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Research Topic : missing data
Status : Active
Socio-Economic Objective : Expanding Knowledge in Engineering
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  • Researchers (91)
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT140100219

    Funder
    Australian Research Council
    Funding Amount
    $656,967.00
    Summary
    Readying Wireless Networks for Future Communications Systems: From Ubiquitous Computing to the Internet of Things. This project aims to prepare wireless networks for future communications systems, by improving the data transmission rates of wireless networks, through developing new coding schemes based on the synergy of noisy-channel coding and index coding. This will allow wireless networks, used in conjunction with the fibre-optic National Broadband Network, to support future high-data-rate an .... Readying Wireless Networks for Future Communications Systems: From Ubiquitous Computing to the Internet of Things. This project aims to prepare wireless networks for future communications systems, by improving the data transmission rates of wireless networks, through developing new coding schemes based on the synergy of noisy-channel coding and index coding. This will allow wireless networks, used in conjunction with the fibre-optic National Broadband Network, to support future high-data-rate and ubiquitous communication services. This project aims to produce new theoretical results in the field of communication theory, and efficient practical coding schemes for wireless communications.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP210100271

    Funder
    Australian Research Council
    Funding Amount
    $290,831.00
    Summary
    Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through dee .... Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through deep learning with structural conditions and load-carrying capacities obtained from vibration tests and finite element model analysis for efficient structural damage detection and quantification. The project will lead to effective structural health monitoring and enhance structural safety and reduce maintenance costs.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190101363

    Funder
    Australian Research Council
    Funding Amount
    $450,000.00
    Summary
    Millimetre wave communications for mobile broadband systems. This project aims to develop innovative millimetre wave (mmWave) communication theories and techniques, in order to significantly improve the data rate and network capacity for mobile broadband. Pragmatic transceiver designs, channel estimation algorithms, and network optimisation tools will be developed to quantify the potential of this promising wireless infrastructure. The technologies are designed to exploit the abundant mmWave spe .... Millimetre wave communications for mobile broadband systems. This project aims to develop innovative millimetre wave (mmWave) communication theories and techniques, in order to significantly improve the data rate and network capacity for mobile broadband. Pragmatic transceiver designs, channel estimation algorithms, and network optimisation tools will be developed to quantify the potential of this promising wireless infrastructure. The technologies are designed to exploit the abundant mmWave spectrum and complement the state-of-the-art cellular systems to fulfil the formidable demand for ultra-fast data services. The project outcomes are expected to increase mobile broadband speed by an order of magnitude which can benefit end-user experience and open up new opportunities for network providers.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP180100872

    Funder
    Australian Research Council
    Funding Amount
    $402,993.00
    Summary
    Optical wireless communications: solving the spectrum crunch. This project aims to make optical wireless communication to handheld mobile receivers a reality by developing systems which combine holographic filters and microsystems to realise a new form of receiver. This will be based on analysis of all of the complex interactions of transmitter, receiver and channel properties. The new receivers will exploit the narrow field of view of holographic optical filters. This project will generate know .... Optical wireless communications: solving the spectrum crunch. This project aims to make optical wireless communication to handheld mobile receivers a reality by developing systems which combine holographic filters and microsystems to realise a new form of receiver. This will be based on analysis of all of the complex interactions of transmitter, receiver and channel properties. The new receivers will exploit the narrow field of view of holographic optical filters. This project will generate knowledge in the fields of communications theory and on the use of holographic filters and microsystems. This solution to the lack of available radio frequency spectrum which conventional wireless face will provide significant practical and commercial benefits.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT190100525

    Funder
    Australian Research Council
    Funding Amount
    $988,200.00
    Summary
    Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collabor .... Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190101576

    Funder
    Australian Research Council
    Funding Amount
    $440,000.00
    Summary
    Low-energy electro-photonics: novel materials, devices and systems. This project aims to develop low-power technologies for programming and tuning photonic integrated circuits (PICs). By replacing thermal tuning, the project will reduce power consumption from watts to milliwatts, which also eliminates the thermal crosstalk that limits the complexity of today's PICs. The expected outcome will be the basis for a generic field-programmable photonic chip, which can be used to rapidly prototype desig .... Low-energy electro-photonics: novel materials, devices and systems. This project aims to develop low-power technologies for programming and tuning photonic integrated circuits (PICs). By replacing thermal tuning, the project will reduce power consumption from watts to milliwatts, which also eliminates the thermal crosstalk that limits the complexity of today's PICs. The expected outcome will be the basis for a generic field-programmable photonic chip, which can be used to rapidly prototype designs for production as full custom chips as part of a new Australian industry capability. The expected benefits will be a faster innovation cycle, greater adoption of photonic technologies, and support of research into, for example, neuromorphic optical processing, and advanced communications and sensing systems.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220101318

    Funder
    Australian Research Council
    Funding Amount
    $473,000.00
    Summary
    Physical Layer Security for Wireless Machine-Type Communications. This project aims to provide new understanding and design guidelines to secure wireless communications among low-cost resource-constrained devices. This is achieved by advancing the fundamental theory of an emerging security paradigm named physical layer security. Expected outcomes of this project include a communication-theoretic framework to characterise the secrecy performance of communications over wireless networks, followed .... Physical Layer Security for Wireless Machine-Type Communications. This project aims to provide new understanding and design guidelines to secure wireless communications among low-cost resource-constrained devices. This is achieved by advancing the fundamental theory of an emerging security paradigm named physical layer security. Expected outcomes of this project include a communication-theoretic framework to characterise the secrecy performance of communications over wireless networks, followed by novel signal processing and transmission designs. The research outcomes should provide innovative solutions to safeguard commercial and industry Internet of Things networks, benefiting Australia's digital transformation.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190102963

    Funder
    Australian Research Council
    Funding Amount
    $505,000.00
    Summary
    Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This .... Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This project will benefit robotics, control engineering, infrastructure automation, and other fields that demand the capability to model physical systems from limited data. It will also improve cybersecurity by making learning algorithms resilient to deliberate attacks with false data.
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    Showing 1-8 of 8 Funded Activites

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