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Field
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environment-specific algorithms and machine learning approaches. At the end of the project a technology demonstrator will be built using UAV- and USV- mounted radar sensors, and it will be tested in real world
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aims and objectives This project aims to develop an optimised, fault-tolerant implementation of the Falcon post-quantum digital signature algorithm for spaceborne FPGAs/processors. The key objectives
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the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including
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like the target voice, such as competing voices, which are particularly challenging for noise reduction algorithms currently employed in hearing aids. With multimodal hearing aids, which capture a
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workspaces under positional restrictions. Develop smart control algorithms that will allow the robotics end-effectors to communicate with the central control system and coordinate tasks with other end
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precision Mechatronics systems and algorithms. Ability to develop kinematic and/or dynamic analysis of Mechanical/Robotic systems. Ability to implement control and kinematics with hardware-in-the–loop
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(developed by B.J. Evans, O. Hassan and K. Morgan). This solver directly solves the Boltzmann-BGK model equation for the velocity distribution function, which is a fundamental quantity in rarefied gas
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Sustainability Post-COVID-19 Project Supervisors: Professor Rebecca Randell, Dr Joshua Pink Project Description: The COVID-19 pandemic accelerated the drive to home working and acceptance of the distributed
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institutions and governmental practices to ensure that support meets local needs. Structural challenges such as resource distribution, access to finance, corruption, multilevel governance systems and
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. This research will use distributed field data collection (macroinvertebrates, sediment character and dynamics) and a complementary set of flume experiments to quantify these impacts and to systematically