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ECE PhD Defense: "High-Performance Optimization Algorithms for Multi-Physics Compressive Sensing, Imaging, and Communications," Richard Obermeier

Abstract: According to Compressive Sensing (CS) theory, sparse signals can be accurately recovered from a small set of linear measurements using efficient L1-norm minimization techniques. The success of such techniques depends upon the “quality” of the sensing matrix as determined by some metric. The Restricted Isometry Property (RIP) establishes some of the tightest reconstruction guarantees that are currently known. These guarantees come at a cost: it is NP-hard to evaluate the RIP. To overcome this issue, researchers typically utilize random matrix theory to create matrices...

Monday, December 10, 2018 at 10:00am

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