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

You might also read

Related Articles

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

Sort by
Same journal

RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Ma et al. A Lightweight, Low-Frequency, Broadband Underwater Acoustic Transducer with Ternary Symmetric Excitation: Integrating KNN and Terfenol-D for Enhanced Performance. <i>2026</i>, <i>26</i>, 3645.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Tu et al. Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography. <i>Sensors</i> 2024, <i>24</i>, 3097.

Sensors (Basel, Switzerland)·2026
Same journal

Real-Time Detection System for Road Roughness Based on Ultrasonic Technology.

Sensors (Basel, Switzerland)·2026
Same journal

FedHSFV: Federated Learning for Finger Vein Recognition via Hierarchical Decoupling and Subspace Metric.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Nov 23, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K

Localization of Scattering Objects Using Neural Networks.

Domonkos Haffner1, Ferenc Izsák2

  • 1Institute of Physics, Eötvös Loránd University, Pázmány P. stny. 1A, 1117 Budapest, Hungary.

Sensors (Basel, Switzerland)
|December 30, 2020
PubMed
Summary

This study uses advanced neural networks to pinpoint multiple scattering objects by analyzing scattered waves. The novel approach effectively estimates object locations using simulated data and a specialized neural network structure.

Keywords:
inverse scatteringlocalization problemneural networksscattered data

More Related Videos

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology
21:47

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

Published on: December 19, 2010

13.0K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Related Experiment Videos

Last Updated: Nov 23, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology
21:47

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

Published on: December 19, 2010

13.0K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Area of Science:

  • Computational physics
  • Wave scattering phenomena
  • Machine learning applications

Background:

  • Localization of multiple scattering objects using scattered waves.
  • Assumptions include known incident plane waves, fully reflecting balls with known diameters, and scattered wave measurements.
  • Utilizes the μ-diff simulation package in Matlab for generating training data.

Discussion:

  • Develops an advanced neural network architecture for object localization.
  • Introduces a complex locally connected layer as the core component.
  • Employs appropriate data preprocessing to manage network parameters efficiently.

Key Insights:

  • Neural networks effectively estimate the locations of multiple scattering objects.
  • A refined neural network structure with a locally connected layer enables precise localization.
  • Low parameter count achieved through specialized architecture and data preprocessing.

Outlook:

  • Potential for real-world applications in non-destructive testing and imaging.
  • Further research into optimizing neural network architectures for scattering problems.
  • Expanding the method to handle more complex scattering scenarios and object types.