Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Simultaneous Structural Monitoring over Optical Ground Wire and Optical Phase Conductor via Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry.

Sensors (Basel, Switzerland)·2024
Same author

Deep Learning for chaos detection.

Chaos (Woodbury, N.Y.)·2023
Same author

Monitoring of a Highly Flexible Aircraft Model Wing Using Time-Expanded Phase-Sensitive OTDR.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

Unsupervised Anomaly Detection Applied to Φ-OTDR.

Antonio Almudévar1, Pascual Sevillano2, Luis Vicente1

  • 1ViVoLab, Aragón Institute for Engineering Research (I3A), University of Zaragoza, 50009 Zaragoza, Spain.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

Distributed acoustic sensing (DAS) using Φ-OTDR detects mechanical events. Unsupervised deep learning anomaly detection effectively removes noise from Φ-OTDR signals, improving event isolation without labeled data.

Keywords:
Unsupervised Anomaly Detectionautoencoderdeep learningdistributed acoustic sensorsΦ-OTDR

More Related Videos

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
06:42

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Published on: January 19, 2019

10.5K
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.3K

Related Experiment Videos

Last Updated: Aug 29, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
06:42

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Published on: January 19, 2019

10.5K
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.3K

Area of Science:

  • Fiber Optic Sensing
  • Signal Processing
  • Machine Learning

Background:

  • Distributed acoustic sensors (DAS) based on Φ-OTDR detect mechanical events via light-matter interactions in optical fibers.
  • High sensitivity leads to reduced signal-to-noise ratio, necessitating advanced processing techniques.
  • Current methods struggle with noise, hindering accurate event detection.

Purpose of the Study:

  • To propose and evaluate an unsupervised anomaly detection method for noise reduction in Φ-OTDR signals.
  • To leverage deep learning concepts for enhanced signal processing in DAS.
  • To demonstrate the efficacy of the method using real-world data.

Main Methods:

  • Implementation of an unsupervised anomaly detection algorithm based on deep learning.
  • Training the model exclusively on event-free Φ-OTDR data.
  • Testing the method's performance on real-world distributed acoustic sensing data.

Main Results:

  • Significant noise reduction observed in Φ-OTDR signals after applying the unsupervised anomaly detection method.
  • Improved isolation of mechanical events within the processed signals.
  • Promising performance demonstrated with real-world data, validating the approach.

Conclusions:

  • Unsupervised deep learning anomaly detection offers an effective solution for noise reduction in Φ-OTDR signals.
  • The proposed method enhances the reliability of distributed acoustic sensing systems.
  • No human-labeled data is required, simplifying the training process.