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

Mesh Analysis01:20

Mesh Analysis

986
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
986
Convolution Properties I01:20

Convolution Properties I

259
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:
259
Convolution Properties II01:17

Convolution Properties II

306
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...
306
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

308
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
308
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

471
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...
471
Structural Classification of Joints01:20

Structural Classification of Joints

4.6K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
4.6K

You might also read

Related Articles

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

Sort by
Same author

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

Multi-Dimensional Quality Assessment for Single-Image-to-3D Contents: Dataset and Model.

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

Transferable human mobility network reconstruction with neuroGravity.

Nature computational science·2026
Same author

Cell-free DNA methylation biomarkers for the early detection and tumor burden monitoring of gastric cancer.

NPJ precision oncology·2026
Same author

SGFormer: Simplifying and Scaling Graph Transformers with Single-Layer Attention and Approximation-Free Linear Complexity.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Oct 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

538

Robust Mesh Representation Learning via Efficient Local Structure-Aware Anisotropic Convolution.

Zhongpai Gao, Junchi Yan, Guangtao Zhai

    IEEE Transactions on Neural Networks and Learning Systems
    |February 28, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel convolutional operation for 3D meshes, enhancing representation learning. The proposed method improves 3D shape reconstruction accuracy over existing techniques.

    Related Experiment Videos

    Last Updated: Oct 2, 2025

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    538

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • 3D mesh representation learning is crucial for computer vision and graphics.
    • Existing graph neural networks for 3D shapes have limitations in representation power due to isotropic filters or predefined local coordinate systems.

    Purpose of the Study:

    • To propose a novel local structure-aware anisotropic convolutional operation (LSA-Conv) for improved 3D shape representation learning.
    • To address the limitations of existing methods in handling irregular 3D shape data.

    Main Methods:

    • Introduced LSA-Conv, which learns adaptive weighting matrices based on local neighborhood structure.
    • Developed LSA-small using matrix factorization to reduce parameter size for high-resolution 3D shapes.
    • Incorporated a residual connection with linear transformation to enhance LSA-Conv performance.

    Main Results:

    • The proposed LSA-Conv and LSA-small models demonstrate significant improvements in 3D shape reconstruction.
    • Achieved superior performance compared to state-of-the-art methods in comprehensive experiments.

    Conclusions:

    • LSA-Conv offers a more powerful approach to 3D shape representation learning by considering local structure.
    • The proposed methods effectively overcome limitations of previous graph neural networks for 3D data.