Deep learning assisted robust detection techniques for a chipless RFID sensor tag

Simple item page
dc.contributor.authorRather, Nadeemen
dc.contributor.authorSimorangkir, Roy B. V. B.en
dc.contributor.authorBuckley, John L.en
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.accessioned2023-12-01T11:45:02Z
dc.date.available2023-12-01T11:45:02Z
dc.date.issued2023-11-28en
dc.description.abstractIn this paper, we present a new approach for robust reading of identification and sensor data from chipless RFID sensor tags. For the first time, Machine Learning (ML) and Deep Learning (DL) regression modelling techniques are applied to a dataset of measured Radar Cross Section (RCS) data that has been derived from large-scale robotic measurements of custom-designed, 3-bit chipless RFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A data set of 9,600 Electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-dimensional Convolutional Neural Network (1D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of identification (ID) and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalise well on the given data. However, the 1D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1D CNN model architectures performed well with a low Root Mean Square Error (RSME) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for the capacitive sensing.en
dc.description.sponsorshipEnterprise Ireland (EI-DT20180291-A)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid2502710
dc.identifier.citationRather, N., Simorangkir, R. B. V. B., Buckley, J. L., O’Flynn, B. and Tedesco, S. (2023) 'Deep learning assisted robust detection techniques for a chipless RFID sensor tag', IEEE Transactions on Instrumentation and Measurement, 73, 2502710 (10pp). doi: 10.1109/TIM.2023.3334378en
dc.identifier.doi10.1109/tim.2023.3334378en
dc.identifier.eissn1557-9662en
dc.identifier.endpage10
dc.identifier.issn0018-9456en
dc.identifier.journaltitleIEEE Transactions on Instrumentation and Measurementen
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/15287
dc.identifier.volume73
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)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.rights© 2023, the Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectChipless RFIDen
dc.subjectConvolutional neural networksen
dc.subjectDeep learningen
dc.subjectElectromagneticsen
dc.subjectMachine learningen
dc.subjectRadar cross sectionen
dc.subjectRFIDen
dc.subjectRobotsen
dc.titleDeep learning assisted robust detection techniques for a chipless RFID sensor tagen
dc.typeArticle (peer-reviewed)en
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Deep_Learning_Assisted_Robust_Detection_Techniques_for_a_Chipless_RFID_Sensor_Tag.pdf
Size:
9.26 MB
Format:
Adobe Portable Document Format
Description:
Accepted Version
Thumbnail Image
Name:
Deep-Learning-Assisted_Robust_Detection_Techniques_for_a_Chipless_RFID_Sensor_Tag.pdf
Size:
3.3 MB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Tyndall National Institute - Journal Articles