Nature's mechanisms for leaching and remobilising metals. This project aims to understand the chemical and physical processes that govern reactive transport and metal scavenging in rocky environments. Much of Australia's mineral wealth is the result of the interaction of warm fluids with rocks deep in the Earth over geological timescales. The formation of ore deposits is governed by the physical chemistry of mineral dissolution and crystallisation, and by fluid flow through porous rocks and frac ....Nature's mechanisms for leaching and remobilising metals. This project aims to understand the chemical and physical processes that govern reactive transport and metal scavenging in rocky environments. Much of Australia's mineral wealth is the result of the interaction of warm fluids with rocks deep in the Earth over geological timescales. The formation of ore deposits is governed by the physical chemistry of mineral dissolution and crystallisation, and by fluid flow through porous rocks and fractures. This project integrates innovation in geology, chemistry, and mineral engineering, and will deliver mineral-scale reaction models that will increase efficiency of in-situ mining and leaching technologies. Knowledge generated can be applied to improve mineral exploration, mining, and processing, contributing to unlocking billions of dollars’ worth of resources tied up in low grade, mineralogically complex ores.Read moreRead less
Climate and environmental history of SE Queensland dunefields. This project aims to generate fundamental information about the timing and mode of formation of sand dunes in the world's largest downdrift sand system, Cooloola and Fraser Island, Queensland. The project aims to provide a world class record of climate variability, sea-level change and long term climate change from the sub-tropics of Australia, an area critical to understanding global climate links and sea-level change but where high ....Climate and environmental history of SE Queensland dunefields. This project aims to generate fundamental information about the timing and mode of formation of sand dunes in the world's largest downdrift sand system, Cooloola and Fraser Island, Queensland. The project aims to provide a world class record of climate variability, sea-level change and long term climate change from the sub-tropics of Australia, an area critical to understanding global climate links and sea-level change but where high quality long-term records are sparse and little investigated. This project will also underpin the outstanding universal value of the Fraser Island World Heritage Area which is based on the area being the world's largest sand island, but for which scientific understanding of the sand dunes is remarkably poor.Read moreRead less
Learning human activities through low cost, unobtrusive RFID technology. A rapidly growing aged population presents many challenges to Australia's health and aged care services. The outcomes of this project will help aging Australians live in their own homes longer, with greater independence and safety by providing an automated, unobtrusive means for health professionals to monitor activity and intervene as required.
Patchy colloidosomes at interfaces: correlation of particle surface heterogeneity, wettability, and chemical activity at the nanoscale. The surfaces of natural mineral particles are made up of spots with such different chemical and physical properties. The complexity makes it hard to predict their behaviour. This project will provide insights into how the 'patchy' nature of particle surfaces affects their behaviour in processes such as flotation separation and bio-fuel production.
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
Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, te ....Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, tested on real datasets, that combines new techniques in data modelling, algorithm development, and system design. Likely benefits are enhanced Australia's competence in data science through student training and new, robust data tools relevant to critical sectors such as cybersecurity, healthcare, and defence.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to ....Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to represent the extracted semantic relationships and novel linkage-analysis based algorithms will be developed for ranking objects. The results from this project will underpin many critical applications such as healthcare.Read moreRead less