The ever increasing demands placed upon machine performance have resulted in the need for more comprehensive particle accelerator modeling. Computer simulations are key to the success of particle accelerators. Many aspects of particle accelerators rely on computer modeling at some point, sometimes requiring complex simulation tools and massively parallel supercomputing.
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
A system for detecting and/or tracking a moving object uses signals received from the object at two or more receivers, each of which is dual polarized. At each receiver, the component of the received signal associated with the polarization thereof is separated from the non-polarization-based component, and the modulation of
We present a fast, large-scale algorithm for the simulation of electromagnetic waves (Maxwell’s equations) in three-dimensional inhomogeneous media. The algorithm has a complexity of O(N log(N)) and runs in parallel. Numerical simulations show the rapid treatment of problems with tens of millions of unknowns on a small shared-memory cluster (
A signal pre-compensation system analyzes one or more properties of a communication medium and, taking advantage of the locality of propagation, generates using sparse fast Fourier transform (sFFT) a sparse kernel based on the medium properties. The system models propagation of data signals through the medium as a fixed-point iteration
We present the sparse multidimensional FFT (sMFFT) for positive real vectors with application to image processing. Our algorithm works in any fixed dimension, requires an (almost) – optimal number of samples and runs in complexity (to first order) for unknowns and nonzeros. It is stable to noise and exhibits an
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
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
As distributed memory systems grow larger, communication demands have increased. Unfortunately, while the costs of arithmetic operations continue to decrease rapidly, communication costs have not. As a result, there has been a growing interest in communication-avoiding algorithms for some of the classic problems in numerical computing, including communication-avoiding Fast Fourier
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