An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving futur ....An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving future secure key distribution. Multi-agent interactions in higher dimensional spaces are considered intractable using traditional matrix methods and this project will build on our exciting new breakthrough showing that such interactions are tractable using geometric multivectors.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100415
Funder
Australian Research Council
Funding Amount
$432,483.00
Summary
Cross-layer Design for Ultra-reliable Low-latency Communications. This project aims to develop fundamental theories and practical technologies for ultra-reliable low-latency communications – one of the grand challenges in 5G cellular networks. Due to the dynamic nature of wireless networks, existing approaches dividing networks into multiple layers cannot guarantee a hard deadline with high reliability. The outcomes of the project will be cross-layer models for characterising the end-to-end perf ....Cross-layer Design for Ultra-reliable Low-latency Communications. This project aims to develop fundamental theories and practical technologies for ultra-reliable low-latency communications – one of the grand challenges in 5G cellular networks. Due to the dynamic nature of wireless networks, existing approaches dividing networks into multiple layers cannot guarantee a hard deadline with high reliability. The outcomes of the project will be cross-layer models for characterising the end-to-end performance, a prediction and communication co-design framework for improving the delay-reliability trade-off, and an online architecture for implementing model-based algorithms in real networks. They will underpin the development of remote control and advancing automation in manufacturing, transportation, mining, etc.Read moreRead less
Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental ....Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental policy formulation, network design, engineering, defence and cybersecurity; offering significant benefits to the researchers and practitioners in these fields. In addition to research outputs, it will strengthen international collaboration and build research capacity to put Australia at the forefront of this research.
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Evolutionary computation for robust multi-objective engineering design. This project aims to develop an evolutionary computation framework for robust multi-objective design, a critical pursuit in engineering industries. Such problems are characterised by multiple conflicting performance objectives
and constraints which are highly nonlinear, often black-box, and prone to unavoidable real-life uncertainties. The existing evolutionary algorithms are often computationally impractical and have a numb ....Evolutionary computation for robust multi-objective engineering design. This project aims to develop an evolutionary computation framework for robust multi-objective design, a critical pursuit in engineering industries. Such problems are characterised by multiple conflicting performance objectives
and constraints which are highly nonlinear, often black-box, and prone to unavoidable real-life uncertainties. The existing evolutionary algorithms are often computationally impractical and have a number of fundamental
shortcomings which restrict their use in real applications. This project aims to investigate and overcome the underlying key challenges to advance knowledge and contribute towards diverse domains such as energy, transport and space research, helping deliver high quality robust designs.
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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
The role of strong duality in computer vision. This project aims to undertake research in the fields of computer vision and optimization that will have a significant impact on the design of numerical algorithms for solving a wide range of problems in Computer Vision, Virtual Reality and Robotic Navigation. This project will advance understanding of a broad class of problems related to how computers interpret images. An expected outcome is the generation of novel mathematical theory and numerical ....The role of strong duality in computer vision. This project aims to undertake research in the fields of computer vision and optimization that will have a significant impact on the design of numerical algorithms for solving a wide range of problems in Computer Vision, Virtual Reality and Robotic Navigation. This project will advance understanding of a broad class of problems related to how computers interpret images. An expected outcome is the generation of novel mathematical theory and numerical algorithms capable of fundamentally changing the way problems relevant to a wide range of vision-related applications are solved. This should offer Australia a strong competitive advantage as a leader in scientific innovation in the areas of Computer Vision, Virtual Reality and Robotics and Autonomous Systems.Read moreRead less
Faithful Visual Analytics: models, metrics and algorithms. This project aims to deliver new models, metrics and algorithms for Faithful Visual Analytics of complex data. For a purported visual representation of some data, "faithfulness" measures how accurately the visual representation describes the data. This project will develop new models for Faithful Visual Analytics, design new faithfulness metrics for faithful visual analytics of complex networks, design new algorithms to compute faithful ....Faithful Visual Analytics: models, metrics and algorithms. This project aims to deliver new models, metrics and algorithms for Faithful Visual Analytics of complex data. For a purported visual representation of some data, "faithfulness" measures how accurately the visual representation describes the data. This project will develop new models for Faithful Visual Analytics, design new faithfulness metrics for faithful visual analytics of complex networks, design new algorithms to compute faithful visualisations, and evaluate using real world social network and biological network data sets. The new models, metrics and algorithms produced by this project will be used in the next generation Visual Analytic tools to enable analysts develop accurate insights and new knowledge of complex data.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101181
Funder
Australian Research Council
Funding Amount
$403,775.00
Summary
Information Fusion for Tracking Objects in Large-Scale Sensor Network. This project aims to develop a mathematical framework to combine multi-modal information coming from multiple sensors. These mobile sensors will be spatially distributed over a large-scale area for the purpose of multi-object tracking. The main application of this framework is for cooperative perception for intelligent decision making. Expected outcomes include a novel technique to integrate receiving information from multipl ....Information Fusion for Tracking Objects in Large-Scale Sensor Network. This project aims to develop a mathematical framework to combine multi-modal information coming from multiple sensors. These mobile sensors will be spatially distributed over a large-scale area for the purpose of multi-object tracking. The main application of this framework is for cooperative perception for intelligent decision making. Expected outcomes include a novel technique to integrate receiving information from multiple mobile agents (e.g. vehicle) to enhance their ability to anticipate situations in dynamic environments and to act effectively to enhance safety. This should provide benefits for the development of cooperative autonomous driving to enhance road safety.Read moreRead less
A novel and efficient approach for optimisation involving iterative solvers. Computationally expensive simulations involving iterative solvers are increasingly being used in industry to assess performance of products and processes. Repeated use of such simulations is necessary to identify optimum solutions. Even with today's computing power, many such tasks remain computationally prohibitive. This project presents a novel approach to solve optimisation problems involving iterative solvers with l ....A novel and efficient approach for optimisation involving iterative solvers. Computationally expensive simulations involving iterative solvers are increasingly being used in industry to assess performance of products and processes. Repeated use of such simulations is necessary to identify optimum solutions. Even with today's computing power, many such tasks remain computationally prohibitive. This project presents a novel approach to solve optimisation problems involving iterative solvers with limited computing budget. A wide range of industries involved in product and process design would gain a significant competitive advantage from this unique technical innovation. In addition, this technology will be invaluable to uncover and understand complex natural phenomena.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101249
Funder
Australian Research Council
Funding Amount
$468,582.00
Summary
Fusing wearables and advanced computational models for real world analysis. This project aims to solve a major technological problem: our inability to study human skeletal, muscular, and neural function in the real world. This project expects to, for the first time globally, integrate wearable sensors with neuromusculoskeletal computational models and artificial intelligence, and validate this technology. Expected project outcomes include an integrated system for future commercialisation and new ....Fusing wearables and advanced computational models for real world analysis. This project aims to solve a major technological problem: our inability to study human skeletal, muscular, and neural function in the real world. This project expects to, for the first time globally, integrate wearable sensors with neuromusculoskeletal computational models and artificial intelligence, and validate this technology. Expected project outcomes include an integrated system for future commercialisation and new understanding of how whole-body behavioural choices affect tissue mechanics during daily and sporting activities. Project outcomes should provide significant benefits, such as the ability to escape the laboratory to understand human performance for defence, sport, industrial, and health settings.Read moreRead less