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A Modified Adaptive Stochastic Resonance for Detecting Faint Signal in Sensors

View Article: PubMed Central

ABSTRACT

In this paper, an approach is presented to detect faint signals with strong noises in sensors by stochastic resonance (SR). We adopt the power spectrum as the evaluation tool of SR, which can be obtained by the fast Fourier transform (FFT). Furthermore, we introduce the adaptive filtering scheme to realize signal processing automatically. The key of the scheme is how to adjust the barrier height to satisfy the optimal condition of SR in the presence of any input. For the given input signal, we present an operable procedure to execute the adjustment scheme. An example utilizing one audio sensor to detect the fault information from the power supply is given. Simulation results show that the modified stochastic resonance scheme can effectively detect fault signal with strong noise.

No MeSH data available.


Input signal spectrum (upper figure) and output response spectrum (lower figure) of SR system when signal amplitude A=0.1V, frequency f=100Hz, noise intensity D=0.5, a = 6 andb = 1.
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f1-sensors-07-00157: Input signal spectrum (upper figure) and output response spectrum (lower figure) of SR system when signal amplitude A=0.1V, frequency f=100Hz, noise intensity D=0.5, a = 6 andb = 1.

Mentions: Fig.1 and Fig.2 illustrate the influence of SR on detecting signal A cos 2π ft. The parameters relating with Fig.1 are a =6, b=1, A= 0.1 V, f =100Hz and the gaussian white noise intensity D=0.5.In term of these parameters and the aforementioned expression (ΔV = a2/4b), the potential barrier ΔV = 9 is obtained. The parameters of Fig.2 are the same with those of Fig.1 except that the parameters a=1, correspondingly, ΔV = 0.25. Here SR will occur (shown in Fig.2).


A Modified Adaptive Stochastic Resonance for Detecting Faint Signal in Sensors
Input signal spectrum (upper figure) and output response spectrum (lower figure) of SR system when signal amplitude A=0.1V, frequency f=100Hz, noise intensity D=0.5, a = 6 andb = 1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756698&req=5

f1-sensors-07-00157: Input signal spectrum (upper figure) and output response spectrum (lower figure) of SR system when signal amplitude A=0.1V, frequency f=100Hz, noise intensity D=0.5, a = 6 andb = 1.
Mentions: Fig.1 and Fig.2 illustrate the influence of SR on detecting signal A cos 2π ft. The parameters relating with Fig.1 are a =6, b=1, A= 0.1 V, f =100Hz and the gaussian white noise intensity D=0.5.In term of these parameters and the aforementioned expression (ΔV = a2/4b), the potential barrier ΔV = 9 is obtained. The parameters of Fig.2 are the same with those of Fig.1 except that the parameters a=1, correspondingly, ΔV = 0.25. Here SR will occur (shown in Fig.2).

View Article: PubMed Central

ABSTRACT

In this paper, an approach is presented to detect faint signals with strong noises in sensors by stochastic resonance (SR). We adopt the power spectrum as the evaluation tool of SR, which can be obtained by the fast Fourier transform (FFT). Furthermore, we introduce the adaptive filtering scheme to realize signal processing automatically. The key of the scheme is how to adjust the barrier height to satisfy the optimal condition of SR in the presence of any input. For the given input signal, we present an operable procedure to execute the adjustment scheme. An example utilizing one audio sensor to detect the fault information from the power supply is given. Simulation results show that the modified stochastic resonance scheme can effectively detect fault signal with strong noise.

No MeSH data available.