Linkage Infrastructure, Equipment And Facilities - Grant ID: LE100100235
Funder
Australian Research Council
Funding Amount
$280,000.00
Summary
Accelerating Australia's large scale video surveillance research programmes. The research to be conducted using this infrastructure will bring immense benefits to Australia in terms of increased levels of public safety and in the protection of critical facilities from terrorism and other crimes, by developing better surveillance systems. This will provide both increases in measurable research outputs and opportunities for Australian business to commercialise these systems. The infrastructure wil ....Accelerating Australia's large scale video surveillance research programmes. The research to be conducted using this infrastructure will bring immense benefits to Australia in terms of increased levels of public safety and in the protection of critical facilities from terrorism and other crimes, by developing better surveillance systems. This will provide both increases in measurable research outputs and opportunities for Australian business to commercialise these systems. The infrastructure will accelerate the pace of surveillance research and development in Australia, enhancing the competitiveness of both Australia's researchers and the businesses that will commercialise these researchers' discoveries.Read moreRead less
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
Bad tastes, odours and toxins in our drinking water reservoirs: are benthic cyanobacteria the culprits? Cyanobacteria (blue-green algae) produce toxins and bad tastes that contaminate drinking water sources, cause public concern about water quality. This project will address a critical knowledge gap by investigating species that grow on the sediments of reservoirs, thus providing more comprehensive management solutions to the water industry.
Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are compl ....Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.Read moreRead less
Examinations of the relationship between accreditation and clinical and organisational performance. Accreditation of organisations is now commonplace. It involves assessing organisations against pre-defined standards. This is a highly significant issue because of the millions of dollars of investment in accreditation. We do not know if we achieve value for money or whether positive change is associated with accreditation. Few studies have examined this in detail. We aim to do so in this stud ....Examinations of the relationship between accreditation and clinical and organisational performance. Accreditation of organisations is now commonplace. It involves assessing organisations against pre-defined standards. This is a highly significant issue because of the millions of dollars of investment in accreditation. We do not know if we achieve value for money or whether positive change is associated with accreditation. Few studies have examined this in detail. We aim to do so in this study. We will examine organisational and individual performance associated with accreditation status in order to illuminate the process and uncover any associations between accreditation and organisational culture, consumer participation and clinical (individual) performance indicators.
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Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the bui ....Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the building of next generation of computer vision systems to work in open and dynamic environments. This should be able to produce solid benefits to the science, society, and economy of Australian via the application of these advanced intelligent systems.Read moreRead less
Establishing advanced networks for air quality sensing and analyses. Establishing advanced networks for air quality sensing and analyses. This project aims to develop innovative, cost-effective, high resolution air quality networks. Recent developments in sensor technologies improve the ability to harvest atmospheric data. This project will develop, validate and implement methods for high sensitivity atmospheric sensing and apply cutting-edge statistical and analytic techniques to the data sets, ....Establishing advanced networks for air quality sensing and analyses. Establishing advanced networks for air quality sensing and analyses. This project aims to develop innovative, cost-effective, high resolution air quality networks. Recent developments in sensor technologies improve the ability to harvest atmospheric data. This project will develop, validate and implement methods for high sensitivity atmospheric sensing and apply cutting-edge statistical and analytic techniques to the data sets, unprecedented in scope and resolution. Outcomes include an open access database to quantify and visualise intra-urban air pollution and human exposure and develop air quality maps and smoke pollution management tools. It is expected to advance the evidence-based management of air as a resource, increasing economic prosperity and enhancing human health and quality of life.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE170100093
Funder
Australian Research Council
Funding Amount
$510,000.00
Summary
High-throughput technology targeting antimicrobial resistance in animals. This project aims to establish reference laboratories as biobanks for resistant isolate collections from veterinary diagnostic laboratories / surveillance programmes and a national research network to mitigate antimicrobial resistance in animals. Antimicrobial resistance in zoonotic/foodborne pathogens and livestock commensals is a global issue. This project will use mass-spectroscopy biotypers, information management soft ....High-throughput technology targeting antimicrobial resistance in animals. This project aims to establish reference laboratories as biobanks for resistant isolate collections from veterinary diagnostic laboratories / surveillance programmes and a national research network to mitigate antimicrobial resistance in animals. Antimicrobial resistance in zoonotic/foodborne pathogens and livestock commensals is a global issue. This project will use mass-spectroscopy biotypers, information management software, robotic liquid handling and a research dairy to develop high-throughput screening technologies to rapidly determine major animal species’ resistance status, and research anti-infectives and vaccines for livestock diseases. This will improve the health and production of Australian livestock, leading to greater market access for high quality products.Read moreRead less
Improving water quality modelling by better understanding solute transport. Poor stream water quality is a critical problem in Australia and globally. Stream water quality depends directly on pathways and time taken for water to transport pollutants through catchments. Predicting these pathways is highly challenging and currently requires specialised data. This project aims to better model the movement of water from rainfall to streams, enable greatly improved use of water quality data routinely ....Improving water quality modelling by better understanding solute transport. Poor stream water quality is a critical problem in Australia and globally. Stream water quality depends directly on pathways and time taken for water to transport pollutants through catchments. Predicting these pathways is highly challenging and currently requires specialised data. This project aims to better model the movement of water from rainfall to streams, enable greatly improved use of water quality data routinely collected in Australia's catchments and thereby better predict water quality behaviour. Proposed field studies aim to support this development. The outcomes sought are improved planning and management of water quality in our rivers, lakes and estuaries, improved health of these water bodies and improved water supplies.Read moreRead less