The aim of this module is to develop student expertise in technical design, planning and management of a multi-disciplinary team engineering research focused project, providing students with an opportunity to consolidate knowledge and skills developed throughout their programme.

The aim of this module is to enable the students to act as mentors for teams that are completing projects in part B.

The aim of this module is for the students to understand the options available with advanced control methodologies that can overcome practical implementation issues with classical control design due to parameter uncertainty and system non-linearities.

The aims of this module are:

• To provide critical overview of statistical methods and machine learning required for analysing data.
• To develop a systematic and practical understanding of regression and classification analysis.

The aims of this module are for students:

• To understand the options available and the issues related to selection of sensors and actuators for the design and control of mechatronic systems.
• To design, model and specify a complex fault tolerant mechatronic and control system.

This module aims to give students:

• Practical knowledge of systems from a model based and architectural viewpoint.
• An understanding of system and enterprise architecture frameworks.
• Knowledge of and practice with software modelling languages, methods, and commercially available tools.
• An introduction to model driven architecture and analysis. The students will learn a system definition and architecture design process aligned to ISO/IEC 15288 and how to model the architecture of a system and use it to assess system functionality and behaviour.

The aims of this module are to:

• Provide an understanding of advanced electronic engineering applications.
• Provide insight into practicalities of advanced sensor systems in real world applications using underwater acoustics applications as a case study.

The core aim of this module is to ensure students are able to take advantage of Machine Learning (ML) techniques to solve practical engineering problems. Towards that end the following aims are established:

• Provide a base understanding of Machine Learning (ML) in the wider context of Artificial Intelligence (AI).
• Establish ML approaches and algorithms.
• Explore ML techniques in practical engineering contexts.
• Establish the challenges with ML in engineering.
• Deliver ML solutions in engineering, ensuring proficient use of essential tools for practical applications.

The aim of this module is for students to develop an understanding of recent and future applications of robotics with and for humans, with particular emphasis on sport and healthcare applications. The module will analyse several problems and what specific robotic solution are being developed for them, how the solutions are derived, what results are currently achieved and what the next challenges are.