Blue energy harvesting and storage technology for wearable electronics. This project aims to develop new self-charging power devices that can harvest and store body energy generated during body motions, and power smart and implantable medical electronics. The project will develop new Piezo-supercapacitors by designing new electrode materials and cell designs. The charge storage and transport kinetics will be uncovered using advanced in-situ characterisation techniques and modern simulation metho ....Blue energy harvesting and storage technology for wearable electronics. This project aims to develop new self-charging power devices that can harvest and store body energy generated during body motions, and power smart and implantable medical electronics. The project will develop new Piezo-supercapacitors by designing new electrode materials and cell designs. The charge storage and transport kinetics will be uncovered using advanced in-situ characterisation techniques and modern simulation methods. The project expects to generate new knowledge in blue energy harvesting and storage systems, training for young scientists, and generate intellectual property with potential commercialised products to be used in implantable devices, placing Australia at the forefront of new technology.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE210100153
Funder
Australian Research Council
Funding Amount
$497,264.00
Summary
Integrated In situ Characterisation Facilities for Energy Studies. This project aims to establish a new capability to reveal catalytic behaviour of materials under practical working conditions at multi-scale levels. Through in situ monitoring of surface, interface and structural properties of catalysts, this unique integrated facility will overcome current limitations due to a lack of understanding of reaction mechanism, by ex situ and/or individual in situ characterisations. This world-class fa ....Integrated In situ Characterisation Facilities for Energy Studies. This project aims to establish a new capability to reveal catalytic behaviour of materials under practical working conditions at multi-scale levels. Through in situ monitoring of surface, interface and structural properties of catalysts, this unique integrated facility will overcome current limitations due to a lack of understanding of reaction mechanism, by ex situ and/or individual in situ characterisations. This world-class facility will significantly advance a range of electrocatalysis, photocatalysis and battery applications for renewable energy-storage and clean-fuel generation. This will be Australia’s only platform; it will benefit a number of innovative research projects in energy, catalysis and environmental and materials science.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.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. 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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE170100137
Funder
Australian Research Council
Funding Amount
$358,275.00
Summary
Integrated thin film facility for catalysis and energy materials research. This project aims to establish thin film fabrication with catalytic/gas sorption characterisation needed for energy research. This project will overcome current limitations in advanced energy materials design via wet chemical methods. It will enable materials synthesis and characterisation toward thermal/photo/electro-catalytic, hydrogen storage, and battery technologies. The facility is expected to drive fundamental conc ....Integrated thin film facility for catalysis and energy materials research. This project aims to establish thin film fabrication with catalytic/gas sorption characterisation needed for energy research. This project will overcome current limitations in advanced energy materials design via wet chemical methods. It will enable materials synthesis and characterisation toward thermal/photo/electro-catalytic, hydrogen storage, and battery technologies. The facility is expected to drive fundamental concepts, and enable combinatorial search and new thin film technology. It is anticipated that this facility will increase Australia’s international competitiveness in the development of advanced energy materials.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
Multi-modal virtual microscopy for quantitative diagnostic pathology. This project will contribute to the next generation of virtual microscopy systems that provide innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of diagnostic pathology. These tools will especially contribute to the screening and diagnosis of cervical, lung and bladder cancer.
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.