Optimizing electrode positions in 2D electrical impedance tomography using deep learning

Smyl, D. orcid.org/0000-0002-6730-5277 and Liu, D. (2020) Optimizing electrode positions in 2D electrical impedance tomography using deep learning. IEEE Transactions on Instrumentation and Measurement. ISSN 0018-9456

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Copyright, Publisher and Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Deep learning; electrical impedance tomography; electrode positioning; inverse problems; neural networks; nondestructive evaluation
Dates:
  • Accepted: 21 January 2020
  • Published (online): 30 January 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 28 Jan 2020 15:36
Last Modified: 13 Feb 2020 14:04
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: https://doi.org/10.1109/TIM.2020.2970371

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