Collaborative Sensing and Learning for Maritime Situational Awareness. We aim to demonstrate coordinated autonomous sensing of naval assets in dynamic maritime environments, reducing the operational load required to deliver a high quality maritime situational awareness. A realistic simulation based approach will help us develop novel artificial intelligence technology including: self-adaptive strategies for dynamic asset allocation, embedded smart sensing capabilities for naval observation syste ....Collaborative Sensing and Learning for Maritime Situational Awareness. We aim to demonstrate coordinated autonomous sensing of naval assets in dynamic maritime environments, reducing the operational load required to deliver a high quality maritime situational awareness. A realistic simulation based approach will help us develop novel artificial intelligence technology including: self-adaptive strategies for dynamic asset allocation, embedded smart sensing capabilities for naval observation systems and novel approaches to continuous collaborative learning from multi-spectral media. In addition to the emerging partnership between participants, the project will advance sovereign capability to develop maritime intelligence gathering technology for the Royal Australian Navy to underpin stability in our region. Read moreRead less
An integrated and real-time passenger travel and public transport service information system. This project will help the Department of Transport provide improved services to the public through a better understanding of journey planning demands in comparison to public transport services. By integrating research through design methods with technological solutions, the project will deliver better quality of service and higher customer satisfaction.
Discovering Patterns using Near Unsupervised Leaning to Support the Quick Detection of New Animal Disease Outbreaks Caused by Viruses. Without the capability to identify and study the vast majority of extant viruses using traditional laboratory techniques, emerging threats to Australian livestock health cannot be efficiently diagnosed or treated. New approaches based on high-throughput sequencing have recently been developed to study such viruses, but making sense of the sequence data is still a ....Discovering Patterns using Near Unsupervised Leaning to Support the Quick Detection of New Animal Disease Outbreaks Caused by Viruses. Without the capability to identify and study the vast majority of extant viruses using traditional laboratory techniques, emerging threats to Australian livestock health cannot be efficiently diagnosed or treated. New approaches based on high-throughput sequencing have recently been developed to study such viruses, but making sense of the sequence data is still a complex problem. Together with the project's Partner Organisations, including YourGene Biosciences Australia and the CSIRO Australian Animal Health Laboratory, this project aims to develop new computational methods to broaden the scope of detection and analysis of unknown viruses, enhancing the capability for research into the causative viral agents of animal diseases.Read moreRead less
Data mining complex transactional and criminal networks. Money laundering, if undetected, poses a major concern for governments and communities. The software system platform for detecting money laundering networks from this project will be the first that can assist intelligence data analysts to detect unknown money laundering networks faster and more accurately, helping fight crimes more efficiently.
Machine learning techniques for fuel loss detection at service stations. This project aims to develop effective techniques to identify the sources of fuel losses, such as leaks and calibration errors in underground storage tanks at service stations. Monitoring fuel losses at service stations is influenced by many external factors which can be difficult to predict. The project expects to use machine learning to develop the techniques and test them with live data at service stations. The expected ....Machine learning techniques for fuel loss detection at service stations. This project aims to develop effective techniques to identify the sources of fuel losses, such as leaks and calibration errors in underground storage tanks at service stations. Monitoring fuel losses at service stations is influenced by many external factors which can be difficult to predict. The project expects to use machine learning to develop the techniques and test them with live data at service stations. The expected outcomes are a set of tailor-made machine learning techniques for effective fuel loss detection and a software suite that can be easily incorporated into the normal operation of service stations. This should reduce the costs to the petroleum industry from wasteful leaks and the environmental damage caused by these leaks. Read moreRead less
