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Nonlinear Bayesian Inference Problems by Dr. Samuel Shapero

Explore Bayesian inference challenges with Dr. Samuel Shapero, covering topics like hypothesis optimization and applications in target tracking, compressed sensing, and more. Dive into the world of sparse coding and neuromorphic hardware.

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Nonlinear Bayesian Inference Problems by Dr. Samuel Shapero

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  1. Problems in NonlinearBayesian Inference Dr. Samuel Shapero Samuel.Shapero@gtri.gatech.edu June 18th, 2014

  2. Bayesian Inference How do we find an optimal hypothesis given available evidence? H2 H3 H1

  3. Applications Multiple Hypothesis Tracking • Target tracking with range-denied measurements (sponsored project) • Agile emitter identification (FY2015 IRAD) Sparse Coding • Subnyquist RWR (FY2015 IRAD) • Compressed Sensing Recovery

  4. N M y â Applications Compressed Sensing Recovery for Medical Imaging [Vasanawala et al. 2011, Shapero et al. 2012] Sparse Components Low Cost Measurement Original Object Compressed Sensing Sparse Approximation x Φ = Recovered Image Bottleneck! Requires speed [Shapero et al., JETCAS, 2012]

  5. Neuromorphic Hardware – RAIN Sparse approximation problems Spiking Neural Networks LOTS OF MATH TESTING CHIP DESIGN RAIN Chip -developed @GT

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