Electromagnetic Classification

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dc.contributor.authorSimsek, Ergun
dc.date.accessioned2024-09-04T19:58:34Z
dc.date.available2024-09-04T19:58:34Z
dc.date.issued2024
dc.description.abstractRather than reconstructing the properties or parameters of a medium as it is done in electromagnetic inversion, this work aims to classify objects with neural networks that are trained with scattered field data and labels (classes). The study demonstrates the feasibility of achieving an 86% accuracy, showcasing potential applications in robotics and environmental perception.
dc.description.urihttps://userpages.cs.umbc.edu/simsek/cps/2024_aps_ursi_C_w_EMW.pdf
dc.format.extent2 pages
dc.genrejournal articles
dc.genrepostprints
dc.identifierdoi:10.13016/m2jamh-wnq4
dc.identifier.urihttp://hdl.handle.net/11603/35974
dc.language.isoen
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.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.titleElectromagnetic Classification
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0001-9075-7071

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