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

Deconvolution01:20

Deconvolution

233
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.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
233
Convolution Properties II01:17

Convolution Properties II

265
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
265
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

349
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
349
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.0K
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...
1.0K
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Convolution Properties I01:20

Convolution Properties I

218
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
218

You might also read

Related Articles

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

Sort by
Same author

Dynamic tailoring of an optical skyrmion lattice in surface plasmon polaritons.

Optics express·2020
Same author

High-Efficiency, Broadband, Near Diffraction-Limited, Dielectric Metalens in Ultraviolet Spectrum.

Nanomaterials (Basel, Switzerland)·2020
Same author

Experimental Examination of Electrical Characteristics for Portland Cement Mortar Frost Damage Evaluation.

Materials (Basel, Switzerland)·2020
Same author

Synthesis of Imidazole-Based Medicinal Molecules Utilizing the van Leusen Imidazole Synthesis.

Pharmaceuticals (Basel, Switzerland)·2020
Same author

Mutagenesis for Improvement of Activity and Stability of Prolyl Aminopeptidase from Aspergillus oryzae.

Applied biochemistry and biotechnology·2020
Same author

Improved Acid Resistance of a Metal-Organic Cage Enables Cargo Release and Exchange between Hosts.

Angewandte Chemie (International ed. in English)·2020

Related Experiment Video

Updated: Aug 25, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Feature recognition of a 2D array vortex interferogram using a convolutional neural network.

Yong Li, You Li, Dawei Zhang

    Applied Optics
    |October 18, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Detecting vortex arrays is crucial for applications needing multiple vortex elements. A convolution neural network (CNN) decodes interferograms to simultaneously determine topological charge, chirality, and angle, proving effective for vortex array classification and detection.

    More Related Videos

    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
    08:47

    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

    Published on: February 9, 2024

    1.6K
    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
    06:25

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

    Published on: February 23, 2024

    679

    Related Experiment Videos

    Last Updated: Aug 25, 2025

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.2K
    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
    08:47

    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

    Published on: February 9, 2024

    1.6K
    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
    06:25

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

    Published on: February 23, 2024

    679

    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Image Processing

    Background:

    • Vortex arrays, featuring multiple optical vortex elements, are essential for advanced optical applications.
    • Simultaneous detection of topological charge, chirality, and initial angle in vortex arrays is a significant challenge.

    Purpose of the Study:

    • To develop a novel method for the simultaneous detection of vortex array properties.
    • To investigate the efficacy of a convolution neural network (CNN) for vortex array analysis.

    Main Methods:

    • Generating interferograms between an off-axis Walsh-phase plate and a vortex array.
    • Utilizing a CNN for decoding the interferogram to extract vortex array parameters.
    • Conducting theoretical analysis and experimental validation.

    Main Results:

    • The developed method successfully decodes interferograms to determine topological charge, chirality, and initial angle.
    • The CNN demonstrated high accuracy in classifying and detecting vortex arrays.
    • Both theoretical and experimental results confirmed the effectiveness of the CNN approach.

    Conclusions:

    • A CNN-based approach provides an effective solution for the simultaneous detection of vortex array properties.
    • This method enhances the capability for analyzing complex vortex arrays in optical systems.
    • The findings highlight the potential of AI in optical metrology and characterization.