Honours and Minor Thesis projects

Convolutional neural network (CNN) has exhibited its significance in addressing large-scale vision tasks such as action recognition, image classification, super-resolution and denoising. Recently, many researchers have reported the potential of employing deep learning techniques to refine high-quality clinical images for diagnosis and treatment.

This is a research-heavy project, looking for students with an interest in human behaviour and data analysis.

Our research explores the use of data visualisation to visually analyse complex eye tracking data and have spent the last few years developing open-source eye tracking analysis software along side feedback from those who use eye tracking in their work.

This project aims to develop a method based on computer vision and machine learning techniques for bridge cheating detection. Bridge is a trick-taking card game using a standard 52-card deck. In its basic format, it is played by four players in two competing partnerships, with partners sitting opposite each other around a table. Cheating in bridge refers to a deliberate violation of the rules of the game of bridge or other unethical behaviour that is intended to give an unfair advantage to a player or team.

In this project, we will look into how to make the physiological data of VR users visible to externals, e.g., a teacher watching a student learning in VR, or a supervisor watching a worker fulfil a task in VR.

Different studies have shown that fluctuations in cognitive load are expressed through changes in human physiology, such as temperature patterns on the face or pupil dilation. However, in users of VR headsets, the face and eyes are covered by the HMD.

Sport Analytics is a lucrative area in the professional sports arenas, however, amateur players and coaches can benefit from some of this relatively affordable technology. Can we learn valuable knowledge about sports players' positions on the field through the use of affordable GPS trackers and open software for analysis without the need for stadium cameras and professional tracking equipment.
 

Human Perception and Cognition in Visualization is a field of research that focuses on understanding how people perceive and understand visual representations of data. Many questions about how investigating visual representations of data can influence user perceptions, decision-making, and memory, are unanswered.

Recently, program generation and optimisation techniques have been adapted to performance critical subroutines in cryptography. Codes generated/optimised by these techniques are both secure and their performance is highly competitive compared to hand-optimised code by experts [1].

The global health crisis has put mental health, emotional well-being, and the risks and importance of digital technologies into the global focus. According to a Lancet Commission report, the number of people with mental disorders is increasing in every country of the world and will cost the global economy US$16 trillion by 2030. Emotions have strong implications not only for mental well-being but also for physical health, e.g., down-regulating negative emotions can lower the risk of heart disease.

Software testing is a crucial part of the software development process, ensuring that the developed software is of high quality and meets the requirements of the users. However, testing can be a complex and time-consuming task, especially when it comes to testing software in multiple languages. ChatGPT is trained in multiple languages, making it easier for understanding and detect bugs in multilingual software.