Jove
Visualize
Contact Us

Related Concept Videos

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

17.3K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
17.3K
Inertia Tensor01:24

Inertia Tensor

743
The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...
743

You might also read

Related Articles

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

Sort by
Same author

EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.

IEEE transactions on cybernetics·2026
Same author

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks With Pyramid Squeeze Attention.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.

IEEE journal of biomedical and health informatics·2026
Same author

Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model.

IEEE transactions on cybernetics·2026
Same author

TBMSCCN: Two-Branch Multi-Scale Convolutional Correlation Network for Steady-State Visual Evoked Potential Classification.

IEEE transactions on bio-medical engineering·2026
Same journal

Logic, inference, understanding: cross-domain generalization for generative language models.

Frontiers in artificial intelligence·2026
Same journal

Label tree semantic losses for rich multi-class medical image segmentation.

Frontiers in artificial intelligence·2026
Same journal

Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.

Frontiers in artificial intelligence·2026
Same journal

Resource-efficient retrieval-augmented question answering for the Indian Lok Sabha dataset.

Frontiers in artificial intelligence·2026
Same journal

Violation detection in power operation sites based on multi-scale detection and few-shot learning.

Frontiers in artificial intelligence·2026
Same journal

Deep reinforcement learning-based reversible medical image encryption framework for secure IoMT environments.

Frontiers in artificial intelligence·2026
See all related articles
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 21, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.5K

Image Completion in Embedded Space Using Multistage Tensor Ring Decomposition.

Farnaz Sedighin1,2, Andrzej Cichocki2,3

  • 1Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Frontiers in Artificial Intelligence
|September 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Tensor Completion method using Tensor Ring decomposition. The approach effectively recovers missing data in big data processing, outperforming existing algorithms, especially in noisy and incomplete datasets.

Keywords:
Hankelizationimage completionmultistage strategytensor ring decompositiontensorization

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.6K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

908

Related Experiment Videos

Last Updated: Oct 21, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.5K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.6K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

908

Area of Science:

  • Big Data Processing
  • Multivariate Data Analysis
  • Machine Learning

Background:

  • Incomplete datasets are common in big data due to sensor limitations or errors.
  • Recovering missing data is crucial for accurate analysis and efficient processing.
  • Existing tensor completion methods face challenges with high missing ratios and noise.

Purpose of the Study:

  • To propose a new tensor completion approach.
  • To enhance data recovery in incomplete datasets using Tensor Ring decomposition.
  • To address limitations of current methods in handling high missing data and noise.

Main Methods:

  • Block Hankelization to transform incomplete tensors into higher-order tensors.
  • Tensor Ring (TR) decomposition applied in an embedded space.
  • Rank-incremental and multistage strategies for efficient TR decomposition.

Main Results:

  • The proposed method demonstrates superior performance in tensor completion.
  • Effectiveness is particularly notable for datasets with very high missing ratios.
  • The approach shows robustness in handling noisy data.

Conclusions:

  • The novel Tensor Completion approach using embedded Tensor Ring decomposition is effective.
  • This method offers significant improvements over state-of-the-art algorithms.
  • It provides a robust solution for big data processing with incomplete and noisy tensors.