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Sensors 2011, 11(4), 3466-3482; doi:10.3390/s110403466

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

1
Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89223-100, Brazil
2
Graduate School of Electrical Engineering and Computer Science, Federal University of Technology-Paran√° (UTFPR), Curitiba 80230-901, Brazil
*
Author to whom correspondence should be addressed.
Received: 11 January 2011 / Revised: 10 March 2011 / Accepted: 14 March 2011 / Published: 24 March 2011
(This article belongs to the Section Physical Sensors)

Abstract

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. View Full-Text
Keywords: fiber Bragg grating; optical sensing; peak detection; fitting; optimization fiber Bragg grating; optical sensing; peak detection; fitting; optimization
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Negri, L.; Nied, A.; Kalinowski, H.; Paterno, A. Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement. Sensors 2011, 11, 3466-3482.

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