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

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

95
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...
95
Flow Cytometry01:23

Flow Cytometry

13.1K
The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
13.1K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

85
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...
85
Classification of Signals01:30

Classification of Signals

519
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
519
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.1K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.1K
Rapidly Varying Flow01:24

Rapidly Varying Flow

96
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...
96

You might also read

Related Articles

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

Sort by
Same author

Rethinking Semantic Segmentation With Multi-Grained Logical Prototype.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Deep Hierarchical Vision Transformer for Hyperspectral and LiDAR Data Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Limited Health Literacy Is Associated With Worse Patient-Reported Outcomes in Inflammatory Bowel Disease.

Inflammatory bowel diseases·2018
Same author

Sequential release of autophagy inhibitor and chemotherapeutic drug with polymeric delivery system for oral squamous cell carcinoma therapy.

Molecular pharmaceutics·2014
Same author

Proteomic analysis of differentially expressed skin proteins in iRhom2(Uncv) mice.

BMB reports·2014
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jul 16, 2025

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry
11:08

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Published on: May 5, 2017

13.0K

Hyperspectral Meets Optical Flow: Spectral Flow Extraction for Hyperspectral Image Classification.

Bing Liu, Yifan Sun, Anzhu Yu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces SpectralFlow, a novel method for hyperspectral image (HSI) classification. SpectralFlow enhances classification accuracy by analyzing spectral variations, outperforming existing techniques.

    More Related Videos

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    00:07

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.1K
    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.5K

    Related Experiment Videos

    Last Updated: Jul 16, 2025

    Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry
    11:08

    Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

    Published on: May 5, 2017

    13.0K
    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    00:07

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.1K
    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.5K

    Area of Science:

    • Remote Sensing
    • Image Processing
    • Computer Vision

    Background:

    • Hyperspectral image (HSI) classification is challenging due to the complexity of spectral and spatial features.
    • Existing methods struggle to fully capture the nuanced spectral variations crucial for accurate classification.

    Purpose of the Study:

    • To develop a new method for HSI classification by analyzing spectral variations from a sequential data perspective.
    • To introduce 'SpectralFlow' for extracting distinguishable spectral features.

    Main Methods:

    • Introduced an optical flow technique to extract 'spectral flow,' representing spectral variation.
    • Employed a dense optical flow extraction method based on deep matching.
    • Combined spectral flow features with original spectral features for Support Vector Machine (SVM) classification.

    Main Results:

    • The proposed SpectralFlow method achieved higher classification accuracy compared to traditional spatial and texture feature extraction methods.
    • SpectralFlow outperformed the latest deep-learning-based methods in benchmark HSI datasets.
    • The method generated finer classification thematic maps, indicating practical applicability.

    Conclusions:

    • SpectralFlow effectively captures spectral variations, leading to improved HSI classification accuracy.
    • The method demonstrates significant potential for practical applications in remote sensing image analysis.
    • This sequential data perspective offers a promising direction for future HSI classification research.