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

• Evolution of Robotics
• Microcontrollers: Arduino and Raspberry Pi
• Computer Vision
• Agents and Multi-Agent Systems
• Machine learning and robotics

• To introduce the concept of artificial intelligence (AI) and to evaluate its role in the development of robotics.
• To introduce theoretical approaches to the development of intelligent robots.
• To undertake practical tasks to demonstrate how AI techniques can be implemented for robotics.
• To analyse methods for designing and deploying robotic systems.
• To critically evaluate the ways in which intelligent robots can be used in real world situations

1. Speaking Cyber - Core understanding of Cybersecurity terminologies
2. Introduction to programming with cybersecurity perspective
3. Implementing secure systems. Illustrative of what may be covered include:
   1. Design and development considerations
   2. Selecting and applying core technologies
   3. Cryptography
   4. Human factors
   5. Auditing (security audits, logging, digital investigation
      etc.)
   6. Defense-in-depth
   7. Google hacking
   8. Pentesting
4. Threats and resolutions 

1. To empower the students with core understanding of the key terminologies in cybersecurity
2. To introduce students to programming with a cybersecurity perspective 
3. To provide students with the understanding of the common cybersecurity concepts and technical security controls available to prevent, detect and recover from security incidents and to mitigate risks
4. To give students a solid foundation in the practical technical skills which can then be harnessed later in CO7604 and CO7605 

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

Intelligent Systems, Sensors and Control            
The last decade has seen an upsurge in the development of intelligent modelling and control structures over their counterpart mathematical model-based structures due to their success in dealing with complex multivariable uncertain systems without the need for extensive dynamic modelling.   The aim of this module is to introduce intelligent sensors, their interface into systems and the use of intelligent control.

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.

The aim of this module is to introduce and analyse electrical machines and power electronic systems for medium to high -power applications; such as, industrial and traction drives, small-scale power generation, and power system control. To examine the operation, characteristics, and capabilities of commonly used systems and their control methods.

Each student undertakes an individual project, at Masters level, in an area related to the MSc programme. The project area is chosen by the student at the beginning of the academic programme in consultation and agreement with potential supervisors. The project may be university-based or carried out in an engineering-related organisation. A key aspect of the MSc project is that it requires students to inform the project activities with critically-appraised new and emerging knowledge of the engineering discipline.

 The project process would involve students employing advanced skill in problem definition, delineation of deliverables, project planning, systematic, comprehensive and critical review of literature, research methodology, robust analysis and discussion of results, generation of evidence-informed engineering solutions and conclusions.

The project would include consideration of the non-technical context of the problem such as environmental, economic and social sustainability impacts. 

The engagement with project supervisor, enables student to develop in-depth understanding of the chosen area of the engineering discipline as well as acquire important skills of effective communication within a community of practice. The assessment of the MSc project enables the student to develop key skills on clear communication of complex technical information to specialist and non-specialist audience.

The individual project provides students with a learning experience  to solve challenging engineering problems by employing critically-appraised current, new and emerging knowledge.

It affords students the opportunity to demonstrate to peers and to current and potential employers the student’s ability to develop innovative engineering solutions informed by good quality academic research, in a particular field, relevant to engineering. This may involve the application of existing research within a novel context.

Embedded Systems and Field Programmable Gate Array (FPGA) Technology

• Concurrent assignment statements and unintended memory
• Adopting proper hardware description language (HDL) coding style and taking a divide and conquer approach for code development
• The need for design simplification.
• Regular sequential circuit block system and registers 
• Building test-benches for sequential circuits
• Timing, clocking, operating frequency and clock tree considerations
• The Finite-state machine (FSM), its representation and FSM HDL code development
• The Finite-state machine with data path (FSMD), its representation and FSM HDL code development

Internet of Things (IoT)

• Automatic Identification Technology and Radio-frequency identification (RFID)
• Wireless Sensor Network
• Location System
• Internet and Mobile Internet
• Wireless Access Technology
• Big Data, IoT & Cloud Computing
• Information Security for IoT
• IoT Application Case Studies
• Arduino Opla, Espressif System on Chip (SoC) & Raspberry Pi IoT technologies

The Embedded Systems part of this module aims to develop the skill in students of digital systems design with the VHDL hardware description language (HDL) and Field Programmable Gate Array (FPGA) reprogrammable and fast prototyping technologies. Students will be able to produce design, synthesis of circuits and implement system on chip (SoC) using modern electronic design automation (EDA) tools.

The Internet of Things (IoT) contents aim to develop the advanced practical skills in building and designing IoT related components and network systems, going from the sensors layer to the cloud processing. This part also develops expertise in the use of a hardware/software tools to create and simulate practical systems, considering big data, energy consumption control and network security aspects.

This L7 module is specially designed for the MSc programme with due consideration to the fact that most students may not have a sufficient computing background from their undergraduate study. The course is structured in such a way that all the essential pre-requisites shall be covered on an ‘learn-when-you-need’ basis.

The overall module will be roughly divided into the following sub-modules: Background (including the mathematical foundations); Contemporary AI applications on Scalar Data (e.g. cybersecurity, financial predictions, financial fraud detection,
etc.); AI for Multimedia applications (e.g. activity detection in CCTV videos, video summarisation,
etc); and Ethical and Legal Considerations.

The AI applications considered in the module would cover a broad range, including engineering, biology, finance, and society, with use-cases drawn from contemporary application scenarios. Open-source dataset(s) will be used for the purpose of training and testing the machine learning models.

The introduction of this module stems from the realisation that while AI skills are in demand in almost all employment sectors, many engineering graduates do not have sufficient skills to leverage AI in real-life applications. This module therefore assumes no prior knowledge of machine learning theory and/or implementations. On the completion of this module, the students should be able to design and implement effective and efficient AI models for a wide variety of applications scenarios. Lastly, the course will also empower the students to take informed actions/decisions while applying AI for their intended use-case in due consideration of the legal and ethical issues that may be involved.