Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Control systems for irrigation networks in storage critical operations. The aim of the project is to further develop automatic control technologies for irrigation channels, with particular focus on supply mode operations for channels with critical limits on storage and inflow. The significance relates to the role of irrigation channels in food and fibre production. New knowledge generated will help Rubicon Water expand its Total Channel Control product, already used extensively in Australia, to ....Control systems for irrigation networks in storage critical operations. The aim of the project is to further develop automatic control technologies for irrigation channels, with particular focus on supply mode operations for channels with critical limits on storage and inflow. The significance relates to the role of irrigation channels in food and fibre production. New knowledge generated will help Rubicon Water expand its Total Channel Control product, already used extensively in Australia, to suit emerging markets with significant export potential. Beyond the commercial impact, expected benefits include improved service, reduced environmental footprint, the safeguarding of assets in extreme events, and the training of engineers in the important areas of modelling and control for infrastructure management.
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