Abstract
Fluctuation-enhanced sensing (FES) comprises the analysis of the stochastic component of the sensor signal and the utilization of the microscopic dynamics of the interaction between the agent and the sensor. We study the relationship between the measurement time window and the statistical error of the measurement data in the simplest case, when the output is the mean-square value of the stochastic signal. This situation is relevant at any practical case when the time window is finite, for example, when a sampling of the output of a fluctuation-enhanced array takes place; or a single sensor's activation (temperature, etc.) is stepped up; or a single sensor's output is monitored by sampling subsequently in different frequency windows. Our study provides a lower limit of the relative error versus data window size with different types of power density spectra: white noise, 1/f(flicker, pink) noise, and 1/f2 (red) noise spectra.
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