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

Convolution Properties II01:17

Convolution Properties II

597
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...
597
Methods of Classification and Identification01:28

Methods of Classification and Identification

1.3K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
1.3K
Convolution Properties I01:20

Convolution Properties I

621
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:
621
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Ogive Graph01:07

Ogive Graph

6.9K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.9K
Graphing Antiderivatives01:30

Graphing Antiderivatives

77
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
77

You might also read

Related Articles

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

Sort by
Same author

CVIWM: A Tightly Coupled State Estimation Method for Poultry House Inspection Robots in Structurally Degraded Environments.

Animals : an open access journal from MDPI·2026
Same author

Heat Stress Influences Immunity Through <i>DUSP1</i> and <i>HSPA5</i> Mediated Antigen Presentation in Chickens.

Animals : an open access journal from MDPI·2025
Same author

Enhanced Methodology and Experimental Research for Caged Chicken Counting Based on YOLOv8.

Animals : an open access journal from MDPI·2025
Same author

Application of mRNA-Seq and Metagenomic Sequencing to Study <i>Salmonella pullorum</i> Infections in Chickens.

International journal of molecular sciences·2025
Same author

The Posture Detection Method of Caged Chickens Based on Computer Vision.

Animals : an open access journal from MDPI·2024
Same author

Integrated Transcriptomic-Metabolomic Analysis Reveals the Effect of Different Light Intensities on Ovarian Development in Chickens.

International journal of molecular sciences·2024
Same journal

Correction: Gernhardt et al. Ex Vivo Computed Tomographic Morphometry and Motion of the Native and Fractured Equine Accessory Carpal Bone. <i>Animals</i> 2026, <i>16</i>, 1132.

Animals : an open access journal from MDPI·2026
Same journal

Camera-Trap Assessment of Terrestrial Mammals and Ground-Dwelling Birds in the Zhangjiajie Chinese Giant Salamander National Nature Reserve, China.

Animals : an open access journal from MDPI·2026
Same journal

Beyond the Mission: Long-Term Endocrine Dynamics in Search and Rescue Dog-Handler Teams.

Animals : an open access journal from MDPI·2026
Same journal

Phenotypic Characterisation of the Abruzzo Donkey (<i>Equus asinus</i>), an Endangered Italian Genetic Resource: Body Measurements.

Animals : an open access journal from MDPI·2026
Same journal

Assessment of Maternal Genetic Diversity and Mitochondrial Population Structure of Endangered Indigenous Chicken Breeds in China.

Animals : an open access journal from MDPI·2026
Same journal

Effects of Expected Progeny Difference and Feeding Systems on Carcass Characteristics in Hanwoo Steers.

Animals : an open access journal from MDPI·2026
See all related articles

Related Experiment Video

Updated: Feb 14, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.6K

Dead Chicken Identification Method Based on a Spatial-Temporal Graph Convolution Network.

Jikang Yang1, Chuang Ma1, Haikun Zheng2

  • 1Guangdong Laboratory for Lingnan Modern Agriculture, College of Engineering, South China Agricultural University, Guangzhou 510642, China.

Animals : an Open Access Journal From MDPI
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

Accurate detection of deceased hens in complex cage systems is challenging. This study introduces a novel method using spatial-temporal graph convolutional networks (STGCN) for precise identification, improving poultry health monitoring.

Keywords:
caged dead chickenmultimodal fusionpose estimationspatial-temporal graph convolution network

More Related Videos

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

1.6K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.3K

Related Experiment Videos

Last Updated: Feb 14, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.6K
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

1.6K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.3K

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Animal Science

Background:

  • Intensive cage rearing presents challenges for accurate dead hen detection.
  • Occlusion and visual similarity between live and dead hens complicate identification.

Purpose of the Study:

  • To develop an automated dead hen identification method for intensive cage systems.
  • To leverage spatial-temporal information for improved detection accuracy.

Main Methods:

  • Utilized multimodal fusion of visible light and thermal infrared images.
  • Employed an improved YOLOv7-Pose algorithm for keypoint extraction and ByteTrack for tracking.
  • Constructed spatial-temporal graph data and applied a graph convolutional network for identification.

Main Results:

  • Achieved 92.8% average precision in keypoint detection.
  • Reached 99.0% overall classification accuracy for dead hen identification.
  • Demonstrated high accuracy (98.9%) for the dead hen category.

Conclusions:

  • The proposed STGCN method effectively overcomes occlusion and visual ambiguity.
  • Dynamic spatial-temporal modeling significantly enhances the robustness and accuracy of dead hen detection.
  • Provides a new technical approach for intelligent poultry health monitoring.