Premium
Iterative Sinc − convolution method for solving planar D‐bar equation with application to EIT
Author(s) -
Abbasi Mahdi,
NaghshNilchi AhmadReza
Publication year - 2012
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.1495
Subject(s) - sinc function , convolution (computer science) , integral equation , mathematics , collocation (remote sensing) , collocation method , rate of convergence , algebraic equation , mathematical analysis , computer science , differential equation , key (lock) , physics , artificial neural network , ordinary differential equation , computer security , nonlinear system , quantum mechanics , machine learning
SUMMARY The numerical solution of D‐bar integral equations is the key in inverse scattering solution of many complex problems in science and engineering including conductivity imaging. Recently, a couple of methodologies were considered for the numerical solution of D‐bar integral equation, namely product integrals and multigrid. The first one involves high computational complexity and other one has low convergence rate disadvantages. In this paper, a new and efficient sinc‐convolution algorithm is introduced to solve the two‐dimensional D‐bar integral equation to overcome both of these disadvantages and to resolve the singularity problem not tackled before effectively. The method of sinc‐convolution is based on using collocation to replace multidimensional convolution‐form integrals‐ including the two‐dimensional D‐bar integral equations ‐ by a system of algebraic equations. Separation of variables in the proposed method allows elimination of the formulation of the huge full matrices and therefore reduces the computational complexity drastically. In addition, the sinc‐convolution method converges exponentially with a convergence rate of O (e − c N) . Simulation results on solving a test electrical impedance tomography problem confirm the efficiency of the proposed sinc‐convolution‐based algorithm. Copyright © 2012 John Wiley & Sons, Ltd.