Electrical Engineering

This course deals with signals, systems, and transforms, from their theoretical mathematical foundations to practical implementation in EE applications. This course covers the mathematics and practical issues of signals in continuous and discrete time, linear time invariant systems, convolution, and signal transforms. 

This course explores how machine learning addresses the problem of how computers can learn and extract information automatically from data. It further explores the methods used in artificial intelligence, data mining, and adaptive system design. Students learn how machine learning is used in most disciplines where data is available, including, e.g., electrical engineering, computer science, or medicine. The course introduces students to the theory and practice of modern machine learning methods.

This course presents students with a comprehensive introduction to advanced topics in Linear Algebra as needed in the more advanced literature on Signals, Signal Processing, Systems and Control. The emphasis is on fundamental notions related to vector spaces, inner product spaces, normed spaces, matrix algebras, and computations with matrices.

This course examines communication reference models (TCP/IP and OSI); circuit switched and packet switched communication; network node functions and building blocks; LAN, MAN, WAN, WLAN technologies; protocols fundamental mechanisms; the TCP/IP core protocols (IP, ICMP, DHCP, ARP, TCP, UDP etc. ); applications and protocols (ftP, Telnet, SMTP, HTTP etc. ); network management and security.

This course focuses on the advanced operation of smart grids with an emphasis on the management of electricity networks through the application of Information and Communication Technologies (ICT).

This course examines the design, the architecture and the development of web applications using technologies currently popular in the marketplace including Java and . NET environments. There are three key themes examined in the unit: Presentation layer, Persistence layer, and Interoperability. The course will examine practical technologies such as JSP and Servlets, the model-view-controller (MVC) architecture, database programming with ADO. NET and JDBC, advanced persistence using ORM, XML for interoperability, and XML-based SOAP services and Ajax, in support of the theoretical themes identified. 

This course examines the application of feedback control to continuous-time, linear time-invariant systems. It covers modelling of physical systems using state space, differential equations, and transfer functions, dynamic response of linear time invariant systems and the role of system poles and zeros on it, simplification of complex systems, stability of feedback systems and their steady state performance, Routh-Hurwitz stability criterion, sketching of root locus and controller design using the root locus, Proportional, integral and derivative control, lead and lag compensators, frequency response techniques, Nyquist stability criterion, gain and phase margins, compensator design in the frequency domain, state space design for single input single-output systems, and pole placement state variable feedback control and observer design.

The course offers students the opportunity to apply their knowledge in a major group design project which considers the full design process; from the client brief to the demonstration of a prototype. You must work systematically from high-level goals to detailed design, drawing upon knowledge and skills learned in other courses. Communication with the client occurs throughout and involves an assessed presentation, report, and demonstration. Team-working is crucial, as groups must develop several subsystems in parallel and integrate them together, as well as carry out non-technical tasks such as documentation and cost management. In addition, students develop problem solving skills, project and time management, and communication.

This course introduces students to the fundamentals of deep learning and illustrates how it is contributing to the practical design of intelligent machines. Deep learning is currently the most active area of research and development and in high demand for experts by hi-tech start-ups, large companies as well as academia. It is the preferred approach for modern AI and machine learning in any domain. This course demonstrates how deep learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalization, robot control, time series forecasting, and much more.

With the advancement in Integrated Circuit (IC) fabrication and integration technologies, design-for-test and system-level testing have become indispensable. This course discusses concepts and technologies in VLSI testing. It starts with fault modeling, fault simulation, and test pattern generation. Then, the course introduces design-for-test, built-in-self-test, and memory testing. Finally, the course will address challenges and solutions to system-level tests.