Accuracy versus speed in fluctuation-enhanced sensing

TL;DR

This paper investigates how the measurement time window affects the statistical error in fluctuation-enhanced sensing, providing a lower limit of error for different noise spectra types.

Contribution

It offers a theoretical analysis of the relationship between data window size and measurement error for various noise spectra in fluctuation-enhanced sensing.

Findings

Derived lower bounds of relative error for white, pink, and red noise spectra.

Analyzed the impact of finite measurement windows on sensing accuracy.

Provided insights applicable to practical fluctuation-enhanced sensing scenarios.

Abstract

Fluctuation-enhanced sensing 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/f^2 (red) noise spectra.