Optimizing autonomous system control with brain-like hierarchical control systems. Autonomous robotic systems, those requiring minimal ongoing supervision, have enormous commercial, medical and industry potential. A robotic hand, permitting manipulation of material objects is an integral part of robot function. Many aspects of human hand control, such as learning, fine motor control, context-specific adaptation and recovery from system damage would be greatly beneficial to a robotic hand. Likewi ....Optimizing autonomous system control with brain-like hierarchical control systems. Autonomous robotic systems, those requiring minimal ongoing supervision, have enormous commercial, medical and industry potential. A robotic hand, permitting manipulation of material objects is an integral part of robot function. Many aspects of human hand control, such as learning, fine motor control, context-specific adaptation and recovery from system damage would be greatly beneficial to a robotic hand. Likewise, theories of human hand control could be subject to empirical testing by implementing them in a robotic hand. These advances will greatly benefit our understanding of the human brain, with potentially wide-ranging medical benefits such as novel prosthetic limb design and rehabilitation strategies for stroke patients.Read moreRead less
Structural-functional connectivity in the brain. This project aims to develop magnetic resonance imaging analysis methods to non-invasively study brain connectivity. Recent advances in imaging can comprehensively describe the brain’s complex network of functional and structural connections (the brain ‘connectome’). This project will simultaneously investigate structural and functional connectivity, and characterise the dynamic properties of the connectome using graph-theoretic approaches. This p ....Structural-functional connectivity in the brain. This project aims to develop magnetic resonance imaging analysis methods to non-invasively study brain connectivity. Recent advances in imaging can comprehensively describe the brain’s complex network of functional and structural connections (the brain ‘connectome’). This project will simultaneously investigate structural and functional connectivity, and characterise the dynamic properties of the connectome using graph-theoretic approaches. This project should give neuroscientists computational tools to comprehensively map the network architecture of the human brain.Read moreRead less
The plasticity of neural codes. Information about the world is represented in the brain by the combined activity of populations of many neurons. However, the basic principles underlying how such population activity codes information are largely unknown. Using the map from the eye to the brain of the zebrafish as a model, the project aims to combine experimental measurements of neural activity with mathematical modelling in order to discover these basic principles. Of particular interest is how t ....The plasticity of neural codes. Information about the world is represented in the brain by the combined activity of populations of many neurons. However, the basic principles underlying how such population activity codes information are largely unknown. Using the map from the eye to the brain of the zebrafish as a model, the project aims to combine experimental measurements of neural activity with mathematical modelling in order to discover these basic principles. Of particular interest is how these coding principles change during development and their plasticity after disruptions to the visual map. Besides improving our understanding of how brains process information, the knowledge gained could help optimise the design of brain-computer interfaces and artificial computing devices.Read moreRead less
A novel approach to diffusion MRI for greatly improved imaging of brain white matter and its connectivity. In this project, innovative new imaging and reconstruction techniques will be developed to provide images of brain connectivity, with unprecedented detail. Such images will allow extremely detailed investigations into the white matter connections that allow brain regions to communicate, and improve our understanding of how the brain operates.
Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent sys ....Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent systems that facilitate adaptation and change.
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Quantitative measurement of Schizophrenia using Electrovestibulography. Schizophrenia was estimated to cost approximately $1.85billion in 2001 (0.3% of GDP and nearly $50k for each of the 37,000 Australians with the illness). Over one third of the cost is borne by sufferers and their carers. Misdiagnosis and incorrect therapy are common. To date quantitative assessment of Schizophrenics has been impossible making this tool potentially invaluable. An accurate diagnostic test could facilitate earl ....Quantitative measurement of Schizophrenia using Electrovestibulography. Schizophrenia was estimated to cost approximately $1.85billion in 2001 (0.3% of GDP and nearly $50k for each of the 37,000 Australians with the illness). Over one third of the cost is borne by sufferers and their carers. Misdiagnosis and incorrect therapy are common. To date quantitative assessment of Schizophrenics has been impossible making this tool potentially invaluable. An accurate diagnostic test could facilitate earlier diagnosis, more accurate treatment plans, and prevention of debilitating psychotic episodes for the sufferer. By being able to monitor drug efficacy the community can benefit by reduced drug costs, confinement times and hastened new drug development. Read moreRead less
Classification and Prediction Modelling for Financial Distress, Tax Debt and Insolvency for ATO Clients. The Australian Taxation Office (ATO) has clients who are not able to meet their taxation debts, resulting in revenue shortfalls for both the State and Federal Governments. Through this project, we will develop predictive models and techniques which identify client classes and clusters in the ATO client population and the defining attributes of these collections - especially those which are a ....Classification and Prediction Modelling for Financial Distress, Tax Debt and Insolvency for ATO Clients. The Australian Taxation Office (ATO) has clients who are not able to meet their taxation debts, resulting in revenue shortfalls for both the State and Federal Governments. Through this project, we will develop predictive models and techniques which identify client classes and clusters in the ATO client population and the defining attributes of these collections - especially those which are at high risk of incurring debt and defaulting on paying taxes. In turn, the early identification of clients in financial distress will allow the ATO to give them assistance so that they can reduce their debts and meet their financial obligations.Read moreRead less
AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing n ....AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.Read moreRead less
Automatic Brain Tissue Segmentation in Magnetic Resonance Images based on Knowledge-guided Constrained Clustering. Accurate volumetric measurement of brain tissues is of critical importance in the study of many brain disorders, disease diagnosis, disease progression tracking and treatment monitoring. The study in this research will result in the development of a powerful computational technique that allows automatic volumetric measurement and analysis of brain tissues. The software developed in ....Automatic Brain Tissue Segmentation in Magnetic Resonance Images based on Knowledge-guided Constrained Clustering. Accurate volumetric measurement of brain tissues is of critical importance in the study of many brain disorders, disease diagnosis, disease progression tracking and treatment monitoring. The study in this research will result in the development of a powerful computational technique that allows automatic volumetric measurement and analysis of brain tissues. The software developed in this project will expedite early clinical diagnosis and treatment of neural diseases for patients, hence saving life and reducing health cost both at the personal and the national level. Read moreRead less
Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in ....Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.Read moreRead less