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
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Creating perceptual experts in Australia's policing and security agencies. This project aims to create the next generation of experts in Australia’s policing and national security agencies, by improving crime scene evidence interpretation. Agencies are under pressure to develop more rigorous training practices that go beyond mere intuition and tradition. This project will use a novel approach that directs learning toward the most diagnostic perceptual cues. Expected outcomes include a solid empi ....Creating perceptual experts in Australia's policing and security agencies. This project aims to create the next generation of experts in Australia’s policing and national security agencies, by improving crime scene evidence interpretation. Agencies are under pressure to develop more rigorous training practices that go beyond mere intuition and tradition. This project will use a novel approach that directs learning toward the most diagnostic perceptual cues. Expected outcomes include a solid empirical basis for national training programs designed to create experts that are accurate, reliable, and continuously improving. Improving the training of experts will ensure the integrity of forensics as evidentiary tools available to police, lead to more reliable courtroom convictions and help safeguard Australia from terrorism and crime.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
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150101108
Funder
Australian Research Council
Funding Amount
$352,000.00
Summary
The ups and downs of visuospatial attention. The brain has a remarkable capacity to provide a coherent experience of the world by seamlessly integrating sights and sounds from different locations. It is only after brain damage, or when faced with a high attentional load, that our limitations become apparent. The project aims to investigate these limitations by determining how spatial location influences attention in relation to distractibility, cross-modal input and emotionality. Eye tracking an ....The ups and downs of visuospatial attention. The brain has a remarkable capacity to provide a coherent experience of the world by seamlessly integrating sights and sounds from different locations. It is only after brain damage, or when faced with a high attentional load, that our limitations become apparent. The project aims to investigate these limitations by determining how spatial location influences attention in relation to distractibility, cross-modal input and emotionality. Eye tracking and physiological measures of arousal will be combined with traditional cognitive measures to provide a deeper understanding of spatial attention. This project aims to improve attentional models and develop innovative strategies to increase safety by decreasing inattention and distraction.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
Temporal interactions of dorsal/ventral visual streams. This project aims to understand the temporal interactions between the dorsal and ventral visual streams that control skilled actions. The neural pathways for visual perception of objects may be distinct from those associated with movements towards the object, but the speed of activation and interactions of these two cortical visual streams have not been investigated. This project will use the temporal sensitivity of neuroscience brain imagi ....Temporal interactions of dorsal/ventral visual streams. This project aims to understand the temporal interactions between the dorsal and ventral visual streams that control skilled actions. The neural pathways for visual perception of objects may be distinct from those associated with movements towards the object, but the speed of activation and interactions of these two cortical visual streams have not been investigated. This project will use the temporal sensitivity of neuroscience brain imaging techniques (MEG, EEG, fMRI) to measure the real-time sequence of interactions between the two visual streams during goal-directed grasping. It intends to extend the most influential model of visual processing by discovering ‘when’ these pathways activate and interact. Such knowledge will affect delivery of social and commercial outcomes, by providing new directions for the rehabilitation of sensorimotor performance in many neurodevelopmental disorders, and by improving design of control systems for robotic effectors, prosthetic limbs, and more seamless human-machine interfaces.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC170100008
Funder
Australian Research Council
Funding Amount
$4,459,672.00
Summary
ARC Training Centre for Innovative Wine Production. The ARC Training Centre for Innovative Wine Production aims to tackle challenges to wine production through innovative, multi-disciplinary research. Australia’s grape and wine industry is a multi-billion dollar industry, yet in some areas profitability is low. Reasons include extreme weather events, soil salinity and diseases, inefficient practices, a low level of technological innovation and high input costs. New technologies and process effic ....ARC Training Centre for Innovative Wine Production. The ARC Training Centre for Innovative Wine Production aims to tackle challenges to wine production through innovative, multi-disciplinary research. Australia’s grape and wine industry is a multi-billion dollar industry, yet in some areas profitability is low. Reasons include extreme weather events, soil salinity and diseases, inefficient practices, a low level of technological innovation and high input costs. New technologies and process efficiencies developed as part of this project will reduce environmental impact, drive production costs down and profits and employment up. The project will mount a suite of industry-led projects to deliver outcomes to boost Australia’s competitiveness as a supplier of sustainably-produced premium branded wine to the world.Read moreRead less