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 Experiment Video

Updated: Oct 9, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Toward generalized forked gratings via deep learning.

Yue Zhao, Enliang Wang, Fulin Cao

    Optics Letters
    |December 16, 2021
    PubMed
    Summary
    This summary is machine-generated.

    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

    Isomerization dynamics of dicationic CS2 and OCS driven by electron-impact.

    The Journal of chemical physics·2026
    Same author

    Fragmentation dynamics of sulfur dioxide dication in intense femtosecond laser fields.

    The Journal of chemical physics·2026
    Same author

    Ultrafast Bimolecular Reaction in Acetylene Dimer Induced by Femtosecond Strong-Laser-Field Ionization.

    Physical review letters·2026
    Same author

    Imaging transient molecular configurations in UV-excited diiodomethane.

    The Journal of chemical physics·2025
    Same author

    The effect of cooling rate and supplying water condition on the freezing properties of silty clay in seasonal freezing zones.

    PloS one·2025
    Same author

    Visualizing the Three-Dimensional Arrangement of Hydrogen Atoms in Organic Molecules by Coulomb Explosion Imaging.

    Journal of the American Chemical Society·2025
    Same journal

    Gaussian-modulated continuous-variable quantum key distribution over 60 km fiber using an integrated silicon photonic receiver.

    Optics letters·2026
    Same journal

    E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

    Optics letters·2026
    Same journal

    Holographic generation of panoramic 3D scenes by concave ellipsoidal mirror reflection.

    Optics letters·2026
    Same journal

    Dual-pilot phase recovery with pair-wise maximum-ratio combining for coherent PONs.

    Optics letters·2026
    Same journal

    Mapping the whispering gallery modes of a CaF<sub>2</sub> disk resonator with half-tapered fibers to estimate the fundamental mode volume.

    Optics letters·2026
    Same journal

    Quantitative estimation of deep-subwavelength scale via dark-field scattering axial energy concentration decay profiles.

    Optics letters·2026
    See all related articles

    Researchers developed generalized forked gratings to suppress unwanted light diffraction orders. A deep convolutional neural network achieved a 92.3% recovery rate, significantly reducing unwanted light intensity for improved optical applications.

    Area of Science:

    • Optics and Photonics
    • Diffractive Optics
    • Machine Learning in Optics

    Background:

    • Forked gratings are optical elements used to shape light beams.
    • High diffraction orders can interfere with desired optical functions.
    • Controlling light propagation and beam characteristics is crucial in many scientific fields.

    Purpose of the Study:

    • To develop a novel generalized forked grating capable of suppressing high diffraction orders.
    • To utilize a deep convolutional neural network for optimizing grating parameters.
    • To demonstrate the effectiveness of the proposed method for generating vortex beams with suppressed unwanted orders.

    Main Methods:

    • Distribution of rectangular holes within conventional forked gratings.
    • Implementation of a deep convolutional neural network (CNN) for parameter reconstruction.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    677

    Related Experiment Videos

    Last Updated: Oct 9, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.4K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    677
  • Experimental verification of the generated helical phase structures and topological charges.

    • Main Results:

    • Achieved a 92.3% recovery rate using the neural network.
    • Reduced the 3rd order diffracted light intensity to 0.067% of the 1st order.
    • Experimentally confirmed the generation of vortex beams with specific topological charges and suppressed higher orders.

    Conclusions:

    • Generalized forked gratings effectively suppress high diffraction orders for vortex beams.
    • Deep convolutional neural networks provide an efficient method for optimizing diffractive optical elements.
    • The developed gratings show potential for applications in imaging, microscopy, and fundamental physics.