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:
Online Access:http://www.mdpi.com/1424-8220/11/4/3466/
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author Aleksander Paterno
Hypolito Kalinowski
Lucas Negri
Ademir Nied
author_facet Aleksander Paterno
Hypolito Kalinowski
Lucas Negri
Ademir Nied
author_sort Aleksander Paterno
collection DOAJ
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.
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spelling doaj.art-5baf1cf991804b2f9f4ec2e1462ac3dc2022-12-22T03:10:29ZengMDPI 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
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
title 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_short 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
topic fiber Bragg grating
optical sensing
peak detection
fitting
optimization
url http://www.mdpi.com/1424-8220/11/4/3466/
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AT lucasnegri benchmarkforpeakdetectionalgorithmsinfiberbragggratinginterrogationandanewneuralnetworkforitsperformanceimprovement
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