A Concurrent Multiscale Model for Improved Prediction of Drying Process. This project aims to develop an innovative multiscale model for food drying, which integrates spatial and temporal nonlinear behaviours at different scales. The proposed unifying theory will capture dynamic micro level features and upscale them to macro level features through a concurrent bridging scheme. As cellular elements critically govern the drying process, the fundamental understanding captured through this theory wi ....A Concurrent Multiscale Model for Improved Prediction of Drying Process. This project aims to develop an innovative multiscale model for food drying, which integrates spatial and temporal nonlinear behaviours at different scales. The proposed unifying theory will capture dynamic micro level features and upscale them to macro level features through a concurrent bridging scheme. As cellular elements critically govern the drying process, the fundamental understanding captured through this theory will lead to more accurate prediction of drying kinetics, deformation and quality changes, and hence the development of efficient drying systems. This project will overcome a longstanding research problem and position Australia at the forefront in world drying research to reap substantial economic benefits for Australia.Read moreRead less
Transforming Australian bio-based industries through multiscale modelling. Agricultural and forestry biomass can be converted into feedstocks for production of biofuels and biomaterials via synthetic biology. A key challenge is the complex biomass microstructure renders it highly resistant to conversion, and pretreatment is crucial for enhancing process efficiency. Micro-CT imaging will enable particle characterisation and identification of changes in the fibre composition during pretreatment. T ....Transforming Australian bio-based industries through multiscale modelling. Agricultural and forestry biomass can be converted into feedstocks for production of biofuels and biomaterials via synthetic biology. A key challenge is the complex biomass microstructure renders it highly resistant to conversion, and pretreatment is crucial for enhancing process efficiency. Micro-CT imaging will enable particle characterisation and identification of changes in the fibre composition during pretreatment. This information will be used to create a virtual biomass particle model for an in silico investigation to inform optimal process design. The framework will transform the way biomass is processed, contributing to the growth of the Australian bio-manufacturing industry by making it more productive, profitable and sustainable.Read moreRead less
Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising ap ....Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising approach, but its accuracy is far from satisfactory. The software systems developed in this project will be used in structural identification of target proteins in drug design. This will make drug design process more efficient, saving time and cost, potentially saving lives.Read moreRead less
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less
Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries. Partially Observable Markov Decision Processes (POMDPs) provide a general mathematical framework for sequential decision making under uncertainty. However, solving POMDPs effectively under realistic assumptions remains a challenging problem. This project aims to develop new efficient Monte Carlo algorithms to significantly advance the application of POMDPs to real-world decision problems involving complex action spaces an ....Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries. Partially Observable Markov Decision Processes (POMDPs) provide a general mathematical framework for sequential decision making under uncertainty. However, solving POMDPs effectively under realistic assumptions remains a challenging problem. This project aims to develop new efficient Monte Carlo algorithms to significantly advance the application of POMDPs to real-world decision problems involving complex action spaces and system dynamics. Both theoretical and algorithmic approaches will be applied to sustainable fishery management --- an important problem for Australia and an ideal context for POMDPs. The project will advance research in artificial intelligence, dynamical systems, and fishery operations, and benefit the national economy.Read moreRead less
Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global com ....Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global competitiveness. Expected outcomes of this project will help to break the communication barriers between the Deaf and hearing population. This should provide significant benefits to Deaf communities through enhanced communication and improved quality-of-life, leading to a fair, more inclusive and resilient Australian society.Read moreRead less
A next-generation whole parasite bovine Babesia vaccine. . In Australia, Babesia parasites cause most of the severe and often fatal cases of cattle-tick fever, a globally significant tick-borne disease. It can be prevented by a live-attenuated parasite vaccine which has critical limitations of a 4-day shelf-life and risk of severe disease if administered to adult cattle. This project aims to evaluate in cattle a novel whole parasite Babesia bovis vaccine that cannot cause disease and can be pres ....A next-generation whole parasite bovine Babesia vaccine. . In Australia, Babesia parasites cause most of the severe and often fatal cases of cattle-tick fever, a globally significant tick-borne disease. It can be prevented by a live-attenuated parasite vaccine which has critical limitations of a 4-day shelf-life and risk of severe disease if administered to adult cattle. This project aims to evaluate in cattle a novel whole parasite Babesia bovis vaccine that cannot cause disease and can be preserved as an off-the-shelf product without losing efficacy. The expected outcome is a significantly improved vaccine for a major infectious disease that affects primary food production. As the disease imposes a major economic burden, it will have great benefit for the Australian livestock industry.
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Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
Defining the molecular basis for Salmonella persistence. Salmonella infections in animals and humans place significant burdens on the agri-food and healthcare sectors. All mammals and avian species can become chronically infected with Salmonella and such chronic carriage is a reservoir for disease and outbreaks in other animals and humans. Significant gaps in our understanding of Salmonella infection remain, including the molecular mechanisms involved in establishing a chronic carrier state. We ....Defining the molecular basis for Salmonella persistence. Salmonella infections in animals and humans place significant burdens on the agri-food and healthcare sectors. All mammals and avian species can become chronically infected with Salmonella and such chronic carriage is a reservoir for disease and outbreaks in other animals and humans. Significant gaps in our understanding of Salmonella infection remain, including the molecular mechanisms involved in establishing a chronic carrier state. We identified several Salmonella specific genes and subsequent murine studies revealed that a Salmonella mutant lacking these genes is attenuated in mice and especially in the gallbladder. In this project we seek to understand the molecular basis for attenuation and the contribution of each protein to diseaseRead moreRead less
Linking human brain structure to function with ultra-high resolution fMRI. This project will examine the structure and function of the sensory cortex of the human brain using ultra-high resolution functional magnetic resonance imaging (7 Tesla MRI). The project pushes new boundaries for resolution with ultra-high field MRI (7 Tesla) and, as such, will advance techniques for the acquisition, analysis, and computational modelling of high-resolution fMRI brain imaging, providing detail of the funct ....Linking human brain structure to function with ultra-high resolution fMRI. This project will examine the structure and function of the sensory cortex of the human brain using ultra-high resolution functional magnetic resonance imaging (7 Tesla MRI). The project pushes new boundaries for resolution with ultra-high field MRI (7 Tesla) and, as such, will advance techniques for the acquisition, analysis, and computational modelling of high-resolution fMRI brain imaging, providing detail of the functional organisation of the sensory cortex at a level never previously possible in the living human brain. This will provide new understanding of the neural-level networks that underpin attention and touch perception in the human brain.Read moreRead less