Multiview video coding using cuboid data compression. This project investigates novel approaches to multiview video coding that use new data compression techniques and explicit occlusion handling. These new approaches complement the state-of-the-art, improving interactivity with instantaneous view change and VCR functionality, reducing encoding complexity, and increasing compression efficiency.
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
Methodologies for face recognition under varying imaging conditions. Face recognition systems are heavily dependent on the nature of the input to the system. Variability in appearance due to changes in illumination, expression, pose, etc. can reduce the recognition results of the existing systems. The aim of this project is to develop new techniques to improve the recognition accuracy in natural environment where unwanted image variations exist. The development of such techniques will be of grea ....Methodologies for face recognition under varying imaging conditions. Face recognition systems are heavily dependent on the nature of the input to the system. Variability in appearance due to changes in illumination, expression, pose, etc. can reduce the recognition results of the existing systems. The aim of this project is to develop new techniques to improve the recognition accuracy in natural environment where unwanted image variations exist. The development of such techniques will be of great importance to Australia's security and safety. The outcome of this research will provide the first steps towards formulating the next generation recognition systems that will improve the suitability of the face recognition for use in security, surveillance, intelligent robotics, banking, and smart environments.Read moreRead less
Constrained and Stable Solutions of Nonlinear and Semismooth Equations. In this project, comprehensive models for designing safe power system parameters will be proposed, efficient algorthms for solving these models will be constructed. The new models and algorithms in this project will provide efficient tools to prevent catastrophic events in power systems, which is related with national security. This project will also strengthen collaboration of Australian applied
mathematians with inter ....Constrained and Stable Solutions of Nonlinear and Semismooth Equations. In this project, comprehensive models for designing safe power system parameters will be proposed, efficient algorthms for solving these models will be constructed. The new models and algorithms in this project will provide efficient tools to prevent catastrophic events in power systems, which is related with national security. This project will also strengthen collaboration of Australian applied
mathematians with international researchers and engineering scientists. This is important for the advance of science and technology in
Australia.Read moreRead less
Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising ap ....Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising approach, but its accuracy is far from satisfactory. The software systems developed in this project will be used in structural identification of target proteins in drug design. This will make drug design process more efficient, saving time and cost, potentially saving lives.Read moreRead less
Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored ....Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.Read moreRead less
Efficient multi-view video coding with cuboids and base anchored models. This project aims to address current deficiencies in multi-view video coding technology to achieve the ultra-compression efficiency demanded by increasing display resolutions and synchronised viewpoints. The project expects to generate new knowledge, by moving from the current pixel-centric approach to methods that concentrate information common to many view-frames. The project is expected to improve compression of audio-vi ....Efficient multi-view video coding with cuboids and base anchored models. This project aims to address current deficiencies in multi-view video coding technology to achieve the ultra-compression efficiency demanded by increasing display resolutions and synchronised viewpoints. The project expects to generate new knowledge, by moving from the current pixel-centric approach to methods that concentrate information common to many view-frames. The project is expected to improve compression of audio-visual services that are of great interest to international standards bodies and industry, while facilitating free interaction and augmented reality. This project will provide significant benefits to broadcast, entertainment, surveillance and health industries and position Australia as a world leader in this field.Read moreRead less
Multi-Group Stochastic Modelling of Population Balance for Gas-Liquid Flows. Multiphase flow systems are encountered in many process industries such as chemical, petroleum, mining, nuclear, energy, food and pharmaceutical, which are fundamental to the Australian economy. Commercially available computer codes for simulating such systems are currently widely used in many Australian industrial sectors. This research project will address the prevalent deficiency in many of these computer codes and ....Multi-Group Stochastic Modelling of Population Balance for Gas-Liquid Flows. Multiphase flow systems are encountered in many process industries such as chemical, petroleum, mining, nuclear, energy, food and pharmaceutical, which are fundamental to the Australian economy. Commercially available computer codes for simulating such systems are currently widely used in many Australian industrial sectors. This research project will address the prevalent deficiency in many of these computer codes and develop new models capable of predicting a wide range of industrial bubbly flow problems. The resultant improved computer codes will provide industries with significant benefits and, in particular, reduce times and costs in their design and production. Read moreRead less
Robust AI Planning for Hybrid Systems. Automated planning, a core area of Artificial Intelligence, can effectively deal with the automatic synthesis of optimised action strategies for discrete system models. Extending the reach of planning to hybrid discrete/continuous systems, under exogenous uncertainty, is a widely open problem which this project will address. This will enable the proactive, and therefore more effective, management of microgrids and other cyber-physical systems, based on fore ....Robust AI Planning for Hybrid Systems. Automated planning, a core area of Artificial Intelligence, can effectively deal with the automatic synthesis of optimised action strategies for discrete system models. Extending the reach of planning to hybrid discrete/continuous systems, under exogenous uncertainty, is a widely open problem which this project will address. This will enable the proactive, and therefore more effective, management of microgrids and other cyber-physical systems, based on forecast information.Read moreRead less
Sustainable fiscal federalism and reform of the GST distribution system. The primary source of funds for Australian States and Territories is GST revenue distributed by the Commonwealth using an equalisation formula that has proved to be politically unsustainable and in recent times manifestly inadequate to provide the revenue needed in response to crises and natural disasters. A tipping point has been reached and reform is urgently needed. Drawing on international experience with GST distributi ....Sustainable fiscal federalism and reform of the GST distribution system. The primary source of funds for Australian States and Territories is GST revenue distributed by the Commonwealth using an equalisation formula that has proved to be politically unsustainable and in recent times manifestly inadequate to provide the revenue needed in response to crises and natural disasters. A tipping point has been reached and reform is urgently needed. Drawing on international experience with GST distributions specifically and fiscal federalism more generally, the project aims to develop a reform blueprint for a sustainable and equitable fiscal federalism regime in Australia that best aligns with Australia’s current and long-term fiscal needs.Read moreRead less