Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128761
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Type: Journal article
Title: Binary signal transmission in nonlinear sensors: stochastic resonance and human hand balance
Author: Duan, F.
Duan, L.
Chapeau-Blondeau, F.
Ren, Y.
Abbott, D.
Citation: IEEE Instrumentation and Measurement Magazine, 2020; 23(1):44-49
Publisher: IEEE
Issue Date: 2020
ISSN: 1094-6969
1941-0123
Statement of
Responsibility: 
Fabing Duan, Lingling Duan, François Chapeau-Blondeau, Yuhao Ren, and Derek Abbott
Abstract: Many sensors exhibit nonlinear characteristics [1]-[5] and are deployed in noisy environments [1]-[7]. In terms of device design and forming standards, this is a challenging area. However, it also presents opportunities for non-conventional signal processing methods based on stochastic resonance that have been shown to be of benefit for individual nonlinear sensors [1]-[7], sensor arrays [3]-[10], sensor networks [3], [8], [11], and even portable devices for people with reduced sensory capacity [12]-[14]. The most fascinating property of stochastic resonance is that nonlinear sensors connected in parallel or in a network yield improved performance over that achieved by using individual sensors [1]-[10]. Studies in stochastic resonance have led to evidence of noise-enhanced signal transmission and processing in nonlinear sensors, and noise can be exploited in the design of engineered devices [2]-[7], [10] and biological systems [1], [11]-[13]. This paper studies noise-enhanced signal transmission and processing in nonlinear sensors and also exploits the positive role of noise in the design of engineered devices that enhance the sensitivity of hand movements.
Keywords: Sensor arrays; noise level; vibrations; neurons; Hopfield neural networks; sensor phenomena and characterization
Rights: © 2020 IEEE
RMID: 1000017286
DOI: 10.1109/MIM.2020.8979523
Appears in Collections:Electrical and Electronic Engineering publications

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