Enhancement of Non-Destructive Measurement Resolution with Neural Networks

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dc.contributor.authorSimsek, Ergun
dc.contributor.authorCho, Emerson K.
dc.date.accessioned2024-09-04T19:58:33Z
dc.date.available2024-09-04T19:58:33Z
dc.date.issued2024
dc.description.abstractSimilar 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%.
dc.description.urihttps://userpages.cs.umbc.edu/simsek/cps/2024_aps_ursi_Resolution.pdf
dc.format.extent2 pages
dc.genrejournal articles
dc.genrepostprints
dc.identifierdoi:10.13016/m2bx0b-aikt
dc.identifier.urihttp://hdl.handle.net/11603/35973
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleEnhancement of Non-Destructive Measurement Resolution with Neural Networks
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0001-9075-7071

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