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[2021-2022] (DSTL: £96.7K) EO Image Processing

[2021-2025] (EU H2020: €777.4K) EXPONENTIAL ANALYSIS EMPOWERING INNOVATION

The EXPOWER project (led by Annie Cuyt, University of Antwerp) combines a broad spectrum of key research and training activities on Multi-Exponential Analysis with applications in industry, that are currently being undertaken in some premier research institutes. The network is interdisciplinary, intersectoral, unconventional and ambitious. It is unconventional in the sense that it connects stakeholders from seemingly separately developed fields: computational harmonic analysis, numerical linear algebra, computer algebra, nonlinear approximation theory, digital signal processing and their applications, in one and more variables. It is ambitious because the consortium stretches from mathematics to computational science and engineering and industry.

[2022-2023] (Innovate UK: £120K) INtelligent FOod Recognition and Monitoring for patient wellbeing (INFORM) in collaboration with Falcon Food Services Equipment

INFORM is an industrial project and aims to develop an embedded hardware that uses computer vision and IoT to automatically detect and analyse food consumption. It will assist care providers to monitor patients’ wellbeing and provide increased quality of care and reduce the quantity of food waste, preserving valuable health service and care homes resources and reducing environmental impacts.

[2019-2022] (ST Microelectronics/Univresity of Stirling: £143.75K) PhD studentship: Domain specific optimisations for real-time image processing on heterogeneous FPGA+CPU+GPU

[2019-2023] (Scotland's Rural College/Univresity of Stirling: £79K) PhD studentship: Improving animal health in marine ecosystem using data mining and signal/image processing

[2018-2022] (ESRC: £370K) Identifying Novel Markers of Concealed Face Recognition

The project develops new ways through eye-tracking, physiological responses and micro-expressions, to detect recognition of familiar faces when people deny recognition of someone they know. It combines the Concealed Information Test (CIT) with theoretical models of familiar and unfamiliar face recognition to create novel and simple techniques that have the potential for use in a wide range of security settings.