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on leveraging advanced computer vision (CV) and deep learning (DL) techniques to develop a state-of-the-art computer-aided detection and diagnosis (CAD) system for early-stage Alzheimer's disease (AD) detection
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capture from the atmosphere. Central to this endeavour is the enhancement of gas separation performance in cyclic gas adsorption/desorption processes, achieved through the design and development of 3D
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into electricity grids while maintaining grid stability. Project Description: This project aims to design and prototype a multi-port commercial scale converter for efficient grid integration of photovoltaic
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and implementing algorithms for processing, imaging or inversion of seismic data. Experience, willingness and ability to participate in field acquisition of land and downhole seismic data. Demonstrated
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test the sensors and actuators used in the system. Develop the sensor calibration protocol and mechanisms. Develop the control software. Develop the automated system prototype and test it. Optimise
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images of drill cores and cuttings. They will be involved in dataset acquisition, including core/cuttings preparation, imaging, and labelling. Computer vision algorithms, such as R-CNN, YOLO, SSD, will be
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accordance with set standards, for content updates, including changes to text and images, uploading documents etc, to Student Services web pages Facilitate online and face-to-face training for core content
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from bauxite ores via the Bayer process. However, Australian bauxite ores contain high content of organics, which, if not effectively managed, can impact the Bayer process and alumina product quality. A
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, improve workflows and provide solutions. The John de Laeter Centre provides quantitative data used to understand processes of Earth and planetary evolution, characterise the nature of resources and
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) (specifically coronary computed tomography angiography [CCTA]) is a widely used less-invasive medical imaging modality for coronary artery disease (CAD) diagnosis [1]. Despite recent advancements of the CT