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Advances in Distributed Optical Fiber Sensing Systems
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Advances in Distributed Optical Fiber Sensing Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 1687

Special Issue Editor

Dr. Huijuan Wu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: sensor information acquisition and processing; pattern recognition; optical fiber sensing and Internet of Things technology

Special Issue Information

Dear Colleagues,

Distributed optical fiber sensing (DOFS) has been extensively studied and widely used in natural disaster prediction and urban infrastructure security guarding, including intrusion detection, structure health monitoring, etc., with superior advantages such as fully distributed sensing in long range, low operation cost and long service lifetime. Significant advances in the research and development of DOFS have recently been made. Therefore, for this Special Issue we invite all papers and contributions on all topics related to DOFS, such as new designs of sensing fibers or cables, instrumentation, high-fidelity demodulation technologies and advanced signal processing methods related to different types of distributed sensors. We also welcome papers presenting important application advances and challenges of DOFS at present and in the future.

Dr. Huijuan Wu
Guest Editor

Manuscript Submission Information

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Keywords

  • distributed optical fiber sensor
  • novel fiber cable
  • smart sensing
  • demodulation
  • detection
  • identification
  • distributed signal processing
  • safety monitoring
  • environment monitoring
  • structural health monitoring
  • smart city
  • field applications

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Published Papers (1 paper)

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Research

17 pages, 15276 KiB  
Article
Multichannel Classifier for Recognizing Acoustic Impacts Recorded with a phi-OTDR
by Ivan Alekseevich Barantsov, Alexey Borisovich Pnev, Kirill Igorevich Koshelev, Egor Olegovich Garin, Nickolai Olegovich Pozhar and Roman Igorevich Khan
Sensors 2023, 23(14), 6402; https://doi.org/10.3390/s23146402 - 14 Jul 2023
Cited by 1 | Viewed by 1224
Abstract
The purpose of this work is to increase the security of the perimeter of an area from unauthorized intrusions by creating an improved algorithm for classifying acoustic impacts recorded with a sensor system based on a phase-sensitive optical time reflectometer (phi-OTDR). The algorithm [...] Read more.
The purpose of this work is to increase the security of the perimeter of an area from unauthorized intrusions by creating an improved algorithm for classifying acoustic impacts recorded with a sensor system based on a phase-sensitive optical time reflectometer (phi-OTDR). The algorithm includes machine learning, so a dataset consisting of two classes was assembled. The dataset consists of two classes. The first class is the data of the steps, and the second class is other non-stepping influences (engine noise, a passing car, a passing cyclist, etc.). As an intrusion signal, a human walking signal is analyzed and recorded in frames of 5 s, which passed the threshold condition. Since, in most cases, the intruder moves on foot to overcome the perimeter, the analysis of the acoustic effects generated during the step will increase the efficiency of the perimeter detection tools. When walking quietly, step signals can be quite weak, and background signals can contain high energy and visually resemble the signals you are looking for. Therefore, an algorithm was created that processes space–time diagrams developed in real time, which are grayscale images. At the same time, during the processing of one image, two more images are calculated, which are the result of processing the denoised autoencoder and the created mathematical model of the adaptive correlation. Then, the three obtained images are fed to the input of the created three-channel neural network classifier, which includes convolutional layers for the automatic extraction of spatial features. The probability of correctly detecting steps is 98.3% and that of background actions is 97.93%. Full article
(This article belongs to the Special Issue Advances in Distributed Optical Fiber Sensing Systems)
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