Abstract
Advances in sensor technologies have been instrumental in being able to observe and analyse complex dynamical systems. Examples include LiDAR measurements of wind velocities in Engineering, high throughput high-resolution cell screens in Life Sciences, array electrode potential field signals from the surface of the organs in Healthcare. In all of these examples, there is not only temporal variation that characterizes the dynamics in the systems, but there are also spatial fields of interest. Analysis of signals from such systems demand developments in estimation and identification of spatio-temporal systems. The talk will first introduce the generic framework of estimation and identification, motivated from a statistical signal processing perspective, which underpins the methodologies used in a variety of problems. Following this, examples from Engineering, Life Science and Healthcare will be used to define estimation problem classes. For each of these problems are reduced to a state and/or parameter estimation problems of different complexities. The statistical signal-processing framework is then employed to solve the problems. The talk concludes by further examples that are currently under investigation.
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