topics covered in this course include basics of convex analysis, such as convex sets, convex functions and convex optimization problems; convexity with respect to generalized inequality; least-squares; linear and quadratic programs; semi-definite programming; geometric programming; minimax; optimality conditions; Lagrange dual functions and problems; duality theory; theorems of alternative and applications; and algorithms for solving unconstrained and constrained optimization problems. Students complete hands-on exercises using high-level numerical software

The objective of this course is to introduce students to fundamentals and applications of microfluidics. The course will provide knowledge on physics and theory of microfluidic principles, design and micro-/nano-fabrication of polymer microfluidic devices, materials and microfluidic components, and a wide range of applications of microfluidics through lectures and research case studies

This course is designed to train the students to RF and Microwave circuits & systems design techniques for radio communications leading towards the advancements in the domain of advanced radio technology. The focus of the radio technology concepts treated in this course is on the consumer electronics (5G and beyond) and aerospace electronics segments.

In the first half of the course, the students will be exposed to the fundamental concepts of passive and active circuits design at radio frequencies. These fundamental concepts require substantial understanding of transmission line theory and Smith chart and hence a thorough discussion on these two topics will be the starting point of this course. This half of the course also includes learning the commercial software tools such as Advanced Design System (ADS), and CST Microwave Studio. This learning will be achieved through design assignments.
In the second half of the course, students will be introduced to design aspects of components such as matching networks, couplers, power dividers, high frequency amplifiers etc. As part of the design assignments, the students will be required to carry out projects from conception to realization, and then demonstrate the performance through measurements.

The Optical Communications course provides an in-depth overview of fiber-optic communications for high bit-rate/high-throughput. The course provides a description of fiber-optic communication networks. Starting from an in-depth analysis of the devices that are fundamental part of the optical communication network, such as single mode optical fibers, fiber amplifiers, optical switches, filters, multiplexers, and photodetectors, the course aims to set the basis for assessment of network performance and for the consequent network design. The effect of attenuation, dispersion, and non-linearities will be quantified and standard methods of compensation of those effects will be discussed. Protocols for 10-100 Gbit/s data communication over long-haul links are discussed.

Finally new trends on signal modulations, applied to optical communication, will be exposed as an overview of future optical fibers network

This course provides comprehensive knowledge on semiconductor fabrication techniques for VLSI (Very-Large-Scale Integration) applications. Key topics include crystal growth, wafer preparation, epitaxial growth, oxidation processes, and lithography. Students will learn various advanced techniques such as etching, thin-film deposition, diffusion, ion implantation, and metallization. The course also covers critical aspects of testing, assembly, and packaging of semiconductor devices. Students will gain insights into the fabrication steps required for developing high-performance VLSI circuits, with emphasis on addressing modern challenges such as scaling limitations and process control. This course also explores the latest innovations in material and process engineering, which are essential for the continuous advancement of semiconductor technology. This course is designed for PhD students who aspire to pursue careers in semiconductor processing, VLSI design, and related research fields.