Coding for Distributed Storage: Fundamental Limits and Code Designs. Applications such as file sharing, large-scale scientific projects, and social networking are fuelling the need for reliable and sustainable distributed storage systems. This project aims to develop the theory and the code designs for next-generation storage systems that are specifically optimised for the storage needs in such applications. This project is well placed to provide cost-effective, home-grown solutions for Australi ....Coding for Distributed Storage: Fundamental Limits and Code Designs. Applications such as file sharing, large-scale scientific projects, and social networking are fuelling the need for reliable and sustainable distributed storage systems. This project aims to develop the theory and the code designs for next-generation storage systems that are specifically optimised for the storage needs in such applications. This project is well placed to provide cost-effective, home-grown solutions for Australia's future data centre needs. Its potential immediate benefits are: contribution to the knowledge base and fundamental capabilities in storage systems; practical codes tailor-made for different storage applications; IP creation and commercialisation; and, education of future Australian academic and industrial innovators.Read moreRead less
Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed ....Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed methodology enables quantification of confidence in the predictions. This will provide ship owners, directly to their vessels and/or at the fleet management centres, information such as weather reports, reliable collision/no-collision warnings and avoidance strategies, on-the-fly. Read moreRead less
Transmission beyond linear capacity in fibre optics. This project aims to develop the concept and demonstrate the practicality of a new fibre optic communication technology that allows data transmission rates beyond currently accepted fundamental limits. This project aims to design and demonstrate the feasibility and practicality of utilising nonlinear modes of data transmission. This would assist in the management of fibre impairments that fundamentally limit further increase in data rate causi ....Transmission beyond linear capacity in fibre optics. This project aims to develop the concept and demonstrate the practicality of a new fibre optic communication technology that allows data transmission rates beyond currently accepted fundamental limits. This project aims to design and demonstrate the feasibility and practicality of utilising nonlinear modes of data transmission. This would assist in the management of fibre impairments that fundamentally limit further increase in data rate causing the capacity crunch problem. This is expected to present Australia with leading edge technology to compete in the area of high-speed, high-capacity communication, which is the backbone of our economy, heath, education, social participation, and security.Read moreRead less
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.Read moreRead less
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.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
Multiscale modelling of systems with complex microscale detail. In modern science and engineering many complex systems are described by distinctly different microscale physical models within different regions of space. This project is to develop systematic mathematical and computational methods for the compact and accurate macroscale modelling and computation of such systems for application in industrial research and development. Our sparse simulations, justified with mathematical analysis, use ....Multiscale modelling of systems with complex microscale detail. In modern science and engineering many complex systems are described by distinctly different microscale physical models within different regions of space. This project is to develop systematic mathematical and computational methods for the compact and accurate macroscale modelling and computation of such systems for application in industrial research and development. Our sparse simulations, justified with mathematical analysis, use small bursts of particle/agent simulations, PDEs, or difference equations, to efficiently evaluate macroscale system-level behaviour. The objective is to accurately interface between disparate microscale models and establish provable predictions on how the microscale parameter spaces resolve at the macroscale.Read moreRead less
Multiscale modelling of systems with complex microscale detail. This project aims to develop systematic mathematical and computational methods for the compact and accurate macroscale modelling of systems with microscopic irregular details. The methodology, justified with mathematical analysis and computation, uses small bursts of particle/agent simulations, partial differential equation (PDEs), or difference equations, to efficiently predict macroscale behaviour. This project’s mathematical meth ....Multiscale modelling of systems with complex microscale detail. This project aims to develop systematic mathematical and computational methods for the compact and accurate macroscale modelling of systems with microscopic irregular details. The methodology, justified with mathematical analysis and computation, uses small bursts of particle/agent simulations, partial differential equation (PDEs), or difference equations, to efficiently predict macroscale behaviour. This project’s mathematical methodology aims to efficiently and accurately extract and simulate the collective dynamics which emerge on macroscales, leading to improved prediction and understanding of the significant features of these complex systems at the scale relevant to engineers and scientists.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
Multi-modal virtual microscopy for quantitative diagnostic pathology. This project will contribute to the next generation of virtual microscopy systems that provide innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of diagnostic pathology. These tools will especially contribute to the screening and diagnosis of cervical, lung and bladder cancer.