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Semenov VV, Zakharova A. Multiplexing-based control of stochastic resonance. CHAOS (WOODBURY, N.Y.) 2022; 32:121106. [PMID: 36587355 DOI: 10.1063/5.0123886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
We show that multiplexing (Here, the term "multiplexing" means a special network topology where a one-layer network is connected to another one-layer networks through coupling between replica nodes. In the present paper, this term does not refer to the signal processing issues and telecommunications.) allows us to control noise-induced dynamics of multilayer networks in the regime of stochastic resonance. We illustrate this effect on an example of two- and multi-layer networks of bistable overdamped oscillators. In particular, we demonstrate that multiplexing suppresses the effect of stochastic resonance if the periodic forcing is present in only one layer. In contrast, multiplexing allows us to enhance the stochastic resonance if the periodic forcing and noise are present in all the interacting layers. In such a case, the impact of multiplexing has a resonant character: the most pronounced effect of stochastic resonance is achieved for an appropriate intermediate value of coupling strength between the layers. Moreover, multiplexing-induced enhancement of the stochastic resonance can become more pronounced for the increasing number of coupled layers. To visualize the revealed phenomena, we use the evolution of the dependence of the signal-to-noise ratio on the noise intensity for varying strength of coupling between the layers.
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Affiliation(s)
- Vladimir V Semenov
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
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Zhang Y, Zheng R, Shimono K, Kaizuka T, Nakano K. Effectiveness Testing of a Piezoelectric Energy Harvester for an Automobile Wheel Using Stochastic Resonance. SENSORS 2016; 16:s16101727. [PMID: 27763522 PMCID: PMC5087514 DOI: 10.3390/s16101727] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 10/06/2016] [Accepted: 10/13/2016] [Indexed: 11/16/2022]
Abstract
The collection of clean power from ambient vibrations is considered a promising method for energy harvesting. For the case of wheel rotation, the present study investigates the effectiveness of a piezoelectric energy harvester, with the application of stochastic resonance to optimize the efficiency of energy harvesting. It is hypothesized that when the wheel rotates at variable speeds, the energy harvester is subjected to on-road noise as ambient excitations and a tangentially acting gravity force as a periodic modulation force, which can stimulate stochastic resonance. The energy harvester was miniaturized with a bistable cantilever structure, and the on-road noise was measured for the implementation of a vibrator in an experimental setting. A validation experiment revealed that the harvesting system was optimized to capture power that was approximately 12 times that captured under only on-road noise excitation and 50 times that captured under only the periodic gravity force. Moreover, the investigation of up-sweep excitations with increasing rotational frequency confirmed that stochastic resonance is effective in optimizing the performance of the energy harvester, with a certain bandwidth of vehicle speeds. An actual-vehicle experiment validates that the prototype harvester using stochastic resonance is capable of improving power generation performance for practical tire application.
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Affiliation(s)
- Yunshun Zhang
- Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro, Tokyo 153-8505, Japan.
| | - Rencheng Zheng
- Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro, Tokyo 153-8505, Japan.
| | - Keisuke Shimono
- Department of Mechanical Systems Engineering, Graduate School of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei-shi, Tokyo 184-8588, Japan.
| | - Tsutomu Kaizuka
- Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro, Tokyo 153-8505, Japan.
| | - Kimihiko Nakano
- Interfaculty Initiative in Information Studies, The University of Tokyo, 4-6-1, Komaba, Meguro, Tokyo 153-8505, Japan.
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Chapeau-Blondeau F, Rousseau D, Delahaies A. Rényi entropy measure of noise-aided information transmission in a binary channel. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2010; 81:051112. [PMID: 20866190 DOI: 10.1103/physreve.81.051112] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2010] [Indexed: 05/29/2023]
Abstract
This paper analyzes a binary channel by means of information measures based on the Rényi entropy. The analysis extends, and contains as a special case, the classic reference model of binary information transmission based on the Shannon entropy measure. The extended model is used to investigate further possibilities and properties of stochastic resonance or noise-aided information transmission. The results demonstrate that stochastic resonance occurs in the information channel and is registered by the Rényi entropy measures at any finite order, including the Shannon order. Furthermore, in definite conditions, when seeking the Rényi information measures that best exploit stochastic resonance, then nontrivial orders differing from the Shannon case usually emerge. In this way, through binary information transmission, stochastic resonance identifies optimal Rényi measures of information differing from the classic Shannon measure. A confrontation of the quantitative information measures with visual perception is also proposed in an experiment of noise-aided binary image transmission.
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Affiliation(s)
- François Chapeau-Blondeau
- Laboratoire d'Ingénierie des Systèmes Automatisés (LISA), Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France
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McDonnell MD, Abbott D. What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Comput Biol 2009; 5:e1000348. [PMID: 19562010 PMCID: PMC2660436 DOI: 10.1371/journal.pcbi.1000348] [Citation(s) in RCA: 364] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations--e.g., random noise--cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being "suboptimal". Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the "neural code". Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise--via stochastic resonance or otherwise--than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing "noise benefits", and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology.
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Affiliation(s)
- Mark D McDonnell
- Institute for Telecommunications Research, University of South Australia, Mawson Lakes, South Australia, Australia.
