Developing A Smart Farming Oriented Secure Data Infrastructure. Smart farming is the future of agriculture. However, recently the Federal Bureau of Investigation has issued a
warning that the lack of data privacy and cyber security mechanisms in the field runs a high risk of disaster. This
project aims to establish an innovative secure data infrastructure for smart farming including secure and automated smart farming supply-chain management. The deliverables of this project will include the cutt ....Developing A Smart Farming Oriented Secure Data Infrastructure. Smart farming is the future of agriculture. However, recently the Federal Bureau of Investigation has issued a
warning that the lack of data privacy and cyber security mechanisms in the field runs a high risk of disaster. This
project aims to establish an innovative secure data infrastructure for smart farming including secure and automated smart farming supply-chain management. The deliverables of this project will include the cutting-edge Blockchain based secure IoT data management and privacy-preserving smart contracts for smart farming supply-chain management. This data infrastructure will be the first of its kind which will lay a solid foundation for smart farming technology.Read moreRead less
Space-based space surveillance with robust computer vision algorithms. Space-based space surveillance with robust computer vision algorithms. This project aims to develop computer vision algorithms to detect man-made objects in space. These algorithms function on nanosatellite platforms, enabling space-based space surveillance. This technology is expected to provide always-on monitoring of the Earth's orbit to enhance existing defence infrastructure and protect vital space assets, including comm ....Space-based space surveillance with robust computer vision algorithms. Space-based space surveillance with robust computer vision algorithms. This project aims to develop computer vision algorithms to detect man-made objects in space. These algorithms function on nanosatellite platforms, enabling space-based space surveillance. This technology is expected to provide always-on monitoring of the Earth's orbit to enhance existing defence infrastructure and protect vital space assets, including communications and navigational satellites, in Earth’s orbit from collisions and covert sabotage. Increased space use by government and civilian agencies opens up opportunities for the space industry. This project is expected to develop Australia’s space surveillance capabilities, protect space assets and capture a growing market.Read moreRead less
Control and Optimization of Distributed Multiagent Formations. The project aims to develop a conceptual framework and algorithms for handling multi-vehicle formation control. Formations of unmanned airborne vehicles are currently used by defence forces and swarms of micro-vehicles are beginning to find increasing use in defence and for civilian emergency response, largely for surveillance purposes. Vehicles must cooperate to achieve a global formation objective, while respecting constraints on s ....Control and Optimization of Distributed Multiagent Formations. The project aims to develop a conceptual framework and algorithms for handling multi-vehicle formation control. Formations of unmanned airborne vehicles are currently used by defence forces and swarms of micro-vehicles are beginning to find increasing use in defence and for civilian emergency response, largely for surveillance purposes. Vehicles must cooperate to achieve a global formation objective, while respecting constraints on sensors, energy, and general mechanical limitations. The project aims to resolve the challenges of deciding what a single vehicle should observe, what and to where it should communicate, and how it should move in relation to what it sees. The conceptual framework developed may also be relevant in guiding future defence acquisitions and civilian applications.Read moreRead less
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
Early Career Industry Fellowships - Grant ID: IE230100672
Funder
Australian Research Council
Funding Amount
$470,337.00
Summary
Measuring real-time mental workload to improve our Defence capability. This project aims to develop a novel platform for measuring real-time variation in the cognitive workload of humans working with advanced Defence technologies. The project expects to combine innovative statistical techniques with cutting-edge psychological and neuroscience developments to measure and process workload-related brain activity in real-time. Expected outcomes of the project include an enhanced capacity to measure ....Measuring real-time mental workload to improve our Defence capability. This project aims to develop a novel platform for measuring real-time variation in the cognitive workload of humans working with advanced Defence technologies. The project expects to combine innovative statistical techniques with cutting-edge psychological and neuroscience developments to measure and process workload-related brain activity in real-time. Expected outcomes of the project include an enhanced capacity to measure and respond to cognitive workload in the field. This should provide significant benefits such as enhanced performance and safety outcomes, which will provide a strategic advantage to the Australian Defence Force by facilitating the development of advanced technologies that respond to the capabilities of the human user.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH210100030
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub in Intelligent Robotic Systems for Real-Time Asset Management. This hub aims to transform the way assets and infrastructure are managed by developing new capabilities for intelligent robotic systems for inspection, monitoring, and maintenance. The hub expects to generate new knowledge in robotics and associated fields including sensing, planning, data processing, and machine learning using interdisciplinary approaches and tight collaboration between academia and industry. The ex ....ARC Research Hub in Intelligent Robotic Systems for Real-Time Asset Management. This hub aims to transform the way assets and infrastructure are managed by developing new capabilities for intelligent robotic systems for inspection, monitoring, and maintenance. The hub expects to generate new knowledge in robotics and associated fields including sensing, planning, data processing, and machine learning using interdisciplinary approaches and tight collaboration between academia and industry. The expected outcomes are robots with the ability to autonomously collect data for integration into a digital twin that provides a real-time representation of the true state of a physical asset. The benefits include both improved asset management and establishing Australia as a leading manufacturer of advanced robotic systems.Read moreRead less
Cooperative control of networked systems with constraints. This project aims to address the challenge of networked systems in deploying teams of robotic agents. Control of the networked system is extremely difficult due to real world constraints imposed on each agent. This project will focus on motion constraints, equipment/capability constraints, and spatial constraints. In addition to theoretical advances, the wider scientific community will benefit directly, because the control algorithms dev ....Cooperative control of networked systems with constraints. This project aims to address the challenge of networked systems in deploying teams of robotic agents. Control of the networked system is extremely difficult due to real world constraints imposed on each agent. This project will focus on motion constraints, equipment/capability constraints, and spatial constraints. In addition to theoretical advances, the wider scientific community will benefit directly, because the control algorithms developed are expected to allow straightforward deployment of robotic teams. There are myriad applications for cooperative robotic agents, ranging from surveillance, to environmental monitoring using underwater and aerial drone formations – with an array of benefits and impacts including economic, commercial and societal. The results are intended to ensure and cement Australia’s front-line position in the current technological revolution known as “Industry 4.0”.Read moreRead less
A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.
Discovery Early Career Researcher Award - Grant ID: DE170101081
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis t ....Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis to efficiently and precisely analyse large-scale programs according to clients’ needs, thereby allowing compilers to generate safe, reliable and secure code. This project is expected to advance value-flow analysis for industrial-sized software, improve software reliability and security, and benefit Australian software systems and industries.Read moreRead less
Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less