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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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exposure risks to consumers. The research will involve method development using LC-MS/MS for toxin detection, sample collection, data analysis, and comparison of results against current safety thresholds
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trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By
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, government, and wider society. In the REF2021 review of UK university research, 88% of Cranfield’s research was rated as ‘world-leading’ or ‘internationally excellent’. This project will develop a robust
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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dietary exposure risks to consumers. The research will involve method development using LC-MS/MS for toxin detection, sample collection, data analysis, and comparison of results against current safety
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will handle real human waste samples to develop robust protocols for solid and liquid waste characterization, microbial profiling, and safety validation, including pathogen screening. Focus areas include
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potential health impacts. Water utilities across the UK, including Anglian Water, are developing strategies to meet new regulatory guidelines and enhance the resilience of water supply systems. Anglian Water
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very well the behaviour of these cryogenic hydrogen pumps, in order to master their integration into the hydrogen system. The primary objective of this research in collaboration with Airbus is to develop