Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed ....Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed methodology enables quantification of confidence in the predictions. This will provide ship owners, directly to their vessels and/or at the fleet management centres, information such as weather reports, reliable collision/no-collision warnings and avoidance strategies, on-the-fly. Read moreRead less
Statistical analyses for spatial organisation in T cell signalling networks. This project aims to reveal how nanoscale spatial organisation encodes plasticity in the T cell signalling network, and how T cells exploit this plasticity to regulate sensitivity to antigens. In adoptive immunity, T cells respond appropriately to any given antigen, but how they make decisions is unclear. This project will define how nanoscale spatial organisation of signalling molecules shapes signalling strength and p ....Statistical analyses for spatial organisation in T cell signalling networks. This project aims to reveal how nanoscale spatial organisation encodes plasticity in the T cell signalling network, and how T cells exploit this plasticity to regulate sensitivity to antigens. In adoptive immunity, T cells respond appropriately to any given antigen, but how they make decisions is unclear. This project will define how nanoscale spatial organisation of signalling molecules shapes signalling strength and plasticity in the T cell antigen receptor (TCR) network; and infer rules linking spatial organisation and signalling activities in intact T cells. Contextualising the TCR signalling network is expected to reveal the origin and use of network plasticity for T cell decision-making. Such information could be invaluable for the design of vaccines and immune-modulating drugs.Read moreRead less
Implementation of cognitive radar techniques in resource limited radar systems. Cognitive radar technology enables a multiple functional radar system to be built on a single chip, to be of high efficiency and low cost. Waveform design and scheduling play a key role in such a system. This project will investigate and design waveforms and scheduling methods for building a real cognitive radar system in the extremely high frequency band.
A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, th ....A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, this project seeks to develop new distributed solutions for statistical estimation, which are specifically designed for dynamic systems with multiple object states, and are inherently scalable and robust. The potential benefits include new technologies for smart cities, autonomous infrastructure, and digital productivity.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102388
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
From Bayesian filtering to smoothing and prediction for multiple object systems. This project will develop new and improved algorithms for tracking multiple targets, such as tanks, submarines or planes, using the state of the art in mathematical and computational design. These will enable more efficient and accurate technologies for defence related applications including intelligence, surveillance and reconnaissance.
Parameter estimation for multi-object systems. Parameter estimation in multi-object system is essential to the application of multi-object filtering to a wider range of practical problems with social and commercial benefits. This project develops the necessary parameter estimation techniques for complete 'plug-and-play' multi-object filtering solutions that facilitates widespread applications.
Multi-object Estimation for Live-Cell Microscopy. The objective of this project is to develop new tools for the inference of biological information from live-cell data to facilitate analysis of experiments and speed up discovery in cell biology. The new tools would provide reliable, consistent inference requiring no manual intervention and able to process large volumes of data in a timely manner. This would equip biologists with a vehicle that could move them closer to the goal of understanding ....Multi-object Estimation for Live-Cell Microscopy. The objective of this project is to develop new tools for the inference of biological information from live-cell data to facilitate analysis of experiments and speed up discovery in cell biology. The new tools would provide reliable, consistent inference requiring no manual intervention and able to process large volumes of data in a timely manner. This would equip biologists with a vehicle that could move them closer to the goal of understanding the mechanism behind biological processes.Read moreRead less
A stochastic geometric framework for Bayesian sensor array processing. This project develops a mathematical framework, and a new generation of techniques, for sensor array processing to address real-world problems with uncertainty in array parameters and number of signals. The outcomes will enhance the capability of sensors in many application areas including, radar, sonar, astronomy and wireless communications.
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.