@inproceedings{424ecf35dbcb473bb7a35753b2132606,
title = "1D-CNN Enabled Depolarizing Chipless RFID",
abstract = "This paper presents the application of a deep learning (DL) regression model to a specialised dataset of depolarised Radar Cross Section (RCS) measurements of a custom 3-bit CRFID tag. A dataset of 12,600 Electromagnetic (EM) RCS signatures were utilized to train and validate a 1-dimensional Convolutional Neural Network (1D CNN) architecture. For the first time, DL implementation approach for reliably detecting identification (ID) information from depolarized signals is presented. In addition, we report the first case 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 the encoded tag ID information. It was observed that the developed model was able to generalize well to given data, achieving a low RMSE of 0.040(0.66\%) on an unseen test dataset.",
keywords = "Artificial Intelligence, Automation, Chipless RFID, CNNs, Data Acquisition, Deep Learning, Electromagnetic Systems, Machine Learning, Radio Frequency, RCS, RFID, Robot",
author = "Nadeem Rather and Simorangkir, \{Roy B.V.B.\} and Buckley, \{John L.\} and Brendan O'Flynn and Salvatore Tedesco",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on RFID Technology and Applications, RFID-TA 2025 ; Conference date: 28-10-2025 Through 31-10-2025",
year = "2025",
doi = "10.1109/RFID-TA63091.2025.11265818",
language = "English",
series = "2025 IEEE International Conference on RFID Technology and Applications, RFID-TA 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE International Conference on RFID Technology and Applications, RFID-TA 2025",
address = "United States",
}