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
Industrial Transformation Research Hubs - Grant ID: IH130200012
Funder
Australian Research Council
Funding Amount
$2,748,358.00
Summary
ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). This Research Hub aims to undertake simultaneous modelling of deep Earth and surface processes, spanning basin scales to individual sediment grains. The Hub will develop and apply cutting-edge basin simulation approaches to transform the seeding and testing of basin exploration models, extending their viability to complex, ....ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). This Research Hub aims to undertake simultaneous modelling of deep Earth and surface processes, spanning basin scales to individual sediment grains. The Hub will develop and apply cutting-edge basin simulation approaches to transform the seeding and testing of basin exploration models, extending their viability to complex, inaccessible remote and deep exploration targets. The Hub will fuse multidimensional data into five dimensional basin models (space and time, with uncertainty estimates) by coupling the evolution of mantle flow, crustal deformation, erosion and sedimentary processes, achieving a quantum leap in basin modelling and petroleum systems analysis.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
Industrial Transformation Training Centres - Grant ID: IC180100030
Funder
Australian Research Council
Funding Amount
$3,925,357.00
Summary
ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching ....ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching objectives are to enable development and adoption of new practices to improve productivity and asset reliability for industry and to foster a new maintenance technology service sector for national and international markets.Read moreRead less
Reducing direct greenhouse gas emissions from urban wastewater systems. This project aims to develop a systematic framework for water utilities to monitor and reduce direct greenhouse gas (GHG) emissions from wastewater systems. A standardised monitoring protocol will be developed to conduct an unprecedented nationwide sampling campaign. The obtained data, with microbial characterisation and mechanism analysis, will be used to develop novel models for accurate prediction of GHG emissions. Expect ....Reducing direct greenhouse gas emissions from urban wastewater systems. This project aims to develop a systematic framework for water utilities to monitor and reduce direct greenhouse gas (GHG) emissions from wastewater systems. A standardised monitoring protocol will be developed to conduct an unprecedented nationwide sampling campaign. The obtained data, with microbial characterisation and mechanism analysis, will be used to develop novel models for accurate prediction of GHG emissions. Expected outcomes include protocol to accurately monitor emissions, models to predict emission under various conditions, and mitigation guideline for typical plant configurations. The anticipated benefit is a significant reduction in GHG emissions from urban water industry and support it to meet net-zero-emission goal by 2050.Read moreRead less
ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this te ....ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this team with the foremost international authorities and leading industry players in the area of sensor networks. This research network will guide collaborative research that will ensure Australia to play a world leading role in sensor network development and implementation.
Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.
ARC Molecular and Materials Structure Research Network. The Network will build powerful e-Science resources for the structural sciences. Collaborative remote access will be developed for sophisticated instrumentation, including instruments planned for the Replacement Research Reactor and Australian Synchrotron. A structure database service with cross disciplinary content and versatile visualisation and analysis capabilities will further exemplify smart information use. The internet services will ....ARC Molecular and Materials Structure Research Network. The Network will build powerful e-Science resources for the structural sciences. Collaborative remote access will be developed for sophisticated instrumentation, including instruments planned for the Replacement Research Reactor and Australian Synchrotron. A structure database service with cross disciplinary content and versatile visualisation and analysis capabilities will further exemplify smart information use. The internet services will ultimately harness the Grid, enabling linkage into other national and international Grid systems. Encompassing physics, computer science, applied mathematics, chemistry and biochemistry, and catalysing interaction across these disciplines, the MMSN will impact all five National Research Priority 3 goals.Read moreRead less
Photogrammetric Reconstruction for Underwater Virtual Heritage Experiences. This project aims to enable significant underwater cultural heritage sites such as shipwrecks to be recreated in immersive underwater virtual heritage experiences. Photogrammetric 3D reconstruction techniques will be used to generate complex digital 3D models of shipwreck sites from hundreds of thousands of underwater images. This will allow vivid experiences to be created which explain the stories of these wrecks. The p ....Photogrammetric Reconstruction for Underwater Virtual Heritage Experiences. This project aims to enable significant underwater cultural heritage sites such as shipwrecks to be recreated in immersive underwater virtual heritage experiences. Photogrammetric 3D reconstruction techniques will be used to generate complex digital 3D models of shipwreck sites from hundreds of thousands of underwater images. This will allow vivid experiences to be created which explain the stories of these wrecks. The project will conduct audience engagement studies to recommend the most appropriate methods to implement underwater virtual heritage experiences for Australian audiences. The sites which will be used as test datasets are some of the most significant Australian shipwreck sites, including HMAS Sydney (II) and HMAS AE1.Read moreRead less