Low-profile FSS design methodology to increase isolation between vehicle-borne multifrequency antennas

The present work describes a new approach for the design of a Frequency-Selective Surface (FSS) in the context of frequency filters to increase isolation between two vehicle-borne antennas. A compact FSS design based on nested square meandered resonators is optimized for multifrequency operation. Furthermore, a design workflow is proposed. In general, the measurement of low-profile FSS does not correspond to simulation through Floquet modes based on periodic boundary conditions due to the lack of uniformity of mutual coupling among the FSS unit cells. The proposed method demonstrates the agreement between the infinite simulation and the measurement of the finite prototype once a convenient scale factor is applied, which facilitates the design workflow. In this case, an FSS is used as an efficient filter to increase the isolation between antennas by 6 dB in three representative bands (3GPP, WiFI I and II). In this way, multifrequency antennas can be placed at approximately half their actual distance with the same performance in spatial-constrained vehicular environments

Low-profile FSS design methodology to increase isolation between vehicle-borne multifrequency antennas

The present work describes a new approach for the design of a Frequency-Selective Surface (FSS) in the context of frequency filters to increase isolation between two vehicle-borne antennas. A compact FSS design based on nested square meandered resonators is optimized for multifrequency operation. Furthermore, a design workflow is proposed. In general, the measurement of low-profile FSS does not correspond to simulation through Floquet modes based on periodic boundary conditions due to the lack of uniformity of mutual coupling among the FSS unit cells. The proposed method demonstrates the agreement between the infinite simulation and the measurement of the finite prototype once a convenient scale factor is applied, which facilitates the design workflow. In this case, an FSS is used as an efficient filter to increase the isolation between antennas by 6 dB in three representative bands (3GPP, WiFI I and II). In this way, multifrequency antennas can be placed at approximately half their actual distance with the same performance in spatial-constrained vehicular environments

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

Modeling the impact on wheel sensor readouts by Eddy Current Brakes installed in high-speed trains

This paper presents a model to anticipate the impact of Eddy Current Brakes (ECBs) installed in high-speed trains on the readouts of rail-side wheel sensors. The purpose is to anticipate false positive readouts of train wheels when traversing, one of the main obstacles for full ECB deployment. The ECB type EWB 154 from Knorr-Bremse and Wheel Sensor types RSR180 and RSR123 from Frauscher Sensor Technology are represented in a comprehensive model, integrating LTSpice and CST Microwave Studio. The wheel sensor predicted readout error is 4% compared to measurements when DC current is not applied to the ECB (passive case). It is demonstrated that the RSR180 is not compatible with ECBs, whereas the RSR123 is. The impact of active (DC current fed) brakes is analyzed when performing running tests with a high-speed ICE 3 train equipped with ECBs. The model is adjusted to study the saturation of the rail and ECB pole cores. The extra damping of the wheel sensor fingerprint is modeled by an extra 6% drop that may well be applicable to passive tests in a laboratory setting to shift to active tests without actually performing them. In this way, cost and time would be saved. Based on the model outcomes, a test bench is recommended for laboratory tests to emulate active behavior