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Research Topic : Flow analysis
Status : Active
Socio-Economic Objective : Expanding Knowledge in Technology
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  • Researchers (74)
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT210100506

    Funder
    Australian Research Council
    Funding Amount
    $1,011,851.00
    Summary
    A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, th .... A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, this project seeks to develop new distributed solutions for statistical estimation, which are specifically designed for dynamic systems with multiple object states, and are inherently scalable and robust. The potential benefits include new technologies for smart cities, autonomous infrastructure, and digital productivity.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190103301

    Funder
    Australian Research Council
    Funding Amount
    $420,000.00
    Summary
    Faithful Visual Analytics: models, metrics and algorithms. This project aims to deliver new models, metrics and algorithms for Faithful Visual Analytics of complex data. For a purported visual representation of some data, "faithfulness" measures how accurately the visual representation describes the data. This project will develop new models for Faithful Visual Analytics, design new faithfulness metrics for faithful visual analytics of complex networks, design new algorithms to compute faithful .... Faithful Visual Analytics: models, metrics and algorithms. This project aims to deliver new models, metrics and algorithms for Faithful Visual Analytics of complex data. For a purported visual representation of some data, "faithfulness" measures how accurately the visual representation describes the data. This project will develop new models for Faithful Visual Analytics, design new faithfulness metrics for faithful visual analytics of complex networks, design new algorithms to compute faithful visualisations, and evaluate using real world social network and biological network data sets. The new models, metrics and algorithms produced by this project will be used in the next generation Visual Analytic tools to enable analysts develop accurate insights and new knowledge of complex data.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT180100447

    Funder
    Australian Research Council
    Funding Amount
    $993,643.00
    Summary
    Modelling human perceptual-motor interaction for human-machine applications. This project aims to develop a new modelling framework for identifying the perceptual-motor processes that underlie cooperative and competitive human interaction. The project will also determine whether this modelling framework can be combined with modern machine-learning methods to develop artificial agents capable of human level performance. Expected outcomes will include a practical methodology for rapidly generating .... Modelling human perceptual-motor interaction for human-machine applications. This project aims to develop a new modelling framework for identifying the perceptual-motor processes that underlie cooperative and competitive human interaction. The project will also determine whether this modelling framework can be combined with modern machine-learning methods to develop artificial agents capable of human level performance. Expected outcomes will include a practical methodology for rapidly generating models of effective human interaction that can be easily implemented in human-machine systems. This will provide a richer understanding of the fundamental perceptual-motor processes that support robust human interaction and enhanced the effectiveness of human-machine collaboration and training technologies.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220101255

    Funder
    Australian Research Council
    Funding Amount
    $499,630.00
    Summary
    Responsible Urban Innovation with Local Government Artificial Intelligence. Artificial intelligence (AI) is not only becoming an integral part of urban services, but also impacting and shaping the future of cities and societies. However, the current AI practice has shown that urban innovation without responsibility generates more problems than it solves. Especially, the absence of a deep understanding of the costs, benefits, risks and impacts of deploying government AI systems creates negative e .... Responsible Urban Innovation with Local Government Artificial Intelligence. Artificial intelligence (AI) is not only becoming an integral part of urban services, but also impacting and shaping the future of cities and societies. However, the current AI practice has shown that urban innovation without responsibility generates more problems than it solves. Especially, the absence of a deep understanding of the costs, benefits, risks and impacts of deploying government AI systems creates negative externalities and serious concerns in the society. This project will generate new knowledge on the most appropriate approaches for local governments to engage with AI to achieve responsible urban innovation. The project outcomes will include responsible AI adoption and implementation pathways for Australian local governments.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP190100619

    Funder
    Australian Research Council
    Funding Amount
    $230,703.00
    Summary
    Digital technologies and the private rental sector in Australia. This project aims to show how digital technologies are transforming the private rental sector in Australia. This project expects to generate new knowledge about the growing global reach of digital technologies aimed at private renters, landlords and property managers. The expected outcomes of this project include the production of social scientific knowledge about the potential of digital technologies to be both socially pernicious .... Digital technologies and the private rental sector in Australia. This project aims to show how digital technologies are transforming the private rental sector in Australia. This project expects to generate new knowledge about the growing global reach of digital technologies aimed at private renters, landlords and property managers. The expected outcomes of this project include the production of social scientific knowledge about the potential of digital technologies to be both socially pernicious and socially progressive. This project should provide significant benefits for Australian renters and our tenant advocacy partners who represent them, and to show how digital technologies can be used to create a better housing system.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP180102553

    Funder
    Australian Research Council
    Funding Amount
    $421,097.00
    Summary
    Sublinear algorithms for visual analytics of extreme-scale networks. This project aims to design new sublinear algorithms for the visual analytics of extreme-scale networks, involving billions of nodes. Based on algorithmics for graph drawing, integrating sublinear algorithms and distributed algorithms, the project will introduce new quality metrics for good visualisation of extreme-scale networks, design new sublinear-time algorithms to compute good visualisation, implement them in a distribute .... Sublinear algorithms for visual analytics of extreme-scale networks. This project aims to design new sublinear algorithms for the visual analytics of extreme-scale networks, involving billions of nodes. Based on algorithmics for graph drawing, integrating sublinear algorithms and distributed algorithms, the project will introduce new quality metrics for good visualisation of extreme-scale networks, design new sublinear-time algorithms to compute good visualisation, implement them in a distributed computing environment, and evaluate with a real world social network and biological network data sets. The new algorithms produced by this project will be used in the next generation visual analytic tools for extreme-scale data to enable analysts develop new insights and new knowledge of extreme-scale data.
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    Showing 1-6 of 6 Funded Activites

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