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

204
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...
204
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.9K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
6.9K
Sampling Theorem01:15

Sampling Theorem

302
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
302
Aliasing01:18

Aliasing

119
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...
119

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

Diffusion in liquid mixtures.

NPJ microgravity·2023
Same author

Soft matter dynamics: A versatile microgravity platform to study dynamics in soft matter.

The Review of scientific instruments·2022
Same author

Effect of size disparity on the structure and dynamics of the small component in concentrated binary colloidal mixtures.

The Journal of chemical physics·2019
Same author

Accounting for effective interactions among charged microgels.

Physical review. E·2019
Same author

Microliter viscometry using a bright-field microscope: η-DDM.

Soft matter·2018
Same author

Dense colloidal mixtures in an external sinusoidal potential.

The Journal of chemical physics·2018

相关实验视频

Updated: Jun 6, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.3K

在数字里埃显微镜分析中通过快速统计趋同改进数据采样.

A B Zuccolotto-Bernez, L F Rojas-Ochoa, S U Egelhaaf

    Applied optics
    |November 27, 2024
    PubMed
    概括

    研究人员开发了一种更快的算法来分析软物质数据. 这种新方法显著加快了相关函数的计算速度,比如使用数字里埃显微镜计算的中间散射函数.

    科学领域:

    • 软物质物理学 软物质物理学
    • 材料科学是一种材料科学.
    • 统计力学就是统计力学.

    背景情况:

    • 软物质研究经常分析相关函数,例如中间散射函数.
    • 传统的方法,如波散射或数字里埃显微镜产生大量数据,需要耗时的分析.
    • 优化数据分析对于最小化计算和确保统计有效性至关重要.

    研究的目的:

    • 开发一种更有效的算法来分析软物质研究中的相关函数.
    • 为了减少计算负载,加快数字里埃显微镜中的数据分析.
    • 为了在更短的时间内获得统计学上有效的结果.

    主要方法:

    • 开发一种使用高效采样技术的新型算法.
    • 将算法应用于数字里埃显微镜数据分析.
    • 用传统分析方法比较计算时间和结果.

    主要成果:

    • 新的算法大大减少了统计趋同所需的计算次数.
    • 实现的分析速度比传统方法快两倍.
    • 该算法提供了与传统分析技术相当的信息.

    结论:

    更多相关视频

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K
    Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
    05:04

    Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

    Published on: June 13, 2023

    1.4K

    相关实验视频

    Last Updated: Jun 6, 2025

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    16.3K
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K
    Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
    05:04

    Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

    Published on: June 13, 2023

    1.4K
    • 开发的算法为软物质相关函数的分析提供了实质性的加速.
    • 高效的采样技术可以大大提高数字里埃显微镜数据处理的速度.
    • 这一进步促进了软物质科学领域的更快,更有效的研究.