Linkage Infrastructure, Equipment And Facilities - Grant ID: LE150100030
Funder
Australian Research Council
Funding Amount
$270,000.00
Summary
Test-bed for Wide-Area Software Defined Networking Research. Test bed for wide-area software defined networking research: This project aims to develop a wide-area test bed, spanning ten organisations, for conducting research and experimentation in the emerging disruptive technology of Software Defined Networking (SDN). SDN is likely to bring long-term transformation to the networking industry, much like cloud computing did, by enabling dynamic virtualised elastic network services under software ....Test-bed for Wide-Area Software Defined Networking Research. Test bed for wide-area software defined networking research: This project aims to develop a wide-area test bed, spanning ten organisations, for conducting research and experimentation in the emerging disruptive technology of Software Defined Networking (SDN). SDN is likely to bring long-term transformation to the networking industry, much like cloud computing did, by enabling dynamic virtualised elastic network services under software control. The test bed will empower Australian researchers in network technologies and dependent applications (for example, multimedia and security) to collaboratively develop and demonstrate novel ideas at scale. This is expected to benefit Australia by giving our researchers international recognition in this nascent area, and developing a national talent pool for local industry.Read moreRead less
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0453434
Funder
Australian Research Council
Funding Amount
$395,077.00
Summary
A new airborne facility for environmental, hydrological, atmospheric and oceanic research: high resolution measurement of soil moisture, temperature and salinity. This proposal seeks to establish a new national capability for airborne remote sensing of key environmental variables. It will enable high-resolution mapping of near-surface soil moisture, land surface salinity and temperature, and ocean surface salinity and temperature. It will be a new tool for hydrologic, atmospheric and oceanic r ....A new airborne facility for environmental, hydrological, atmospheric and oceanic research: high resolution measurement of soil moisture, temperature and salinity. This proposal seeks to establish a new national capability for airborne remote sensing of key environmental variables. It will enable high-resolution mapping of near-surface soil moisture, land surface salinity and temperature, and ocean surface salinity and temperature. It will be a new tool for hydrologic, atmospheric and oceanic researchers, providing unprecedented detail on characteristics critical to our understanding and management of the environment. The small instrument size and weight will enable use of a light aircraft as the observing platform, providing the national (and international) research community with an affordable tool, hitherto unavailable.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Funder
Australian Research Council
Funding Amount
$348,026.00
Summary
A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and ....A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and evaluation of IoT technologies and services. The project will also serve as a vehicle for the education and training of Australia’s next generation of scholars and engineers, and contribute to Australia’s scientific visibility.Read moreRead less
ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this ....ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this Centre is to create innovative mathematical and statistical models that can uncover the knowledge concealed within the size and complexity of these big data sets, with a focus on using the models to deliver insight into problems vital to the Centre's Collaborative Domains: Healthy People, Sustainable Environments and Prosperous Societies.Read moreRead less