Convex optimisation for control, signal processing and communication systems. Renewable control of complex systems, signal processing, telecommunication and in general any industries interested in these applications stand to benefit from our research. In particular, the automotive and defence industries stand to benefit from the nonlinear control design aspect of the proposed project outcomes. The
telecommunications industries, on the other hand, benefit from the signal processing and communicat ....Convex optimisation for control, signal processing and communication systems. Renewable control of complex systems, signal processing, telecommunication and in general any industries interested in these applications stand to benefit from our research. In particular, the automotive and defence industries stand to benefit from the nonlinear control design aspect of the proposed project outcomes. The
telecommunications industries, on the other hand, benefit from the signal processing and communications aspects. We also build a core expertise in optimisation and its applications in Australia by training PhD students and Postdoctoral researchers. The research collaborations will cement and maintain the international linkages which will improve applied research in AustraliaRead moreRead less
Analysis of Polynomial Phase Signals with Missing Observations. Many non-stationary signals in radar, physics and communications can be modelled as polynomial phase signals. These signals are often incomplete due to missing observations from intermittent sensor failures, outliers, receiver errors, periodic interference and inaccessibility of data. The aim of this project is to develop robust and computationally efficient methods for recovering such signals from small data sets when there is a la ....Analysis of Polynomial Phase Signals with Missing Observations. Many non-stationary signals in radar, physics and communications can be modelled as polynomial phase signals. These signals are often incomplete due to missing observations from intermittent sensor failures, outliers, receiver errors, periodic interference and inaccessibility of data. The aim of this project is to develop robust and computationally efficient methods for recovering such signals from small data sets when there is a large proportion of missing observations. This will contribute to a conceptual advancement in the field of signal processing and will provide new methods for use in applications such as radar, astrophysics, seismology, vibration analysis and communications.Read moreRead less