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

Reducing Line Loss01:18

Reducing Line Loss

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 in...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Maximizing the Directional Derivative01:25

Maximizing the Directional Derivative

The directional derivative is a central concept in multivariable calculus that describes how a function changes at a given point when moving in a specified direction. This direction is represented by a unit vector, ensuring that only the orientation influences the rate of change. By varying the direction, different rates of change can be observed, demonstrating that the directional derivative depends strongly on the chosen direction.The directional derivative is computed using the gradient...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

You might also read

Related Articles

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

Sort by
Same author

A programmable multi-stage microfluidic platform with micro-dam prefiltration and micro-nano hierarchical filter for synergetic capture of CTCs and SERS-based phenotype analysis.

Biosensors & bioelectronics·2026
Same author

Dynamic Biomass-Based Hydrogel with Dual pH/Glucose Responsiveness for Controlled Nitric Oxide Release and Diabetic Wound Healing.

Biomacromolecules·2026
Same author

Adhesion Force Evolution Mediated by Capillary Condensation under Varied Macroscopic Humidity or Interface Micromorphology: Measurement via Micrometer-Scale Probe.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Multimodal Dissection of UV-B-Induced Plant Defense Against Insect in Tea Plants.

Plant, cell & environment·2026
Same author

Correction: Guo et al. Shikonin as a WT1 Inhibitor Promotes Promyeloid Leukemia Cell Differentiation. <i>Molecules</i> 2022, <i>27</i>, 8264.

Molecules (Basel, Switzerland)·2026
Same author

Breaking dense integration limits: inverse-designed lithium niobate multimode photonic circuits.

Nature communications·2025
Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026
Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026
Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026
Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026
Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026
Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 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

Point cloud denoising method with low-rank recovery and point optimization.

Pei Yang, Xiangyue Wang, Jin Zhang

    Optics Express
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel nonlocal point cloud denoising method using low-rank recovery (LMR) and point optimization. The technique effectively refines noisy point cloud data for improved laser scanning reconstruction and reverse engineering precision.

    Related Experiment Videos

    Last Updated: Jun 12, 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

    Area of Science:

    • Computer Vision
    • Geometric Modeling
    • Data Processing

    Background:

    • Point clouds are essential for 3D reconstruction and reverse engineering.
    • Acquiring noise-free point clouds is challenging due to environmental and system errors.
    • Existing denoising methods are sensitive to noise and often focus on local features.

    Purpose of the Study:

    • To develop an advanced nonlocal point cloud denoising method.
    • To enhance the precision of 3D reconstruction and reverse engineering.
    • To overcome the limitations of existing noise-sensitive denoising techniques.

    Main Methods:

    • Formulation of a local feature descriptor (NDA) capturing geometric information.
    • Construction of an NDA matrix by identifying similar feature vectors globally.
    • Application of an adaptive weighted low-rank recovery (LMR) model.
    • Refinement of the denoised point cloud using a multi-constraint optimization method.

    Main Results:

    • The proposed method demonstrates significant effectiveness in denoising point clouds.
    • Successful validation in both simulated and real-world application scenarios.
    • Improved precision in laser scanning reconstruction and reverse engineering outcomes.

    Conclusions:

    • The nonlocal denoising method with LMR and point optimization offers a robust solution for noisy point cloud data.
    • This approach enhances the accuracy and reliability of 3D reconstruction.
    • The method shows promise for practical applications in various fields requiring precise 3D models.