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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
Finding Volume Using Cross-Sectional Area01:25

Finding Volume Using Cross-Sectional Area

For solids whose cross-sectional areas vary in a predictable way, volume can be determined by integrating these areas along an axis perpendicular to the slices. This approach is particularly useful for polyhedral solids, where classical geometric formulas may not be immediately applicable. A tetrahedron provides a clear example of how cross-sectional integration can be applied to a three-dimensional object with continuously changing geometry.Consider a tetrahedron with height h and a base that...

You might also read

Related Articles

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

Sort by
Same author

Granulosa cell glycogen fuels the avascular corpus luteum.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Multidimensional nano-ion composite hydrogel based on enzymatic blood glucose control, gas therapy and ion liquid permeation for repairing diabetic wounds.

Materials today. Bio·2026
Same author

A biohybrid platform integrating bacterial propulsion and photoresponsive nanomedicine for adequate intratumoral drug delivery.

Journal of nanobiotechnology·2026
Same author

VolSegGS: Segmentation and Tracking in Dynamic Volumetric Scenes via Deformable 3D Gaussians.

IEEE transactions on visualization and computer graphics·2025
Same author

AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation.

IEEE transactions on visualization and computer graphics·2025
Same author

MoE-INR: Implicit Neural Representation with Mixture-of-Experts for Time-Varying Volumetric Data Compression.

IEEE transactions on visualization and computer graphics·2025

Related Experiment Video

Updated: Jul 20, 2026

3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache
10:39

3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache

Published on: June 2, 2014

18.3K

CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural

Jun Han, Chaoli Wang

    IEEE Transactions on Visualization and Computer Graphics
    |August 8, 2022
    PubMed
    Summary

    CoordNet is a novel framework for scientific visualization, enhancing generalization across diverse tasks. This coordinate-based approach improves time-varying volumetric data analysis for better data and visualization generation.

    More Related Videos

    Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
    08:32

    Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

    Published on: May 4, 2018

    6.4K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.2K

    Related Experiment Videos

    Last Updated: Jul 20, 2026

    3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache
    10:39

    3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache

    Published on: June 2, 2014

    18.3K
    Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
    08:32

    Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

    Published on: May 4, 2018

    6.4K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.2K

    Area of Science:

    • Scientific Visualization
    • Deep Learning
    • Volumetric Data Analysis

    Background:

    • Deep learning models often lack generalization across different scientific visualization tasks.
    • Existing methods require task-specific architectures, limiting adaptability.
    • Time-varying volumetric data presents unique challenges for visualization.

    Purpose of the Study:

    • To introduce CoordNet, a unified coordinate-based framework for diverse scientific visualization tasks.
    • To enhance the generalization power of deep learning models in visualizing time-varying volumetric data.
    • To provide a single network architecture capable of handling multiple visualization-related tasks.

    Main Methods:

    • Developed CoordNet, a single coordinate-based framework utilizing a unified representation (coordinates and values).
    • Employed a residual block-based implicit neural representation architecture with periodic activation functions.
    • Evaluated on data generation (temporal/spatial super-resolution) and visualization generation (view synthesis, ambient occlusion prediction) tasks.

    Main Results:

    • CoordNet demonstrated superior quantitative and qualitative performance across all evaluated tasks.
    • The framework achieved state-of-the-art results in both data generation and visualization generation.
    • Successfully applied to diverse time-varying volumetric datasets.

    Conclusions:

    • CoordNet offers a generalized solution for time-varying volumetric data visualization.
    • The unified coordinate-based approach significantly improves model generalization and performance.
    • The framework provides a flexible and effective tool for scientific visualization challenges.