The Industrial Informatics and Signal Processing group (iisp) promote and develop innovative ideas and place them into commercially viable environments. With multi-disciplinary expertise and the latest technological processes, we provide systems-level solutions to challenging problems in industry, government & business.
The iisp research group is conducting research into computer vsion human action tracking, understanding and surveillance using deep learning. We have a focus on integrated multi-camera systems that are robust to lighting changes and occlusion. The group researches integrated IT systems that are capable of supporting multi sensor environments and providing decision support for a number of applications. Image and signal processing is also applied in biomedical diagnostics and prognostics incorporating impedance spectroscopy, tomography systems & fluorescence microscopy.
Our collaboration on Image Processing of CT and MRI images with the Brighton & Sussex Medical School has led to many high quality publications in leading journals that have influenced healthcare. As a result of this collaboration we have developed texture analysis algorithms that can detect cancer, schizophrenia and autism. There is a spin out company stemming from this collaboration called TexRAD Ltd.
CMOS imaging sensors and systems are under development for applications in proteomics. Networked control systems integrate with this research and currently focus on smart grids that will be able to exploit fluctuating, sustainable power generation sources.
We work on the interoperability of business IT systems and have contributed to its evolution to its current state, the group has worked with commercial systems and more recently has been exploiting open source technology and data standardization in the context of health care systems. Research into the interoperability of National Health Service data and services provides highly pertinent know-how using HL7 and SNOMED in an SOA architecture. We are collaborating with the University of Utah, School of Medicine on the design, development, and validation of a standards-based task management service to enable a Service-Oriented Approach to Clinical Decision Support. In this project we will integrate the Tolven EHR/PHR system with Utah’s Open CDS (Clinical Decision Support).
Click on the projects box to access more detailed information
Members: Young, Birch, Yang, Chatwin
News
TexRad wins the University of Sussex Achieving impact award
A video of Prof Chris Chatwin's invited IET talk on Future Cities can be seen here.