| Qualifications |
Duration |
Start dates |
Application period |
PhD
(MPhil also available) |
Full-time: 3–4 years
Part-time: 6–8 years |
February and October |
January to April |
Qualifications
PhD (MPhil also available) |
Duration
Full-time: 3–4 years
Part-time: 6–8 years |
Start dates
February and October |
Application period
January to April |
Next generation (XG) technologies are characterised as being intelligent, scalable, adaptive, interoperable, reconfigurable and dependable from a variety of perspectives including safety, privacy and security. Next generation video systems for instance, will offer more views, more pixels, greater interactive functionality and increasingly multisensory experiences like augmented and virtual reality.
Similarly, XG networks like 6G technology will offer intelligent connectivity and extend mobility into new multimedia domains driven by the integration of Artificial Intelligence and Machine Learning towards the vision of a hyperconnected society with everyone and everything connected. This is the exciting backdrop in which the Next Generation Multimedia Technologies (XGMT) Research Group undertakes its research.
Entry requirements
Minimum 2:1 undergraduate degree (or equivalent). If you are not a UK citizen, you may need to prove your knowledge of English.
Potential research projects
- NeXt Generation (XG) multimedia technologies
- Cognitive Radio networks
- 5G and 6G wireless technologies
- IoT, wireless sensor networks and mobile ad hoc network (MANET) security
- Green wireless communications
- Multimodal medical imaging
- Distributed video coding
- Remote image sensing of road surface quality
- 3D image/video inpainting and multi-view video coding
- Clinical tracking of vulnerable patients in care scenarios
Current/recent research projects
- An Investigation of the Effect of COVID-19 on the Digital Divide in Regions of North Wale
- A Novel Semi-Autonomous Time Series Analysis App for Polar Environmental Monitoring and Predictive Decision-Making
- A Novel Approach to Anomaly Detection in Cloud FinTech Systems
- Has Coronavirus changed primary care appointments forever or will it revert to the old, trusted methods?
- Machine-learning techniques to detect and identify cybercrimes
- The development of an active clinical tracking and alert system for vulnerable patients in care
- A novel framework for improving Intrusion Detection in IoT-enabled MANETs
- Plants identification using convolutional neural networks through leaves images in a natural environment
- Understanding unpaved road condition for asset management by Earth Observation in Low Income Countries
- A Distributed Video Coding Framework for Higher Resolution Sequences
- A Novel Multi-View Tennis Table Umpiring Framework
- Sustainable Low-Carbon Isolated Island Electricity Systems – Policy and Investment Impacts Assessed Using System Dynamics
- A Cognitive Radio Compressive Sensing Framework
- A Novel Inpainting Framework for Virtual View Synthesis
- A Hybrid Similarity Measure Framework for Multimodal Medical Image Registration
- A Knowledge Integration Framework for 3D Shape Reconstruction
- Scalable base station switching framework for green cellular networks
- A Unified Wormhole Attack Detection Framework for Mobile Ad hoc Networks
- Intelligent Side Information Generation in Distributed Video Coding
- A Cognitive TV White Space Access Framework
- Interference Aware Cognitive Femtocell Networks
Potential supervisors
Fees and funding
| UK fee |
International fee |
| Full-time: £4,786 per year |
Full-time: £15,698 per year |
| Part-time: £2,393 per year |
Part-time: £7,849 per year |
Some of our research students are funded via Doctoral Training Partnership EPSRC and the STEM Faculty; others are self-funded.
For detailed information about fees and funding, visit Fees and studentships.
To see current funded studentship vacancies across all research areas, see Current studentships.
Links