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Telecommunications and Radio Engineering, 78(16), 1435–1443. https://doi. org/10.1615/TelecomRadEng.v78.i16.20" }, { "key": "ref15", "doi": "10.3390/s20072135", "unstructured": "Mulloni, V., & Donelli, M. (2020). Chipless RFID Sensors for the Internet of Things: Challenges and Opportunities. Sensors, 20(7), 2135. https://doi. org/10.3390/s20072135" }, { "key": "ref16", "doi": "10.3390/s20216385", "unstructured": "Nastasiu, D., Scripcaru, R., Digulescu, A., Ioana, C., De Amorim, R., Barbot, N., Siragusa, R., Perret, E., & Popescu, F. (2020). A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches. Sensors, 20(21), 6385. https://doi.org/10.3390/s20216385" }, { "key": "ref17", "doi": "10.1109/tap.2014.2330586", "unstructured": "Nijas, C. M., Deepak, U., Vinesh, P. V., Sujith, R., Mridula, S., Vasudevan, K., & Mohanan, P. (2014). Low-Cost Multiple-Bit Encoded Chipless RFID Tag Using Stepped Impedance Resonator. Athul Thomas, James Kurian: Interception amelioration of Chipless RFID Tags Using Deep Learning Techniques IEEE Transactions on Antennas and Propagation, 62(9), 4762–4770. https://doi.org/10.1109/ TAP.2014.2330586" }, { "key": "ref18", "doi": "10.1109/tap.2012.2207081", "unstructured": "Nijas, C. M., Dinesh, R., Deepak, U., Rasheed, A., Mridula, S., Vasudevan, K., & Mohanan, P. (2012). Chipless RFID Tag Using Multiple Microstrip Open Stub Resonators. IEEE Transactions on Antennas and Propagation, 60(9), 4429–4432. https://doi.org/10.1109/TAP.2012.2207081" }, { "key": "ref19", "doi": "10.1109/map.2006.323323", "unstructured": "Nikitin, P. V., & Rao, K. V. S. (2006). Theory and measurement of backscattering from RFID tags. IEEE Antennas and Propagation Magazine, 48(6), 212–218. https://doi.org/10.1109/ MAP.2006.323323" }, { "key": "ref20", "doi": "10.26438/ijcse/v6i10.7478", "unstructured": "Patel, H. H., & Prajapati, P. (2018). Study and Analysis of Decision Tree Based Classification Algorithms. International Journal of Computer Sciences and Engineering, 6(10), 74–78. https:// doi.org/10.26438/ijcse/v6i10.7478" }, { "key": "ref21", "doi": "10.1109/tmtt.2009.2017323", "unstructured": "Preradovic, S., Balbin, I., Karmakar, N. C., & Swiegers, G. F. (2009). Multiresonator-Based Chipless RFID System for Low-Cost Item Tracking. IEEE Transactions on Microwave Theory and Techniques, 57(5), 1411–1419. https://doi. org/10.1109/TMTT.2009.2017323" }, { "key": "ref22", "doi": "10.1016/b978-1-78548-144-4.50003-8", "unstructured": "Rance, O., Perret, E., Siragusa, R., & Lema??tre-Auger, P. (2017). RCS synthesis for chipless RFID: Theory and design. http://search.ebscohost.com/login" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/767", "sha256_before": null, "sha256_after": "65c4bcd4862c8ae710e1fec5163a5224e70520ec70c9e6c0ccdca04babc36840" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:46", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i05.pp22-32", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Interception amelioration of Chipless RFID Tags Using Deep Learning Techniques", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i05.pp22-32" } ], "agents": [ { "role": "author", "name": { "given": "Athul Thomas", "family": "Athul Thomas" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-10-05", "date_type": "PublicationDate", "online": "2023-10-05" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "5", "pages": { "first": "22", "last": "32" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/767", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/767/694", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/767/1826", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/admin/uploads/JSt%20Final.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "In this paper, we have proposed data-driven methods to enhance the interrogation and reliability of Chipless Radio Frequency Identification systems. Six binary combinations of an 8-bit RFID tag are fabricated on a Rogers RT / Duroid ® 5880 substrate with a permittivity of 2.2 and a loss tangent of 0.0009 to create the model dataset. The tag’s frequency response encompasses eight identifiable frequency resonances in the 3–10 GHz frequency band. By analyzing the spectral signature of the backscattered chip-less RFID tags at possible reach and orientation, two distinct datasets corresponding to ideal (within the anechoic chamber) and natural environments are prepared. The data sets are trained and evaluated using the SVM, KNN, DT and DNN approaches. A validation accuracy of 97.5% is obtained for the DNN model in actual environmental conditions in the presence of clutter and noises. The DNN model attains an accuracy of 99.5% on the ideal dataset and 97.5% on the actual dataset. The model extracted tag information up to 70cm from the interrogator, which is about a 20% increase in reading range compared to conventional interrogation methods", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-10-05", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00017", "unstructured": "Abdullah, A., Stroulia, E., & Nawaz, F. (2020). Efficiency Optimization in Supply Chain Using RFID Technology. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 1–6. https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00017" }, { "key": "ref2", "doi": "10.1109/ccece.2015.7129326", "unstructured": "Akbari, A., Mirshahi, S., & Hashemipour, M. (2015). 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RCS synthesis for chipless RFID: Theory and design. http://search.ebscohost.com/login" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/767", "sha256_before": "65c4bcd4862c8ae710e1fec5163a5224e70520ec70c9e6c0ccdca04babc36840", "sha256_after": "a8d8cb65249c36286cd8d70ba4718e52e64475ae5953932abf43c583e6db367c" } ] }