title: Benchmark for peak detection algorithms in fiber Bragg grating interrogation and a new neural network for its performance improvement.
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.
authors.affiliation: Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville, Brazil. lucashnegri@gmail.com
authors.forename: Lucas
authors.initials: L
authors.lastname: Negri
authors.forename: Ademir
authors.initials: A
authors.lastname: Nied
authors.forename: Hypolito
authors.initials: H
authors.lastname: Kalinowski
authors.forename: Aleksander
authors.initials: A
authors.lastname: Paterno
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019985:Benchmarking; D003198:Computer Simulation; D004777:Environment; D006801:Humans; D016571:Neural Networks, Computer