Melbourne School of Engineering
Nonlinear Signal Processing Laboratory


Collaboration with NSP Lab brings the skills of a highly competent and interdisciplinary team of researchers to support and advance industrial projects. Novel and comprehensive solutions will be provided through a diverse yet focused approach.

Prospective industrial partners seeking consultancy or long term linkage projects will especially benefit from our expertise in signal processing, control engineering and information theory. Technologies and applications where work is currently pursued include the following.


RADAR and Telecommunications

Developing rigorous methods adapted to applications in tracking, filtering, synchronisation, equalization, decision and fast simulation. A comprehensive approach to filtering type applications is based on Information Geometry and modern simulation methods. Guaranteed performance is provided through systematic use of advanced tools based on Large Deviations, which quantify exactly the necessary resources for applicability of statistical algorithms in low signal to noise ratio situations.


Medical applications

We develop a systems based approach to medical applications ranging from automated and objective diagnosis to control of medication dosing in a surgery situation. We aim to provide portable, optimised digital solutions which guarantee a high degree of precision inaccessible through traditional subjective practice.


Inverse problems and Imaging

The concept of inverse problem involves the processing of physical data in its broad sense. Its applications include medical imaging and geophysical exploration. It involves problems of parameter identification, of deconvolution and of distributed signal processing. We use a standard Bayesian approach emphasizing stability and regularity of solutions.



Almost any Engineering problem will involve optimisation. Our strong background in mathematics allows us to go beyond traditional numerical optimisation and provide global algorithms specifically taylored for required convergence rates and classes of objective functions.