Jove
Visualize
Contact Us

Related Concept Videos

Parallel Processing01:20

Parallel Processing

182
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
182
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

455
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
455

You might also read

Related Articles

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

Sort by
Same author

6G-Enabling the New Smart City: A Survey.

Sensors (Basel, Switzerland)·2023
Same author

When Social Networks Meet D2D Communications: A Survey.

Sensors (Basel, Switzerland)·2019
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: Jul 23, 2025

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

241

Hardware-Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization.

Corneliu Zaharia1, Vlad Popescu1, Florin Sandu1

  • 1Department of Electronics and Computers, Transilvania University, Bdul Eroilor 29, 500068 Brașov, Romania.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
Summary

This study introduces a hybrid hardware-software approach to enhance real-time object detection. Artificial intelligence manages hardware components on FPGAs, improving computer vision algorithm performance and efficiency.

Keywords:
FPGAadaptive hardware resources integrationartificial intelligencecomputer visionembedded systemshardware acceleratorshybrid implementationsmemory bandwidthobject detection

More Related Videos

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

581
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

9.0K

Related Experiment Videos

Last Updated: Jul 23, 2025

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

241
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

581
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

9.0K

Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Real-time computer vision algorithms face challenges in memory bandwidth and energy consumption.
  • Current implementations in devices like smartphones and automotive systems require optimization.
  • Object detection is crucial for various monitoring and security applications.

Purpose of the Study:

  • To improve the quality of real-time object detection computer vision algorithms.
  • To propose a hybrid hardware-software implementation for enhanced performance.
  • To explore efficient allocation of algorithm components to hardware (IP Cores) and software.

Main Methods:

  • Developed a hybrid hardware-software implementation strategy.
  • Investigated methods for allocating algorithm components to hardware IP Cores.
  • Utilized embedded artificial intelligence for dynamic hardware configuration and parameter adjustment.
  • Implemented the solution on a Xilinx Zynq-7000 SoC FPGA demonstrator.

Main Results:

  • Demonstrated significant performance gains in object detection use-cases.
  • Showcased the benefits of hybrid hardware-software implementations.
  • Highlighted major improvements achieved by AI-managed IP Cores.
  • Validated the approach on a practical FPGA system.

Conclusions:

  • Hybrid hardware-software implementations offer substantial benefits for real-time computer vision.
  • AI-driven management of hardware IP Cores leads to major performance gains.
  • The proposed method effectively addresses challenges in memory bandwidth and energy consumption for object detection.