Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroi...

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Main Authors:	Aleksander Paterno, Hypolito Kalinowski, Lucas Negri, Ademir Nied
Format:	Article
Language:	English
Published:	MDPI AG 2011-03-01
Series:	Sensors
Subjects:	fiber Bragg grating optical sensing peak detection fitting optimization
Online Access:	http://www.mdpi.com/1424-8220/11/4/3466/

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spelling	doaj-5baf1cf991804b2f9f4ec2e1462ac3dc2020-11-24T20:48:14ZengMDPI AGSensors1424-82202011-03-011143466348210.3390/s110403466Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance ImprovementAleksander PaternoHypolito KalinowskiLucas NegriAdemir NiedThis paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented. http://www.mdpi.com/1424-8220/11/4/3466/fiber Bragg gratingoptical sensingpeak detectionfittingoptimization
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Aleksander Paterno Hypolito Kalinowski Lucas Negri Ademir Nied
spellingShingle	Aleksander Paterno Hypolito Kalinowski Lucas Negri Ademir Nied Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement Sensors fiber Bragg grating optical sensing peak detection fitting optimization
author_facet	Aleksander Paterno Hypolito Kalinowski Lucas Negri Ademir Nied
author_sort	Aleksander Paterno
title	Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement
title_short	Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement
title_full	Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement
title_fullStr	Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement
title_full_unstemmed	Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement
title_sort	benchmark for peak detection algorithms in fiber bragg grating interrogation and a new neural network for its performance improvement
publisher	MDPI AG
series	Sensors
issn	1424-8220
publishDate	2011-03-01
description	This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented.
topic	fiber Bragg grating optical sensing peak detection fitting optimization
url	http://www.mdpi.com/1424-8220/11/4/3466/
work_keys_str_mv	AT aleksanderpaterno benchmarkforpeakdetectionalgorithmsinfiberbragggratinginterrogationandanewneuralnetworkforitsperformanceimprovement AT hypolitokalinowski benchmarkforpeakdetectionalgorithmsinfiberbragggratinginterrogationandanewneuralnetworkforitsperformanceimprovement AT lucasnegri benchmarkforpeakdetectionalgorithmsinfiberbragggratinginterrogationandanewneuralnetworkforitsperformanceimprovement AT ademirnied benchmarkforpeakdetectionalgorithmsinfiberbragggratinginterrogationandanewneuralnetworkforitsperformanceimprovement
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Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

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