Electronic and Electrical Engineering (with optional Placement/Project year) MSc

Embedded Systems Introduction:

1. Embedded Architecture (ARM): Focuses on the ARM architecture, the dominant platform for modern embedded and IoT devices.
2. C Programming & STM32 HAL: Teaches embedded C programming using the STM32 Hardware Abstraction Layer (HAL), covering I/O operations, pulse-width modulation (PWM), and interrupt handling.
3. Digital Communication Interfaces: Details key serial and parallel communication standards for embedded systems, including UART/Serial, SPI, I2C, and CAN.
4. Data Collection & Filtering: Covers techniques for acquiring and processing sensor data, including analog/digital filtering (low-pass, high-pass, band-pass, band-stop, notch filters) and frequency analysis via FFT.
5. Multithreading & Real-Time Programming: Introduces concepts of concurrent execution and real-time systems, critical for developing responsive, reliable embedded applications.

IoT Fundamentals & Applications:

1. Introduces the core principles of the Internet of Things, alongside real-world use cases and application scenarios.
2. Electromagnetics & Sensor Design: Explores the electromagnetic principles underpinning radio-frequency design technology.
3. Networking Fundamentals: Covers core networking components including Ethernet, web servers, and web clients, establishing the backbone for IoT connectivity.
4. IoT Communication Technologies: Surveys a wide range of wired and wireless protocols for IoT devices, including RFID, BLE, LiFi, 6LoWPAN, ZigBee, Z-Wave, LoRa, HTTP, and WebSockets.

The module introduces key cyber security concepts and their associated techniques. The modules introduces the analysis of computer programming from a cyber security perspective. The module introduces a variety of different cyber security threats and possible resolutions to them. In addition the module provides a fundamental introduction to implementing secure computing systems.

Power Conversion

• AC-DC power conversion: thyristor converters, rectification, inversion, HVDC and drive applications, harmonic analysis.
• DC-AC power conversion: inverter types, managing output waveform distortion; and application in drives, reactive-power compensation and power-flow control.

High-Power Semiconductor Devices

• Characteristics, performance and application requirements.

Machines and Drives

• Induction machines: operation as motors and generators; space harmonic effects, and dynamic model.
• Large synchronous machines; operating characteristics, dynamic model, and introduction to vector control.

Power System Protection

• The protection overlay: protection and metering transducers. Fuses.
• Overcurrent protection: relay types, operating characteristics and equations, grading, applications.
• Differential protection: voltage balance and circulating current schemes, biased characteristics and high impedance schemes. Applications to the protection of transformers, feeders and busbars.
• Distance protection: basic principle, block average comparator, zones of protection, residual compensation, power swing blocking.
• Digital protection: relay hardware. digital signal processing in protection relays. Digital distance protection. Digital differential protection.

Simulation

Practical workshops will be provided at the computer to design and simulate electrical systems, including the representation of components.  Students will design, test and evaluate different electrical machines, methods of protection for specified applications.

Control Systems Engineering and Design

1.   System Network Diagrams and input/output port types and causality, Functional Analysis, Function Means Trees, Working Principles and the Feedback Servomechanism Principle in details. Impact of design constraints.

2. Introduction to functional design view of controllers with Tracking, Stabilisation and Prediction (TSP) approaches and the use of Relative Degree in Conceptual Controller Design.

3. Controller Design with Phase and Gain Compensation, Phase Advance, Notch Filtering and Digital filtering using z-transforms

Multi-input Multi-output (MIMO) State-Space Controller Design Methods

4.   Continuous-time MIMO State-space controller design using full and partial state feedback

5.   Discrete-time MIMO State-space controller design using full and partial state feedback

6.   MIMO Inverse Dynamics in Continuous and Discrete-Time

7.   MIMO PI, Pseudo Derivative Feedback (PDF) control and Robust Inverse Dynamics Estimation in state-space and role of integrator in inverse dynamics

8.   MIMO functional TPS algorithms

Nonlinear systems.

9.    Use of State-Space Design for Nonlinear Control e.g. Variable Structure Control

10.   Nonlinear Inverse Dynamics

11.   Stability of Nonlinear Inverse Dynamics using Small Perturbation Linearisation with State-Space Modelling.

Intelligent Control and Optimisation

12.   Deterministic Methods to design and Tune MIMO linear and nonlinear control algorithms.

11.. Stability of Nonlinear Inverse Dynamics using Small Perturbation Linearization with State-Space Modelling.

13.   Use of optimisation methods (e.g. Gradient based methods) and nonlinear system modelling and simulation to tune MIMO controllers

14.   Introduction to Neural networks and their design for use in MIMO control systems for learning algorithms.

15.   Introduction to Machine Learning

The Robotics module provides an introduction to the foundational principles of robotics, exploring the theoretical aspects that underpin the design, application, and ethical considerations of robotic systems.

You will begin by examining the fundamental question: What is a robot? This includes understanding the diverse applications of robots across industries and their role in society. The module also delves into the ethical implications of robotics, such as their impact on employment, privacy, and safety.

Key technical topics include an overview of mechatronics, which integrates mechanical, electronic, and computer engineering; sensors, which enable robots to perceive their environment; and control systems, which ensure robots can perform tasks accurately and autonomously.

This module introduces students to the fundamentals and applications of artificial intelligence, particularly in engineering and real-world problem-solving. It covers key AI techniques, including machine learning algorithms, data-driven decision-making, and their applications in cybersecurity, financial predictions, and multimedia analysis. The module emphasises both theoretical understanding and hands-on implementation through programming exercises, group projects, and open-source datasets. Ethical and legal considerations in AI are also explored, ensuring responsible application. By the end of the course, students will be equipped to design, implement, and evaluate AI models for a variety of modern use cases.

In this module, students undertake an individual project to solve a problem requiring critically-appraised new and emerging knowledge of the engineering discipline.