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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

108
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
108
Response Surface Methodology01:16

Response Surface Methodology

214
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
214
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

118
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
118
Modeling and Similitude01:12

Modeling and Similitude

316
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
316
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

101
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
101
Typical Model Studies01:30

Typical Model Studies

417
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
417

You might also read

Related Articles

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

Sort by
Same author

FGFR1 but not S6K1/2 drives intrinsic BRAF inhibitor resistance in melanoma.

Cell death discovery·2026
Same author

A Time-Synchronized Multi-Sensor drone dataset acquired from multiple radars and RF receiver.

Scientific data·2026
Same author

Efficacy of RCI001 as a Therapeutic Candidate in a Primary Sjögren Syndrome Mouse Model.

Cornea·2024
Same author

Convolutional Neural Network-Based Drone Detection and Classification Using Overlaid Frequency-Modulated Continuous-Wave (FMCW) Range-Doppler Images.

Sensors (Basel, Switzerland)·2024
Same author

Efficacy of RCI001 as a therapeutic candidate of dry eye disease in a modified mixed dry eye model.

Eye and vision (London, England)·2024
Same author

CRISPR screening identifies BET and mTOR inhibitor synergy in cholangiocarcinoma through serine glycine one carbon.

JCI insight·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 17, 2025

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

1.6K

Machine Learning-Based Air-to-Ground Channel Model Selection Method for UAV Communications Using Digital Surface

Young-Eun Kang1, Young-Ho Jung1

  • 1School of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Republic of Korea.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for selecting air-to-ground (A2G) channel models using machine learning and digital surface model (DSM) data. This improves performance evaluations for new services like Urban Air Mobility (UAM) and 6G communications.

Keywords:
UAMUAV communicationair-to-ground (A2G) channel modelchannel model selectionconvolutional neural network (CNN)deep neural network (DNN)digital surface modelmachine learning

More Related Videos

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

8.1K
Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K

Related Experiment Videos

Last Updated: Aug 17, 2025

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

1.6K
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

8.1K
Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K

Area of Science:

  • Wireless communication
  • Machine learning applications
  • Geographic information systems

Background:

  • Accurate air-to-ground (A2G) channel modeling is crucial for verifying communication network performance, especially for emerging services like Urban Air Mobility (UAM).
  • Existing A2G channel models require accurate terrain environment classification for effective simulation, but practical automatic methods are lacking.
  • Simulations must reflect diverse terrain characteristics for reliable performance evaluation.

Purpose of the Study:

  • To propose a novel, practical, and automatic method for air-to-ground (A2G) channel model selection using machine learning (ML).
  • To develop an automated topography classification system for accurate simulation environments.
  • To enhance the performance evaluation of non-terrestrial communication networks.

Main Methods:

  • A two-step neural network-based classifier was developed for automatic topography classification.
  • Digital Surface Model (DSM) terrain data and various geographic features were utilized as input for the ML model.
  • A new dataset comprising five topography classes representative of Korea's terrain was created for evaluation.

Main Results:

  • The proposed ML-based method significantly improved the accuracy of A2G channel model selection compared to manual or cross-correlation methods.
  • The developed method effectively classifies terrain types, enabling more realistic communication simulations.
  • The use of publicly available DSM data allows for easy adaptation to different countries' terrain characteristics.

Conclusions:

  • The developed ML-based approach provides an effective solution for automatic A2G channel model selection.
  • This method enhances the realistic performance evaluation of advanced communication systems like UAM and 6G.
  • The approach is adaptable globally due to its reliance on accessible DSM data.