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 17, 2025

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

13.0K

Machine learning-based leaky momentum prediction of plasmonic random nanosubstrate.

Jooyoung Kim, Hongki Lee, Seongmin Im

    Optics Express
    |October 7, 2021
    PubMed
    Summary

    Machine learning simplifies constructing leakage radiation images from nanoisland bright-field images. This method analyzes surface plasmon polariton propagation using a neural network, reducing the need for complex equipment.

    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

    Screening for insulin resistance in children with abdominal obesity: sex-specific performance of TyG index, FGIR, and VAI.

    Physical activity and nutrition·2026
    Same author

    Lower-neck involvement and post-concurrent chemoradiotherapy response predict distant metastasis in head and neck cancer.

    Radiation oncology journal·2026
    Same author

    Generation of Functional Oligodendrocyte Progenitor Cells Through Serial Replating of iPSC-Derived NPC Spheres.

    Cells·2026
    Same author

    Habitual Ultra-processed Food Intake Is Associated with Gut Dysbiosis and Pro-inflammatory Metabolite Profiles in Korean Patients with IBD.

    Digestive diseases and sciences·2026
    Same author

    Population Pharmacokinetics and Model-Informed Precision Dosing of Lenalidomide Incorporating Total and Unbound Plasma Concentrations in Renally Impaired Patients with Multiple Myeloma.

    Drug design, development and therapy·2026
    Same author

    Advancing Small-Molecule Immunotherapy Through Polymeric Micelle Delivery.

    Pharmaceutics·2026

    Area of Science:

    • Optoelectronics and Nanophotonics
    • Machine Learning Applications
    • Plasmonics

    Background:

    • Surface plasmon polariton (SPP) propagation is crucial for nanophotonic devices.
    • Leakage radiation offers a method for visualizing and analyzing SPP modes.
    • Current methods for leakage radiation characterization can be complex and equipment-intensive.

    Purpose of the Study:

    • To develop a machine learning framework for constructing leakage radiation characteristics from bright-field images of nanoislands.
    • To simplify the process of leakage radiation image generation.
    • To enable efficient analysis of SPP propagation.

    Main Methods:

    • Utilized a plasmonic random nanosubstrate.
    • Employed a fast-learning two-layer neural network.

    More Related Videos

    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires
    09:00

    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

    Published on: December 11, 2013

    5.4K
    Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment
    09:13

    Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment

    Published on: April 4, 2017

    7.8K

    Related Experiment Videos

    Last Updated: Oct 17, 2025

    Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
    15:06

    Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

    Published on: January 3, 2016

    13.0K
    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires
    09:00

    Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

    Published on: December 11, 2013

    5.4K
    Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment
    09:13

    Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment

    Published on: April 4, 2017

    7.8K
  • Trained the network to predict leakage radiation characteristics from bright-field images using limited data.

    • Main Results:

    • Successfully demonstrated the prediction of leakage radiation characteristics from bright-field images.
    • Showcased the effectiveness of a two-layer neural network for this image-to-image prediction task.
    • Achieved this with a limited number of training samples.

    Conclusions:

    • The proposed machine learning approach significantly simplifies leakage radiation image construction.
    • This method reduces the reliance on sophisticated experimental equipment.
    • The framework has potential for broad applications in nanophotonics and image prediction.