2010 URSI International Symposium on Electromagnetic Theory 2010
DOI: 10.1109/ursi-emts.2010.5637093
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On the numerical evaluation of the testing integrals for the Galerkin discretization of surface integral equations

Abstract: The testing integrals used to discretize surface integral equations by application of the Moment Method are usually considered as regular 'trivial' integrals to be computed. This paper discusses some fundamental problems relevant to the numerical evaluation of these integrals and presents several test cases.

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Cited by 2 publications
(2 citation statements)
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“…Both strategies can be implemented using existing MoM library routines, since the evaluation of the potentials at the NG points and near regions is based on source integrals [9], while the evaluation of MoM generalized impedance requires already existing library routines for testing integrals optimized on a distance base [15]. Thus an efficient and precise approximate version of the matrix-vector multiplication in (3) is obtained.…”
Section: Nonunform Grid Algorithmmentioning
confidence: 99%
“…Both strategies can be implemented using existing MoM library routines, since the evaluation of the potentials at the NG points and near regions is based on source integrals [9], while the evaluation of MoM generalized impedance requires already existing library routines for testing integrals optimized on a distance base [15]. Thus an efficient and precise approximate version of the matrix-vector multiplication in (3) is obtained.…”
Section: Nonunform Grid Algorithmmentioning
confidence: 99%
“…From the implementation point of view, the algorithm is designed to compute potentials on either an RWG base or on a single triangle base. Both strategies can be implemented using existing MoM library routines, since the evaluation of the potentials at the NG points and near regions is based on source integrals [9], while the evaluation of MoM generalized impedance requires already existing library routines for testing integrals optimized on a distance base [15]. Thus an efficient and precise approximate version of the matrix-vector multiplication in (3) is obtained.…”
Section: Nonunform Grid Algorithmmentioning
confidence: 99%