Linkage Infrastructure, Equipment And Facilities - Grant ID: LE120100006
Funder
Australian Research Council
Funding Amount
$600,000.00
Summary
An adaptable and dedicated linear accelerator for medical radiation research. Leading radiation scientists developing innovative methods and devices for treating cancer patients will collaborate in future research using this highly adaptable linear accelerator for medical radiation research. Innovations in tumour targeting, better patient safety, new medical devices and improved cancer outcomes are expected.
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
Ring constructions and algorithms for enhancing performance of BCH codes. BCH codes form a major class of codes used in modern communication systems. The aim of this project is to enhance the efficiency of this class of codes by combining them in constructions enabling correction of deletion and insertion errors, and develop efficient implementations of encoding and decoding algorithms incorporating soft decision methods for enhanced error correction. Significance of the project is explained by ....Ring constructions and algorithms for enhancing performance of BCH codes. BCH codes form a major class of codes used in modern communication systems. The aim of this project is to enhance the efficiency of this class of codes by combining them in constructions enabling correction of deletion and insertion errors, and develop efficient implementations of encoding and decoding algorithms incorporating soft decision methods for enhanced error correction. Significance of the project is explained by the role of fast, secure and reliable communications in modern information and communication technology. Expected outcomes include new efficient algorithms and commercial modules available for symbolic computation systems with applications in telecommunications industry.
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Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data ar ....Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data are needed. This project will design, implement and evaluate visualisation methods for massive multivariate network data sets. This research is expected to be used by Australian software development, biotechnology and security companies to exploit their data.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less
Machine Assisted, Multi-scale Spatial and Temporal Observation and Modeling of Marine Benthic Habitats. The Integrated Marine Observing System (IMOS) science plans include sampling campaigns reliant on Autonomous Underwater Vehicle (AUV) Facility data and designed to address the issues of marine biodiversity quantification and assurance. The proposed research will directly enhance the effectiveness of these programs by speeding labour-intensive analyses, aggregating the results, and searching f ....Machine Assisted, Multi-scale Spatial and Temporal Observation and Modeling of Marine Benthic Habitats. The Integrated Marine Observing System (IMOS) science plans include sampling campaigns reliant on Autonomous Underwater Vehicle (AUV) Facility data and designed to address the issues of marine biodiversity quantification and assurance. The proposed research will directly enhance the effectiveness of these programs by speeding labour-intensive analyses, aggregating the results, and searching for ecological patterns on a national scale that would be difficult to identify using traditional approaches tuned to process-scale studies. Australian society stands to benefit by virtue of improved large-scale models of ecosystem function and reduced cost for conducting marine ecosystem investigations.Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less
Powerful knowledge: mapping out standards of teachers' knowledge for teaching mathematics and English to achieve the goals of the curriculum. Setting standards is an important step to raising teacher quality. This project will establish benchmarks for teachers' knowledge at all levels of schooling in two key areas of the curriculum: English and mathematics.
Modelling with data: Advancing STEM in the primary curriculum. Improving the nation's skills in Science, Technology, Engineering, and Mathematics (STEM) remains a continuing concern, especially given the decline in international test results. The project aims to introduce a new approach to promoting this learning across grades 3-6 through modelling with data. With a focus on inquiry processes involving data variation and uncertainty within STEM-based contexts, the project aims to develop the imp ....Modelling with data: Advancing STEM in the primary curriculum. Improving the nation's skills in Science, Technology, Engineering, and Mathematics (STEM) remains a continuing concern, especially given the decline in international test results. The project aims to introduce a new approach to promoting this learning across grades 3-6 through modelling with data. With a focus on inquiry processes involving data variation and uncertainty within STEM-based contexts, the project aims to develop the important mathematical and statistical literacies needed for lifting student achievements. In advancing both theory and practice, the project aims to contribute to knowledge of primary students' capabilities for STEM problem solving and ways of enhancing implementation of the Australian Curriculum.Read moreRead less