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

Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Neural Circuits01:25

Neural Circuits

1.8K
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...
1.8K
Aggregates Classification01:29

Aggregates Classification

413
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
413
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.8K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.8K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

169
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
169
Classification of Signals01:30

Classification of Signals

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models.

Journal of computer-aided molecular design·2026
Same author

Acetylcholinesterase inhibitory activity of phthalimide derivatives as anti-alzheimer agents: QSAR, ARKA, Hybrid ARKA-RASAR, virtual screening, molecular docking and ADMET studies.

Molecular diversity·2026
Same author

A hyperspectral imaging framework integrating band selection and deep learning for beverage stain classification in forensic analysis.

Scientific reports·2026
Same author

Structural insights into TNF-α inhibition by bioactive compounds found in plants of North East India: in vitro validation and in silico investigations using QSAR, molecular docking, and dynamics simulations.

Molecular diversity·2026
Same author

Soil degradation toxicity potential (DT<sub>50</sub>) of VPs followed by biodegradability and leaching: Exploration of possible aquatic and terrestrial component toxicity through QSAR, q-RASAR, and comprehensive screening.

Environmental science and pollution research international·2026
Same author

Machine learning-based heat flux estimation from high-speed video during saturated pool boiling over vertical tube.

Scientific reports·2026
Same journal

A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths.

IEEE transactions on cybernetics·2026
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Oct 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

682

Crowd Characterization in Surveillance Videos Using Deep-Graph Convolutional Neural Network.

Shreetam Behera, Debi Prosad Dogra, Malay Kumar Bandyopadhyay

    IEEE Transactions on Cybernetics
    |December 1, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel deep learning approach for crowd characterization, improving movement classification accuracy by 4-5%. The method enhances crowd monitoring and management systems using graph convolutional neural networks.

    More Related Videos

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.2K

    Related Experiment Videos

    Last Updated: Oct 11, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    682
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.2K

    Area of Science:

    • Computational social science
    • Artificial intelligence
    • Computer vision

    Background:

    • Crowd behavior analysis is crucial for understanding group dynamics and enabling effective monitoring.
    • Parametric modeling offers a pathway to robust crowd monitoring systems.
    • Characterizing crowd movement requires sophisticated analytical tools.

    Purpose of the Study:

    • To develop an advanced framework for crowd characterization using deep learning.
    • To classify crowd movements based on physical parameters and dynamics.
    • To enhance the accuracy and efficiency of crowd monitoring systems.

    Main Methods:

    • Crowd characterization was framed as a graph classification problem.
    • Graphs were constructed from motion groups derived via an active Langevin framework.
    • A deep graph convolutional neural network processed these graphs for classification.

    Main Results:

    • The proposed deep graph convolutional neural network framework demonstrated improved performance.
    • Achieved a 4%-5% enhancement in accuracy and Area Under the Curve (AUC) compared to existing methods.
    • Validated on a diverse dataset including public and custom-recorded videos.

    Conclusions:

    • The developed framework offers a significant advancement in crowd characterization accuracy.
    • The insights gained can directly inform and improve crowd monitoring and management strategies.
    • Deep graph convolutional networks provide a powerful tool for analyzing complex crowd dynamics.