Advanced Electronic and Electrical Engineering

Core Modules

• Team Project

  This is a major group project in which students will work on an agreed practical problem that is relevant to tomorrow's societal needs and agreed with their supervisor. The working practice of the groups will be modelled on industrial practices in terms of planning, keeping proper records of meetings and the progress of work, and students will each take on a responsibility within the team that is vital to the professional and successful running of the group project. The overall aim is to provide students with a full appreciation of mechanisms that can support professional group working and its management in engineering practice in the context of exploring and researching solutions to a topic relevant to society.

• Power Systems

  The aim of this module is to cover the entire process of using a primary source of energy, converting it to electricity and delivering the generated electricity to where it is required. It provides students with useful knowledge and skills. This course covers the power system basics, such as complex power, calculations including phasors, reactive compensation, power factor, conversion of circuits to phasor domain, instantaneous values, three phase circuits, calculations related to transmission lines, transformers, per unit calculations, synchronous and induction machines, powerflow and optimal power flow. Labs are designed to allow students to actively engage with the covered material and to work through the calculations using Matlab as well as hand calculations.

• Agile Engineering
• System on Chip Design

  The module extends the knowledge acquired in digital systems with advanced topics in the emergent area of FPGA based system on chip design. The module will cover state-of-the-art features available in modern FPGAs exploring their fine-grained internal architecture and embedded macro blocks such as DSP slices, IPs and hardcore/softcore processors. A design language based on C/C++ will be presented as an alternative to traditional RTL design (VHDL). High level synthesis tools will be used to compute signal processing applications.

• Engineering Project

  The aim of this module is to provide students with the opportunity to carry out an in-depth engineering project, potentially in collaboration with industry, to solve a real-world problem or create a novel product. For specialised MScs, the project will be related to the specialisation topic.

Optional Modules

Below is a taster of some of the exciting optional modules that students on the course could choose from during this academic year. Please be aware these do change over time, and optional modules may be withdrawn or new ones added.

• Advanced Communication Systems

  This module covers advanced communications systems, focusing on microwave, optical, and broadband technologies. Students will gain knowledge and understanding of these systems and their applications in high-speed networks. They will learn to analyse complex microwave systems using mathematical and computational tools, such as estimating satellite link budgets. The module also covers the breakdown and categorisation of communication system elements, including high-speed optical data network architectures. Students will study radio propagation, design principles of advanced microwave systems, and sources of degradation, distortion, and losses. Additionally, they will be introduced to the latest CAD tools for evaluating and synthesising practical microwave systems.

• Sustainable Power Generation

  In this module you will develop an understanding of the various types of power generators, focussing on renewable technologies. You will look at the main benefits and drawbacks of different generation types and examine why a generation mix is desirable. You will consider the technical, environmental, sustainability, cost and political factors driving engineering and commercialisation decisions, and evaluate the objectives and constraints that are involved in optimisation procedures for power system applications, such as optimal wind farm layout.

• Fundamentals of Biomedical Engineering

  The aim of this module is to provide theoretical and practical knowledge relating to biomedical engineering, human physiological phenomena and the electronics used to acquire them safely and accurately. The indicative content for this module includes the study of fundamental modern healthcare technologies in relation to the data acquisition of EEG, ECG, EMG and PPG signals. The nature of these signals will be studied along with engineering techniques for developing diagnostic and therapeutic devices from them. Further, medical imaging technologies such as ultrasound, MRI and CT scanners will be studied. Signal processing and pattern recognition techniques relevant to biomedical engineering applications will also be investigated. Finally, students will undertake group projects over the last few weeks of term which will revolve around developing complete end-to-end bioinstrumentation systems.

• User-Centred Design

  This module focuses on user-centred design aspects and challenges in interactive traditional, augmented and virtual reality contexts, and addresses the approaches that can be used to create displays and interfaces to enhance user experience.

• Pattern Recognition

  The aim of this module is to provide theoretical and practical knowledge relating to pattern recognition. The indicative content for this module includes the study of fundamental pattern recognition in relation to supervised and unsupervised learning. Topics will include Bayesian decision theory, Artificial Neural Networks and Support Vector Machines (amongst others). The nature of these algorithms will be studied along with engineering techniques for developing smart applications. Further, deep learning for engineering applictions (e.g. classification of electrocardiograms) will be studied. Finally, students will undertake coursework to an apply an appropriate machine learning methodology to solve a real-world pattern recognition problem.

• Pattern Recognition for Biomedicine

  The aim of this module is to provide theoretical and practical knowledge relating to pattern recognition. The indicative content for this module includes the study of fundamental pattern recognition in relation to supervised and unsupervised learning. Topics will include Bayesian decision theory, Artificial Neural Networks and Support Vector Machines (amongst others). The nature of these algorithms will be studied along with engineering techniques for developing smart applications. Further, deep learning for biomedical engineering applications (e.g. classification of electrocardiograms) will be visited. Finally, students will undertake a coursework to an apply an appropriate machine learning methodology to solve a real-world biomedical problem.

• Real-Time DSP

  This module develops a comprehensive understanding of the theory behind digital signal processing (DSP) and progresses to include how DSP can be implemented in real-time and embedded systems. Initially, students will be introduced to the theory behind signal processing progressively increasing the depth and breadth of their knowledge using Matlab software interface. Material will include sampling theorem, digital filtering, the discrete Fourier transform, the z-transform and adaptive filtering. Then, students will gain an understanding of how to program hardware to perform industry-standard DSP algorithms such as filtering, spectral analysis and including Wiener filtering and adaptive echo cancellation. Advanced topics include high-resolution spectral estimation techniques and speech processing. Limitations of hardware such as finite precision, floating point operations will also be studied.

• Mobile and Optical Fibre Communications

  The main aims of the module are to provide guidance and experience of working in Mobile and Fibre Optic high-speed smart communication systems ensuring that user requirements are understood and used to produce suitable advanced solutions. Students will acquire knowledge of fundamental concepts, terminology, techniques, and principles: Radio over Fibre (RoF) components; causes and techniques to remove radio frequency interference; optical multiplexing and switching technologies; link budget calculation; optical network architectures; increased dynamic range. Furthermore, students will be exposed to the importance of RoF systems in the broadband mobile and fibre telecommunication systems, where they are used for applications such as fibre optics, microwave and photonic components, antenna remoting, radar detection, satellite communications, signal generation, and electronic warfare systems. Students will interact with leading UK communication industries and learn from experienced communication leaders on how to apply communication engineering skills to real-world problems.

• Voice Technologies

  The aim of this module is to provide students with advanced knowledge of voice synthesis, recognition and processing in the context of present-day and future engineering systems that make use of a voice input or output. The indicative content for this course includes the synthesis of human speech and singing in terms of the sound source and sound modifiers in practice to create electronic voice signals, standard voice processing techniques, used for example, to enhance speech quality, remove background noise and improve perceived voice quality, to design hearing aids and techniques used for automatic speech recognition, for example, Apple's 'Siri' system.