Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3502
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dc.contributor.authorNakmouche M.F.-
dc.contributor.authorDerbal M.C.-
dc.contributor.authorAllam A.M.M.A.-
dc.contributor.authorFawzy D.E.-
dc.contributor.authorShams S.I.-
dc.contributor.authorNedil M.-
dc.contributor.authorElsaadany M.-
dc.date.accessioned2023-06-16T14:59:32Z-
dc.date.available2023-06-16T14:59:32Z-
dc.date.issued2021-
dc.identifier.isbn9.78173E+12-
dc.identifier.urihttps://doi.org/10.1109/APS/URSI47566.2021.9704795-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3502-
dc.descriptionIEEE Antennas and Propagation Society (AP-S);US National Committee (USNC) of the International Union of Radio Science (URSI)en_US
dc.description2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 -- 4 December 2021 through 10 December 2021 -- 177295en_US
dc.description.abstractMachine learning has been used in this work for the development of a Ku band Ridge Gap Waveguide (RGW) slot antenna loaded with an FSS superstrate for satellite internet applications. The structure operates from 13.25 to 14.75 GHz with a gain beyond 10 dB using FSS superstrate loading. The developed machine learning model aims to predict the optimal length and width of the radiated slot, where both the Fractional Bandwidth (FBW) and the resonance frequency are considered objective parameters. The simulated results and the anticipated results through the machine learning algorithm are in good agreement. © 2021 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectFrequency Selective Surfacesen_US
dc.subjectMachine Learningen_US
dc.subjectRidge Gap Waveguide (RGW)en_US
dc.subjectSuperstrateen_US
dc.subjectBandwidthen_US
dc.subjectLearning algorithmsen_US
dc.subjectMachine learningen_US
dc.subjectMicrowave antennasen_US
dc.subjectRidge waveguidesen_US
dc.subjectSlot antennasen_US
dc.subjectANNen_US
dc.subjectFrequency-selective surfacesen_US
dc.subjectGap waveguidesen_US
dc.subjectInternet applicationen_US
dc.subjectKu banden_US
dc.subjectMachine-learningen_US
dc.subjectRidge gap waveguideen_US
dc.subjectSatellite interneten_US
dc.subjectSuperstratesen_US
dc.subjectWaveguide slot antennasen_US
dc.subjectFrequency selective surfacesen_US
dc.titleMachine Learning Based Design of Ku Band Ridge Gap Waveguide Slot Antenna Loaded with FSS for Satellite Internet Applicationsen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/APS/URSI47566.2021.9704795-
dc.identifier.scopus2-s2.0-85124043097en_US
dc.authorscopusid57206657916-
dc.authorscopusid55582327600-
dc.authorscopusid23011278600-
dc.authorscopusid57201750748-
dc.authorscopusid8320837800-
dc.authorscopusid36190739600-
dc.authorscopusid21833761700-
dc.identifier.startpage1881en_US
dc.identifier.endpage1882en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Object-
item.fulltextWith Fulltext-
item.languageiso639-1en-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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