Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Weak-periodic stochastic resonance in a parallel array of static nonlinearities
Author: Ma, Y.
Duan, F.
Chapeau-Blondeau, F.
Abbott, D.
Citation: PLoS One, 2013; 8(3):1-7
Publisher: Public Library of Science
Issue Date: 2013
ISSN: 1932-6203
Statement of
Yumei Ma, Fabing Duan, François Chapeau-Blondeau and Derek Abbott
Abstract: This paper studies the output-input signal-to-noise ratio (SNR) gain of an uncoupled parallel array of static, yet arbitrary, nonlinear elements for transmitting a weak periodic signal in additive white noise. In the small-signal limit, an explicit expression for the SNR gain is derived. It serves to prove that the SNR gain is always a monotonically increasing function of the array size for any given nonlinearity and noisy environment. It also determines the SNR gain maximized by the locally optimal nonlinearity as the upper bound of the SNR gain achieved by an array of static nonlinear elements. With locally optimal nonlinearity, it is demonstrated that stochastic resonance cannot occur, i.e. adding internal noise into the array never improves the SNR gain. However, in an array of suboptimal but easily implemented threshold nonlinearities, we show the feasibility of situations where stochastic resonance occurs, and also the possibility of the SNR gain exceeding unity for a wide range of input noise distributions.
Keywords: Models, Theoretical; Nonlinear Dynamics; Algorithms; Signal-To-Noise Ratio
Rights: Copyright: © 2013 Ma et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0020126605
DOI: 10.1371/journal.pone.0058507
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
File Description SizeFormat 
hdl_78322.pdfPublished version365.58 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.