[Back]

A. Amad, T. Betcke, P. Ledger, D. Praetorius:
"Benchmark computations for the polarization tensor characterization of small conducting objects";
Applied Mathematical Modelling, 111 (2022), 94 - 107.

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 Poyla-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.

http://dx.doi.org/10.1016/j.apm.2022.06.024