Self-Monitoring Ultrasonic Gas Flow Meter Based on Vortex and Correlation Method

Lin, Yaoying; Hans, Volker

doi:10.1109/tim.2007.908137

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…Presently, the primary approach to bolster signal accuracy in flow rate sensors is signal filtering [14]. The field of signal processing for flow rate sensors has already established well-developed methodologies in both the time and frequency domains, including Kalman filtering [15,16], wavelet transform [17][18][19], Hilbert transform [20,21], and Fourier transform [22,23]. The crux of these conventional filtering algorithms lies in noise suppression, where diverse filtering techniques are employed to extract the signal based on the spectral distribution of interfering signals.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of bionic cilia flow rate sensor signals by single-well stochastic resonance

Fu,

Zhang,

et al. 2024

Meas. Sci. Technol.

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Based on the characteristics of non-periodic signals in bionic cilia flow rate sensors, an investigation on the real-time signal processing methodologies is conducted in single-well stochastic resonance. In this research, we derive a model for an adaptive single-well stochastic resonance system featuring nonlinear recuperation. To assess the scientific robustness and practical viability of the algorithm, a validation experiment was formulated utilizing the single-well stochastic resonance capacitance online detection and processing hardware system. The experimental findings show a notable reduction in noise interference, a marked enhancement in signal quality, and an approximate 0.55 increase in the maximum cross-correlation coefficient among sensor signals. Consequently, the model fulfills the requirements for effectively handling non-periodic signals from sensors.

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