Random noise helps make signals clearer

December 6, 2011

Scientists have shown the energy conditions, under which a weak signal supplied to a physical system emerges as a stronger signal at the output thanks to the presence of random noise (a process known as stochastic resonance), in a paper that has just been published in European Physical Journal B.

Stochastic resonance goes against the intuitive idea that where noise is present, the signal tends to fade. It occurs in systems where the response is not proportional to the applied input signal, known as nonlinear systems.

The authors, Shubhashis Rana, Sourabh Lahiri and Arun M. Jayannavar from the Institute of Physics, in Bhubaneswar, India, used a model consisting of a symmetric double-well energy potential in which a particle moves randomly. They studied the effect of the steepness of the walls of the confining energy potential by observing the movement of the particle, which they subjected to an external sinusoidal signal that alternately lowers either of the wells.

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The authors selected a quantifier – the average work done on the system by the signal – to determine the conditions under which the particle moving from one well to the opposite side well and back at every cycle of the signal reaches stochastic resonance. They found that it only occurs when the potential is "hard", meaning that it has sufficiently steep walls, but breaks down otherwise. Previous work used different quantifiers and found similar results, confirming their findings using numerical simulations.

This study contributes to improving scientists' understanding of stochastic resonance. It could, ultimately, contribute to gaining deeper insights into physics-related phenomena such as the processing of unclear images to increase their resolution* and biological systems, including mechanoreceptor cells in crayfish and the functioning of sensory neurons in humans.

More information: Rana S, Lahiri S, Jayannavar A M (2011). The role of soft versus hard bistable systems on stochastic resonance using average cycle energy as a quantifier. European Physical Journal B (EPJ B) 84, 2. DOI 10.1140/epjb/e2011-20802-9

Journal reference: European Physical Journal B search and more info website

Provided by Springer

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MentalHealthNut
Dec 06, 2011

Rank: not rated yet
Isn't this old? Dithering has been a long known tool.
Isaacsname
Dec 06, 2011

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Well, interestingly enough, people who have visual static, AKA stochastic resonance in visual perception, often feel they are " suffering " from it.

My own experience, ( I have it quite heavily ) is that it is likely behind peoples' abilities to notice small details, hence being known as " detail-oriented " or " anal retentive ", etc. People who have it don't make the connection and instead seek to suppress it with medicines.

Possibly there is an explanation for such personality traits directly traceable to this effect.

I didn't know what it was until a few years ago, but I knew growing up that I didn't see things like most people.

BTW, I already posted a link to this video at least a month ago :P
Callippo
Dec 06, 2011

Rank: 1 / 5 (1)
This research has a wide range applications. For example, at their tipping point the financial markets are becoming unstable and they tend to behave chaotically. You can improve the stability of financial markets somewhat paradoxically by introduction of chaotic component into stock prices. It's actually quite easy to understand - if the brokers will perceive small fluctuations as a normal, they will not react to every tiny fluctuation of prices so desperately. It's particularly important at the situation, when the stock exchange is realized with computers who are following the tiniest fluctuations of stock prices. This automatized stock exchange enables to profit from subtlest fluctuations of stock prices (which is why it's used, after all) - but it's doing stock market very unstable. An artificial introduction of the noise into stock prices will effectively prevent it.
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stochastic resonance, nonlinear systems, random noise, input signal, particles, signaling
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