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Mtetwa N, Smith LS. Precision Constrained Stochastic Resonance in a Feedforward Neural Network. ACTA ACUST UNITED AC 2005; 16:250-62. [PMID: 15732404 DOI: 10.1109/tnn.2004.836195] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Stochastic resonance (SR) is a phenomenon in which the response of a nonlinear system to a subthreshold information-bearing signal is optimized by the presence of noise. By considering a nonlinear system (network of leaky integrate-and-fire (LIF) neurons) that captures the functional dynamics of neuronal firing, we demonstrate that sensory neurons could, in principle harness SR to optimize the detection and transmission of weak stimuli. We have previously characterized this effect by use of signal-to-noise ratio (SNR). Here in addition to SNR, we apply an entropy-based measure (Fisher information) and compare the two measures of quantifying SR. We also discuss the performance of these two SR measures in a full precision floating point model simulated in Java and in a precision limited integer model simulated on a field programmable gate array (FPGA). We report in this study that stochastic resonance which is mainly associated with floating point implementations is possible in both a single LIF neuron and a network of LIF neurons implemented on lower resolution integer based digital hardware. We also report that such a network can improve the SNR and Fisher information of the output over a single LIF neuron.
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Affiliation(s)
- Nhamoinesu Mtetwa
- Department of Computing Science, University of Stirling, Stirling FK9 4LA, UK.
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Zhang XJ. Limit cycles and stochastic resonance in a periodically driven Langevin equation subject to white noise. ACTA ACUST UNITED AC 2004. [DOI: 10.1088/0305-4470/37/30/006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Rousseau D, Duan F, Chapeau-Blondeau F. Suprathreshold stochastic resonance and noise-enhanced Fisher information in arrays of threshold devices. ACTA ACUST UNITED AC 2003; 68:031107. [PMID: 14524750 DOI: 10.1103/physreve.68.031107] [Citation(s) in RCA: 49] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2003] [Revised: 06/17/2003] [Indexed: 11/07/2022]
Abstract
We analyze the parametric estimation that can be performed on a signal buried in noise based on the parsimonious representation provided by a parallel array of threshold devices. The Fisher information contained in the array output about the input parameter is used as the measure of performance in the estimation task. For estimation on a suprathreshold input signal, we establish that enhancement of the Fisher information can be obtained by addition of independent noises to the thresholds in the array. Similar improvement by noise is also shown to be possible for the estimation error of the maximum likelihood estimator. These results extend the applicability of the recently introduced nonlinear phenomenon of suprathreshold stochastic resonance.
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Affiliation(s)
- David Rousseau
- Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France
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Rousseau D, Rojas Varela J, Chapeau-Blondeau F. Stochastic resonance for nonlinear sensors with saturation. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2003; 67:021102. [PMID: 12636648 DOI: 10.1103/physreve.67.021102] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2002] [Indexed: 05/24/2023]
Abstract
We analyze the transmission of a noisy signal by sensor devices which are linear for small inputs and saturate at large inputs. Large information-carrying signals are thus distorted in their transmission. We demonstrate conditions where addition of noise to such large input signals can reduce the distortion that they undergo in the transmission. This is established for periodic, as well as aperiodic, and random information-carrying signals. Various measures characterizing the transmission, such as signal-to-noise ratio, input-output cross correlation, and mutual information, are shown improvable by addition of noise. These results constitute another instance of the nonlinear phenomenon of stochastic resonance where addition of noise enhances the signal.
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Affiliation(s)
- David Rousseau
- Laboratoire d'Ingénierie des Systèmes Automatisés (LISA), Université d'Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France
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Bandrivskyy A, Luchinsky DG, McClintock PVE. Simple approximation of the singular probability distribution in a nonadiabatically driven system. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2002; 66:021108. [PMID: 12241151 DOI: 10.1103/physreve.66.021108] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2001] [Revised: 04/03/2002] [Indexed: 11/07/2022]
Abstract
Singular behavior and the formation of plateaus in the probability distribution in a nonadiabatically driven system are investigated numerically in the weak noise limit. A simple extension of the recently introduced logarithmic susceptibility theory is proposed to construct an approximation of the nonequilibrium potential that is valid throughout whole of the phase space.
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Affiliation(s)
- A Bandrivskyy
- Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom
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Chapeau-Blondeau F. Noise-aided nonlinear Bayesian estimation. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2002; 66:032101. [PMID: 12366162 DOI: 10.1103/physreve.66.032101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2002] [Indexed: 05/23/2023]
Abstract
Estimation on a noisy signal observed by a nonlinear sensor taking the form of a threshold quantizer is considered. The optimal Bayesian estimator with minimal error is derived in this nonlinear setting. The existence of conditions where the performance of this estimator can be improved by raising the level of noise is established, both theoretically and numerically. These results constitute a different instance of the nonlinear phenomenon of stochastic resonance for signal enhancement by noise.
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Affiliation(s)
- François Chapeau-Blondeau
- Laboratoire d'Ingénierie des Systèmes Automatisés (LISA), Université d'Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France
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Luchinsky D, Mannella R, McClintock P, Stocks N. Stochastic resonance in electrical circuits. II. Nonconventional stochastic resonance. ACTA ACUST UNITED AC 1999. [DOI: 10.1109/82.793711] [Citation(s) in RCA: 23] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Luchinsky D, Mannella R, McClintock P, Stocks N. Stochastic resonance in electrical circuits. I. Conventional stochastic resonance. ACTA ACUST UNITED AC 1999. [DOI: 10.1109/82.793710] [Citation(s) in RCA: 61] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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