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

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.0K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.0K
Discrete Fourier Transform01:15

Discrete Fourier Transform

272
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
272
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

879
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
879
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

489
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
489
Fast Fourier Transform01:10

Fast Fourier Transform

317
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
317
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

332
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
332

You might also read

Related Articles

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

Sort by
Same author

Phenylpropionamides of Cannabis sativa L. seeds exert a cytoprotective effect through modulation of the AMPK/mTOR/ULK1 autophagy pathway and attenuate apoptosis in MPP<sup>+</sup>-induced SH-SY5Y cells.

Tissue & cell·2026
Same author

A unified ligand-dimensional design to halt cation migration in perovskite photovoltaics.

Science advances·2026
Same author

RNA helicase associated with AU-rich elements (RHAU) regulates hepatic glucose homeostasis via a novel microRNA-150-Notch3-peroxisome proliferator-activated receptor γ pathway.

International journal of biological macromolecules·2026
Same author

Pharmacodynamics and mechanisms of triterpene components from the leaves of <i>Astragalus mongholicus</i> Bunge in the treatment of Alzheimer's disease.

Natural product research·2026
Same author

Extracellular Vesicles as Immunotherapeutic Mediators in Gastrointestinal Cancers and Diseases: From Mechanisms to Clinical Translation.

Clinical pharmacology and therapeutics·2026
Same author

HuR coordinates systemic aging through platelet infiltration.

Nature communications·2026

Related Experiment Video

Updated: Jun 29, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

9.9K

Optical fiber vibration signal recognition based on an efficient multidimensional feature extraction network.

Yuzhou Du, Banglian Xu, Leihong Zhang

    Applied Optics
    |April 3, 2024
    PubMed
    Summary

    This study introduces an efficient multidimensional feature extraction network for optical fiber vibration signal recognition. The novel method achieves a high average recognition rate of 98.67% for classifying vibration signals.

    More Related Videos

    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
    05:52

    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

    Published on: October 20, 2019

    36.2K
    Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver
    04:33

    Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver

    Published on: August 21, 2018

    10.4K

    Related Experiment Videos

    Last Updated: Jun 29, 2025

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
    09:43

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

    Published on: March 20, 2017

    9.9K
    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
    05:52

    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

    Published on: October 20, 2019

    36.2K
    Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver
    04:33

    Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver

    Published on: August 21, 2018

    10.4K

    Area of Science:

    • Optical Fiber Sensing
    • Signal Processing
    • Machine Learning

    Background:

    • One-dimensional optical fiber vibration signals possess limited features, hindering recognition accuracy with shallow neural networks.
    • Existing complex algorithms and data processing methods complicate optical vibration signal recognition.

    Purpose of the Study:

    • To propose an efficient multidimensional feature extraction network for improved optical fiber vibration signal recognition.
    • To enhance feature extraction capabilities for more effective vibration signal classification.

    Main Methods:

    • Utilized ResNet-50 with efficient channel attention (ECA) for improved image feature extraction.
    • Integrated a long short-term memory (LSTM) network to enhance temporal feature extraction.
    • Converted one-dimensional vibration signals into 128x128 grayscale images for richer information.

    Main Results:

    • Successfully collected and processed three distinct vibration signals using a phase-sensitive optical time-domain reflectometry (Φ-OTDR) system.
    • The proposed network effectively recognized and classified the different vibration signal types.
    • Achieved a high average recognition rate of 98.67%.

    Conclusions:

    • The proposed efficient multidimensional feature extraction network significantly improves optical fiber vibration signal recognition.
    • Converting signals to grayscale images enhances information content for better classification.
    • The integration of ECA and LSTM networks offers a robust solution for complex vibration signal analysis.