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being implemented in many practical applications such as image processing, AI and machine learning where exact results are not critical and intrinsic errors are acceptable. However, approximate computing
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environments alongside personal wearables provides exciting opportunities to support individuals who have difficulty regulating their emotions. This research will leverage machine learning to monitor and
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equivalent) are NOT eligible to apply for an award. Due consideration should be given to financing your studies . Recommended reading 1. Abbasi, B. and Goldenholz, D.M., 2019. Machine learning applications in
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. Consideration will be given security issues on the model deployment in the Digital Twin solution, including data poisoning. The project will investigate solutions, including decentralized machine learning
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Apply and key information This project is funded by: Department for the Economy (DfE) Vice Chancellor's Research Scholarship (VCRS) Summary Affordable sewing machines and commercial patterns made
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roles in intelligence systems for our daily lives, such as computer vision, autonomous car driving, earth observation, etc. Deep learning is typically data driven, there is little domain knowledge
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Summary Recognizing human activities accurately is crucial in smart environments, supporting applications in healthcare, security, and automation. Traditional machine learning struggles with
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. The integrated AI-based feedback system is a focal point, employing machine learning and sensor fusion to process diverse data. The goal is to design a system that dynamically responds to data inputs, allowing
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-WAAM machine and experimentally observing the picture of deposition process. The monitoring technology will be used for optimising tool path planning of Robot to ensure a higher quality deposition. A
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. In collaboration with LoweConex, a leading software and analytics provider for connected building assets, this project combines machine learning, physics-based models, and expert domain knowledge