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Research Topic : CDNA LIBRARY
Field of Research : Pattern Recognition and Data Mining
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Control of Plant Pests, Diseases and Exotic Species in Farmland, Arable Cropland and Permanent Cropland Environments (1)
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  • Researchers (25)
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  • Active Funded Activity

    Linkage Projects - Grant ID: LP160100387

    Funder
    Australian Research Council
    Funding Amount
    $252,000.00
    Summary
    Legal and social dynamics of eBook lending in Australia’s public libraries. Legal and social dynamics of eBook lending in Australia’s public libraries. This project aims to develop an evidence base of quantitative and qualitative data about how eBooks are used in libraries. EBooks have tremendous beneficial potential, particularly for Australians in remote areas and those with impaired mobility or vision. However, libraries’ rights to acquire and lend them are more restricted than for physical b .... Legal and social dynamics of eBook lending in Australia’s public libraries. Legal and social dynamics of eBook lending in Australia’s public libraries. This project aims to develop an evidence base of quantitative and qualitative data about how eBooks are used in libraries. EBooks have tremendous beneficial potential, particularly for Australians in remote areas and those with impaired mobility or vision. However, libraries’ rights to acquire and lend them are more restricted than for physical books. Libraries and legal, social and data science researchers will investigate eBook lending practices and understand their social impacts. The project will identify ways of reforming policy, law, and practice to help libraries fulfil their public interest missions. This project is expected to enable libraries to extract more value from existing public investments.
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    Funded Activity

    Discovery Projects - Grant ID: DP130100024

    Funder
    Australian Research Council
    Funding Amount
    $270,000.00
    Summary
    Effective image search and retrieval through automatic image annotation. This project aims to develop an effective and efficient image retrieval system, so that images are retrieved as easily as textual data. The project researches and develops several key know-hows in image retrieval. It enables Australia to maintain significant advantage in the frontier technology of information processing.
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    Funded Activity

    Australian Laureate Fellowships - Grant ID: FL140100012

    Funder
    Australian Research Council
    Funding Amount
    $2,830,000.00
    Summary
    Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorith .... Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorithm testing for combinatorial optimisation, described as 'ground-breaking,' this project aims to tackle the challenges needed to generalise the paradigm to other fields such as machine learning, forecasting, software testing, and other branches of optimisation. An online repository of test instances and tools aim to provide a valuable resource to improve research practice and support new insights into algorithm performance.
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    Funded Activity

    Discovery Projects - Grant ID: DP140101075

    Funder
    Australian Research Council
    Funding Amount
    $405,000.00
    Summary
    Intelligent Image Retrieval from Distorted and Partial Queries for Rapid Mobile Identification of Pests Threatening Food and the Environment. Pests and diseases are major threats to the Australian food industry and environmental biosecurity. A rapid and mobile pest information retrieval system is critical to prevent a pest becoming established and devastating the region. However, automated insect image retrieval remains an unsolved challenge in the research community. This project addresses the .... Intelligent Image Retrieval from Distorted and Partial Queries for Rapid Mobile Identification of Pests Threatening Food and the Environment. Pests and diseases are major threats to the Australian food industry and environmental biosecurity. A rapid and mobile pest information retrieval system is critical to prevent a pest becoming established and devastating the region. However, automated insect image retrieval remains an unsolved challenge in the research community. This project addresses the fundamental problem of distorted and partial image query in cluttered background in order to achieve pest identification at a much earlier on-site stage. The success of this research will not only make a technical breakthrough towards retrieving objects with movable body parts, but also revolutionise the current pest detection and monitoring process.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP180100958

    Funder
    Australian Research Council
    Funding Amount
    $403,052.00
    Summary
    Single model irregular-region retrieval for rapid plant disease detection. This project aims to study the major technical barrier in plant disease image retrieval to build a pervasive rapid plant disease identification system. The techniques are designed to function on one or very few sample images, thus enabling on-line in field disease identification linked to authoritative plant disease image libraries. The success of this project will not only make significant contributions to fundamental th .... Single model irregular-region retrieval for rapid plant disease detection. This project aims to study the major technical barrier in plant disease image retrieval to build a pervasive rapid plant disease identification system. The techniques are designed to function on one or very few sample images, thus enabling on-line in field disease identification linked to authoritative plant disease image libraries. The success of this project will not only make significant contributions to fundamental theory in single model image retrieval, but also create a revolution in plant disease early detection for effective and efficient crop protection.
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