In this module, students will become familiar with both hardware and software aspects of ARM Cortex M microcontrollers. They will learn the distinctive features of embedded systems and their design procedure, as well as how to programme a microcontroller using ARM assembly language and embedded C.

Throughout the module, students undertake several hands-on, practical exercises as well as a group design project. This will allow them to demonstrate their understanding of the fundamental concepts of embedded systems and become familiar with hardware architecture and instruction sets. They will also learn to identify and use Keil IDE capabilities effectively for debugging and programming an ARM microcontroller.

Students will acquire the fundamentals of design strategy, debugging and structural efficiency required to be a skilled microcontroller engineer. Familiarity with the architecture and programming of this microcontroller is of increasing importance for graduates with electrical and electronics degrees.

Students will be able to demonstrate a good understanding of the architecture and programming of the KL025Z board and ARM CortexM0+ microcontroller; use web-based aids for programming these MCUs; and be able to choose a particular device, integrate it into a system, and write working programmes.

Projects are obtained from local companies who have a genuine engineering problem, design or development requirement. The three-week project commences with a team and project assignment and briefing lecture. Each team then meets their company and is assigned an industrial contact and academic supervisor for the project. Communication with the company and academic supervisor for most of the project is at the discretion of the team. The modules ends with a presentation session to which the company and all academic project supervisors are invited.

This module gives the opportunity to apply the technical, problem analysis and project management skills learned in earlier modules to a real industrial environment.

Gaining professional experience solving problems in the industry can greatly increase the employability of postgraduates. Students can also forge useful connections within the industry during their communication with the company.

During the project, students will learn how to structure a technical problem; assess the technologies required to meet the requirements using available literature and resources; work creatively to develop possible solutions; and apply multidisciplinary scientific and engineering skills to assess the technical validity of those solutions.

This module is only available to full time MSc students.

The design and application of intelligent control systems, with a focus on modern algorithmic computer-aided design methods, is what students will be introduced to during this module. Starting from the well-known proportional-integral algorithm, essential concepts such as digital and optimal control will be familiarised using straightforward algebra and block diagrams.

The module addresses the needs of students across the engineering discipline who would like to advance their knowledge of automatic control and optimisation, with the lectures being supported by practical worked-examples based on recent research into robotics, mechatronic and environmental systems, among other areas.

Students shall also be taught statistical modelling concepts that have a wide ranging application for control, signal processing and forecasting, with applications beyond engineering into health and medicine, economics, etc.

The concept of state variable feedback is utilised as a unifying framework for generalised digital control system design. This approach provides a relatively gentle learning curve, from which potentially difficult topics, such as optimal, stochastic and multivariable control, can be introduced and assimilated in an interesting and straightforward manner. The module also aims to develop an appreciation of the constraints under which industrial applications of control operate, and to introduce the computational tools needed for designing these control systems.

Major global companies across the engineering discipline, including automotive and communications companies, have positions for graduates with a control engineering background. This module is also very useful for those who wish to work with robotics and autonomous systems.

Ultimately, students will come to understand various hierarchical architectures of intelligent control. They will also be able to design optimal model-based control systems and design and evaluate system performance for practical applications.

During this module, students will learn the basics of how the behaviour of device structures change as dimensions shrink, along with how to design these structures and predict their static and dynamic behaviour across a range of energy domains (electro-magnetic, electro-static, thermal, mechanical etc.).

In addition to this, the module will also teach the principles of sensing and actuation in the main application areas that range from mobile communications to bio-chemical analysis. Students will gain knowledge regarding the manufacturing technologies for micro & nanoscale technologies; the product engineering process, including how to achieve high reliability; and the emerging technologies that utilise nanoscale structures.

Semiconductor industry companies like Intel and Texas Instruments have positions across the entire design and manufacturing flow for graduates with a microengineering background. Most of the smaller design companies have activity in MEMS and microengineering.

As a result of undertaking this module, students will come to understand the underpinning engineering science associated with micro-mechanics and microfluidics. Other topics they will discuss include the fundamental principles of solid state physics and materials used within devices involving sub 100nm dimensions; micropackaging concepts; and the mechanics of scaling across multiple energy domains down to sub 100nm dimensions.

In the end, students will be able to demonstrate a wide knowledge and comprehensive understanding of design processes and methodologies for microsystems, this will include an awareness of developing technologies in the areas of microsystems and heterogeneous systems.

This course provides students with comprehensive knowledge and understanding of electrical power system control, modelling and simulation. It develops understanding of scientific principles and methodology of power control, load flow, transient performance and distribution generation systems. Finally, it provides the opportunity to students to use their knowledge and understanding to design inverters to connect PV panels / wind turbines to the grid.

This module focuses on design methods for distributed circuits. Students will develop an understanding of RF transmission line theory by considering impedance matching, S-parameters, and Smith charts, as well as RF measurements and detection. They will also explore high frequency circuit design, which includes RF amplifier and filter design, noise calculations, and applications of RF components.

There is a strong practical element to the module that involves students using Microwave Office to build and test a microwave amplifier. This provides students with practical skills in high frequency electronics and related fields.

In completing these tasks, students will develop an understanding of the impact, importance and application of high frequency electronics in the field of communications, remote control and wireless interface.

By the end of this module, students will be able to design RF circuits using analytical techniques and computational design software including filters and amplifiers; design impedance matching networks; and understand high frequency distributed circuits.

Pre-requisites of this module include BSc degree level understanding of AC Theory.

Students will become familiar with the hardware and software skills necessary to interface with and integrate electro-mechanical system to a digital computer for software based control during this module.

The definition of interfacing, interfacing-integration requirements, digital and analogue signal conditioning, D/A and A/D conversion, power switching techniques and devices, hybrid HW/SW solutions and National Instruments LabVIEW programming are just some of the topics that will be explained and expanded upon during this module. By exposing students to typical real-world problems and solutions when combining circuit techniques and LabVIEW programming, their understanding and confidence when dealing with such problems will be increased.

On successful completion of this module, students will understand the principles of digital and analogue interfacing; be able to define and interpret interfacing requirements and device specifications; and be able to design appropriate interface hardware, resolving issues of signal amplitude, level shifting, polarity, impedance and drive, using passive and active circuitry; and able to independently debug and programs in LabVIEW.

In addition to these skills, students will also gain an understanding of the problems associated with integration within engineering systems, as well as an experience and appreciation of the interactions between hardware and software.

This module introduces students to the recent advances in artificial intelligence, machine learning, and cutting-edge deep learning methods. Students will learn how to examine the technologies that apply to various aspects of engineering, such as searching and planning algorithms, supervised learning, unsupervised learning, reinforcement learning, deep neural networks, convolution neural networks, recurrent neural networks, and generative adversarial network.

The module aims to equip students with key knowledge and understanding of their application in industrial robots, smart manufacturing, predictive maintenance, design optimisation and digital twin. Students will also learn how to implement the machine learning algorithms by practicing this in our labs, keeping the legal, social and ethical considerations in mind when applying machine learning technologies.

On successful completion of this module, students will be able to demonstrate the impact of emerging machine learning technologies by understanding the underlying principles of machine learning, typical algorithms, and deep learning methods. Students will be able to analyse real-world problems, such as design optimisation, manufacturing process optimisation, fault diagnosis and prognosis, and be able to design machine learning models to solve them.