Intelligent real time multi-site controller for conserving energy in remote areas and in the resource industry. This project researches the issues in achieving demand response for electricity usage in remote regions of Australia through the use of smart meters and web of things framework to provide ubiquitous monitoring and control of devices, intelligent control systems to dynamically change energy usage patterns and community-based social network architecture. This will lead to several benefit ....Intelligent real time multi-site controller for conserving energy in remote areas and in the resource industry. This project researches the issues in achieving demand response for electricity usage in remote regions of Australia through the use of smart meters and web of things framework to provide ubiquitous monitoring and control of devices, intelligent control systems to dynamically change energy usage patterns and community-based social network architecture. This will lead to several benefits, such as (a) the strengthening of Australian business competitiveness in these regions by reducing energy costs and increasing energy trading, (b) reduction in ecological impact through smarter utilisation of energy and shifting to renewable sources, (c) encourage local generation and distribution of electricity where communities can trade excess energy.Read moreRead less
A Socially Conscious Smart IoT Platform for Water Quality Monitoring. This project will develop an intelligent remote water quality monitoring platform based on a new sensor network architectural paradigm. Expected outcomes include an artificially intelligent water quality monitoring system that is produced via a unique social enterprise business model. This approach will facilitate widespread remote water quality monitoring, leading to an enhanced understanding of the environment, whilst provid ....A Socially Conscious Smart IoT Platform for Water Quality Monitoring. This project will develop an intelligent remote water quality monitoring platform based on a new sensor network architectural paradigm. Expected outcomes include an artificially intelligent water quality monitoring system that is produced via a unique social enterprise business model. This approach will facilitate widespread remote water quality monitoring, leading to an enhanced understanding of the environment, whilst providing valuable training/education for the community stakeholders involved in the production of the system. The research outcome will be globally significant, enabling end users to meet key water quality objectives over time, and considerably increase productivity in the Australian agriculture/aquaculture industries.Read moreRead less
Conservation genomics of a critically endangered insect. This project aims to develop tools genotyping large animal genomes, focusing on the case of the Lord Howe Island stick insect, once thought to be extinct and now critically endangered. This project expects to generate molecular tools to monitor the genetic health the insect which has a large, complex and poorly understood genome. Expected outcomes include the development of a preservation and reintroduction strategy for the insect. This pr ....Conservation genomics of a critically endangered insect. This project aims to develop tools genotyping large animal genomes, focusing on the case of the Lord Howe Island stick insect, once thought to be extinct and now critically endangered. This project expects to generate molecular tools to monitor the genetic health the insect which has a large, complex and poorly understood genome. Expected outcomes include the development of a preservation and reintroduction strategy for the insect. This project will benefit ongoing conservation efforts, and is timely given the ongoing eradication of rats from Lord Howe Island where this species once lived. Read moreRead less
Mathematics and computing for integrated stockyard-centric management of mining supply chains. Blended mineral products, such as coal and iron ore, make a strong contribution to Australia's economy. Blending occurs in stockpiles, so to realise product value, stockyard and supply chain operational plans must align with blend targets. This project will provide new mathematical and computational planning tools to maximise this value.
Evolution of halophytes: a phyloinformatic approach to understanding and exploiting the traits underlying salt-tolerance in plants. Salinity is an increasing burden on the Australian economy & environment, with >2 million ha of salt-affected land, at an annual cost to agriculture over $187 million. One solution is to exploit naturally salt-tolerant plants to increase productive agricultural land and restore salt-affected environments. To do this, we must increase basic knowledge of the diversity ....Evolution of halophytes: a phyloinformatic approach to understanding and exploiting the traits underlying salt-tolerance in plants. Salinity is an increasing burden on the Australian economy & environment, with >2 million ha of salt-affected land, at an annual cost to agriculture over $187 million. One solution is to exploit naturally salt-tolerant plants to increase productive agricultural land and restore salt-affected environments. To do this, we must increase basic knowledge of the diversity & distribution of salt-tolerance. This project is the first to use DNA sequences from thousands of species to understand the evolution of salt-tolerance in order to provide the foundation for the development of new crop varieties, selection of species that can be developed for bioremediation, and identification of traits that will be profitable targets for breeding programs. Read moreRead less