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Socio-Economic Objective : Mathematical sciences
Australian State/Territory : QLD
Research Topic : image processing
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  • Funded Activity

    Discovery Projects - Grant ID: DP0666107

    Funder
    Australian Research Council
    Funding Amount
    $201,000.00
    Summary
    Towards automated and intelligent processing of web-based information. The successful outcome of this project will enhance Australia's research reputation in an important, practical area of ICT, will contribute to emerging Web standards, will produce frontier technology that will eventually be of benefit to Australian industry, and will train several postgraduate students.
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    Funded Activity

    Discovery Projects - Grant ID: DP1093652

    Funder
    Australian Research Council
    Funding Amount
    $150,000.00
    Summary
    Rule-based reasoning systems for complex and dynamic ontologies. The successful outcome of this project will enhance Australia's research reputation in an important, practical area of ICT, will contribute to emerging Web technologies that will eventually be of benefit to Australian industry, and will train several postgraduate students.
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    Funded Activity

    Discovery Projects - Grant ID: DP0345901

    Funder
    Australian Research Council
    Funding Amount
    $165,000.00
    Summary
    Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discove .... Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discovery and validation of group structure in data mining applications will considerably enhance knowledge management and decision support in science, industry, and government.
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    Funded Activity

    Discovery Projects - Grant ID: DP0452551

    Funder
    Australian Research Council
    Funding Amount
    $150,000.00
    Summary
    New lattice approach for digital broadband communications. A main limiting factor in supplying future broadband communications is overcoming signal dispersion in the transmission channel. Recent preliminary collaboration by the chief investigators has uncovered a novel approach to this problem based on powerful mathematical lattice theory. The techniques have potential to significantly increase bandwidth and reliability compared to current technologies. This project will use lattice theory to pr .... New lattice approach for digital broadband communications. A main limiting factor in supplying future broadband communications is overcoming signal dispersion in the transmission channel. Recent preliminary collaboration by the chief investigators has uncovered a novel approach to this problem based on powerful mathematical lattice theory. The techniques have potential to significantly increase bandwidth and reliability compared to current technologies. This project will use lattice theory to propose, develop, analyse and test new data transmission techniques including joint coding, modulation and equalisation. The research will include theoretical analysis and hardware implementation. The overall aim is to dramatically improve reliability and throughput of data communication systems.
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