Advanced Sonar Sensing for Robotics. Robotics research is heavily dependent on fast, accurate, reliable and cheap sensors. Sonar sensing can fulfil these requirements in air and underwater environments. This project will advance this sensor technology by providing sonar with high-speed accurate measurement and classification capabilities that function on moving platforms. The sonar will adapt and monitor differing environmental conditions allowing the sensor data to be integrated better with ....Advanced Sonar Sensing for Robotics. Robotics research is heavily dependent on fast, accurate, reliable and cheap sensors. Sonar sensing can fulfil these requirements in air and underwater environments. This project will advance this sensor technology by providing sonar with high-speed accurate measurement and classification capabilities that function on moving platforms. The sonar will adapt and monitor differing environmental conditions allowing the sensor data to be integrated better with other sensors, such as laser and stereo vision. Interference rejection will be incorporated that will allow the sensor to operate in conjunction with other sonar. Applications of the technology will be robotic mapping, localisation, navigation and exploration.Read moreRead less
Estimation and Control of Noisy Riemannian Systems. Many application areas such as satellite control, computer vision, coordination of rigid bodies, require the estimation and control of systems subject to geometric constraints. Most current algorithms for doing this are deterministic and can fail catastrophically in the presence of noise. This project aims to provide:
(i) Methods for analysing and then redesigning deterministic algorithms to ensure stability in the presence of noise;
(ii) New ....Estimation and Control of Noisy Riemannian Systems. Many application areas such as satellite control, computer vision, coordination of rigid bodies, require the estimation and control of systems subject to geometric constraints. Most current algorithms for doing this are deterministic and can fail catastrophically in the presence of noise. This project aims to provide:
(i) Methods for analysing and then redesigning deterministic algorithms to ensure stability in the presence of noise;
(ii) New design methods that deal with noise in an optimal way;
(iii) Noise resistant methods for distributed consensus seeking systems and cooperative control systems.
The outcomes will advance and benefit spatio-temporal data analysis and coordination in areas such as transport, health and video-security.Read moreRead less
Point processes system identification under simultaneity. Neuroscientists study neuronal brain dynamics of mammals via recordings from scores of tiny electrodes. But analysing these experiments is a problem because current methods cannot handle the common case where neurons discharge simultaneously. This project aims to provide powerful new tools to overcome this bottleneck.
Riemannian System Identification. A growing number of applications such as satellite attitude estimation, pose estimation in computer vision and direction estimation in statistics require the study of random processes in Riemannian manifolds and Lie Groups. This project will provide: methods for the construction/ numerical simulation of such processes; methods of system identification and their asymptotic performance analysis; and, algorithms for process state estimation.
Modeling stochastic systems in Riemannian manifolds. This project aims to develop new statistical signal processing and control engineering algorithms and tools that will enable tracking of objects remotely on land and in space. A growing number of applications require the study of random processes in Riemannian manifolds, that is processes that evolve subject to a geometric constraint. This project aims to provide methods for the numerical simulation of such processes, methods of online and off ....Modeling stochastic systems in Riemannian manifolds. This project aims to develop new statistical signal processing and control engineering algorithms and tools that will enable tracking of objects remotely on land and in space. A growing number of applications require the study of random processes in Riemannian manifolds, that is processes that evolve subject to a geometric constraint. This project aims to provide methods for the numerical simulation of such processes, methods of online and offline system identification from data on such processes and asymptotic performance analysis; and algorithms for process state estimation that obeys the geometry. The outcomes will advance and benefit spatio-temporal data analysis in areas such as transport, health and video-security.Read moreRead less
Vector network system identification. This machine learning project aims to provide more reliable ways to identify the structure and function of dynamic networks from both continuous and discrete network data. The project will use all the data and create principled new metrics. This could enable early diagnosis of network faults across a range of applications for example in power systems or diseased human brains. It could also enable discovery of critical functional subnetworks affecting reliabl ....Vector network system identification. This machine learning project aims to provide more reliable ways to identify the structure and function of dynamic networks from both continuous and discrete network data. The project will use all the data and create principled new metrics. This could enable early diagnosis of network faults across a range of applications for example in power systems or diseased human brains. It could also enable discovery of critical functional subnetworks affecting reliable operation in large complex human systems (such as financial systems) or natural systems (such as gene regulatory networks).Read moreRead less
DC optimisation based synthesis of systems in control, signal processing and wireless communication network. The conceptual advances with new optimisation based solvers to be developed in the area of control, signal processing and wireless communication. Major benefits of this project will be its direct applications to renewable technologies in automobile, health care, digital and communication network industries.
Real-time signal processing and distributed robotic telescope networking for co-detection of gravitational waves and their optical counterparts. An international collaboration of scientists will employ a global network of telescopes and detectors to search for ripples in space-time. The project will use novel computational tools to study exotic phenomena in the distant Universe.
Distributed signal processing and control in sensor networks. Distributed sensor networks find wide applications in smart electricity grids, traffic systems, industrial plants and security systems. Massive amounts of data need be collected, transmitted and processed. This project aims to develop advanced techniques for the monitoring, diagnosis and control for these networks.