Abstract
Abstract 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.
Published Version
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