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Jiming Song

Researcher at Iowa State University

Publications -  211
Citations -  8512

Jiming Song is an academic researcher from Iowa State University. The author has contributed to research in topics: Integral equation & Fast multipole method. The author has an hindex of 32, co-authored 194 publications receiving 7765 citations. Previous affiliations of Jiming Song include Motorola & Nanjing University.

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Acceleration of Spectral Domain Approach for Generalized Multilayered Shielded Microstrip Interconnects Using Two Fast Convergent Series

TL;DR: In this article, a simple and versatile approach for the acceleration of the spectral domain approach for generalized shielded microstrip interconnects is presented, which uses asymptotic expansion for the Bessel's function and the Green's function followed by an approximation of infinite summation using two fast convergent sine cosine series.
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Midpoint Summation: A Method for Accurate and Efficient Summation of Series Appearing in Electromagnetics

TL;DR: In this article, a novel approach for fast approximation of a summation to an integral with midpoint summation (MPS) has been developed, which requires one term less than the existing Euler-Maclaurin formula (EMF) for obtaining the same order of accuracy.
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Integral equation fast solver with truncated and degenerated kernel for computing flaw signals in eddy current non-destructive testing

TL;DR: In this paper, the truncated and degenerated (TD) kernel function method is applied as the mathematical framework for solving 3D arbitrary shaped eddy current NDE forward problems, which results in a significant reduction in computational burden with nearly linear complexity.
Proceedings ArticleDOI

Fast Fourier transform of sparse spatial data to sparse Fourier data

TL;DR: The present algorithm is motivated by the multilevel fast multipole algorithm (MLFMA), but is different from that described by Brandt (1991), where the restriction of the Fourier data to an Ewald sphere is lifted so that it can be arbitrarily distributed as well.
Proceedings ArticleDOI

Point-based implementation of multilevel fast multipole algorithm for higher-order Galerkin's method

TL;DR: By implementing the multilevel fast multipole algorithm based on point-to-point interactions, the number of levels used is not limited by the size of basis functions, making MLFMA more efficient.