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

116
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...
116
Reducing Line Loss01:18

Reducing Line Loss

188
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...
188
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

126
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...
126
Modeling and Similitude01:12

Modeling and Similitude

323
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...
323
Differential Leveling01:12

Differential Leveling

255
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
255
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

866
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
866

You might also read

Related Articles

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

Sort by
Same author

Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments.

Sensors (Basel, Switzerland)·2021
Same author

High-Fidelity Depth Upsampling Using the Self-Learning Framework.

Sensors (Basel, Switzerland)·2018
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 25, 2025

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

608

Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation.

Yongseop Jeong1, Jinsun Park2, Donghyeon Cho3

  • 1The Robotics Program, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight depth completion network for autonomous driving. It uses local similarity-preserving knowledge distillation (LSPKD) to reduce computational costs while maintaining accurate depth perception.

Keywords:
depth completionknowledge distillationlocal similaritymodel compressionmultimodal learningsensor fusion

Related Experiment Videos

Last Updated: Aug 25, 2025

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

608

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Accurate depth perception is crucial for autonomous driving systems.
  • High computational costs hinder real-world depth estimation.
  • Existing methods often struggle with efficiency and accuracy.

Purpose of the Study:

  • To develop a lightweight depth completion network for efficient real-world depth perception.
  • To improve the accuracy of depth estimation in autonomous driving platforms.
  • To reduce computational overhead during training and inference.

Main Methods:

  • Proposed a lightweight depth completion network.
  • Introduced local similarity-preserving knowledge distillation (LSPKD) for knowledge transfer.
  • Utilized a heavy teacher network to guide a lightweight student network.

Main Results:

  • The proposed method significantly reduces computational costs.
  • Achieved superior performance compared to other lightweight networks.
  • Demonstrated effective depth completion regardless of ground-truth data density.

Conclusions:

  • The lightweight network with LSPKD offers an efficient solution for real-world depth perception.
  • This approach enables accurate depth estimation for autonomous driving with reduced computational demands.
  • The method shows promise for practical deployment in autonomous systems.