Work

Experimental Dataset for Fiber Optic Specklegram Sensing Under Thermal Conditions and Use in a Deep Learning Interrogation Scheme

Year: 2025

Type: article

Abstract: This dataset comprises specklegram images acquired from a multimode optical fiber subjected to varying thermal conditions. Designed for training neural networks focused on developing Fiber Optic Specklegram Sensors (FSSs), these experimental data enable the detection of changes in speckle patterns corresponding to applied temperature variations. The dataset includes 24,528 images captured over a temperature range from 25 °C to 200 °C, with incremental steps of approximately 0.175 °C. Key acquisition parameters include a wavelength of 633 nm, a sensing zone length of 20 mm, and a multimode fiber with a core diameter of 62.5 μm. This dataset supports developing and validating temperature-sensing models using fiber optic technology and can facilitate benchmarking against other experimental or synthetic datasets. Finally, an implementation is presented for utilizing the dataset in a deep learning interrogation scheme.

Source: Data

DOI: 10.3390/data10040044

Authors: Francisco J. Vélez, Juan D. Arango, Víctor H. Aristizábal, Carlos Trujillo, Jorge A. Herrera-Ramírez

Institutions: Universidad Cooperativa de Colombia, Universidad EAFIT, Institución Universitaria ITM

Language: English

Topic: Advanced Fiber Optic Sensors

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funders: Universidad EAFIT, Instituto Tecnológico Metropolitano, Universidad Cooperativa de Colombia