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
ARC Complex Open Systems Research Network. Complexity is the common frontier in the physical, biological and social sciences. This Network will link specialists in all three sciences through five generic conceptual and mathematical theme activities. It will promote research into how subsystems self-organise into new emergent structures when assembled into an open, non-equilibrium system. Outcomes will include new technologies and software tools and deeper understanding of fundamental questions i ....ARC Complex Open Systems Research Network. Complexity is the common frontier in the physical, biological and social sciences. This Network will link specialists in all three sciences through five generic conceptual and mathematical theme activities. It will promote research into how subsystems self-organise into new emergent structures when assembled into an open, non-equilibrium system. Outcomes will include new technologies and software tools and deeper understanding of fundamental questions in science. An essential function of the network will be introducing researchers end users to new tools and broadening the horizons of graduate students.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0347797
Funder
Australian Research Council
Funding Amount
$263,000.00
Summary
A Versatile High-resolution X-ray Diffractometer for Materials Research. The aim of this project is to establish a state-of-the-art triple-axis x-ray diffraction facility capable of non-destructively analysing complex semiconductor materials and structures investigated by all Australian semiconductor-growing groups. Growers and device engineers will be able to control growth processes accurately and correlate device performance with structural analysis. Modern triple-axis instruments can also b ....A Versatile High-resolution X-ray Diffractometer for Materials Research. The aim of this project is to establish a state-of-the-art triple-axis x-ray diffraction facility capable of non-destructively analysing complex semiconductor materials and structures investigated by all Australian semiconductor-growing groups. Growers and device engineers will be able to control growth processes accurately and correlate device performance with structural analysis. Modern triple-axis instruments can also be used for high-resolution texture analysis and surface reflectivity measurements on numerous types of materials. Thus chemists, geologists, and materials scientists with interests outside of the semiconductor growth community will gain substantial benefit from this instrument for the investigation of materials of technological and economic importance.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
Stochastic Analysis with a View to Applications in Financial Risk Processes. Recent decades have seen explosive growth in applications of probability theory and statistics to the modelling of risk in finance and insurance. An intensive theoretical investigation into passage time and other problems for Levy and other continuous time processes will be applied to financial risk analyses. Related investigations will involve perpetuities and stochastic volatility models for price series. Outcomes ....Stochastic Analysis with a View to Applications in Financial Risk Processes. Recent decades have seen explosive growth in applications of probability theory and statistics to the modelling of risk in finance and insurance. An intensive theoretical investigation into passage time and other problems for Levy and other continuous time processes will be applied to financial risk analyses. Related investigations will involve perpetuities and stochastic volatility models for price series. Outcomes will include the development of new theory in probability and statistics, the initiation and reinforcement of collaborative ties with major international research figures, and the fostering of contacts with the finance industry.Read moreRead less
Seasonal adjustment using disaggregated short time span data. Seasonally adjusted economic and social times series are vital information used by governments and businesses in decision making. This project will develop statistical methods to estimate and remove seasonal factors from economic and social time series using finely disaggregated data for a relatively small number of time periods. This will enable better and quicker estimation of seasonal factors when new series are introduced or there ....Seasonal adjustment using disaggregated short time span data. Seasonally adjusted economic and social times series are vital information used by governments and businesses in decision making. This project will develop statistical methods to estimate and remove seasonal factors from economic and social time series using finely disaggregated data for a relatively small number of time periods. This will enable better and quicker estimation of seasonal factors when new series are introduced or there a major changes to existing series, improving the analysis of such series and the decisions based on them.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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0346878
Funder
Australian Research Council
Funding Amount
$190,000.00
Summary
GeoWulf: An Inference Engine for Complex Earth Systems. The project is to build a `Beowulf' cluster as a platform for solving
complex data inference problems in the Earth sciences, and in
particular the fields of thermochronology, seismology, crustal and
mantle dynamics, and landform evolution. A Beowulf cluster is a
network-linked set of commonly available `off-the-shelf' PC-computers
configured to give unprecedented performance/cost ratio. Projects
using the Beowulf facility will combine ....GeoWulf: An Inference Engine for Complex Earth Systems. The project is to build a `Beowulf' cluster as a platform for solving
complex data inference problems in the Earth sciences, and in
particular the fields of thermochronology, seismology, crustal and
mantle dynamics, and landform evolution. A Beowulf cluster is a
network-linked set of commonly available `off-the-shelf' PC-computers
configured to give unprecedented performance/cost ratio. Projects
using the Beowulf facility will combine state-of-the-art computational
techniques recently developed at ANU, and high quality data sets
collected over the past decade to address fundamental questions in
the Geosciences.Read moreRead less
ARC Research Network for a Secure Australia. The Research Network for a Secure Australia (RNSA) is a multi-disciplinary collaboration established to strengthen Australia's research capacity for protecting critical infrastructure from natural or human-caused disasters including terrorist acts. The RNSA will facilitate a knowledge-sharing network for research organisations, government and the private sector to develop research tools and methods to mitigate emerging safety and security issues relat ....ARC Research Network for a Secure Australia. The Research Network for a Secure Australia (RNSA) is a multi-disciplinary collaboration established to strengthen Australia's research capacity for protecting critical infrastructure from natural or human-caused disasters including terrorist acts. The RNSA will facilitate a knowledge-sharing network for research organisations, government and the private sector to develop research tools and methods to mitigate emerging safety and security issues relating to critical infrastructure. World-leaders with extensive national and international linkages in relevant scientific, engineering and technological research will lead this collaboration. The RNSA will launch various activities to foster research collaboration and nurture young investigators.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.