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

Deconvolution01:20

Deconvolution

212
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...
212
Neural Circuits01:25

Neural Circuits

1.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.4K
Introduction to Learning01:18

Introduction to Learning

492
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
492
State Space Representation01:27

State Space Representation

251
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
251
Position Vectors01:29

Position Vectors

987
A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
For instance, we want to locate a point P(x, y, z) relative to the origin of coordinates O. In that case, we can define a position...
987
Upsampling01:22

Upsampling

275
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
275

You might also read

Related Articles

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

Sort by
Same author

Selective Targeting of ApisOBP7 with Indole-2-Carboxylates as a Strategy for Developing Novel Aphid Repellents.

Journal of agricultural and food chemistry·2026
Same author

Evolutionary Tree for All Bumblebee Species World-Wide Estimated by Combining Information from Fast-Evolving Genes, Slow-Evolving Genes, and Genomic Data (Apidae, <i>Bombus</i>).

Insects·2026
Same author

Metal-Organic Framework as a Bioorthogonal Catalyst for Gene Editing.

Journal of the American Chemical Society·2026
Same author

Dietary niche partitioning and convergent gut microbiota in sympatric <i>Vespa</i>.

Frontiers in microbiology·2026
Same author

Marine Saliency Segmenter: Object-Focused Conditional Diffusion With Region-Level Semantic Knowledge Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Conformal graphene coatings on ordinary fabrics for wearable electronic devices.

Nature communications·2026

Related Experiment Video

Updated: Aug 3, 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

592

Unsupervised Point Cloud Representation Learning With Deep Neural Networks: A Survey.

Aoran Xiao, Jiaxing Huang, Dayan Guan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 8, 2023
    PubMed
    Summary

    This review covers unsupervised point cloud representation learning using deep neural networks (DNNs). It examines methods for learning from unlabeled data, addressing the challenge of large-scale point cloud labeling.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K

    Related Experiment Videos

    Last Updated: Aug 3, 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

    592
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Data Science

    Background:

    • Point cloud data offers superior accuracy and robustness in challenging environments.
    • Deep neural networks (DNNs) excel in applications like autonomous driving and surveillance.
    • Training deep point cloud models typically requires extensive labeled data, which is resource-intensive.

    Purpose of the Study:

    • To provide a comprehensive review of unsupervised point cloud representation learning using DNNs.
    • To discuss the motivation, pipelines, and terminology of recent studies in this field.
    • To highlight the growing importance of learning from unlabeled point cloud data.

    Main Methods:

    • Reviewing existing unsupervised point cloud representation learning methods based on their technical approaches.
    • Presenting background information on widely used point cloud datasets and DNN architectures.
    • Quantitatively benchmarking and discussing reviewed methods across multiple datasets.

    Main Results:

    • A detailed overview of various unsupervised point cloud representation learning techniques.
    • Comparative analysis and performance benchmarking of different methods on standard datasets.
    • Identification of common challenges and future research directions.

    Conclusions:

    • Unsupervised learning is crucial for overcoming the limitations of labeled point cloud data.
    • The field is rapidly evolving, with diverse methods offering unique advantages.
    • Future research should focus on addressing current challenges to advance the capabilities of point cloud analysis.