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
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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Funder
Australian Research Council
Funding Amount
$348,026.00
Summary
A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and ....A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and evaluation of IoT technologies and services. The project will also serve as a vehicle for the education and training of Australia’s next generation of scholars and engineers, and contribute to Australia’s scientific visibility.Read moreRead less