Neural Activity Shaping for Retinal and Cochlear Implants. This project aims to develop methods to control and optimise the spatial patterns of neural activity evoked by neural prostheses in order to improve the resolution of neuroprostheses. A major problem for neural prostheses is that the electrical current used to stimulate neurons causes a diffuse spread of activity in the neural tissue, which limits the resolution of the device. For patients this translates into limitations in sound qualit ....Neural Activity Shaping for Retinal and Cochlear Implants. This project aims to develop methods to control and optimise the spatial patterns of neural activity evoked by neural prostheses in order to improve the resolution of neuroprostheses. A major problem for neural prostheses is that the electrical current used to stimulate neurons causes a diffuse spread of activity in the neural tissue, which limits the resolution of the device. For patients this translates into limitations in sound quality, in the case of cochlea implants, or visual acuity, for retinal implants. The outcome of the project will be algorithms that optimally choose the currents on each electrode so as to shape neural activity at the finer resolution of electrode spacing rather than the coarser resolution of current spread.Read moreRead less
Individualized cochlear implant sound coding: Optimized algorithms for better hearing. One in six Australians is affected by hearing loss. Hearing loss impacts on a person's educational and employment opportunities, resulting in a significant economic impact upon Australia. Over 10% of people with hearing impairment have a severe or profound hearing loss and may be candidates for a cochlear implant. Current cochlear implant sound processing only offers limited benefit to users. This project repr ....Individualized cochlear implant sound coding: Optimized algorithms for better hearing. One in six Australians is affected by hearing loss. Hearing loss impacts on a person's educational and employment opportunities, resulting in a significant economic impact upon Australia. Over 10% of people with hearing impairment have a severe or profound hearing loss and may be candidates for a cochlear implant. Current cochlear implant sound processing only offers limited benefit to users. This project represents a truly innovative pathway forward in the development of cochlear implant sound coding that could substantially increase the speech perception of users, enabling these people to become and remain active and productive members of our community.Read moreRead less
Computational neural modelling of bottom-up information and top-down attention in auditory perception. The aim of this project is to gain a better understanding of the ways in which our auditory cortex functions. This project will make a significant contribution to this important and fundamental aspect of brain science and brain-inspired computation. The outcome will be to build a computational model of the auditory cortex, through simulation of the detailed neuronal responses using spiking neur ....Computational neural modelling of bottom-up information and top-down attention in auditory perception. The aim of this project is to gain a better understanding of the ways in which our auditory cortex functions. This project will make a significant contribution to this important and fundamental aspect of brain science and brain-inspired computation. The outcome will be to build a computational model of the auditory cortex, through simulation of the detailed neuronal responses using spiking neurons. Applications will develop improved processing strategies for automatic speech recognition, hearing aids, bionic ears (cochlear implants), robotics and other machine processing systems.Read moreRead less
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Dynamic Scheduling and Stochastic Control for Sensor Networks. Sensor networks are rapidly becoming important in applications from environmental monitoring, navigation to border surveillance. However, due to bandwidth constraints, even very simple networks have proven to be very complex to properly control. It is now necessary to efficiently allocate the 'limited available bandwidth' to sensors in order to share the most valuable data over the network. Therefore, this project proposes new techn ....Dynamic Scheduling and Stochastic Control for Sensor Networks. Sensor networks are rapidly becoming important in applications from environmental monitoring, navigation to border surveillance. However, due to bandwidth constraints, even very simple networks have proven to be very complex to properly control. It is now necessary to efficiently allocate the 'limited available bandwidth' to sensors in order to share the most valuable data over the network. Therefore, this project proposes new techniques using concepts of dynamic sensor scheduling and stochastic control to provide computationally feasible and near optimal solutions to the limited and varying bandwidth problem. This work will greatly enhance the operational performance of distributed sensor networks.Read moreRead less
Scalable Robust Video Surveillance over Constrained Networks. Real-time monitoring of large numbers of people is becoming increasingly important for applications such as efficient service delivery and security against both common crime and terrorism. The use of human operators for such tasks is infeasible due to the large amount of data collected. Existing autonomous video surveillance systems are prone to high numbers of false alarms and often require expensive hardware. This proposal seeks ....Scalable Robust Video Surveillance over Constrained Networks. Real-time monitoring of large numbers of people is becoming increasingly important for applications such as efficient service delivery and security against both common crime and terrorism. The use of human operators for such tasks is infeasible due to the large amount of data collected. Existing autonomous video surveillance systems are prone to high numbers of false alarms and often require expensive hardware. This proposal seeks to address both difficulties by using rigorous statistical signal processing methods to optimally fuse information from a network of low-cost cameras.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
Detection and Quantification of General Fetal Movements from Accelerometer Measurements using Nonstationary Signal Processing Techniques. There are approximately 1,750 fetal deaths per year in Australian with about one-third occurring late in gestation and without an apparent cause. The development of an automated system capable of long-term monitoring of fetal health will result in accurate diagnoses and prediction of future outcome. This will, in turn, allow early intervention by the clinicia ....Detection and Quantification of General Fetal Movements from Accelerometer Measurements using Nonstationary Signal Processing Techniques. There are approximately 1,750 fetal deaths per year in Australian with about one-third occurring late in gestation and without an apparent cause. The development of an automated system capable of long-term monitoring of fetal health will result in accurate diagnoses and prediction of future outcome. This will, in turn, allow early intervention by the clinician to reduce fetal deaths and enhance the chances of good outcomes with resultant savings in social and financial costs to the community. The development of such equipment would spawn future research into intervention treatments and contribute to Australia's position as a world leader in computerised health monitoring systems.Read moreRead less