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the question ‘how close to failure is the asset?’. By integrating a suite of state-of-the-art sensors and monitoring technologies with data fusion and AI analytics, this research will enable timely
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sensors that can be independently transmitting, receiving, or both. By acting in unison, rather than in isolation, they can utilise temporal and spatial diversity whilst simultaneously exploiting shared
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated
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an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
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project’s focus is to: Conduct cutting-edge experiments to investigate how surface texture affects seal performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with
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drugs at the target site, using only an internal battery and on-board sensors for fully autonomous operation. The overarching goal is to develop a battery-powered ingestible capsule that autonomously
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining