Efficient signal transmission techniques for large scale antenna systems. This project aims to design efficient signal transmission techniques for large scale antenna wireless systems that can significantly improve network capacity and radio spectrum efficiency. Large scale antenna arrays deployed in cellular networks is a promising technique to accommodate the dramatically increasing data demands for next generation wireless communications. The intended outcome of the project will bring revolut ....Efficient signal transmission techniques for large scale antenna systems. This project aims to design efficient signal transmission techniques for large scale antenna wireless systems that can significantly improve network capacity and radio spectrum efficiency. Large scale antenna arrays deployed in cellular networks is a promising technique to accommodate the dramatically increasing data demands for next generation wireless communications. The intended outcome of the project will bring revolutionary change in mobile wireless communications and benefit billions of people in the world. It will significantly lift productivity and economic growth in Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101347
Funder
Australian Research Council
Funding Amount
$419,162.00
Summary
Empowering 5G Infrastructure with Collocated 3G/4G/5G Base Station Antennas. The Australian government has decided to support the timely rollout of fifth-generation (5G) mobile communication systems due to their potential for producing far-reaching economic and social benefits. This infrastructure rollout requires a quick, efficient deployment of the associated 5G base stations. The integration of 5G antenna arrays into existing 3G/4G base stations would alleviate the substantial cost increases ....Empowering 5G Infrastructure with Collocated 3G/4G/5G Base Station Antennas. The Australian government has decided to support the timely rollout of fifth-generation (5G) mobile communication systems due to their potential for producing far-reaching economic and social benefits. This infrastructure rollout requires a quick, efficient deployment of the associated 5G base stations. The integration of 5G antenna arrays into existing 3G/4G base stations would alleviate the substantial cost increases and negative environmental impacts tied to acquiring new sites for them. This project aims to develop the theory and engineering methodologies necessary to successfully address the significant scientific challenges posed by the detrimental interference effects associated with the compact integration of 3G/4G/5G arrays. Read moreRead less
Reliable bi-directional machine-type communications for smart agriculture. This project aims to develop innovative solutions for agricultural machine-type communications to provide robust and bi-directional coverage for remote agriculture areas with difficult terrain, by leveraging smart-sensor-enabled, energy-efficient uplink transmissions and ultra-reliable downlink transmissions. Machine-type communications have been recognised as a key enabler for the future smart agriculture and smart farms ....Reliable bi-directional machine-type communications for smart agriculture. This project aims to develop innovative solutions for agricultural machine-type communications to provide robust and bi-directional coverage for remote agriculture areas with difficult terrain, by leveraging smart-sensor-enabled, energy-efficient uplink transmissions and ultra-reliable downlink transmissions. Machine-type communications have been recognised as a key enabler for the future smart agriculture and smart farms. The project will use novel agricultural machine-type communication theories and develop test-beds to enable the smart agricultural applications. This is expected to contribute to the crucial communication infrastructures for smart farms, which will lead to higher agricultural productivity and national economy.Read moreRead less
A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driv ....A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driving models. The project will lead to two innovations: in theory design an attack detection and prevention ecosystem for autonomous driving and in application implement a safety analysis toolset for industry-scale autonomous systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101310
Funder
Australian Research Council
Funding Amount
$426,918.00
Summary
Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the ....Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the complex dynamics of supply and demand in transportation. The results should advance both research and technology in academia and the transportation industry and will also provide significant benefits to Australia and the international community by enhancing the energy-efficiency of and access to the mobility of the future.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100274
Funder
Australian Research Council
Funding Amount
$415,675.00
Summary
Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes wil ....Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes will advance big spatiotemporal data analytics and nonlinear optimisation theory for solving decision-making tasks towards a future energy system. This should promote the Australian power industry transition to a sustainable future grid based on a digitalisation approach to efficient energy management against climate changes.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100245
Funder
Australian Research Council
Funding Amount
$410,518.00
Summary
Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep lea ....Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep learning schema for data modelling and sequential decision-making knowledge with a novel deep reinforcement learning methodology. These developments have immediate applications in autonomous vehicles, advanced manufacturing, and dynamic pricing, with scientific, economic, and social benefits for Australia and the world.Read moreRead less
When caring ends: Understanding and supporting informal care trajectories. This project aims to advance understandings of how, why, when, and for whom caring ends, including the socio-cultural and relational factors that shape experiences before, during, and after caring. Using an innovative, multi-method sociological approach, and foregrounding carers’ voices, this project expects to generate new knowledge on the meaning and experience of care and caring. This project is significant in bringing ....When caring ends: Understanding and supporting informal care trajectories. This project aims to advance understandings of how, why, when, and for whom caring ends, including the socio-cultural and relational factors that shape experiences before, during, and after caring. Using an innovative, multi-method sociological approach, and foregrounding carers’ voices, this project expects to generate new knowledge on the meaning and experience of care and caring. This project is significant in bringing together leading researchers and key carer-focused organisations, spanning service sectors and moving across care relationships, life stages and contexts. Expected outcomes include enhanced service capacity with tangible policy and practice benefits that will enable sustainable and fulfilling informal caring experiences.Read moreRead less
Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian geno ....Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian genome analysis model. A federated cross-domain recommender system will be developed to profile individuals and generate personalised recommendations. The outcomes are expected to create a paradigm shift in learning-based prediction and personalised recommendations to support healthcare services in complex environments. Read moreRead less
Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to ....Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to increase prediction accuracy and reliability in dynamic sequences, and to support decision-making in complex situations to achieve robust and adaptive results. Anticipated outcomes can help data scientists with state-of-the-art skills to manage sequential data and benefit data-enabled innovation in Australia.Read moreRead less