Publications/Signal Processing

Low-Frequency Electromagnetic Imaging Using Sensitivity Functions and Beamforming

We present a computational technique for low-frequency electromagnetic imaging in inhomogeneous media that provides superior three-dimensional resolution over existing techniques. The method is enabled through large-scale, fast (low-complexity) algorithms that we introduce for simulating electromagnetic wave propagation. We numerically study the performance of the technique on various problems including the

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A Sparse Multidimensional FFT for Real Positive Vectors

We present a sparse multidimensional FFT (sMFFT) randomized algorithm for positive real vectors. The algorithm works in any fixed dimension, requires an (almost)-optimal number of samples (O (R log (N))) and runs in O (R log (N)) complexity (where N is the total size of the vector in d dimensions

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Embedded Second-Order Cone Programming with Radar Applications

Second-order cone programming (SOCP) is required for the solution of underdetermined systems of linear equations with complex coefficients, subject to the minimization of a convex objective function. This type of computational problem appears in compressed radar sensing, where the goal is to reconstruct a sparse image in a generalized space

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