University of Essex

The Embedded and Intelligent Systems Research Laboratory (EIS@Essex) in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex carries out research in the areas of Embedded Systems and System-on-Chip design with focus on security, power, performance and reliability, advanced embedded systems and processor architectures targeted for cyber physical systems, automotive/industrial, robotics, image processing, networked and distributed sensor nodes / Internet of Things and real-time critical systems.

Furthermore, it works in the area of Big Data Analytics, computer vision and embedded AI for real world problems and application areas.

D5 - Essex

Embedded and Intelligent Systems Laboratory

Lead Investigator: Prof. Klaus McDonald-Maier, Dr Shoaib Ehsan & Dr Xiaojun Zhai

Focus (or foci) of the group for NCNR:

For its work with the NCNR, the University of Essex has focused on measuring and modeling the effects of radiation on the sensors and hardware used in robotics. It has also investigated how firmware and software are affected.

The team measured radiation effects on a number of readily available cameras, sensors and control electronics to identify how these components can be used effectively in radioactive environments. Using equipment that is easy to come by can reduce the cost of robotics hardware and improve the lifespan of what will become consumable equipment. The degradation models produced in Essex can also be used by high-level perception algorithms that filter, or compensate for, the noise and degradation on sensors and cameras.

The novel systems developed allow firmware, operating systems and hardware to physically reallocate resources to unaffected areas. This maximises the lifetime and reliability of mission-critical remote equipment and also allows detailed remote monitoring of the hardware and software at a very low system level.

For its NCNR work, Essex has also developed reconfigurable embedded platforms supporting a variety of AI and machine-learning edge computing models. The team has also made significant contributions to novel visual place recognition methods for challenging environments.

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