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Australian State/Territory : QLD
Research Topic : SIGNAL
Australian State/Territory : ACT
Field of Research : Signal Processing
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  • Funded Activity

    Discovery Projects - Grant ID: DP0558865

    Funder
    Australian Research Council
    Funding Amount
    $150,000.00
    Summary
    Design and analysis of optimum space-frequency-time codes for multi-rate OFDM Systems. This research work contributes to the ones of the major national research priorities, the frontier ICT technology. It addresses the issues of a frontier ICT technology. Output of the project will place the Australia in the map of 4-th generation mobile and wireless communications research. These results will also influence the implementation aspects of future mobile communication systems and attract the att .... Design and analysis of optimum space-frequency-time codes for multi-rate OFDM Systems. This research work contributes to the ones of the major national research priorities, the frontier ICT technology. It addresses the issues of a frontier ICT technology. Output of the project will place the Australia in the map of 4-th generation mobile and wireless communications research. These results will also influence the implementation aspects of future mobile communication systems and attract the attention of the international community. Other major national benefit of the project is the training of PhD students and the production of potential researchers for 4G research.
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    Funded Activity

    Research Networks - Grant ID: RN0459979

    Funder
    Australian Research Council
    Funding Amount
    $2,250,000.00
    Summary
    ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this te .... ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this team with the foremost international authorities and leading industry players in the area of sensor networks. This research network will guide collaborative research that will ensure Australia to play a world leading role in sensor network development and implementation.
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    Funded Activity

    Discovery Projects - Grant ID: DP140100793

    Funder
    Australian Research Council
    Funding Amount
    $270,000.00
    Summary
    Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive. Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than ai .... Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive. Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than aiming to achieve invariance to it. The project will also research new data agnostic feature compaction capabilities to enable scalable learning on the world’s largest and challenging expression dataset available to us through international collaboration. Tackling these two major open problems will make accurate coding of facial expressions in natural environments achievable.
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