Enhancement of Non-Destructive Measurement Resolution with Neural Networks

Files

enhancementofnondestructive.pdf (2.98 MB)

Links to Files

https://ieeexplore.ieee.org/document/10686643

Permanent Link

http://hdl.handle.net/11603/35973
 https://doi.org/10.1109/AP-S/INC-USNC-URSI52054.2024.10686643

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Data Science
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-9075-7071

Date

2024-09-30

Type of Work

conference papers and proceedings
postprints
Text

Department

Program

Citation of Original Publication

Simsek, Ergun, and Emerson K. Cho. “Enhancement of Non-Destructive Measurement Resolution with Neural Networks.” 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), July 2024, 1151–52. https://doi.org/10.1109/AP-S/INC-USNC-URSI52054.2024.10686643.

Rights

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Subjects

Abstract

Similar to image sharpening, the resolution of measured electromagnetic fields can be enhanced with machine learning. We numerically demonstrate that a λ/10 spatial resolution is achievable even with probes that are a few wavelengths wide, while maintaining a maximum relative error of less than 3%.