scholarly journals Effect of humidity on optical fiber distributed sensor based on Brillouin scattering

2008 ◽  
Author(s):  
Carlos A. Galindez ◽  
Francisco J. Madruga ◽  
M. Lomer ◽  
A. Cobo ◽  
Jose M. Lopez-Higuera
Keyword(s):  
Optical Fiber ◽  
Brillouin Scattering ◽  
Distributed Sensor

scholarly journals A Brief Review of Specialty Optical Fibers for Brillouin-Scattering-Based Distributed Sensors

2018 ◽  
Vol 8 (10) ◽  
pp. 1996 ◽  
Author(s):  
Peter Dragic ◽  
John Ballato
Keyword(s):  
Optical Fiber ◽  
Silicate Glass ◽  
Optical Fibers ◽  
Brillouin Scattering ◽  
Distributed Sensors ◽  
Distributed Sensor ◽  
Fiber Design ◽  
New Thinking ◽  
Sensing Application ◽  
Brillouin Gain

Specialty optical fibers employed in Brillouin-based distributed sensors are briefly reviewed. The optical and acoustic waveguide properties of silicate glass optical fiber first are examined with the goal of constructing a designer Brillouin gain spectrum. Next, materials and their effects on the relevant Brillouin scattering properties are discussed. Finally, optical fiber configurations are reviewed, with attention paid to fibers for discriminative or other enhanced sensing configurations. The goal of this brief review is to reinforce the importance of fiber design to distributed sensor systems, generally, and to inspire new thinking in the use of fibers for this sensing application.

scholarly journals Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 474
Author(s):  
Fen Xiao ◽  
Mingxing Lv ◽  
Xinwan Li
Keyword(s):  
Neural Network ◽  
Optical Fiber ◽  
Frequency Shift ◽  
Brillouin Scattering ◽  
Principal Component ◽  
Feedforward Neural Network ◽  
Gain Spectrum ◽  
Fiber Sensors ◽  
Brillouin Gain ◽  
Distributed Optical Fiber

Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.

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