Evaluation of machine learning models for a chipless RFID sensor tag

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dc.contributor.authorRather, Nadeemen
dc.contributor.authorSimorangkir, Roy B. V. B.en
dc.contributor.authorBuckley, Johnen
dc.contributor.authorO’Flynn, Brendanen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2023-06-09T10:36:33Z
dc.date.available2023-06-09T10:36:33Z
dc.date.issued2023-03en
dc.description.abstractRadar cross section (RCS) is a measure of the reflective strength of a radar target. Chipless RFID tags use this principle to create a tag that can be read at a distance without needing a power-hungry radio transceiver chip and/or battery. A chipless tag consists of a pattern of conductive and dielectric materials that backscatter electromagnetic (EM) waves in a distinctive pattern. A chipless tag can be read and identified by analysing the reflected waves and matching it with a predefined EM signature. In this paper, for the first time, several regression-based machine learning (ML) models are evaluated to detect identification and sensing information for an RCS-based chipless RFID tag. The simulated EM RCS signatures containing an 8-bit identification code and six capacitive sensing values are evaluated. The EM RCS signatures are evaluated within the UWB frequency band from 3.1 to 10.6 GHz. A dataset of 1,530 simulated signatures with relevant features are utilised for model training, validation, and testing. Root mean square error (RMSE) is used as the quantitative metric to evaluate their performance. It is found that Support Vector Regression (SVR) models provide the minimum RMSE for the identification code. At the same time, the Gradient Boosted Trees (GBT) regression model performed better in detecting the sensing information.en
dc.description.sponsorshipEnterprise Ireland (Holistics DTIF (Disruptive Technologies Innovation Fund) (EI-DT20180291-A))en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRather, N., Simorangkir, R.B.V.B., Buckley, J., O’Flynn, B. and Tedesco, S. (2023) ‘Evaluation of machine learning models for a chipless RFID sensor tag’, in 2023 17th European Conference on Antennas and Propagation (EuCAP). Florence, Italy: IEEE, pp. 1-5. https://doi.org/10.23919/EuCAP57121.2023.10133043en
dc.identifier.doi10.23919/eucap57121.2023.10133043en
dc.identifier.endpage5en
dc.identifier.isbn978-1-6654-7541-9en
dc.identifier.journaltitle2023 17th European Conference on Antennas and Propagation (EuCAP)en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14551
dc.language.isoenen
dc.publisherEuCAP; IEEEen
dc.relation.ispartof2023 17th European Conference on Antennas and Propagation (EuCAP)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3835/IE/VistaMilk Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.relation.projectEI-DT20180291en
dc.rights© 2023 The Author(s). This paper's copyright is held by the author(s). It is published in these proceedings and included in any archive such as IEEE Xplore under the license granted by the "Agreement Granting EurAAP Rights Related to Publication of Scholarly Work".en
dc.subjectTrainingen
dc.subjectSupport vector machinesen
dc.subjectSemiconductor device measurementen
dc.subjectRadar cross-sectionsen
dc.subjectCodesen
dc.subjectMachine learningen
dc.subjectSensorsen
dc.titleEvaluation of machine learning models for a chipless RFID sensor tagen
dc.typeArticle (peer-reviewed)en
dc.typeproceedings-articleen
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