Compulsory modules

The aim of this module is to develop student expertise in technical design, planning and management of a multi-disciplinary team engineering research focussed 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 aims of this module are to develop the theory and practice of multi/many-core programming in an engineering context.

The aims of this module are to:

  • To present studies on a deep understanding of the specific digital communication technologies critical to IoT systems.
  • To explore the application, strengths, and limitations of various digital communication technologies in IoT.
  • To develop practical skills in designing IoT communication systems using current technologies and protocols.

Optional modules

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.

More information to follow.

The aim of the module is to develop critical understanding of the fundamentals of digital signal processing, as applied to numerous and common-place digital systems, with the use of computer simulation based tools.

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.

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.

More information to follow.

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.