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

Boundary Layer Characteristics01:18

Boundary Layer Characteristics

815
When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
815
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

455
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
455

You might also read

Related Articles

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

Sort by
Same author

Expression of tissue factor pathway inhibitor-2 in gastric stromal tumor and its clinical significance.

Experimental and therapeutic medicine·2014
Same author

Facile access to cytocompatible multicompartment micelles with adjustable Janus-cores from A-block-B-graft-C terpolymers prepared by combination of ROP and ATRP.

Colloids and surfaces. B, Biointerfaces·2014
Same author

Functional layers for Zn(II) ion detection: from molecular design to optical fiber sensors.

The journal of physical chemistry. B·2013
Same author

Expression of the 78 kD glucose-regulated protein is induced by endoplasmic reticulum stress in the development of hepatopulmonary syndrome.

Gene·2013
Same author

Multi-nuclear silver(I) and copper(I) complexes: a novel bonding mode for bispyridylpyrrolides.

Dalton transactions (Cambridge, England : 2003)·2013
Same author

Transcriptome profilings of female Schistosoma japonicum reveal significant differential expression of genes after pairing.

Parasitology research·2013

Related Experiment Video

Updated: Mar 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

840

Nonlinear physical segmentation algorithm for determining the layer boundary from lidar signal.

Feiyue Mao, Jun Li, Chen Li

    Optics Express
    |December 25, 2015
    PubMed
    Summary

    This study introduces an automated lidar layer detection algorithm that accurately identifies cloud and aerosol boundaries, even with low signal-to-noise ratios, improving data processing for optical property retrieval.

    More Related Videos

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    25.1K
    Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
    13:35

    Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

    Published on: March 21, 2021

    12.1K

    Related Experiment Videos

    Last Updated: Mar 28, 2026

    Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
    08:16

    Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

    Published on: October 24, 2025

    840
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    25.1K
    Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
    13:35

    Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

    Published on: March 21, 2021

    12.1K

    Area of Science:

    • Atmospheric Science
    • Remote Sensing
    • Optical Physics

    Background:

    • Accurate layer boundary detection is crucial for processing lidar data and retrieving optical properties.
    • Traditional methods often require manual input and are sensitive to signal-to-noise ratio (SNR).

    Purpose of the Study:

    • To develop a robust, automatic algorithm for detecting layer boundaries (base and top) in lidar signals.
    • To overcome limitations of existing methods, particularly in low SNR conditions.

    Main Methods:

    • A novel lidar signal segmentation and representation algorithm was developed.
    • The approach is grounded in the fundamental lidar equation, enhancing its applicability.

    Main Results:

    • The algorithm accurately detects layer bases and tops in both simulated and real lidar data.
    • Performance remains high even with low signal-to-noise ratios.
    • Classification results are accurate and satisfactory.

    Conclusions:

    • The proposed algorithm offers a reliable, automated solution for lidar data analysis.
    • It enables accurate detection, retrieval, and analysis of lidar datasets, reducing manual intervention.