Benchmark computations for the polarization tensor of small conducting objects Article Swipe

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Alan A. S. Amad , Paul D. Ledger , T. Betcke , D. Praetorius ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2106.15157

The characterisation of small low conducting inclusions in an otherwise uniform background from low-frequency electrical field measurements has important applications in medical imaging using electrical impedance tomography as well as in geological imaging using electrical resistivity tomography. It is known that such objects can be characterised by a Póyla-Szegö (polarizability) tensor. Such characterisations have attracted interest as they can provide object features in a machine learning classification algorithm and provide an alternative imaging solution. However, to be able train machine learning algorithms, large dictionaries are required and it is essential that the characterisations are accurate. In this work, we obtain accurate numerical approximations to the tensor coefficients, by applying an adaptive boundary element method. The goal being to provide a sequence of benchmark {computations} for the tensor coefficients to allow other software developers check the accuracy of their codes.

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Benchmark (Surveying)

Computer Science

Algorithm

Artificial Intelligence

Theoretical Computer Science

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Computation Tensor (intrinsic definition) Benchmark (surveying) Computer science Algorithm Electrical impedance tomography Polarizability Artificial intelligence Theoretical computer science Computational science Tomography Geometry Mathematics Physics Geology Optics Quantum mechanics Molecule Geodesy

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2106.15157
PDF: https://arxiv.org/pdf/2106.15157
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4287111451

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DOI: https://doi.org/10.48550/arxiv.2106.15157

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Title: Benchmark computations for the polarization tensor of small conducting objects

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-06-29

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Authors: Alan A. S. Amad, Paul D. Ledger, T. Betcke, D. Praetorius

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Landing page: https://arxiv.org/abs/2106.15157

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PDF URL: https://arxiv.org/pdf/2106.15157

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Concepts: Computation, Tensor (intrinsic definition), Benchmark (surveying), Computer science, Algorithm, Electrical impedance tomography, Polarizability, Artificial intelligence, Theoretical computer science, Computational science, Tomography, Geometry, Mathematics, Physics, Geology, Optics, Quantum mechanics, Molecule, Geodesy

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