2013
DOI: 10.1364/ol.38.002327
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Recovering a fiber Bragg grating axial strain distribution from its reflection spectrum

Abstract: A processing scheme able to obtain any arbitrary intragrating strain distribution of a fiber Bragg grating (FBG) is proposed and demonstrated. The processing method employs just the intensity of the FBG reflection spectrum to obtain its deformation profile by combining a geometrical processing scheme with the particle swarm optimization technique. The technique has been evaluated using several spectra generated from very heterogeneous strain distributions and with a real spectrum obtained from a 5 mm length FB… Show more

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Cited by 6 publications
(2 citation statements)
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“…(9,10) Recently, Rodriguez-Coboa et al have proposed a new processing scheme based on a comparison metric, geometrical processing, and a blind optimization technique, particle swarm optimization (PSO), to resolve axial perturbation applied to FBGs. (11) The simulation results were consistent with those of experiments. In this paper, the strain-distribution sensing method based only on the Fabry-Perot-like reflective spectra of FBGs is presented.…”
Section: Introductionsupporting
confidence: 84%
“…(9,10) Recently, Rodriguez-Coboa et al have proposed a new processing scheme based on a comparison metric, geometrical processing, and a blind optimization technique, particle swarm optimization (PSO), to resolve axial perturbation applied to FBGs. (11) The simulation results were consistent with those of experiments. In this paper, the strain-distribution sensing method based only on the Fabry-Perot-like reflective spectra of FBGs is presented.…”
Section: Introductionsupporting
confidence: 84%
“…Among heuristic bounded methods without starting point, the PSO method is widely used for optical applications. [31][32][33] The PSO method was proposed in 1995 by Kennedy and Eberhart. 34 It was used for the optimization of gold nanoshells using discrete dipole approximation (DDA) and Mie models for photothermal therapy, 35 and more general problems of plasmonics including the optimization of nanostructures and the resolution of the inverse problem.…”
Section: Combined Constrained Pso With Nelder-mead Algorithmmentioning
confidence: 99%