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fall detection would be from video as it does not require wearing a device and remembering to charge it and so on. But computer vision-based falls detection in the elderly can be problematic due
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as networks or graphs in the hope of reasoning about them - but the tools that we have for understanding such network structured data (whether algorithmic analytics or visualisation tools) remain crude
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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This project will investigate and develop the ways in which AI algorithms and practices can be made transparent and explainable for use in law enforcement and judicial applications The Faculty
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Connected Autonomous Vehicle (MCAV) team. Required knowledge Artificial Intelligence Machine learning Software Testing Genetic Algorithms
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guarantees of FL. In this project, we aim at an ambitious goal - designing secure and privacy-enhancing algorithms and framework for FL and applying our designs into real-world applications. To achieve
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challenging data problem. Weak signals from collisions of compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms
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and verbal communication skills Strong computational, programming, algorithms, and data analysis skills Outstanding research skills Capacity to work independently and as a part of a team Applicants with
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Learning, Algorithms and Data Structures
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on this technology for its automated warehouses and fulfilment centres. The aim of this project is to use discrete optimisation techniques (e.g. Integer Programming) to design new algorithms for MAPF. An example