We are currently focusing on two strongly interlinked and complementary areas of research reflecting a body of activities, strong track record of achievements, external recognition and capacity for successful bidding for external funding. The first theme focuses on the softer aspects of smart systems and technologies covering such areas as computational intelligence, informatics and complex adaptive systems, while the second broad theme is more hardware and device oriented covering sensory and electronic technologies, robotics and bio-medical engineering .
Computational intelligence, informatics and complex adaptive systems
The work in this theme is a continuation of the research carried out within the Computational Intelligence Research Group (CIRG), which was formally established in 2003. The group has been engaged in research of the theoretical and application aspects of advanced intelligent technologies.
The group’s interests and expertise lie in a broad area of intelligent and biologically/nature-inspired learning and complex adaptive systems and networks. Our projects and research activities cover a wide range of machine learning and hybrid intelligent techniques encompassing data and information fusion, learning and adaptation methods, multiple classifier and prediction systems, statistical methods, applied graph theory, processing and modelling of uncertainty in pattern recognition, diagnostic analysis and decision support systems.
Sensory and electronic technologies, robotics and biomedical engineering
The work in this theme continues the research carried out within the Academic Biomedical Engineering Research Group (ABERG), which was established in 1999.
Our primary aim is to improve the quality of life for people with chronic disabilities in developing new treatment methods and assessment techniques to advance cost-effective, evidence-based healthcare. The group has an international reputation in the areas of Functional Electrical Stimulation (FES) and rehabilitation engineering, sensor development and physiological measurements. The use of FES in stroke treatment has been adopted by the Royal College of Physicians. The group was also the first to develop a Laser Doppler sensor for in-shoe plantar blood-flow measurement.