Fast Signal Processing and Control Algorithms for Complex Hierarchical Systems. Complex dynamical behaviour is inherent to many real-world systems including telecommunications networks, financial markets and biological systems. High performance signal processing and control algorithms for such large-scale, complex systems are computationally very expensive in general. An important class of large-scale Markovian models arising in many applications shows a remarkable hierarchical property, display ....Fast Signal Processing and Control Algorithms for Complex Hierarchical Systems. Complex dynamical behaviour is inherent to many real-world systems including telecommunications networks, financial markets and biological systems. High performance signal processing and control algorithms for such large-scale, complex systems are computationally very expensive in general. An important class of large-scale Markovian models arising in many applications shows a remarkable hierarchical property, displaying strong interactions within certain clusters of states and weak interactions among these clusters. By utilizing this property, the proposed project will design and analyze novel reduced-complexity signal processing and control algorithms with rigorous performance guarantees. In addition, this project will explore possibilities of making these algorithms hierarchical such that they are easy to implement through decentralization.Read moreRead less
Stochastic Sensor Scheduling in Statistical Signal Processing. In several statistical signal processing applications, due to computational or communication constraints, at each time instant one can use only a few out of several possible noisy (stochastic) sensors. The stochastic sensor scheduling problem deals with how to dynamically choose which group of sensors to pick at each time instant. This project involves research in sensor scheduling for widely used stochastic dynamical systems such as ....Stochastic Sensor Scheduling in Statistical Signal Processing. In several statistical signal processing applications, due to computational or communication constraints, at each time instant one can use only a few out of several possible noisy (stochastic) sensors. The stochastic sensor scheduling problem deals with how to dynamically choose which group of sensors to pick at each time instant. This project involves research in sensor scheduling for widely used stochastic dynamical systems such as Hidden Markov Models and Jump Markov Linear Systems. It focuses on the design and analysis of stochastic control algorithms such as dynamic programming and simulation based randomized methods. The research will lead to an integrated theory incorporating stochastic control, statistical signal processing and combinatorial optimization. We will also apply the resulting techniques to tracking maneuvering targets given noisy observations.Read moreRead less
Special Research Initiatives - Grant ID: SR0354553
Funder
Australian Research Council
Funding Amount
$30,000.00
Summary
Network on Control, Dynamics and Systems (NCDS). Control systems theory provides principles and methods for design of complex engineering systems that automatically maintain desired performance despite changes in their environment (e.g. autopilot in an aircraft). This field is facing many new exciting challenges at the dawn of new millenium, such as design of complex engineering systems in possibly networked, asynchronous and distributed environments. The network will play a major role in addres ....Network on Control, Dynamics and Systems (NCDS). Control systems theory provides principles and methods for design of complex engineering systems that automatically maintain desired performance despite changes in their environment (e.g. autopilot in an aircraft). This field is facing many new exciting challenges at the dawn of new millenium, such as design of complex engineering systems in possibly networked, asynchronous and distributed environments. The network will play a major role in addressing these challenges by providing a national research focus, facilitating collaboration and the sharing of people and ideas. By delivering a National Graduate School, the network will enhance learning conditions for graduate students. Moreover, it will provide an important catalyst between Australian universities and industry. This initiative will be essential in assessing the present state of control research in Australia and drafting a detailed plan for the network's leading research role in the future. Read moreRead less