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Related Concept Videos

Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Measurements of Strain01:27

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Strain quantifies the deformation of a material under force, typically measured as normal strain, which represents the change in length when compared with the original length. Electrical strain gauges are used for enhanced accuracy. These devices consist of a conductive wire mounted on a paper backing that adheres to the material's surface. These gauges operate on the piezoresistive effect, where the wire's electrical resistance changes in response to mechanical deformation. The strain...
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Related Experiment Video

Updated: Aug 8, 2025

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves
06:26

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Published on: January 12, 2024

458

Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks.

Bo Li1,2,3, Ningjun Jiang1,3, Xiaole Han3

  • 1Institute of Geotechnical Engineering, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary

This study enhances Brillouin optical time domain reflectometry (BOTDR) performance by applying a convolutional neural network (CNN) for image denoising. This improves strain vibration measurement accuracy and reduces frequency uncertainty in optic fibre sensing.

Failed At:

2026-06-19T13:49:04.797609+00:00

Keywords:
Brillouin scatteringconvolutional neural networkfibre optic sensingimage denoising

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