Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101297
Funder
Australian Research Council
Funding Amount
$429,000.00
Summary
A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the e ....A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the emerging field of biological imaging and to deliver an integrated imaging platform for mapping various tissue microscopic components at the cellular level. Successful outcomes have the potential for commercialisation and will accelerate a range of fundamental science and engineering studies requiring imaging techniques.Read moreRead less
Developing key vision technology for automation of aquaculture factory. This project aims to investigate structural, coloured textural, and hyperspectral analysis approaches to achieve automated lobster molt-cycle staging and classification to the level required for commercial production. High labour cost, water contamination, and disease transmission are major barriers in Australian bay lobster aquaculture inhibiting its large scale production. Automation of the production process and reducing ....Developing key vision technology for automation of aquaculture factory. This project aims to investigate structural, coloured textural, and hyperspectral analysis approaches to achieve automated lobster molt-cycle staging and classification to the level required for commercial production. High labour cost, water contamination, and disease transmission are major barriers in Australian bay lobster aquaculture inhibiting its large scale production. Automation of the production process and reducing the human contact with animals are of high priority in the development of this Australian-led emerging industry. The project aims to develop technology to bring this world- first aquaculture factory to large scale production, and create new export opportunities for lobsters and production systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100964
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerg ....Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerging field to strengthen Australia’s world leadership role in data science. Practically, the novel theories and data analytics technologies developed will help to safeguard Australian business, industry, and society from cyberfraud, online rumour-mongering, and financial loss.Read moreRead less
Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts ....Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.Read moreRead less
Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project ....Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project expects to provide practical data analysis approaches and establish the theoretical foundations for data mining with multiple sources of brain data.Read moreRead less
Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imagin ....Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.Read moreRead less
Biologically Inspired Binaural Coupling for Selective Machine Hearing. This project aims to investigate biologically-inspired binaural coupling models in the context of the deep learning paradigm by formulating desirable higher level auditory structures as neural network sub-systems. This project expects to generate new knowledge for developing the next generation of robust speech processing systems that are capable of mimicking the selecting listening ability of humans when faced with realistic ....Biologically Inspired Binaural Coupling for Selective Machine Hearing. This project aims to investigate biologically-inspired binaural coupling models in the context of the deep learning paradigm by formulating desirable higher level auditory structures as neural network sub-systems. This project expects to generate new knowledge for developing the next generation of robust speech processing systems that are capable of mimicking the selecting listening ability of humans when faced with realistic noisy speech signals and the ‘cocktail party problem’ using innovative binaural feedback systems. This work should provide significant benefits, including improved voice biometrics and selective auditory attention capabilities in machines.Read moreRead less
Drone-based Communications for High-speed Beyond 5G Wireless Systems. Drone-based communication is a revolutionised wireless paradigm for the development of highly flexible and cost-effective beyond fifth-generation (B5G) wireless systems. This project aims to develop novel communication theories and practical techniques to realise truly high-speed and ubiquitous communication required in B5G networks. The project intends to deliver resource allocation designs, robust transceiver designs and a s ....Drone-based Communications for High-speed Beyond 5G Wireless Systems. Drone-based communication is a revolutionised wireless paradigm for the development of highly flexible and cost-effective beyond fifth-generation (B5G) wireless systems. This project aims to develop novel communication theories and practical techniques to realise truly high-speed and ubiquitous communication required in B5G networks. The project intends to deliver resource allocation designs, robust transceiver designs and a system-level analysis as the foundations and tools to unlock the potential of this promising paradigm. The outcomes of this project are expected to fundamentally advance the knowledge of drone-based communications with significant economic values to service providers and benefits to mobile users over the world.Read moreRead less
Ultra-reliable and low-latency mission critical communications. This project aims to develop enabling technologies for ultra-reliable and low-latency communications. While the evolution of wireless communication technologies to date has focused on data rate improvement, very little is known on how to achieve ultra-reliability and almost-zero latency which is urgently required for mission critical applications such as smart manufacturing and intelligent vehicles. The outcomes of the project will ....Ultra-reliable and low-latency mission critical communications. This project aims to develop enabling technologies for ultra-reliable and low-latency communications. While the evolution of wireless communication technologies to date has focused on data rate improvement, very little is known on how to achieve ultra-reliability and almost-zero latency which is urgently required for mission critical applications such as smart manufacturing and intelligent vehicles. The outcomes of the project will be new analytical tools and practical guidelines for designing trusted communication platforms to realise these applications, with benefits ranging from improved safety in intelligent transportation systems to digital transformation of the manufacturing industry.Read moreRead less