Abstract:In this paper, the stochastic resonance effect is considered. It is shown that the stochastic resonance effect appears in the conditions of operating on the nonlinear system of additive mixture of desired signal and noise. The numerical simulation of the output signal when exposed to the input of the system of additive mixture harmonic signal and noise with a uniform distribution is given. Analytical relational expressions for signal-to-noise ratio on the output of the nonlinear system are got. The analysis of signal-to-noise ratio is conducted on the output of the nonlinear system depending on the parameters of the input signal and noise. In this paper we have shown the stochastic resonance effect occurs mainly at low frequencies.

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The paper describes the method for electroencephalogram (EEG) analysis based on the stochastic resonance (SR) effect. The numerical computation has provided the separation of low frequency components that fall within the δ-rhythm band. This is currently central in the neuropathology diagnostics, because the presence of low frequencies in the EEG is abnormal and bears witness to the disease. For verification, the data obtained with the use of the SR effect have been compared with the computations based on the autocorrelation function (ACF) processing. The comparison has shown their good agreement.

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The problem of standing out a signal from an additive mixture of a harmonic signal and white Gaussian noise is considered. The analysis is based on the phenomenon of stochastic resonance (SR), which consists in amplifying a periodic signal under the influence of noise of a certain power. SR is a universal physical phenomenon that is typical of some nonlinear systems, and is came out not only in technical, but also in biological and social systems. When calculating the spectral characteristics of the output signal, Volterra series were used. The problem is solved using the transfer functions of Volterra without the initial definition of kernels. Volterra transfer functions are obtained by the harmonic input signal method. The influence of the input signal parameters, in particular the amplitude and frequency of the harmonic signal and the noise power, on the spectral power density of the output signal is studied. Optimal parameters values are determined. Criteria are formulated for using a stochastic filter to standing out a harmonic signal on the background white Gaussian noise.