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

Machines01:19

Machines

498
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
498
Machines: Problem Solving II01:30

Machines: Problem Solving II

566
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
566
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

407
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...
407
Machines: Problem Solving I01:22

Machines: Problem Solving I

593
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
593
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

149
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
149
Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

1.1K
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Identifying Sources of Black Carbon Associated with High Mortality Risk in Beijing, China Based on Long-term Hourly Continuous Measurements.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

An interpretable machine learning model for outpatient referral risk assessment in pediatric biliary atresia after Kasai portoenterostomy.

Journal of pediatric gastroenterology and nutrition·2026
Same author

Occurrence and Dietary Exposure of Legacy and Emerging Per- and Polyfluoroalkyl Substances in Preprepared Meat and Aquatic Dishes - China, 2025.

China CDC weekly·2026
Same author

Toxicokinetic Differences between Dermal and Oral Exposure to Ultraviolet Absorbers: Exploring Pathway-Specific Biomarkers.

Environmental science & technology·2026
Same author

Identifying Tracing Species from Source Regions of PM<sub>2.5</sub> on the Tibetan Plateau by Integrating Measurement and Modeling.

Environmental science & technology·2026
Same author

HDAC5 depletion promotes hyper-acetylation of FOXA1 and potentiates HIF1α transcriptional activation in pancreatic cancer.

Molecular cell·2026

Related Experiment Video

Updated: Dec 10, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.7K

Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics.

Ling-Yu Duan, Jiaying Liu, Wenhan Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 29, 2020
    PubMed
    Summary

    Video Coding for Machines (VCM) bridges the gap between traditional video compression and feature compression for AI. This new approach enables collaborative compression of video and feature streams for enhanced machine vision applications.

    More Related Videos

    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

    9.4K
    Automated Interactive Video Playback for Studies of Animal Communication
    07:21

    Automated Interactive Video Playback for Studies of Animal Communication

    Published on: February 9, 2011

    13.9K

    Related Experiment Videos

    Last Updated: Dec 10, 2025

    Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
    06:32

    Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

    Published on: July 14, 2023

    1.7K
    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

    9.4K
    Automated Interactive Video Playback for Studies of Animal Communication
    07:21

    Automated Interactive Video Playback for Studies of Animal Communication

    Published on: February 9, 2011

    13.9K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Data Compression

    Background:

    • Video compression prioritizes human perception (full fidelity), while feature compression focuses on machine vision efficiency.
    • Existing standards like MPEG-7 address feature compression, and deep learning advances video compression, but a gap remains for collaborative AI-driven applications.

    Purpose of the Study:

    • To introduce and define Video Coding for Machines (VCM), a novel paradigm emerging from MPEG standardization efforts.
    • To explore the potential of VCM in bridging the divide between video coding for human vision and feature coding for machine vision.
    • To provide a foundation for collaborative compression of video and feature streams in AI applications.

    Main Methods:

    • Systematic review of state-of-the-art video compression and feature compression techniques from an MPEG standardization perspective.
    • Definition, formulation, and paradigm proposal for Video Coding for Machines (VCM).
    • Exploration of AI technologies, including prediction and generation models, for VCM.

    Main Results:

    • Proposed potential solutions for Video Coding for Machines (VCM).
    • Preliminary results demonstrate performance and efficiency gains in collaborative compression.
    • Established academic and industrial evidence supporting VCM's viability.

    Conclusions:

    • Video Coding for Machines (VCM) offers a promising new direction for optimizing video and feature stream compression for AI.
    • VCM aligns with emerging trends like 'Analyze then Compress' (e.g., Digital Retina).
    • Further research and development in VCM can significantly benefit a broad range of AI applications.