2013 Academic Year Seminars
We can use spatiotemporal models to describe systems which exist across some physical space, and which change over time. In my talk I will focus on a first-order integro-difference equation based model. This is a popular form of model, due to its applicability to problems in ecology and geostatistics where it is used to model patterns that are difficult to represent with the more traditional reaction-diffusion equations.
I am interested in the model in an engineering context as it lends itself naturally to modelling systems using observations collected irregularly across space and regularly in time: a typical configuration when using sensor networks. My aim is to show that we can represent this spatiotemporal model using a standard linear dynamical system with a manageable state space. Once in this framework, we can use a standard expectation maximization algorithm to estimate the spatial field at each point in time, as well as the integral kernel which governs the dynamics of the system.
Michael Dewar received the M.Eng. degree in control systems engineering and Ph.D. degree in systems engineering both from The University of Sheffield, U.K., in 2002 and 2007 respectively. He is currently working as a Research Associate in the Institute for Adaptive and Neural Computation, School of Informatics, The University of Edinburgh.
He has held a previous research associate position in The University of Sheffield, and in 2004 he was with the Department of Electrical Power and Control Engineering, University of Malta. His interests include the modeling of dynamic spatiotemporal systems and the analysis of dynamic behavioural data.