Chipless wireless sensor coupled with nachine learning for oil temperature monitoring.

Temperature monitoring is essential in several industries driving the need for sensors. Chipless radio frequency identification (RFID) technology has emerged as a cost-effective solution, enabling wireless detection without the need for a power supply or electronics embedded in the sensor tags. However, a significant challenge lies in wirelessly monitoring temperature within liquid environments using chipless RFID tags as resonances vanish due to energy absorption in liquids. This work presents a chipless RFID sensor for wireless detection of oil temperature in a glass container. The temperature monitoring is based on the characterization of the permittivity of oil samples with different concentrations of total polar compounds (TPCs). After evaluating two chipless RFID tag designs, we propose to use a complementary ring resonator (CRR) tag as it exhibits a robust response to oil liquid volume, improving the detection of temperature in low-loss liquids and offering higher sensitivity. When the measurement results are coupled with machine learning (ML), we demonstrate that the response of the proposed tag as a wireless sensor can be used to estimate the temperature of oil samples with different quality (TPC) with an average test RMSE of 4 degrees C (standard deviation < 2 degrees C), in the approximate range 22 degrees C-95 degrees C

Chipless RFID tag implementation and machine-learning workflow for robust identification

In this work, we describe a complete step-by-step workflow to apply machine-learning (ML) classification for chipless radio-frequency identification (RFID) tag identification, covering: 1) the tag implementation criteria for circular ring resonator (CRR) and square ring resonator (SRR) arrays for ML interoperability; 2) the data collection procedure to get a sufficiently representative dataset of real measurements; 3) the ML techniques to visualize the data and reduce its dimensionality; 4) the evaluation of the ML classifier to ensure high-accuracy predictions on new measurements; and 5) a thresholding scheme to increase the certainty of the predictions. The differences in the tags' frequency responses are maximized by optimizing the Hamming distance between the tag identifiers (IDs) and by controlling each resonator array's radar cross section (RCS) level. We show that the proposed workflow achieves perfect accuracy for the identification of four tags at a fixed distance of 160 cm. We also evaluate the performance of the proposed workflow to identify up to 16 tags within a flexible range (up to 140 cm), showcasing the tradeoff between the number of tags that can be correctly classified based on the reading range