Cite this DOI

Athul Thomas, A. T. (2023). Interception amelioration of Chipless RFID Tags Using Deep Learning Techniques. *Journal of Science & Technology*, *8*(5), 22–32. https://doi.org/10.46243/jst.2023.v8.i05.pp22-32

@article{athulthomas2023interception,
  author    = {Athul Thomas, Athul Thomas},
  title     = {{Interception amelioration of Chipless RFID Tags Using Deep Learning Techniques}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {oct},
  volume    = {8},
  number    = {5},
  pages     = {22--32},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i05.pp22-32},
  url       = {https://doi.org/10.46243/jst.2023.v8.i05.pp22-32},
  language  = {en},
  abstract  = {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}
}

TY  - JOUR
TI  - Interception amelioration of Chipless RFID Tags Using Deep Learning Techniques
AU  - Athul Thomas, Athul Thomas
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/10/05/
VL  - 8
IS  - 5
SP  - 22
EP  - 32
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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
DO  - 10.46243/jst.2023.v8.i05.pp22-32
UR  - https://doi.org/10.46243/jst.2023.v8.i05.pp22-32
ER  -

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    "URL": "https://doi.org/10.46243/jst.2023.v8.i05.pp22-32",
    "title": "Interception amelioration of Chipless RFID Tags Using Deep Learning Techniques",
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    "abstract": "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",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i05.pp22-32 gives all four in one JSON answer.