Hybrid DCNN-enabled depolarizing chipless RFID: improving tag detection across varying lossy surfaces and shapes

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dc.contributor.authorRather, Nadeemen
dc.contributor.authorSimorangkir, Roy B. V. B.en
dc.contributor.authorGawade, Dinesh R.en
dc.contributor.authorBuckley, John L.en
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.funderResearch Irelanden
dc.contributor.funderDepartment of Agriculture, Food and the Marine, Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderEnterprise Irelanden
dc.date.accessioned2025-11-12T12:17:11Z
dc.date.available2025-11-12T12:17:11Z
dc.date.issued2025-09-10en
dc.description.abstractThis paper presents a comprehensive design and implementation approach for robust detection of depolarizing chipless RFID (CRFID) tags. Depolarizing tags are advantageous compared to co-polar CRFID tags due to their improved performance on RF-lossy materials. This work introduces the application of deep learning (DL) regression modelling to a specialised dataset of depolarised Radar Cross Section (RCS) measurements of a custom 3-bit CRFID tag, acquired through an extensive robot-based data acquisition method. A dataset of 12,600 depolarised Electromagnetic (EM) RCS signatures were collected using an automated data acquisition system to train and validate a 1-dimensional Convolutional Neural Network (1D CNN) architecture. A novel hybrid 1D CNN with Bi-LSTM and attention mechanism architecture was also implemented to visualize the model attention and improve detection performance. We present, for the first time reported in literature, a comprehensive design and AI implementation approach for reliably detecting identification (ID) information from depolarized signals. Also, we report the first instance of describing the impact of surface permittivity variations, tag deformations, tilt angles, and read ranges, all integrated into model training for enhanced robustness in detecting ID information. The developed models facilitate real-time identification and recording of objects, enhancing IoT applications in varied environments. It was observed that both models were able to generalize well to given data, with Model-1 achieving a low RMSE of 0.040 (0.66%) on an unseen test dataset. However, the hybrid model reduced the error further by 27.5% with a test RMSE of 0.029 (0.48%).en
dc.description.sponsorshipResearch Ireland (Grant 21/RC/10303_P2 (VistaMilk); 13/RC/2077; SFI/12/RC/2289-2; 16/RC/3918-CONFIRM); Enterprise Ireland (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., Gawade, D. R., Buckley, J. L., O'Flynn, B. and Tedesco, S. (2025) 'Hybrid DCNN-enabled depolarizing chipless RFID: improving tag detection across varying lossy surfaces and shapes', IEEE Journal of Radio Frequency Identification, 9, pp. 768-778. https://doi.org/10.1109/JRFID.2025.3608617en
dc.identifier.doi10.1109/jrfid.2025.3608617en
dc.identifier.endpage778en
dc.identifier.issn2469-7281en
dc.identifier.issn2469-729Xen
dc.identifier.journaltitleIEEE Journal of Radio Frequency Identificationen
dc.identifier.startpage768en
dc.identifier.urihttps://hdl.handle.net/10468/18197
dc.identifier.volume9en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofIEEE Journal of Radio Frequency Identificationen
dc.rights© 2025, the Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectChipless RFIDen
dc.subjectCNNsen
dc.subjectDeep learningen
dc.subjectData acquisitionen
dc.subjectElectromagnetic systemsen
dc.subjectMachine learningen
dc.subjectRCSen
dc.subjectRadio frequencyen
dc.subjectRFIDen
dc.subjectRoboten
dc.titleHybrid DCNN-enabled depolarizing chipless RFID: improving tag detection across varying lossy surfaces and shapesen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
oaire.citation.volume9en
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