Dr. Al Wexler Profile

Dr. Al Wexler

President at Quantic EMC Inc

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Proceedings Article | 5 May 2009 Paper

Standoff subterranean high definition impedance imaging

A. Wexler

Proceedings Volume 7303, 73031V (2009) https://doi.org/10.1117/12.818713

KEYWORDS: Electrodes, Image processing, Image quality, Chemical elements, 3D image processing, Land mines, Radar, Matrices, Improvised explosive devices, Inverse problems

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High definition impedance imaging (HDII) is applicable, from d.c. upward for electrical, sonic and elasticity signal excitations. At low frequencies, great depth is achievable in contrast to that provided by radar without HDII. The HDII solution process results in a very large and sparse matrix system and associated algorithms provide convergence with few iterations and high image definition. The methodology solves the three-dimensional image solution rather that by solving in slices. HDII image quality results from the number of linearly independent equations resulting from the number of electrodes and linearly independent measurements that are obtained. To construct a standoff (i.e. contactless) system, the three-dimensional vector Helmholtz equation, i.e. the formulation used in antennal analysis, may be employed. To do this, the same basic HDII imaging algorithm, as used for the contact case, is employed for standoff imaging. Over determination can permit significantly refined image quality.

Proceedings Article | 29 April 2008 Paper

High definition impedance imaging for mines and tunnels

A. Wexler, Patrick O'Connor, J. McFee

Proceedings Volume 6953, 695306 (2008) https://doi.org/10.1117/12.777668

KEYWORDS: Electrodes, Chemical elements, Matrices, Land mines, 3D image processing, Image enhancement, Image restoration, Mining, Reconstruction algorithms, 3D modeling

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The high definition impedance imaging (HDII) Electroscan algorithm casts the error norm problem into the interior of the region and iteratively minimizes the difference norm calculated between solutions achieved for applied currents (i.e. the Neumann problem) and the solution achieved for the measured voltages (i.e. the Dirichlet problem) - in the electrical-excitation case. This results in very sparse matrices instead of densely-packed Jacobian matrices. Minimization of the error yields a three-dimensional image of the conductivity distribution. The paper presents a rapid, sparse-matrix methodology for high definition admittivity imaging involving a very large number of voxels. It is a least-square algorithm, simultaneously involving all excitations, and it is error resilient and well-conditioned. The solution iterative procedure is accelerated by a variety of means such as: solution of mutually-constrained, three-dimensional field equations; successive point-iterative overrelaxation; multi-acceleration factors; measurements at a multiplicity of electrodes; and excitation modification for image enhancement. Laboratory, field, and simulation case studies are presented. Spatially restricted-region and open-region solutions are compared. Signal-source modeling is not required. Conductivity and, generally, admittivity values are able to be determined. And so, the imaging process has diagnostic capability. It is applicable to non-contact standoff excitations, e.g. magnetic fields, microwave/radar, sonic and elasticity wave excitations.

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