ARC Research Network in Enterprise Information Infrastructure. EII targets consolidated research towards the comprehensive development & establishment of advanced information infrastructures. Its prime purpose is to provide a forum for intellectual exchange by diverse yet complementary research groups, to address the fundamental research problems faced by scientific & business communities when dealing with deployment of information technology to globally distributed, and data intensive environme ....ARC Research Network in Enterprise Information Infrastructure. EII targets consolidated research towards the comprehensive development & establishment of advanced information infrastructures. Its prime purpose is to provide a forum for intellectual exchange by diverse yet complementary research groups, to address the fundamental research problems faced by scientific & business communities when dealing with deployment of information technology to globally distributed, and data intensive environments. EII will address 3 tightly coupled research themes: Ability to interoperate across existing heterogenous platforms & applications; Efficient processing of very large data sets; Technology adoption & impact. Generic results will be applicable to e-science and large business information systems installations.Read moreRead less
Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less
The Airport Metropolis: Managing the Interfaces. The project aims to develop coordinated and equitable decision-making to ensure that airport-urban development balances economic, social and environmental issues and produces a sustainable regional (and national) competitive advantage that is both secure and resilient. The project will develop modelling technologies for the innovative management of data to ensure efficient and resilient infrastructure coordination. The outputs will enable an open ....The Airport Metropolis: Managing the Interfaces. The project aims to develop coordinated and equitable decision-making to ensure that airport-urban development balances economic, social and environmental issues and produces a sustainable regional (and national) competitive advantage that is both secure and resilient. The project will develop modelling technologies for the innovative management of data to ensure efficient and resilient infrastructure coordination. The outputs will enable an open planning process whereby all stakeholders are able to provide informed input into decision-making. Strategic decision-making, based on increased certainty about future airport and regional planning and development will improve conditions for growth in a range of industries.Read moreRead less
Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to ....Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to represent the extracted semantic relationships and novel linkage-analysis based algorithms will be developed for ranking objects. The results from this project will underpin many critical applications such as healthcare.Read moreRead less
Active Team – Examining An Online Social Networking Intervention To Increase Physical Activity In Controlled (RCT) And Ecological (ET) Settings
Funder
National Health and Medical Research Council
Funding Amount
$814,041.00
Summary
Lifestyle diseases, such as heart disease and diabetes, are key health problems facing Australia. Effective, low-cost, mass-reach physical activity interventions are urgently needed. This project uses online social networks to deliver an innovative physical activity intervention. This project will determine how effective the software is in changing people’s lifestyle over 12 months, and whether viral marketing techniques can be used to disseminate the program on a mass scale.
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
Semantic change detection through large-scale learning. This project aims to develop technologies which understand the content of images before higher-level analysis is performed. This approach is intended to allow more accurate and reliable decisions to be made using automated image analysis than has previously been possible. The project will particularly investigate the detection of change in the contents of an image.
ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies th ....ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies that will allow robots to see as we do, and overcome the last barrier to the ubiquitous deployment of robots into society for the benefit of all.Read moreRead less
Congestion recovery and optimisation of patient flows. Australian public hospitals often experience congestion due to growing demand and limited resources, resulting in disruptions in service delivery and risks in quality of care. This project will apply advanced techniques and methodologies from mathematical sciences and computer modelling to alleviate this important healthcare delivery problem.
Special Research Initiatives - Grant ID: SR0354735
Funder
Australian Research Council
Funding Amount
$10,000.00
Summary
Australian Network on Microelectronics, Optoelectronics and Microelectromechanical Systems. The Network will encompass semiconductor microelectronics, optoelectronics, sensors and microelectromechanical systems (MEMS). Fundamental research in these areas enables the technological advances that underpin rapidly developing industries such as information and telecommunications technologies, defence, aerospace, medicine, and remote sensing. Exciting challenges exist in designing new devices that exp ....Australian Network on Microelectronics, Optoelectronics and Microelectromechanical Systems. The Network will encompass semiconductor microelectronics, optoelectronics, sensors and microelectromechanical systems (MEMS). Fundamental research in these areas enables the technological advances that underpin rapidly developing industries such as information and telecommunications technologies, defence, aerospace, medicine, and remote sensing. Exciting challenges exist in designing new devices that exploit unique semiconductor systems and technologies. By sharing capabilities and resources (both capital and human), the network will enable the issues associated with such novel materials and devices to be addressed in a targeted manner. The network will also guarantee the ongoing future of research in the area by actively involving early career researchers and postgraduate students.Read moreRead less