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

Wave Parameters01:10

Wave Parameters

7.7K
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
7.7K
Effective Value of a Periodic Waveform01:07

Effective Value of a Periodic Waveform

542
The concept of effective value, the root mean square (RMS) value, is crucial in understanding electrical circuits and power delivery. This idea emerges from the necessity to measure the effectiveness of a voltage or current source in supplying power to a resistive load.
The effective value of a periodic current represents the direct current (DC) that conveys the same average power to a resistor as the periodic current itself. This concept is crucial when assessing AC circuits. To determine the...
542
Graphing the Wave Function01:13

Graphing the Wave Function

1.8K
Consider the wave equation for a sinusoidal wave moving in the positive x-direction. The wave equation is a function of both position and time. From the wave equation, two different graphs can be plotted.
1.8K
Discrete Fourier Transform01:15

Discrete Fourier Transform

284
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
284
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

215
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
215
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

496
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
496

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

Development and internal validation of a nomogram for early prediction of hospital-acquired ESKAPE colonization or infection in very preterm infants using indicators available within 24 hours.

Frontiers in pediatrics·2026
Same author

Nanogel-controlled delivery of caerin peptides enables sustained intratumoural retention and immunometabolic reprogramming.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Clinical effects of deep hyperthermia on patients undergoing postoperative adjuvant chemotherapy for colorectal cancer.

International journal of radiation biology·2026
Same author

Novel Insights Into the Association Between Parkinson's Disease and Constipation: Role of SHMT2 as a Promising Biomarker.

CNS neuroscience & therapeutics·2026
Same author

Extremely low dose norepinephrine in treatment of refractory chronic cluster headache: two case reports and literature review.

Frontiers in neuroscience·2026
Same author

[Correlation analysis between endolymphatic hydrops severity and high jugular bulb with vestibular function in refractory Ménière's disease patients].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2026

相关实验视频

Updated: Jul 2, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.6K

点Wavelet:在光谱域学习为3D点云分析.

Cheng Wen, Jianzhi Long, Baosheng Yu

    IEEE transactions on neural networks and learning systems
    |February 23, 2024
    PubMed
    概括

    PointWavelet引入了一种新的光谱域方法,用于使用可学习的图形波形变换进行3D点云分析. 该方法增强了局部结构表示,以改进3D点云分类和细分.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 3D数据分析 3D数据分析

    背景情况:

    • 深度学习在二维视觉识别方面表现出色,引发了对三维点云分析的兴趣,特别是在自动驾驶方面.
    • 现有的3D点云方法主要关注空间域特征,忽视光谱域局部结构.
    • 研究光谱域特征对于全面的3D点云理解至关重要.

    研究的目的:

    • 介绍PointWavelet,一种用于3D点云分析的新方法.
    • 用可学习的图形波量变换来探索光谱领域的局部图形结构.
    • 为了增强3D点云中的局部结构的表示.

    主要方法:

    • 开发了PointWavelet,一种使用可学习图形波形变换的方法.
    • 引入了多尺度的光谱图卷积,用于学习局部结构表示.
    • 设计了一个可学习的图形波量变换来加速光谱分解和训练.

    主要成果:

    • 在四个基准数据集上证明了PointWavelet的有效性:ModelNet40,ScanObjectNN,ShapeNet-Part和S3DIS.
    • 在点云分类和细分任务中取得了显著的改进.
    • 可学习的图形波段转换大大减少了训练时间.

    更多相关视频

    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
    08:42

    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

    Published on: September 3, 2021

    3.1K
    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
    13:02

    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

    Published on: February 27, 2016

    12.3K

    相关实验视频

    Last Updated: Jul 2, 2025

    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
    11:00

    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

    Published on: July 19, 2016

    11.6K
    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
    08:42

    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

    Published on: September 3, 2021

    3.1K
    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
    13:02

    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

    Published on: February 27, 2016

    12.3K

    结论:

    • 通过利用光谱域信息,PointWavelet为3D点云分析提供了一种有效的方法.
    • 拟议的可学习图形波量变换加速了培训过程,而不会影响性能.
    • 这种方法推进了对3D点云理解的最先进技术,用于诸如自动驾驶等应用.