STOCHASTIC RESONANCE IN SATURATION NONLINEARITIES BASED ON SIGNAL DETECTION

2008 ◽  
Vol 08 (02) ◽  
pp. L229-L235 ◽  
Author(s):  
LEI ZHANG ◽  
JUN HE ◽  
AIGUO SONG
Keyword(s):  
Signal Detection ◽  
White Noise ◽  
Stochastic Resonance ◽  
Gaussian White Noise ◽  
Detection Performance ◽  
Sinusoidal Signal ◽  
Signal Noise ◽  
Noise Ratio

Recently, it was reported that some saturation nonlinearities could effectively act as noise-aided signal-noise-ratio amplifiers. In the letter we consider the signal detection performance of saturation nonlinearities driven by a sinusoidal signal buried in Gaussian white noise. It is showed that the signal detection statistics still undergo a nonmonotonic evolution as noise is raised. We also particularly show that an improvement of the SNR in terms of the first harmonic does not imply the possibility to improve the signal detection performance through stochastic resonance. The study might also complement other reports about stochastic resonance in saturation nonlinearities.

scholarly journals HIGH SIGNAL-TO-NOISE RATIO GAIN BY STOCHASTIC RESONANCE IN A DOUBLE WELL

2001 ◽  
Vol 01 (03) ◽  
pp. L181-L188 ◽  
Author(s):  
ZOLTAN GINGL ◽  
PETER MAKRA ◽  
ROBERT VAJTAI
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Total Power ◽  
High Signal ◽  
Signal To Noise ◽  
Simulation Techniques ◽  
Response Range ◽  
Signal Simulation ◽  
Noise Ratio

We demonstrate that signal-to-noise ratio (SNR) can be significantly improved by stochastic resonance in a double well potential. The overdamped dynamical system was studied using mixed signal simulation techniques. The system was driven by wideband Gaussian white noise and a periodic pulse train with variable amplitude and duty cycle. Operating the system in the non-linear response range, we obtained SNR gains much greater than unity. In addition to the classical SNR definition, the ratio of the total power of the signal to the power of the noise part was also measured and it showed better signal improvement.

Research on Virtual-Switch Characteristics of Noise-Enhanced Stochastic Resonance Using Leaky Integrate-and-Fire Model

2010 ◽  
Vol 439-440 ◽  
pp. 1324-1327
Author(s):  
Guo Hua Hui
Keyword(s):  
White Noise ◽  
Stochastic Resonance ◽  
Noise Intensity ◽  
Neuron Model ◽  
Gaussian White Noise ◽  
Spike Trains ◽  
Neural Data ◽  
Fire Model ◽  
Integrate And Fire ◽  
Lif Neuron

Virtual-switch characteristics of noise-induced stochastic resonance using leaky integrate-and-fire (LIF) model was focused in this article. For LIF neuron model, Gaussian white noise was added to the simulating system. The condition of spike-starting was studied through the system. With the increase of noise intensity, the stimulated spikes became more and more intensive. With proper white noise intensity, the amount of excitatory spikes was enough to generate spike trains. We expected to design a virtual-switch utilizing this mechanism for neural data processing field.

Probabilistic analysis and ghost-stochastic resonance of a hybrid energy harvester under Gaussian White noise

Meccanica ◽  
2020 ◽  
Vol 55 (9) ◽  
pp. 1679-1691
Author(s):  
G. J. Fezeu ◽  
I. S. Mokem Fokou ◽  
C. Nono Dueyou Buckjohn ◽  
M. Siewe Siewe ◽  
C. Tchawoua
Keyword(s):  
White Noise ◽  
Stochastic Resonance ◽  
Probabilistic Analysis ◽  
Energy Harvester ◽  
Gaussian White Noise ◽  
Hybrid Energy ◽  
Hybrid Energy Harvester

Stochastic Resonance in a Time-Delayed Logistic Growth Model Driven by Periodic Signal and White Noise

2011 ◽  
Vol 117-119 ◽  
pp. 703-707
Author(s):  
Yu Rong Zhou ◽  
Chong Qiu Fang
Keyword(s):  
White Noise ◽  
Stochastic Resonance ◽  
Growth Model ◽  
Delay Time ◽  
Signal To Noise Ratio ◽  
Logistic Growth ◽  
Periodic Signal ◽  
Signal To Noise ◽  
Logistic Growth Model ◽  
Noise Ratio

stochastic resonance; time-delayed Logistic growth model; signal-to-noise ratio Abstract. The stochastic resonance in a time-delayed Logistic growth model subject to correlated multiplicative and additive white noise as well as to multiplicative periodic signal is investigated. Using small time delay approximation, we get the expression of the signal-to-noise ratio (SNR). It is found that the SNR is a non-monotonic function of the system parameters, of the intensities of the multiplicative and additive noise, as well as of the correlation strength between the two noises. The effects of the delay time in the random force is in opposition to that of the delay time in the deterministic force.

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