Honours and Minor Thesis projects

The application of AI in sports is widely researched as both coaches and players realise the significance of quantitative analysis that can be extracted from video matches. Detecting and segmenting in-play scenes in sport video
sequences is necessary in various applications such as quantitative game and performance analysis. In studies on video-based game and performance analysis of racket sports, much research efforts have been made to explore the relationships between predefined parameters and sports performance, and the methodologies for effective coaching.

Autonomous electric vehicle (EV) fleets are emerging as an effective way to solve environmental problems and reduce commute costs in smart cities. Due to the complex spatiotemporal behaviors of passengers and their trips, the unmanaged electric charging demand from EV fleets will significantly impact the existing transportation and electric power infrastructure. Reliable charging networks and charging strategies for EV fleets are the prerequisites to the successful adoption of autonomous EV fleets.

We aim to take the first step to

The world’s energy markets are transforming, and more renewable energy is integrated into the electric energy market. The intermittent renewable supply leads to unexpected demand-supply mismatches and results in highly fluctuating energy prices. Energy arbitrage aims to strategically operate energy devices to leverage the temporal price spread to smooth out the price differences in the market, which also generates some revenue.

The aim of this project is to probe, conceptualize, and assess the fusion of generative AI with data comics to augment their narrative potency, user comprehension, and user engagement. Data comics, an innovative blend of data and visual artistry, effectively portray intricate narratives in an accessible comic strip format. By integrating generative AI, we can automate and enrich the storytelling process, further enhancing the visual and narrative depth of data comics. The primary areas of research encompass AI, data visualisation, and user experience.

The research challenge for this project is to curate a dataset captured in a collaborative learning setting in which teams of three students engaged in conversations and created a joint concept map. The goal is to analyse the content of their conversations and concept maps they created at a multi-touch tabletop and model the epistemic constructs reflected in both their conversations and the artefact they jointly create. Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

AI has been growingly used in Medicine. There are big opportunities for AI in medical research, including medical imaging diagnosis. AI and Deep Learning have been used to detect and classify lesions in various diseases, such as cancers. 

This project is in collaboration with the Faculty of Dentistry, Airlangga University, Indonesia. We will explore the use of AI in dentistry, especially in dental medical imaging and periodontology (gum disease). Gum disease often causes bone loss in the furcation of a lower molar (see picture below).

Admission to the Medical degree at Monash is very competitive. The criteria is beyond the ATAR score. It covers many other factors, such as health test, interview, etc. Consequently, the ranking process is very complex.

Are you interested in programming maps, such as GoogleMaps or Open Street Maps? This project uses online maps extensively for visualising routes, and other objects of interest.

Multimedia content such as audio, image, and video are stored and transported in compressed forms. Various standards are designed to encode the content at the highest possible level while minimizing distortion. Some commonly used compression standards include MP3 for audio, JPEG for still image, H.264/AVC for video. Despite the vast differences in signal characteristics, most compression standards have two things in common: transformed-quantized coefficients and scale factor (quantization table in JPEG and AVC). The coefficients are usually coded as a product of sign_bit and magnitude.