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

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.8K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.8K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Reducing Line Loss01:18

Reducing Line Loss

187
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
187
Force Classification01:22

Force Classification

1.4K
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,...
1.4K
Aggregates Classification01:29

Aggregates Classification

366
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...
366
Parallel Processing01:20

Parallel Processing

205
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...
205

You might also read

Related Articles

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

Sort by
Same author

Development of Nipah virus mRNA vaccine for pandemic preparedness.

Frontiers in immunology·2026
Same author

Establishment of a VSV-Based Pseudovirus Platform for In Vitro and In Vivo Evaluation of Nipah Vaccine-Induced Neutralizing Responses.

Viruses·2025
Same author

Light-FER: A Lightweight Facial Emotion Recognition System on Edge Devices.

Sensors (Basel, Switzerland)·2022
Same author

Functional improvement of collagen-based bioscaffold to enhance periodontal-defect healing via combination with dietary antioxidant and COMP-angiopoietin 1.

Materials science & engineering. C, Materials for biological applications·2022
Same author

Review of the Factors Affecting Acceptance of AI-Infused Systems.

Human factors·2022
Same author

No Association between Metabolic Syndrome and Periodontitis in Korean Postmenopausal Women.

International journal of environmental research and public health·2021

Related Experiment Video

Updated: Aug 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Optimizing Face Recognition Inference with a Collaborative Edge-Cloud Network.

Paul P Oroceo1, Jeong-In Kim1, Ej Miguel Francisco Caliwag1

  • 1Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary

This study optimizes edge artificial intelligence by combining edge and cloud computing for faster real-time face recognition. The proposed method significantly boosts inference speed and reduces latency in edge-cloud collaboration.

Keywords:
TCP/IPdeep learningedge–cloudface recognitionreal-time

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

606

Related Experiment Videos

Last Updated: Aug 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
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

606

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Distributed Systems

Background:

  • Deep learning edge AI applications face challenges in optimizing performance through edge-cloud collaboration.
  • Existing research often overlooks real-world implementation and inference speed, focusing instead on simulation or accuracy.
  • Current edge-cloud implementations sometimes fail to leverage the cloud effectively, diminishing the benefits of collaboration.

Purpose of the Study:

  • To propose and evaluate a method for increasing inference speed and reducing latency in edge AI applications.
  • To implement a real-time face recognition system leveraging edge-cloud collaboration.
  • To analyze the performance of this system in terms of frame-per-second (FPS) rate.

Main Methods:

  • Developed a real-time face recognition system where edge devices handle face detection and cloud servers process face recognition.
  • Utilized cropped face images, smaller than full video frames, to reduce data transmission between edge and cloud.
  • Employed the Transmission Control Protocol/Internet Protocol (TCP/IP) for wireless communication between the edge (Jetson Nano GPU) and cloud (PC).

Main Results:

  • The edge-cloud deployment achieved a maximum FPS rate significantly higher than edge-only or cloud-only deployments.
  • Compared to cloud deployment, the combined edge-cloud approach was 1.91 times faster.
  • Compared to edge deployment, the combined edge-cloud approach was 8.5 times faster.

Conclusions:

  • Edge-cloud collaboration is an effective strategy for accelerating AI inferencing processes.
  • Offloading specific tasks (detection on edge, recognition on cloud) optimizes performance and reduces latency.
  • The proposed framework demonstrates practical benefits for real-time AI applications requiring high throughput.