Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0561231
Funder
Australian Research Council
Funding Amount
$671,715.00
Summary
MRI GRID Computing Facility: Design, Optimisation and Image Processing. The MRI Grid Computing Facility provides the IT infrastructure to achieve effective e-research in the area of magnetic resonance (MR) imaging, a field of neuroscience research that revolutionizes the way brain diseases are identified and treated. The facility consists of a dedicated high performance grid compute engine, distributed visualisation workstations, and distributed data warehouse facilities. Software tools acc ....MRI GRID Computing Facility: Design, Optimisation and Image Processing. The MRI Grid Computing Facility provides the IT infrastructure to achieve effective e-research in the area of magnetic resonance (MR) imaging, a field of neuroscience research that revolutionizes the way brain diseases are identified and treated. The facility consists of a dedicated high performance grid compute engine, distributed visualisation workstations, and distributed data warehouse facilities. Software tools accessible through the Internet will enable researchers to archive, retrieve and exchange data and software; access distributed MR image databases and the latest MR image analysis tools; schedule analysis tasks on the grid compute engine, the outcomes of which will be visualized by the visualization workstations.Read moreRead less
ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this te ....ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this team with the foremost international authorities and leading industry players in the area of sensor networks. This research network will guide collaborative research that will ensure Australia to play a world leading role in sensor network development and implementation.
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Development and implementation of biodiversity information for sustainable management of South Australian groundwater. Clean potable water is one of the most important resources for human health and a successful economy. Increasingly, subterranean aquifers are used for storage and recovery of water. These aquifers contain dynamic ecosystems, but little is known about species composition or about the importance of the presence of various species for water quality. We will use the latest laborator ....Development and implementation of biodiversity information for sustainable management of South Australian groundwater. Clean potable water is one of the most important resources for human health and a successful economy. Increasingly, subterranean aquifers are used for storage and recovery of water. These aquifers contain dynamic ecosystems, but little is known about species composition or about the importance of the presence of various species for water quality. We will use the latest laboratory techniques and DNA identification methods to provide a template for determining ground water diversity and food web dynamics throughout Australia. This project will lead to a better understanding of how to manage ground water in a sustainable manner.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101775
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Distributed large-scale optimisation methods in computer vision. With the number of images and video available over the internet reaching billions and growing, the need for new tools for handling and interpreting such huge amounts of data is quickly becoming apparent. This project will focus on developing new optimisation methods for efficiently computing solutions for a broad class of large-scale problems.
Investigation and development of robust rule discovery and classification system. This research focuses on a national research priority, namely smart information use. The expected outcomes of the project will greatly advance intelligent system design, such as automatic decision making, fault detection and problem diagnosis, for finance, medical, telecom and many other areas. It has great potential for commercialisation and earning incomes for the future research. The publications will benefit th ....Investigation and development of robust rule discovery and classification system. This research focuses on a national research priority, namely smart information use. The expected outcomes of the project will greatly advance intelligent system design, such as automatic decision making, fault detection and problem diagnosis, for finance, medical, telecom and many other areas. It has great potential for commercialisation and earning incomes for the future research. The publications will benefit the future development of intelligent systems for dealing with missing data. This project directly supports a PhD student and two research assistants who will most likely continue their higher degree study. These contribute to regional tertiary education.Read moreRead less
ARC Australia-New Zealand Research Network for Vegetation Function. Plant species vary widely in quantitative functional traits, and in their relations to climate, soils and geography. Global generalizations are emerging. Vegetation Function network will reach from plant function into genomics and crop breeding, into palaeoecology and vegetation history, into landscape management for carbon, water and salinity outcomes, into forecasting future ecosystems under global change, and into phylogeny, ....ARC Australia-New Zealand Research Network for Vegetation Function. Plant species vary widely in quantitative functional traits, and in their relations to climate, soils and geography. Global generalizations are emerging. Vegetation Function network will reach from plant function into genomics and crop breeding, into palaeoecology and vegetation history, into landscape management for carbon, water and salinity outcomes, into forecasting future ecosystems under global change, and into phylogeny, ecoinformatics and evolutionary theory. Across this span, working groups will target nine identified opportunities for breakthrough research. Each research target needs input from two or more disciplines. Together, the nine targets link across disciplines, as a network that spans from genomic to planetary scales.Read moreRead less
Semantic change detection through large-scale learning. This project aims to develop technologies which understand the content of images before higher-level analysis is performed. This approach is intended to allow more accurate and reliable decisions to be made using automated image analysis than has previously been possible. The project will particularly investigate the detection of change in the contents of an image.
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Single and dual process models of recognition memory: Reconciliation of behavioural, electrophysiological, and neuroimaging data. Advanced brain scanning technologies are increasingly used to study human memory. As well as being important for our basic understanding of memory, they also tell us how memory is affected by normal development, ageing, disease, and injury. Unfortunately, because these technologies are so new, a gap has opened up between our psychological understanding of memory and t ....Single and dual process models of recognition memory: Reconciliation of behavioural, electrophysiological, and neuroimaging data. Advanced brain scanning technologies are increasingly used to study human memory. As well as being important for our basic understanding of memory, they also tell us how memory is affected by normal development, ageing, disease, and injury. Unfortunately, because these technologies are so new, a gap has opened up between our psychological understanding of memory and the physiological events measured by the scanning technologies. This has created a problem for how we should interpret the results that are found. The present project aims to close this gap by applying new research methodologies and theoretical insights based on our previous research.Read moreRead less