Non-invasive prediction of adverse neural events using brain wave activity. This project aims to develop intelligent decision-making systems for non-invasive identification of adverse neural events (fatigue/freezing of gait) through real-time monitoring of brain wave activity. Analyses of the effectiveness of the changes in physiological parameters associated with electroencephalography (EEG) signals, advanced biomedical instrumentation, and optimal computational intelligence will form a basis f ....Non-invasive prediction of adverse neural events using brain wave activity. This project aims to develop intelligent decision-making systems for non-invasive identification of adverse neural events (fatigue/freezing of gait) through real-time monitoring of brain wave activity. Analyses of the effectiveness of the changes in physiological parameters associated with electroencephalography (EEG) signals, advanced biomedical instrumentation, and optimal computational intelligence will form a basis for the development of platform technology capable of monitoring and detection of neural health status. Success is expected to yield a new generation of smart dynamic non-invasive systems that will be critical for developing effective solutions to counter life threating conditions for a large cross section of the Australian population.Read moreRead less
Long-term Cloud Service Composition. This project proposes an economic model-based framework for the selection and composition of cloud services, thus creating an efficient market for cloud consumers and providers. The project will use economic models that incorporate a range of quality of service (QoS) parameters as a key driver for optimising the selection of cloud services and the acceptance of consumer requests. The main outcomes of this project aim to increase efficiencies in the cloud mark ....Long-term Cloud Service Composition. This project proposes an economic model-based framework for the selection and composition of cloud services, thus creating an efficient market for cloud consumers and providers. The project will use economic models that incorporate a range of quality of service (QoS) parameters as a key driver for optimising the selection of cloud services and the acceptance of consumer requests. The main outcomes of this project aim to increase efficiencies in the cloud market, benefiting consumers and providers.Read moreRead less
Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart g ....Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart grids and optimal charging and discharging tasks in a large network of electric vehicles, helping Australian power industry improve efficiency and security, as well as training the next generation scientists and engineers for Australia in this emerging field.Read moreRead less
Data structures which change with time, a machine learning approach. Visibility of web pages, based on page importance, on the Internet controls their accessibility by users which is critical for e-Commerce applications. The page importance depends on its contents and its link structure to other web pages, both of which can be time varying. This project proposes a novel model in which time varying aspects of the changes to contents and their link structures are captured, thus allowing us a bette ....Data structures which change with time, a machine learning approach. Visibility of web pages, based on page importance, on the Internet controls their accessibility by users which is critical for e-Commerce applications. The page importance depends on its contents and its link structure to other web pages, both of which can be time varying. This project proposes a novel model in which time varying aspects of the changes to contents and their link structures are captured, thus allowing us a better understanding of how these influence the page importance over time. It will also allow us insight on how to improve the visibility of web pages.Read moreRead less
Multiobjective Memetic Algorithms for Multi-task Symbolic Regression. This project aims at developing the new generation of symbolic regression methods using a yet unexplored way to represent mathematical functions. We will use memetic algorithms to create mathematical models for symbolic regression. Our memetic computing approach will be data-driven and will use multi-objective optimization and multi-task evolutionary computation for symbolic regression, addressing a core need of many areas of ....Multiobjective Memetic Algorithms for Multi-task Symbolic Regression. This project aims at developing the new generation of symbolic regression methods using a yet unexplored way to represent mathematical functions. We will use memetic algorithms to create mathematical models for symbolic regression. Our memetic computing approach will be data-driven and will use multi-objective optimization and multi-task evolutionary computation for symbolic regression, addressing a core need of many areas of science and technology. A large number of datasets will be investigated to benchmark the new methods. The expected outcomes will help support our national priorities with new data analytic capabilities. With a strong and interdisciplinary team in three continents, the project will attract international collaboration. Read moreRead less
