Control and Signal Processing Lab

Research Projects

The following list represents a random selection of projects our members are working on.

View our Research Focus for a description of our research interests.

Adaptive learning visual sensor networks for crowd modelling

Researchers: Jayavardhana Gubbi Lakshminarasimha, Marimuthu Palaniswami, Slaven Marusic

The prevalence of camera networks for surveillance, together with the decreasing cost of infrastructure, has produced a significant demand for robust monitoring systems. Current systems offer limited functionality, particularly in their reliance on centralised processing of gathered information. This project addresses end-to-end system challenges of wireless visual sensor networks. Integrating developments across the spatial, spatio-temporal and decision domains, the project will incorporate distributed sensor network technology with intelligent fusion of information, to deliver unique long-term behaviour analysis capabilities for efficient planning in highly crowded environments.

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Finite sample issues in system identification, change detection and filtering

Researchers: Erik Weyer

Models of dynamical systems are commonly used in many fields of science and engineering. In order to make proper use of a model, it is important to know the uncertainties associated with it. An evaluation of the model quality must necessarily be based on a finite number of data points from the true system. In this project we have developed methodologies: Leave-out Sign dominant Correlation Regions (LSCR) and Sign Perturbed Sums (SPS), which stand on a solid theoretical footing in a finite sample context and which deliver useful evaluations of the model uncertainties. In particular, it delivers a probabilistically guaranteed confidence set for the true system parameters for any finite number of data points under very weak assumptions about the noise processes affecting the system. The LSCR and SPS principles are very powerful and they also find applications in filtering and fault detection, and filtering and detection algorithms with guaranteed statistical properties for any finite number of data points that have been developed.

Current research is focused on further developments of the methods and their properties for different types of linear and nonlinear models and identification algorithms (e.g. least squares, prediction error methods, instrumental variables), efficient numerical implementation of the methods and applications of the principles in different areas.

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Large scale multiple antennas for energy-efficient heterogeneous wireless networks

Researchers: Phee Lep Yeoh, Brian Krongold

This project investigates new network architectures for future wireless broadband inspired by recent advances in large scale multiple antenna technology and heterogeneous networks. The aim is to support flexible and scalable wireless services across diverse network regions with energy-efficient management of radio spectrum and interference. Targeted applications include smart energy metering, intelligent transport systems, mobile health monitoring and green data centres. Outcomes of the research will be new wireless protocols and algorithms drawing upon the foundations of random matrix theory, game theory, and large system analysis, which will offer fundamental insights into large scale multiple antennas for heterogeneous wireless networks.

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Robust filtering and estimation

Researchers: Girish Nair

A basic goal in stochastic filtering/estimation problems is to form an estimate Xest of a parameteror state X from noisy past data Y (time indices omitted). A common distortion criterion is the mean-square error (MSE) under a linearity constraint, which is often unsuitable in nonlinear or discrete-valued settings. An alternative approach in the literature is to maximise the Shannon information I[X; Xest ]. However, this can involve an infinite-dimensional optimisation over distributions.

The aim of this project is to explore estimators  that maximise instead the *nonstochastic*  information between X and Xest [Nair, IEEE Trans, Automatic Control, 2013; Nair, IEEE Conf. Decision and Control, Osaka, 2015]. This has the potential to yield filters with reduced computational cost for situations with bounded disturbances that are nonstochastic or have poorly known distributions.

A complementary approach is to finding an estimator that satisfies a nonstochastic “unrelatedness” principle, whereby the estimation error  is “unrelated” to the data Y in the sense of [Nair, IEEE Trans, Automatic Control, 2013; Nair, IEEE Conf. Decision and Control, Osaka, 2015]. Solving these problems is potentially much simpler than the corresponding probabilistic versions.

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Smart devices for ubiquitous health monitoring

Researchers: Jayavardhana Gubbi Lakshminarasimha, Marimuthu Palaniswami, Jayavardhana Gubbi

Provision of healthcare services remains a critical challenge across all levels of society, with the cost burden and constrained resources limiting the accessibility to healthcare. Advances in hardware development have made available efficient, low-cost, low-power miniature devices for use in remote sensing applications. This iconic project will drive the development of smart devices for low-cost monitoring, analysis and treatment of a number of medical conditions addressing continuous monitoring, aged care, and rehabilitation. The project consolidates a number of biomedical engineering initiatives into a single interdisciplinary project.

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Contact Us

Prof Jonathan Manton

Director, Control and Signal Processing Laboratory