Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimizing electrode positions in 2D Electrical Impedance Tomography
  using deep learning   Electrical Impedance Tomography (EIT) is a powerful tool for non-destructive
evaluation, state estimation, and process tomography - among numerous other use
cases. For these applications, and in order to reliably reconstruct images of a
given process using EIT, we must obtain high-quality voltage measurements from
the target of interest. As such, it is obvious that the locations of electrodes
used for measuring plays a key role in this task. Yet, to date, methods for
optimally placing electrodes either require knowledge on the EIT target (which
is, in practice, never fully known) or are computationally difficult to
implement numerically. In this paper, we circumvent these challenges and
present a straightforward deep learning based approach for optimizing
electrodes positions. It is found that the optimized electrode positions
outperformed "standard" uniformly-distributed electrode layouts in all test
cases. Further, it is found that the use of optimized electrode positions
computed using the approach derived herein can reduce errors in EIT
reconstructions as well as improve the distinguishability of EIT measurements.

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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg