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Research Topic : Automation
Field of Research : Signal Processing
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  • Researchers (25)
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  • Funded Activity

    Linkage Projects - Grant ID: LP120200321

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    Scheduling and quality of service in Long Term Evolution telecommunications. There is an explosion of mobile telecommunications with over 50 billion connections expected by 2020. The next generation of mobile broadband will be based on a new technology known as Long Term Evolution (LTE) and, in this context, the goal of this project is to improve the efficiency of these systems by developing new techniques for scheduling.
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    Funded Activity

    Linkage Projects - Grant ID: LP100100302

    Funder
    Australian Research Council
    Funding Amount
    $555,000.00
    Summary
    Automated vision-based aircraft collision warning technologies. Australia is a sparsely populated country with a number of unique airspace features. This project will investigate novel vision-based collision warning systems that can improve safety for piloted aircraft and also help achieve integration of UASs (Uninhabited Aerial Systems) into national airspace. The benefits of UAS technologies are particularly relevant to Australia, as governments and industry struggle to cope with providing equ .... Automated vision-based aircraft collision warning technologies. Australia is a sparsely populated country with a number of unique airspace features. This project will investigate novel vision-based collision warning systems that can improve safety for piloted aircraft and also help achieve integration of UASs (Uninhabited Aerial Systems) into national airspace. The benefits of UAS technologies are particularly relevant to Australia, as governments and industry struggle to cope with providing equivalent levels of service to remote communities over vast distances (or border protection of vast regions). The population base of Australia requires that cost-effective solutions are sought to meet this end.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220102159

    Funder
    Australian Research Council
    Funding Amount
    $480,000.00
    Summary
    Estimation and Control of Noisy Riemannian Systems. Many application areas such as satellite control, computer vision, coordination of rigid bodies, require the estimation and control of systems subject to geometric constraints. Most current algorithms for doing this are deterministic and can fail catastrophically in the presence of noise. This project aims to provide: (i) Methods for analysing and then redesigning deterministic algorithms to ensure stability in the presence of noise; (ii) New .... Estimation and Control of Noisy Riemannian Systems. Many application areas such as satellite control, computer vision, coordination of rigid bodies, require the estimation and control of systems subject to geometric constraints. Most current algorithms for doing this are deterministic and can fail catastrophically in the presence of noise. This project aims to provide: (i) Methods for analysing and then redesigning deterministic algorithms to ensure stability in the presence of noise; (ii) New design methods that deal with noise in an optimal way; (iii) Noise resistant methods for distributed consensus seeking systems and cooperative control systems. The outcomes will advance and benefit spatio-temporal data analysis and coordination in areas such as transport, health and video-security.
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    Funded Activity

    Discovery Projects - Grant ID: DP130103081

    Funder
    Australian Research Council
    Funding Amount
    $330,000.00
    Summary
    Point processes system identification under simultaneity. Neuroscientists study neuronal brain dynamics of mammals via recordings from scores of tiny electrodes. But analysing these experiments is a problem because current methods cannot handle the common case where neurons discharge simultaneously. This project aims to provide powerful new tools to overcome this bottleneck.
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    Funded Activity

    Discovery Projects - Grant ID: DP140102041

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    Riemannian System Identification. A growing number of applications such as satellite attitude estimation, pose estimation in computer vision and direction estimation in statistics require the study of random processes in Riemannian manifolds and Lie Groups. This project will provide: methods for the construction/ numerical simulation of such processes; methods of system identification and their asymptotic performance analysis; and, algorithms for process state estimation.
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    Funded Activity

    Discovery Projects - Grant ID: DP180103058

    Funder
    Australian Research Council
    Funding Amount
    $362,716.00
    Summary
    Modeling stochastic systems in Riemannian manifolds. This project aims to develop new statistical signal processing and control engineering algorithms and tools that will enable tracking of objects remotely on land and in space. A growing number of applications require the study of random processes in Riemannian manifolds, that is processes that evolve subject to a geometric constraint. This project aims to provide methods for the numerical simulation of such processes, methods of online and off .... Modeling stochastic systems in Riemannian manifolds. This project aims to develop new statistical signal processing and control engineering algorithms and tools that will enable tracking of objects remotely on land and in space. A growing number of applications require the study of random processes in Riemannian manifolds, that is processes that evolve subject to a geometric constraint. This project aims to provide methods for the numerical simulation of such processes, methods of online and offline system identification from data on such processes and asymptotic performance analysis; and algorithms for process state estimation that obeys the geometry. The outcomes will advance and benefit spatio-temporal data analysis in areas such as transport, health and video-security.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP180102417

    Funder
    Australian Research Council
    Funding Amount
    $352,616.00
    Summary
    Vector network system identification. This machine learning project aims to provide more reliable ways to identify the structure and function of dynamic networks from both continuous and discrete network data. The project will use all the data and create principled new metrics. This could enable early diagnosis of network faults across a range of applications for example in power systems or diseased human brains. It could also enable discovery of critical functional subnetworks affecting reliabl .... Vector network system identification. This machine learning project aims to provide more reliable ways to identify the structure and function of dynamic networks from both continuous and discrete network data. The project will use all the data and create principled new metrics. This could enable early diagnosis of network faults across a range of applications for example in power systems or diseased human brains. It could also enable discovery of critical functional subnetworks affecting reliable operation in large complex human systems (such as financial systems) or natural systems (such as gene regulatory networks).
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    Funded Activity

    Linkage Projects - Grant ID: LP160100666

    Funder
    Australian Research Council
    Funding Amount
    $470,000.00
    Summary
    Automatic control systems for low-energy pipelines in irrigation networks. Automatic control systems for low-energy pipelines in irrigation networks. This project aims to design automated pipelines to distribute irrigation water from backbone open-channels to end-users. Automation can make irrigation networks more efficient, which is important for food security and the environment. Automation is expected to achieve low-energy distribution, in line with the gravity-powered operation of typical op .... Automatic control systems for low-energy pipelines in irrigation networks. Automatic control systems for low-energy pipelines in irrigation networks. This project aims to design automated pipelines to distribute irrigation water from backbone open-channels to end-users. Automation can make irrigation networks more efficient, which is important for food security and the environment. Automation is expected to achieve low-energy distribution, in line with the gravity-powered operation of typical open-channel networks. The main challenges are the development of suitable models for designing outlet-flow control systems, optimization-based outlet-flow scheduling methods for ensuring operation within hydraulic constraints, and system monitoring techniques. This project will design automatic control systems to enable low-energy water distribution from open-channels to end-users by pipes.
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    Funded Activity

    Discovery Projects - Grant ID: DP130104617

    Funder
    Australian Research Council
    Funding Amount
    $270,000.00
    Summary
    DC optimisation based synthesis of systems in control, signal processing and wireless communication network. The conceptual advances with new optimisation based solvers to be developed in the area of control, signal processing and wireless communication. Major benefits of this project will be its direct applications to renewable technologies in automobile, health care, digital and communication network industries.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP200200917

    Funder
    Australian Research Council
    Funding Amount
    $593,636.00
    Summary
    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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