Watermark Detector Based on Stochastic Resonance Phenomenon

2013 ◽  
Vol 11 (1) ◽  
pp. 396-401
Author(s):  
Sebastian Lanfranco ◽  
Lucas Horacio Mazzini ◽  
Alfredo Eduardo Dominguez ◽  
Jorge Luis Naguil
Author(s):  
V. Sorokin ◽  
I. Demidov

Adding noise to a system can ‘improve’ its dynamic behaviour, for example, it can increase its response or signal-to-noise ratio. The corresponding phenomenon, called stochastic resonance, has found numerous applications in physics, neuroscience, biology, medicine and mechanics. Replacing stochastic excitations with high-frequency ones was shown to be a viable approach to analysing several linear and nonlinear dynamic systems. For these systems, the influence of the stochastic and high-frequency excitations appears to be qualitatively similar. The present paper concerns the discussion of the applicability of this ‘deterministic’ approach to stochastic systems. First, the conventional nonlinear bi-stable system is briefly revisited. Then dynamical systems with multiplicative noise are considered and the validity of replacing stochastic excitations with deterministic ones for such systems is discussed. Finally, we study oscillatory systems with nonlinear damping and analyse the effects of stochastic and deterministic excitations on such systems. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.


2018 ◽  
Vol 30 (5) ◽  
pp. 986-1003 ◽  
Author(s):  
VLADISLAV SOROKIN ◽  
ILIYA BLEKHMAN

The stochastic resonance phenomenon implies “positive” changing of a system behaviour when noise is added to the system. The phenomenon has found numerous applications in physics, neuroscience, biology, medicine, mechanics and other fields. The present paper concerns this phenomenon for parametrically excited stochastic systems, i.e. systems that feature deterministic input signals that affect their parameters, e.g. stiffness, damping or mass properties. Parametrically excited systems are now widely used for signal sensing, filtering and amplification, particularly in micro- and nanoscale applications. And noise and uncertainty can be essential for systems at this scale. Thus, these systems potentially can exhibit stochastic resonance. In the present paper, we use a “deterministic” approach to describe the stochastic resonance phenomenon that implies replacing noise by deterministic high-frequency excitations. By means of the approach, we show that stochastic resonance can occur for parametrically excited systems and determine the corresponding resonance conditions.


Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 1092 ◽  
Author(s):  
Bruno Andò ◽  
Salvatore Baglio ◽  
Adi R. Bulsara ◽  
Vincenzo Marletta

In this paper the possibility to exploit advantageously the Stochastic Resonance phenomenon in a Nonlinear Energy Harvester to scavenge energy from wide band mechanical vibrations is experimentally demonstrated. The device is demonstrated to be capable of scavenging energy in case of a subthreshold sinusoidal vibration and a wideband noise (limited at 100 Hz) superimposed. The existence of an optimal value of the noise intensity maximizing the switching ratio of the bistable beam, then the performances, is experimentally demonstrated. The harvester is observed to generate power up to about 60 µW and 150 µW in case of a subthreshold sinusoidal input at 1 Hz and 3 Hz with a superimposed noise limited at 100 Hz.


2008 ◽  
Vol 22 (30) ◽  
pp. 5365-5373 ◽  
Author(s):  
RENHUAN YANG ◽  
AIGUO SONG

We study stochastic resonance (SR) in Hindmarsh–Rose (HR) neural network with small-world (SW) connections driven by external periodic stimulus, focusing on the dependence of properties of SR on the network structure parameters. It is found that, the SW neural network enhances SR compared with single neuron. By turning coupling strength c, two categories of SR were gained. With the connection-rewiring probability p increasing, the resonance curve becomes more and more sharp and the peak value increases gradually and then reaches saturation. The SW network enhances the SR peak value compared with regular network and widens resonance in ascending range compared with random network. When decreasing node degree k, the resonance range is enlarged, and the signal noise ratio (SNR) curve becomes a two peak one from a classic single peak SR curve, and then the stochastic resonance phenomenon almost disappears.


2011 ◽  
Vol 25 (13) ◽  
pp. 1775-1783 ◽  
Author(s):  
HUI-QING ZHANG ◽  
WEI XU ◽  
CHUN-YAN SUN ◽  
YONG XU

The phenomenon of stochastic resonance in a bistable system with multiple delays is investigated. The analytic expression of approximation stationary probability density is obtained by using small delay approximation based on probability density approach. Numerical simulation is performed and it is shown that the analytic results are in good agreement with Monte Carlo simulation. Then the expression of the signal-to-noise (SNR) is derived by using two-state theory. Finally, the effect of multiple delays on SNR is discussed. It is found that the stochastic resonance phenomenon can be suppressed or promoted when multiple delays are increased.


2008 ◽  
Vol 22 (06) ◽  
pp. 697-708
Author(s):  
YU-RONG ZHOU ◽  
FENG GUO ◽  
SHI-QI JIANG ◽  
XIAO-FENG PANG

The stochastic resonance phenomenon in a linear system subject to multiplicative and additive dichotomous noise is investigated. By the use of the linear-response theory and the properties of the dichotomous noise, the exact expressions have been found for the first two moments and the signal-to-noise ratio (SNR). It is shown that the SNR is a non-monotonic function of the correlation time of the additive dichotomous noise, and it varies non-monotonically with the bias of the external field, with the intensity and asymmetry of the multiplicative dichotomous noise, as well as with the external field frequency. Moreover, the SNR depends on the intensity of the cross noise between the multiplicative and additive dichotomous noise, as well as on the strength and asymmetry of the additive dichotomous noise.


2000 ◽  
Vol 62 (6) ◽  
pp. 4073-4078 ◽  
Author(s):  
M. I. Tsindlekht ◽  
I. Felner ◽  
M. Gitterman ◽  
B. Ya. Shapiro

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