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

Acid-Base Titration Curves02:23

Acid-Base Titration Curves

141.6K
A titration curve is a plot of some solution property versus the amount of added titrant. For acid-base titrations, solution pH is a useful property to monitor because it varies predictably with the solution composition and, therefore, may be used to monitor the titration’s progress and detect its endpoint. Acid-base titration can be performed with a strong acid and a strong base, a strong acid and a weak base, or a strong base and a weak acid.
For a titration carried out for 25.00 mL of...
141.6K
Design Example: Setting a Curve Using Design Data01:09

Design Example: Setting a Curve Using Design Data

251
Designing and plotting a curve using field data requires precise calculations and execution. A horizontal curve with a radius of 200 meters and an intersection angle of 20 degrees is established using the method of perpendicular offsets from the long chord. The long chord, which spans between the curve's endpoints, is calculated to be 69.46 meters in length. To maintain accuracy in plotting, intervals of 3 meters are selected along the chord.The engineer determines the offset distances for each...
251
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Smooth Endoplasmic Reticulum01:21

Smooth Endoplasmic Reticulum

8.1K
Smooth endoplasmic reticulum or smooth ER is a sub-organelle with specialized functions in animal cells and plant cells. It is often associated with the tubule morphology of the endoplasmic reticulum.
The ER provides optimal conditions for synthesizing steroid hormones and lipids, such as phospholipids and triglycerides. Traditionally, lipid metabolism was considered to be a smooth ER function. However, there is no direct evidence to prove that rough ER is completely excluded from lipid...
8.1K
Functions of Smooth Muscles01:23

Functions of Smooth Muscles

3.4K
Smooth muscles are an important type of muscle tissue that plays a vital role in the involuntary movements of internal organs. For example, they help regulate the movement of food through the gut and the flow of blood through the circulatory system.
Function of visceral smooth muscles
Visceral smooth muscle is found in the walls of all hollow organs, except the heart, and is a key player in the involuntary movements that drive the functioning of these internal organs. This tissue is arranged in...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Accumulation characteristics of mineral elements in the fruiting bodies of <i>Lentinula edodes</i> from main production areas in China.

Frontiers in nutrition·2026
Same author

Discovery of 1,3-disubstituted indole derivatives as ATP-competitive inhibitors of sphingosine kinase for tumor therapy.

European journal of medicinal chemistry·2026
Same author

Pharmacoeconomic Evaluation of Netupitant Palonosetron for Chemotherapy-Induced Nausea and Vomiting: Evidence from Economic Analyses.

Therapeutic innovation & regulatory science·2026
Same author

Acoustic-optical joint underwater object detection with multi-modality correlation features matching network.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Wearable Multi-Modal Measurement System with Self-Developed IMUs and Plantar Pressure Sensors for Real-Time Gait Recognition.

Micromachines·2026
Same author

Axes Mapping and Sensor Fusion for Attitude-Unconstrained Pedestrian Dead Reckoning.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Feb 8, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K

Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks.

Wenyu Cai1, Meiyan Zhang2

  • 1School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China. caiwy@hdu.edu.cn.

Sensors (Basel, Switzerland)
|July 4, 2018
PubMed
Summary

This study introduces a novel method for autonomous underwater vehicles (AUVs) to collect data in underwater sensor networks (USNs). The approach enhances path smoothness and reduces energy consumption for efficient mobile data gathering.

Keywords:
3D Dubins curveautonomous underwater vehiclesmobile data gatheringunderwater sensor networks

More Related Videos

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.4K
Direct Bioprinting of 3D Multicellular Breast Spheroids onto Endothelial Networks
06:07

Direct Bioprinting of 3D Multicellular Breast Spheroids onto Endothelial Networks

Published on: November 2, 2020

5.4K

Related Experiment Videos

Last Updated: Feb 8, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.4K
Direct Bioprinting of 3D Multicellular Breast Spheroids onto Endothelial Networks
06:07

Direct Bioprinting of 3D Multicellular Breast Spheroids onto Endothelial Networks

Published on: November 2, 2020

5.4K

Area of Science:

  • Robotics and Autonomous Systems
  • Underwater Sensor Networks
  • Data Acquisition

Background:

  • Underwater sensor networks (USNs) face challenges with full connectivity, necessitating mobile data gathering.
  • Autonomous underwater vehicles (AUVs) are employed to address energy imbalance and data collection in sparse 3D USNs.
  • AUVs mitigate energy-intensive multi-hop transmissions by collecting data from nodes.

Purpose of the Study:

  • To develop a mobile data gathering mechanism for 3D USNs using AUVs.
  • To overcome kinematic nonholonomic constraints of AUVs during data collection.
  • To optimize path planning and movement control strategies for AUVs in USNs.

Main Methods:

  • Proposed a smooth 3D Dubins curves based mobile data gathering mechanism.
  • Utilized continuous Bezier curves for Z-axis interpolation of 2D Dubins curves.
  • Employed AUVs for efficient data collection and transmission between sensor nodes.

Main Results:

  • Achieved significantly smoother data collection paths for AUVs.
  • Demonstrated reduced energy consumption in mobile data gathering operations.
  • Verified the efficiency of the proposed method through extensive simulations.

Conclusions:

  • The proposed smooth 3D Dubins curves method is highly suitable for mobile data collection in 3D underwater sensor networks.
  • This approach enhances overall performance in terms of path smoothness and energy efficiency.
  • Optimized AUV path planning is crucial for effective data acquisition in USNs.