Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Upsampling01:22

Upsampling

214
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...
214
Downsampling01:20

Downsampling

135
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...
135
Deconvolution01:20

Deconvolution

139
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...
139
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

63
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...
63
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

49
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
49
Convolution Properties II01:17

Convolution Properties II

176
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
176

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Microbial photoelectrotrophic denitrification: Development, mechanisms, and applications in wastewater treatment.

Bioresource technology·2026
Same author

Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.

BMC pregnancy and childbirth·2026
Same author

BiOBr/g-C<sub>3</sub>N<sub>4</sub> Planar Heterostructures toward Enhanced Tetracycline Hydrochloride Removal.

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

Application of retrograde distal perfusion via posterior tibial artery in venoarterial extracorporeal membrane oxygenation: A retrospective single-center study.

JTCVS techniques·2026
Same author

Metagenomic characterization of the virome of Aedes albopictus in Anhui Province, China, with phylogenetic analysis of CRESS-DNA viruses and Parvoviridae.

Virus genes·2026
Same author

Network and machine learning analysis of childhood trauma, mental health, and AI-based emotional support needs in adolescents from underdeveloped regions.

BMC psychology·2026

相关实验视频

Updated: Jun 12, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

369

使用通用多级条件编码的多功能点云压缩机 - - 第二部分:属性.

Jianqiang Wang, Ruixiang Xue, Jiaxin Li

    IEEE transactions on pattern analysis and machine intelligence
    |September 17, 2024
    PubMed
    概括

    一个名为"独角兽"的新框架有效地压缩了点云几何和使用多尺度稀疏张量器的属性. 这种基于学习的解决方案为静态和动态点云提供了优越的压缩效率,无论是在无损和有损模式下.

    科学领域:

    • 计算机视觉 计算机视觉
    • 数据压缩数据压缩
    • 机器学习 机器学习

    背景情况:

    • 点云数据需要高效的压缩来进行存储和传输.
    • 像MPEG G-PCC和V-PCC这样的现有方法在压缩效率方面存在局限性.
    • 在点云中的属性压缩由于数据特征的变化而带来了独特的挑战.

    研究的目的:

    • 为点云几何学和属性压缩提出一个名为"独角兽"的通用多级条件编码框架.
    • 为静态和动态点云开发一种多功能,基于学习的解决方案.
    • 在无损和有损模式中实现最先进的压缩效率.

    主要方法:

    • 为voxelized点云属性框架构建多尺度稀疏张量.
    • 在较低规模的重建和当前规模数据之间处理属性残留.
    • 利用较低规模的空间先验和时间参考框架进行条件剩余预测和改进.

    主要成果:

    • 独角兽显著优于符合标准的方法 (MPEG G-PCC,V-PCC) 和其他基于学习的解决方案.
    • 为各种点云类型实现了最先进的压缩效率.
    • 证明了可负担得起的编码和解码运行时间.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    377
    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    9.8K

    相关实验视频

    Last Updated: Jun 12, 2025

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
    09:19

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

    Published on: April 18, 2025

    369
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    377
    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    9.8K

    结论:

    • 独角兽为点云压缩提供了一种多功能且高效的解决方案.
    • 该框架的基于学习的方法很好地适应了多样化的点云数据.
    • 独角兽为压缩效率在现场设定了新的基准.