An integrated model for assessing health effects of nanoparticle inhalation. This project aims to examine the associated risks of nanoparticle inhalation on heath by developing a toxicological predictive tool for health risk assessment. The outcomes of this research will lead to greatly improved preventative measures, thereby reducing occupational diseases and the health socio-economic burden of Australia.
A Multiscale Modelling Platform for Nanoparticle Inhalation Risk Assessment. This project aims to explore the health risks caused by nanoparticle inhalation and its penetration through respiratory mucus and tissue cells. Exposure to nanoparticles has the potential to cause serious and possibly fatal health effects. An understanding of nanoparticle toxicology would enable us to appropriately protect the public’s health and safety. The project plans to consider human respiratory anatomy and physio ....A Multiscale Modelling Platform for Nanoparticle Inhalation Risk Assessment. This project aims to explore the health risks caused by nanoparticle inhalation and its penetration through respiratory mucus and tissue cells. Exposure to nanoparticles has the potential to cause serious and possibly fatal health effects. An understanding of nanoparticle toxicology would enable us to appropriately protect the public’s health and safety. The project plans to consider human respiratory anatomy and physiology and use advanced computer modelling and experimental techniques to evaluate the health risk of exposure to the burgeoning number of nanomaterials found in consumer products. The expected outcome of the project is a predictive tool that determines nanoparticle exposure risk and its health consequences.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
Image search for simulator content creation. The World Wide Web contains tens of billions of images, with personal and industrial collections stretching to may times that number. The potential economic value of these image-based resources is enormous, but largely untapped as we have no practical way of recovering the images we need. This project will develop image search technologies which will allow Australian industry to exploit these important resources. Some of the wide variety of possible ....Image search for simulator content creation. The World Wide Web contains tens of billions of images, with personal and industrial collections stretching to may times that number. The potential economic value of these image-based resources is enormous, but largely untapped as we have no practical way of recovering the images we need. This project will develop image search technologies which will allow Australian industry to exploit these important resources. Some of the wide variety of possible applications might include the searching of surveillance video for objects of interest, vision-based guidance of unmanned vehicles, smart-phone and smart-home systems which understand their environments, and stock tracking systems which can detect spoilage.Read moreRead less
Real-time signal processing and distributed robotic telescope networking for co-detection of gravitational waves and their optical counterparts. An international collaboration of scientists will employ a global network of telescopes and detectors to search for ripples in space-time. The project will use novel computational tools to study exotic phenomena in the distant Universe.
A networked robotic telescope array for coincident detection of transient phenomena in the optical, gravitational wave, neutrino and radio spectra. An international collaboration of scientists will employ a global network of rapid response robotic telescopes and detectors to study exotic transient phenomena in the early Universe. Potential spin-offs include the application of novel image analysis techniques for identifying and tracking dangerous space junk.
Discovery Early Career Researcher Award - Grant ID: DE190100317
Funder
Australian Research Council
Funding Amount
$376,000.00
Summary
Advancing uncertainty prioritisation in water resource management. This project aims to develop a holistic framework for prioritisation of uncertainties in Integrated Water Resource Assessment and Management to assist water resource analysts and planners to improve sustainability of water resource management outcomes. The framework will help scientists, consultants, policy makers and water users better select which sources of uncertainty to address in their work, with what resources, and what me ....Advancing uncertainty prioritisation in water resource management. This project aims to develop a holistic framework for prioritisation of uncertainties in Integrated Water Resource Assessment and Management to assist water resource analysts and planners to improve sustainability of water resource management outcomes. The framework will help scientists, consultants, policy makers and water users better select which sources of uncertainty to address in their work, with what resources, and what methods. Expected outcomes are novel analytical methods to evaluate current practice in uncertainty prioritisation, communicate when and how to use established and novel uncertainty management techniques, and improve prioritisation of uncertainty using proof-of-concept model-based analyses. The project should improve decision making in policy and industry, and societal and environmental outcomes of water management.Read moreRead less
Mapping ear morphology to individualised three dimensional audio. The project aims to develop a practical method to derive a listener's individualised Head Related Transfer Functions from two dimensional images of the head and ears. These are essential for generating high-fidelity three dimensional audio. The project will perceptually evaluate and test the proposed system when applied to teleconferencing, surveillance, and navigational guidance.
Modelling and simulation of self-organised behaviour in biological and bio-inspired systems. Understanding self-organised systems is fundamental in biology and bio-inspired engineering. The project develops sophisticated mathematical modelling techniques and high performance simulation methods for such systems. This will increase our capacity to explain complex biological behaviour and to produce reliable bio-inspired engineering solutions
Individualisation for 3D Audio. The project aim is to allow the general listener to enjoy high-fidelity 3-D sound over headphones. Such 3-D audio is of paramount importance when inter-personal communication requires situational awareness, (eg search and rescue, fire-fighting, and air traffic control). To achieve this, the project aims to address one of the toughest problems in audio signal processing: deriving high-fidelity 3-D audio headphone filters from photos and/or 3D scans of ears. The pro ....Individualisation for 3D Audio. The project aim is to allow the general listener to enjoy high-fidelity 3-D sound over headphones. Such 3-D audio is of paramount importance when inter-personal communication requires situational awareness, (eg search and rescue, fire-fighting, and air traffic control). To achieve this, the project aims to address one of the toughest problems in audio signal processing: deriving high-fidelity 3-D audio headphone filters from photos and/or 3D scans of ears. The project plans to address fundamental research questions in statistical shape and data analysis and to perceptually evaluate the 3-D audio methods developed.Read moreRead less