Efficient Algorithms for Multiple Object Filtering using Stochastic Geometry. The outcomes of this project will enhance our ability to harness advances in sensing and computing technologies and develop automated systems which facilitate rapid and reliable detection and monitoring of potential threats in our air, sea, and land space. Such systems assist our defence personnel in the event of a threat to implement measured and effective responses, and ultimately enhance Australia's operational adva ....Efficient Algorithms for Multiple Object Filtering using Stochastic Geometry. The outcomes of this project will enhance our ability to harness advances in sensing and computing technologies and develop automated systems which facilitate rapid and reliable detection and monitoring of potential threats in our air, sea, and land space. Such systems assist our defence personnel in the event of a threat to implement measured and effective responses, and ultimately enhance Australia's operational advantage, in line with the national research priority of 'Safeguarding Australia' and its associated priority goals. The developed technologies also have significant commercial potential which benefit Australian industries in areas such as robotics, automotive safety and biomedical engineering.Read moreRead less
Model-Reduction Techniques for Control, Communication and Circuits. Model reduction is an important area of study in the analysis and design of dynamical systems. Its objective is to obtain a low-order model given a high-order system model such that the low-order model closely approximates the input-output behaviour of the original high-order system. Although theory and application of model reduction is well developed, there are many unresolved issues such as efficient model reduction techniq ....Model-Reduction Techniques for Control, Communication and Circuits. Model reduction is an important area of study in the analysis and design of dynamical systems. Its objective is to obtain a low-order model given a high-order system model such that the low-order model closely approximates the input-output behaviour of the original high-order system. Although theory and application of model reduction is well developed, there are many unresolved issues such as efficient model reduction techniques for large-scale circuit simulation and communication applications, frequency-weighted model reduction techniques for controller-design applications, and error bounds for the reduction techniques. The project aims to address these issues.Read moreRead less