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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

338
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
338
Sampling Methods: Overview01:06

Sampling Methods: Overview

2.1K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
2.1K
Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.9K
Sampling Theorem01:15

Sampling Theorem

1.3K
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.
1.3K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.5K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.5K
Aliasing01:18

Aliasing

534
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...
534

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

Two-Field Excitation for Contactless Inductive Flow Tomography.

Sensors (Basel, Switzerland)·2024
Same author

Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks.

Sensors (Basel, Switzerland)·2024
Same author

Contactless Inductive Flow Tomography for Real-Time Control of Electromagnetic Actuators in Metal Casting.

Sensors (Basel, Switzerland)·2022
Same author

Laboratory Investigation of Tomography-Controlled Continuous Steel Casting.

Sensors (Basel, Switzerland)·2022
Same author

A Review on Fast Tomographic Imaging Techniques and Their Potential Application in Industrial Process Control.

Sensors (Basel, Switzerland)·2022
Same author

Flow Control Based on Feature Extraction in Continuous Casting Process.

Sensors (Basel, Switzerland)·2020

相关实验视频

Updated: Jan 11, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.0K

使用和扩展Lomb-Scargle方法对不均采样数据的多变量频率和幅度估计.

Martin Seilmayer1, Thomas Wondrak2, Ferran Garcia3

  • 1Staatliche Studienakademie Bautzen, Duale Hochschule Sachsen, Löbauer Strasse 1, 02625 Bautzen, Germany.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
概括

一般化的Lomb-Scargle方法 (LSM) 准确地估计了不规则采样的多变量数据中的频率. 这种强大的技术改进了分析复杂数据集的传统方法,例如太阳活动和超声波测量.

关键词:
多变量数据分析.这是光谱学光谱.不均的抽样采集.

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.8K

相关实验视频

Last Updated: Jan 11, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.8K

科学领域:

  • 数据科学数据科学数据科学
  • 天文学 天文学
  • 信号处理 信号处理

背景情况:

  • 传统的光谱分析方法与不规则采样的数据作斗争,导致显著的偏差.
  • 经典的Lomb-Scargle方法 (LSM) 对单变量,不均采样的时间序列有效.
  • 将LSM扩展到多变量数据对于分析复杂的现实数据集至关重要.

研究的目的:

  • 为了将Lomb-Scargle方法 (LSM) 对具有不规则采样的多变量数据集进行概括.
  • 为了使复杂数据中的频率,相位和振幅向量的同时估计.
  • 保持LSM的统计稳定性和抗噪能力.

主要方法:

  • 重定了移动参数 τ,以保持三角形基础函数在 Rn. 中的直角性.
  • 将一般化的LSM应用于随机抽取的2D太阳活动数据 (太阳黑子).
  • 将一般化的LSM应用于3D超声速概况数据集,其中包括缺失值和时间动.

主要成果:

  • 在太阳活动数据中成功确定了特征频率.
  • 在超声波测量中实现了准确的速度估计,尽管数据不完美.
  • 通过比较分析,与基于富里埃变换的方法相比,表现出优异的性能.

结论:

  • 一般化的LSM提供了一个强大的和统计学上合理的方法,用于在多变量,不规则采样数据中的频率和振幅估计.
  • 这种方法显著提高了复杂数据集的分析在诸如太阳物理和医学超声波等领域.
  • 导出的置信区间和比较分析验证了该方法的有效性和可靠性.