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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.


Frequency spectrum of output. The parameter is: b=1; θ=0.9; a=0.6.
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f6-sensors-07-00157: Frequency spectrum of output. The parameter is: b=1; θ=0.9; a=0.6.

Mentions: According to the abovementioned procedure, the fault signal with noise is considered as the input of the system in Fig.3. The parameter b is taken as the fixed value 1 and θ is equal to 0.9. The parameter a is changed from 0.1 to 1 by the step length 0.1, so the m is set as 10. Because the sample frequency is 500Hz, there are 2000 sample points during the signal of 4 seconds. Correspondingly, n is taken as 2000. The simulation of algorithm is performed by the simulation tool of Matlab software. After adjusting the parameter, the optimal SR can be obtained. Fig.6 shows the power spectrum of the corresponding output when SR occurs. The signals with frequency component 65Hz and 80Hz can be clearly recognize. According to experience, these signals belong to the fault information.


A Modified Adaptive Stochastic Resonance for Detecting Faint Signal in Sensors
Frequency spectrum of output. The parameter is: b=1; θ=0.9; a=0.6.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-07-00157: Frequency spectrum of output. The parameter is: b=1; θ=0.9; a=0.6.
Mentions: According to the abovementioned procedure, the fault signal with noise is considered as the input of the system in Fig.3. The parameter b is taken as the fixed value 1 and θ is equal to 0.9. The parameter a is changed from 0.1 to 1 by the step length 0.1, so the m is set as 10. Because the sample frequency is 500Hz, there are 2000 sample points during the signal of 4 seconds. Correspondingly, n is taken as 2000. The simulation of algorithm is performed by the simulation tool of Matlab software. After adjusting the parameter, the optimal SR can be obtained. Fig.6 shows the power spectrum of the corresponding output when SR occurs. The signals with frequency component 65Hz and 80Hz can be clearly recognize. According to experience, these signals belong to the fault information.

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.