Current Projects from my Research Group:
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Particle Filters: Theory, Implementation and Applications (Mahdi Shabany)
Particle filters, also known as Sequential Monte Carlo filters, rooted in Bayesian estimation, have recently been the focus of research for nonlinear and/or non-Gaussian applications. These filters find applications to problems that can be formulated by dynamic state space (DSS) models. DSS models describe the evolution of a state of interest with time and the observations as a function of the state. Particle filters are used to estimate the unobserved states or their functions based on the given observations. They outperform other filters, such as Kalman filter, Extended Kalman filter, and Grid-based methods, in many important practical situations. As an important advantage and in contrast to alternative approaches, Particle filters are scalable and their complexity increases linearly with the DSS dimension. Of particular interest is the application of Particle filters in digital communication systems, where the system in general is non-linear non-Gaussian, and multi-dimensional. Therefore, Particle filters offer promising performance advantages to different applications in digital communication receivers. These applications require real-time processing of the received data; thus, the major research objective is to develop a good theoretical and analytical understanding of particle filters and their applications in digital communication systems.
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1.5Gbps Baseband Processor for 60 GHz Radio
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A CMOS Bioluminescence/Bioflourescence Detection System-on-Chip