Engineering Ethics and Sustainability:
Ethics and Legal Aspects, Risk and environment management systems, Risk Assessment and engineering failure methods and sustainability.
Advanced Electronic and Electrical Engineering Group Project:
Collaboration and Teamwork interfacing hardware and software. Temperature Systems Controller design.
Advanced Embedded Systems Design:
Electronic Systems Architecture. The aims of this module are to: 1. provide a detailed knowledge of computing for embedded and control computer systems; 2. illustrate and develop an understanding of the various engineering, scientific and economic trade-offs necessary in the design of embedded systems; 3. understand the principles and the role of embedded systems in real world applications; 4. provide familiarity and experience with a range of architectural and programming techniques for embedded engineering systems and their evaluation; 5. understand the process of implementing algorithms on embedded systems.
Advanced Analogue Electronics and Photonics:
This module aims to enable students to focus on photonic systems and electronic systems for sensors in general the use of real-world examples and hence to acquire knowledge and understanding of the characteristics of sensors and associated systems for, and the skills to evaluate, design and implement them.
Embedded DSP for Communication Systems:
The aim of the module is develop in-depth knowledge and understanding of real-time signal processing, reconfigurable computing and embedded DSP system architecture and to develop students’ ability to implement real time algorithms on embedded DSP processors for communication applications.
My Dissertation:
The Processing and Classification of EEG Vowel Signals in
Imagined Speech.
Deep Learning Project, ML sub-set.
Abstract
This report is a response to the previous report by C. Nguyen, G. Karavas, P. Artemiadis, on “Inferred Imagined Speech using EEG signals: a new approach using Riemannian Manifold features", Journal of Neural Engineering, July 2017 [1]. It confirms that Imagined speech has potential within BCI. This report makes an investigation on how to optimise and whether or not this research can justify the decoding of EEG signals from coma patients. The design aspect comes from the manipulation of the LSTM architecture layers and focuses on an investigation of hyperparameters and parameters for optimization of vowel sound training when a patient is in resting condition, only. Vowel classification is improved upon from Nguyen, Artemiadis and Karavas’ work [1].
