Signal Processing and Machine Learning

Signal and image processing algorithms lie at the heart of almost all of today's digital technology, from the mobile phone to advanced satellite imaging. IDCOM's expertise in signal and image processing spans fundamental theory through to algorithm design, with applications in a wide array of sectors across science and technology.

IDCOM delivers world-leading research in signal and image processing, from fundamental theory, algorithms, and architectures to its translation to medical imaging, audio, video, and radar/sonar applications in a true multimodal context.

Machine learning AI computer graphic with blue line face and network representation — Machine Learning and AI in Healthcare

Major research themes:

IDCOM explores theoretical and practical developments in machine learning and artificial intelligence, statistical signal processing and Bayesian estimation, considering applications for:

Medical Imaging & Monitoring
Remote Sensing & Environmental Monitoring
Vision and Robotic systems
Defence and Security
Industrial Condition Monitoring

Academic theme members:

Elliot Crowley – Simplifying Machine Learning
Michael Davies – Sparse Sampling and Computation
Javier Escudero Rodriguez – Signal Processing for Biomedical Data
James Hopgood – Multimodal Signal Processing
Dave Laurenson – Biomedical Imaging
Steven McDonagh – Computer Vision and Machine Learning
Sotirios Tsaftaris – Artificial Intelligence
Mehrdad Yaghoobi – Computational Imaging

Current research projects:

IDCOM leads the Causality in Healthcare AI Hub (CHAI) and the Centre for Doctoral Training in Sensing, Processing and AI for Defence and Security (SPADS)

This article was published on 2024-09-14