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

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

281
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
281
PI Controller: Design01:24

PI Controller: Design

565
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
565
Vision01:24

Vision

55.6K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
55.6K

You might also read

Related Articles

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

Sort by
Same author

A Deep Learning Approach for Pixel-Level Material Classification via Hyperspectral Imaging.

Journal of imaging·2026
Same author

Multi-Area, Multi-Service and Multi-Tier Edge-Cloud Continuum Planning.

Sensors (Basel, Switzerland)·2025
Same author

Wood Waste Valorization and Classification Approaches: A systematic review.

Open research Europe·2025
Same author

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review.

Online journal of public health informatics·2024
Same author

A Robust End-to-End IoT System for Supporting Workers in Mining Industries.

Sensors (Basel, Switzerland)·2024
Same author

Correction: Gligoric et al. IOTA-Based Distributed Ledger in the Mining Industry: Efficiency, Sustainability and Transparency. <i>Sensors</i> 2024, <i>24</i>, 923.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Sep 30, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.2K

Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning

Athanasios Douklias1, Lazaros Karagiannidis1, Fay Misichroni1

  • 1Institute of Communication and Computer Systems, National Technical University of Athens, 157 73 Athens, Greece.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary

We developed a 12.85 kg unmanned aerial system (UAS) for onboard edge computing. This intelligent aerial platform supports computer vision and machine learning applications, enabling autonomous flight capabilities.

Keywords:
UAVairborne computingembedded systemimage processingmachine learningonboard processingtestbed

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.7K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.5K

Related Experiment Videos

Last Updated: Sep 30, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.2K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.7K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.5K

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Aerospace Engineering

Background:

  • Visual sensing is critical for unmanned aerial vehicle (UAV) navigation and advanced applications.
  • Onboard processing on UAVs is essential to reduce data transmission and enable autonomous functions like obstacle avoidance.
  • The trend towards edge computing transforms UAVs into intelligent, locally processing nodes.

Purpose of the Study:

  • To present the rigorous design and implementation of a UAV testbed for edge computing.
  • To detail the system's computational power, sensors, and design rationale.
  • To demonstrate the platform's utility with a sample computer vision application.

Main Methods:

  • Design and construction of a 12.85 kg UAV system.
  • Integration of computational hardware and relevant sensors for onboard processing.
  • Development and deployment of a sample computer vision application on the UAV.

Main Results:

  • A functional UAV testbed capable of supporting complex image processing and machine learning tasks.
  • Successful demonstration of onboard algorithm execution for enhanced UAV capabilities.
  • Validation of the system's suitability for edge computing and airborne applications.

Conclusions:

  • The developed UAV system serves as a robust platform for airborne edge computing research.
  • The testbed facilitates the development and validation of autonomous capabilities for UAVs.
  • This work supports the advancement of intelligent UAVs as capable, independent processing nodes.