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

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
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

402
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
402
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K
Signal Flow Graphs01:18

Signal Flow Graphs

225
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
225
Diffusion on Chromatography Columns01:07

Diffusion on Chromatography Columns

557
In column chromatography, when an analyte is introduced as a narrow band at the top of the column, the solutes begin to separate and broaden, developing a Gaussian profile. This broadening occurs due to various factors, such as longitudinal diffusion.
Longitudinal diffusion occurs when the solute molecules in the mobile phase diffuse from the more concentrated center of the chromatographic band to the more dilute regions on either side, both towards and against the flow direction. This...
557
Aliasing01:18

Aliasing

136
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
136

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

Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Brain microRNAs differentially expressed in age-related cerebral pathologies.

Neurobiology of aging·2025
Same author

Causality-driven candidate identification for reliable DNA methylation biomarker discovery.

Nature communications·2025
Same author

A Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification.

IEEE transactions on neural networks and learning systems·2024
Same author

IL-1ra loaded chondroitin sulfate-functionalized microspheres for minimally invasive treatment of intervertebral disc degeneration.

Acta biomaterialia·2024
Same author

Clustered Task-Aware Meta-Learning by Learning From Learning Paths.

IEEE transactions on pattern analysis and machine intelligence·2023
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jul 8, 2025

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
06:34

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

Published on: September 2, 2016

6.4K

通过光谱扩散进行快速图形生成.

Tianze Luo, Zhanfeng Mo, Sinno Jialin Pan

    IEEE transactions on pattern analysis and machine intelligence
    |December 20, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了用于生成图形结构数据的图谱扩散模型 (GSDM). 通过在图谱上使用低级扩散,GSDM提高了图形拓生成和数据质量,优于现有模型.

    更多相关视频

    Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy
    09:09

    Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy

    Published on: March 5, 2021

    4.4K
    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
    08:17

    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

    Published on: August 16, 2021

    1.9K

    相关实验视频

    Last Updated: Jul 8, 2025

    In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
    06:34

    In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

    Published on: September 2, 2016

    6.4K
    Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy
    09:09

    Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy

    Published on: March 5, 2021

    4.4K
    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
    08:17

    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

    Published on: August 16, 2021

    1.9K

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 图形理论 图形理论

    背景情况:

    • 生成图形结构数据是复杂的,需要准确的分布学习.
    • 扩散模型显示了最先进的性能,但在图形拓生成方面存在局限性.
    • 在相邻矩阵上的全等级扩散阻碍了生成图形数据的质量.

    研究的目的:

    • 提出一个高效和有效的图谱扩散模型 (GSDM).
    • 解决目前用于图表生成的扩散模型的局限性.
    • 提高生成的图形数据的质量和效率.

    主要方法:

    • 开发了一个图谱扩散模型 (GSDM).
    • 在图谱空间上使用低级扩散随机微分方程 (SDEs).
    • 为光谱扩散模型提供理论保证.

    主要成果:

    • 与基线模型相比,GSDM显示出优越的图形生成质量.
    • 拟议的模型实现了显著降低计算消耗.
    • 实验证实了GSDM是各种数据集中的最先进的 (SOTA) 模型.

    结论:

    • 图谱扩散模型 (GSDM) 有效地解决了标准扩散模型的局限性.
    • 在图谱上的低级扩散增强了拓学习和数据质量.
    • GSDM为图形生成提供了更高效和更高质量的解决方案.