A novel and efficient approach for optimisation involving iterative solvers. Computationally expensive simulations involving iterative solvers are increasingly being used in industry to assess performance of products and processes. Repeated use of such simulations is necessary to identify optimum solutions. Even with today's computing power, many such tasks remain computationally prohibitive. This project presents a novel approach to solve optimisation problems involving iterative solvers with l ....A novel and efficient approach for optimisation involving iterative solvers. Computationally expensive simulations involving iterative solvers are increasingly being used in industry to assess performance of products and processes. Repeated use of such simulations is necessary to identify optimum solutions. Even with today's computing power, many such tasks remain computationally prohibitive. This project presents a novel approach to solve optimisation problems involving iterative solvers with limited computing budget. A wide range of industries involved in product and process design would gain a significant competitive advantage from this unique technical innovation. In addition, this technology will be invaluable to uncover and understand complex natural phenomena.Read moreRead less
Temperature sensitivity of soil respiration and its components. This project aims to demonstrate how temperate evergreen forests could buffer against climate change. Soil respiration returns around half the carbon taken up by forests to the atmosphere. This project will characterise and quantify how microbes and roots in soils depend on temperature and substrate supply, and so predict how rising temperatures and drought will affect forests as natural carbon sequestration sinks. This project will ....Temperature sensitivity of soil respiration and its components. This project aims to demonstrate how temperate evergreen forests could buffer against climate change. Soil respiration returns around half the carbon taken up by forests to the atmosphere. This project will characterise and quantify how microbes and roots in soils depend on temperature and substrate supply, and so predict how rising temperatures and drought will affect forests as natural carbon sequestration sinks. This project will resolve the roles of environmental drivers of soil respiration across forests; integrate mechanistic understanding of differing plant and microbial responses to temperature within a common modelling framework; and evaluate the implications of this knowledge in predictions of climatic impacts on terrestrial carbon cycling.Read moreRead less
Tree-mediated methane fluxes: A new frontier in the global carbon cycle. Methane is an extremely potent greenhouse gas. Recent evidence suggests that tree-mediated fluxes may be a significant, but overlooked source of methane to the atmosphere. This project aims to quantify the magnitude and drivers of tree-mediated methane fluxes from Australia’s dominant forest types. Innovatively, we will be using a novel combination of empirical field based measurements, gas tracer experiments, microbial ana ....Tree-mediated methane fluxes: A new frontier in the global carbon cycle. Methane is an extremely potent greenhouse gas. Recent evidence suggests that tree-mediated fluxes may be a significant, but overlooked source of methane to the atmosphere. This project aims to quantify the magnitude and drivers of tree-mediated methane fluxes from Australia’s dominant forest types. Innovatively, we will be using a novel combination of empirical field based measurements, gas tracer experiments, microbial analysis and modelling methods. Expected outcomes are a mechanistic understanding of tree-mediated methane fluxes, helping to constrain regional, national and global methane budgets. The results of this study will help inform publicly funded greenhouse gas abatement strategies, ensuring a maximal return on investment.Read moreRead less
A balancing act: Resolving coastal wetland water, carbon and solute fluxes. Coastal wetlands offer an impressive capacity to regulate the Earth’s climate by altering the way carbon dioxide is extracted from the atmosphere and stored while simultaneously influencing the water cycle, thus providing ecosystem services such as carbon storage, abating flood waters, improving water quality and protecting the coastline from sea level rise. This project aims to address the current gaps in understanding .... A balancing act: Resolving coastal wetland water, carbon and solute fluxes. Coastal wetlands offer an impressive capacity to regulate the Earth’s climate by altering the way carbon dioxide is extracted from the atmosphere and stored while simultaneously influencing the water cycle, thus providing ecosystem services such as carbon storage, abating flood waters, improving water quality and protecting the coastline from sea level rise. This project aims to address the current gaps in understanding the critical exchanges of water and greenhouse gases (GHGs) combining field methodologies and hydrological models, under different climatic conditions. The intended outcomes will benefit management of GHG emissions, coastal flooding and vulnerable groundwater dependent habitats.Read moreRead less
Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by p ....Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by providing solutions meeting each individual learner’s needs. The main outcome will consolidate a cloud based micro learning framework through integrating a group of novel algorithms.Read moreRead less