Articles | Volume 12, issue 2
https://doi.org/10.5194/jsss-12-247-2023
https://doi.org/10.5194/jsss-12-247-2023
Regular research article
 | 
16 Nov 2023
Regular research article |  | 16 Nov 2023

Wireless surface acoustic wave resonator sensors: fast Fourier transform, empirical mode decomposition or wavelets for the frequency estimation in one shot?

Angel Scipioni, Pascal Rischette, and Agnès Santori

Related subject area

Measurement systems: Sensor signal processing and electronics
Extraction of nanometer-scale displacements from noisy signals at frequencies down to 1 mHz obtained by differential laser Doppler vibrometry
Dhyan Kohlmann, Marvin Schewe, Hendrik Wulfmeier, Christian Rembe, and Holger Fritze
J. Sens. Sens. Syst., 13, 167–177, https://doi.org/10.5194/jsss-13-167-2024,https://doi.org/10.5194/jsss-13-167-2024, 2024
Short summary
Simple in-system control of microphone sensitivities in an array
Artem Ivanov
J. Sens. Sens. Syst., 13, 81–88, https://doi.org/10.5194/jsss-13-81-2024,https://doi.org/10.5194/jsss-13-81-2024, 2024
Short summary
Ultrasonic measurement setup for monitoring pre-thawing stages of food
Ruchi Jha, Walter Lang, and Reiner Jedermann
J. Sens. Sens. Syst., 12, 133–139, https://doi.org/10.5194/jsss-12-133-2023,https://doi.org/10.5194/jsss-12-133-2023, 2023
Short summary
Digital twin concepts for linking live sensor data with real-time models
Reiner Jedermann, Kunal Singh, Walter Lang, and Pramod Mahajan
J. Sens. Sens. Syst., 12, 111–121, https://doi.org/10.5194/jsss-12-111-2023,https://doi.org/10.5194/jsss-12-111-2023, 2023
Short summary
Assisting the automated analysis of chemical–analytical measurements in spirits using validated algorithms and an intuitive user interface
Andreas T. Grasskamp, Satnam Singh, Helen Haug, and Tilman Sauerwald
J. Sens. Sens. Syst., 12, 93–101, https://doi.org/10.5194/jsss-12-93-2023,https://doi.org/10.5194/jsss-12-93-2023, 2023
Short summary

Cited articles

Antoniadis, A.: Wavelet methods in statistics: some recent developments and their applications, Statistics Surveys, 1, 16–55, https://doi.org/10.1214/07-SS014, 2007. a
Antoniadis, A., Bigot, J., and Sapatinas, T.: Wavelet estimators in nonparametric regression: A comparative simulation study, J. Stat. Softw., 6, 1–83, https://doi.org/10.18637/jss.v006.i06, 2001. a
Brandt, A.: Noise and vibration analysis: signal analysis and experimental procedures, John Wiley & Sons, ISBN 9780470978160, https://doi.org/10.1002/9780470978160, 2011. a
Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., Chen, B., and He, Z.: Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review, Mech. Syst. Signal Pr., 70–71, 1–35, https://doi.org/10.1016/j.ymssp.2015.08.023, 2016. a
Chen, Y., Li, H., Hou, L., Wang, J., and Bu, X.: An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals, Measurement, 127, 356–365, https://doi.org/10.1016/j.measurement.2018.06.006, 2018. a
Download
Short summary
Many applications which measure physical quantities rely on wireless surface acoustic wave sensors. The accuracy of this sensor depends directly on the measurement of its main frequency. This paper aims to compare three methods for this measurement in one shot: fast Fourier transform, discrete wavelet transform, and empirical mode decomposition. Results show that the choice of the method is conditioned by the disturbance level and that the wavelet method is the best way for harsh environments.