Jove
Visualize
Contact Us

Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...

You might also read

Related Articles

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

Sort by
Same author

Hematemesis; a study of underlying causes.

Gastroenterology·2010
Same author

The combined use of lantoside C and quinidine sulfate in the abolition of established auricular flutter.

American heart journal·2010
Same author

Neoplasms of the stomach other than carcinoma.

Gastroenterology·1950
See all related articles
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 Experiment Video

Updated: Jun 13, 2026

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

Associative network applications to low-level machine vision.

J M Oyster, F Vicuna, W Broadwell

    Applied Optics
    |May 11, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces associative networks, a parallel computing model, for faster low-level machine vision tasks. These networks enable rapid execution of image processing transformations previously too slow for sequential machines.

    More Related Videos

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
    05:47

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

    Published on: August 29, 2025

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    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

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
    05:47

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

    Published on: August 29, 2025

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Vision

    Background:

    • Low-level machine vision tasks are computationally intensive.
    • Sequential processing limits the practical application of many image transformations.
    • Parallel computational models offer potential for accelerating these tasks.

    Purpose of the Study:

    • To explore the application of associative networks to low-level machine vision.
    • To present a formal description of the associative network model.
    • To demonstrate the effectiveness of associative networks for image processing.

    Main Methods:

    • Formal description of the associative network model.
    • Design of associative networks for Boolean functions, edge detection, and Hough transform.
    • Leveraging flexible processor interconnections for enhanced parallelism.

    Main Results:

    • Associative networks were designed for key machine vision tasks.
    • The flexible interconnections facilitate novel parallel algorithm designs.
    • Demonstrated rapid execution of image processing transformations.

    Conclusions:

    • Associative networks are a viable and efficient parallel computational model for machine vision.
    • The model's flexibility surpasses other parallel approaches.
    • Accelerated image processing enhances the practicality of complex vision algorithms.