Electrical Engineering

This course examines selected aspects of computational intelligence methods in-depth and students develop and test intelligent automation systems. Topics include how computational intelligence methods like artificial neural networks, fuzzy systems, deep learning algorithms and computer vision have been extensively applied in the design of intelligent control and automation systems such as autonomous vehicles, visual inspection of industrial products, automated analysis and screening of volumes of medical images. 

This course furthers the fundamental mathematical knowledge and skills that are necessary in engineering. Topics include complex numbers, vectors, matrices, limits and continuity of functions, derivatives and integration and their applications, multivariable calculus, partial derivatives, ordinary differential equations, double integrals in polar coordinates, dot product, and cross product. The course requires students to take prerequisites.

This course focuses on the fundamental principles of circuit theorems and circuit elements, DC/AC and three-phase circuits, transient and steady-state responses, circuit analysis using Laplace transforms. Students learn various techniques ('tools') to analyze the operation of real circuits with a focus on the study of the behavior of the circuit, not the creation of circuits, i.e., the engineering design of the circuit. Topics include capacitors and inductors, Fourier series, Laplace transform, and sinusoids and phasors. 

This course covers working principles and models of basic circuit components such as resistor, capacitor, inductor, diode, and transistor. Students learn to analyze the complex electric circuit problems composed of multiple circuit components using abstractions and various mathematical methods and gain an understanding of the working principles of various logic, memory, and amplifier circuits. The course provides students the ability to understand/modify/write LabView code that can be used to test electric circuits. Topics include Network analysis, Node voltage, Mesh current, Superposition, Impedance, RLC circuit, Diode, MOSFET, Amplifier, Logic and memory devices, Bipolar junction transistor (BJT), BJT small signal model, Lab work via LabView. 

This course provides general knowledge in radio frequency applications, especially those which are common in radio communications. The fundamentals are introduced without penetrating the electronics or design details. The different parts are treated as functional blocks defined by their physical properties. This gives a basic understanding of the radio receiver or the cellular phone but also the requirements put on the used circuits. Thus, this is a compulsory course for those who later want to specialize as radio frequency designers.

This course covers an overview of solid-state microanalysis methods, including elastic and inelastic scattering, identification of phases by morphology, chemical composition, electron diffraction, and microscopy. Principles and functions of different types of microscopes for materials analysis as well as spectroscopy for elemental analysis, analysis of spectra are also reviewed. Methods for surface analysis: Atomic force microscopy, scanning tunnelling microscopy, LEED, X-ray photoelectron spectroscopy (XPS) are covered.

This course introduces the fundamental properties and characteristics of solid-state materials and structures used in modern semiconductor devices and integrated circuit (IC) technologies. 

Topics include Solid-State Electronics and applications, Crystal structure of solids, Introduction to quantum mechanics, Introduction to the quantum theory of solids, Semiconductor in equilibrium, Carrier transport phenomena, Excess carriers in semiconductors, The pn junction. 

The course deals with time discrete signals and systems. Items such as the Fourier Transform, the Discrete Fourier Transform (DFT) and the z-transformed are treated in the course as well as some basic structures for implementation of digital filters. Also, system function and frequency functions are introduced as well as digital filters. Digital processing of analogue signals using A/D and D/A conversion is studied. In the laboratory work, practical applications of digital signal processing such as speech signals processing and biomedical signals processing are treated. Also, the course includes basic filter design using Matlab and digital signal processors (DSP).

Systems do not in general naturally behave in a manner which accords with the user’s wishes. Systems must in general be extended by the addition of a controller in order to force them to behave in an acceptable fashion. The controller may be a human (as in the case of the driver of a car for example), but the controller may also be a human-designed engineering system in its own right. In the latter case the controller is called an automatic controller. This course addresses the need for, the value of and the design of automatic controllers for some of the most common classes of engineering systems. Automatic controllers appear in more or less every engineering environment, from automotive/aerospace to biomedical equipment and including almost everything in between.

This course covers the nature of digital logic and numbering systems. Topics include: Basic gates, Boolean algebra, Karnaugh maps, memory elements, latches, flip-flops, design of combinational and sequential circuits, integrated circuits and logic families, shift registers, counters, multiplexers, demultiplexers, decoders, encoders, and parity circuits, Number systems, 1’s and 2’s complements, arithmetic circuits, fixed-point and floating-point representations, memory types, design of circuits using ROMs and PLAs. The course involves exposure to logic design automation software and an introduction to FPGAs and HDL. Prerequisite: fundamentals of computing.