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Edge AI for Anomaly Detection based on Hilbert Space Transformations

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Funding Programme or Company Name: 
Commercialisation Fund
Funding Body: 
Enterprise Ireland - Commercialisation Award
Description: 
Background This proposed commercialisation project aims to create a new spin-out company that will develop a library of algorithms, i.e. software libraries, that can be used by application developers of IoT technologies to solve the problem of effective, low power anomaly detection on the edge devices themselves without resort to central server processing. This set of algorithms is derived from innovations in new statistical approximations for distribution estimation through the use of Reproducing Kernel Hilbert spaces and Bayesian optimisation. Problem / Opportunity to be Addressed: The problem of low power anomaly detection is experienced by users of IoT technologies, especially for example users of IoT technologies in asset tracking and security applications where important alert features based on abnormal events are implemented either through uploading of data back to central servers for processing where the underlying IoT technologies support the data bandwidth required or through simplified anomaly detection algorithms tuned for high false positive rates supplemented with high supervisory burden human interventions.
Insight Contact: 
Application Domain: 
Research Group: 
Associated Theme for Application Domain: 
Connected Health-Connecting Health & Life Sciences
Connected Health-Chronic Disease Management & Rehabilitation
Involved Institution: 
Dublin City University (DCU)