Displaying 121 - 130 of 187 projects.
We have a range of potential research projects on offer in partnership with VIFM - https://www.vifm.org/ - looking at ML techniques in predicting forensic diagnoses / image analysis, across multiple data types found at VIFM. These include atomic data, text data and text documents, medical images, clinical photographs and digital pathology slides.
This research has high potential to support our IT for social good agenda in addition to its technical attractiveness.
Modern map-based systems and location-based services rely heavily on the ability to efficiently provide navigation services and the capability to search points of interests (POIs) based on their location or textual information. The aim of this project is to build a next-generation navigation system by addressing limitations in the current systems – such as allowing more meaningful distance measures, modeling uncertainty in data sources and queries, and exploiting rich information from several data sources.
On their own, traffic accidents cause 1.3 million fatalities every year – and improper situational awareness is often a major cause. This project aims to exploit big spatio-temporal data to design intelligent techniques for scheduling and offloading tasks to the cloud and peer vehicles. This will ultimately meet the Quality of Service (QoS) requirements of time-critical road safety applications and increase situational awareness by automatically identifying unsafe road conditions and risky driving behaviors – and sending alerts in real time to affected vehicles.
This project aims to harness big data from ubiquitous smartphone sensors to reduce the impact of road transport on the environment. Specifically, we’ll design novel data modelling and indexing techniques to exploit the data and create a next-generation, eco-friendly navigation system which will significantly reduce greenhouse gas emissions and result in fuel saving. The initiative also aims to study the citywide impact of adapting to eco-friendly navigation on traffic, environment and road safety – therefore supporting urban planning and decision-making.
Reasoning, constraint solving and optimisation technologies have made remarkable progress over the last two decades. A number of formalisms like Boolean satisfiability (SAT), satisfiability modulo theories (SMT) and their optimisation extensions (MaxSAT and MaxSMT) as well as constraint programming and optimisation (CP) and mixed-integer linear programming (MILP) can be seen as success stories in computer science.
This project will explore the use of Mixed-Reality (MR) headset technology to support people in performing maintenance tasks in complex environments, where the nature of the work involves close inspection of and interaction with mechanical devices. Examples might include aircraft maintenance or other complex workshop environments. We term work in such situations as "physically embedded" in that the nature of the workflow and the information and data associated with the work is closely tied to the physical machinery. Such maintenance support requires providing the worker with timely and…
This project heavily focuses on maps (e.g. GoogleMaps or Open Street Map). We will explore various properties of road networks, including the granularity of road networks, routes and trajectories on road networks, and query processing on road networks.
A number of inter-disciplinary collaboration exists, including transportation to hospitals, urban sprawl analysis, and geospatial in sustainability (e.g. analysing placement of rubbish bins on streets).
This project investigates 3Vs of Big Data (e.g Volume, Variety, and Velocity).
Volume: Due to the exponential increase in data volume, it is necessary to adopt parallelism techniques to achieve reasonable query response time. The main focus will be on parallel query processing, which is the main driver for Big Data processing
The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem.
The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is revolutionising the field of temporal analytics. We have refocused the field on methods that are both effective and feasible for non-trivial problems.