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相关概念视频

Downsampling01:20

Downsampling

192
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...
192
Properties of Fourier series II01:21

Properties of Fourier series II

190
Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
A function f(t) is...
190
Parseval's Theorem for Fourier transform01:15

Parseval's Theorem for Fourier transform

1.1K
Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
1.1K
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

528
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...
528
Upsampling01:22

Upsampling

265
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...
265
Parseval's Theorem01:18

Parseval's Theorem

560
Parseval's theorem is a fundamental concept in signal processing and harmonic analysis. It asserts that for a periodic function, the average power of the signal over one period equals the sum of the squared magnitudes of all its complex Fourier coefficients. This theorem, named after Marc-Antoine Parseval, provides a powerful tool for analyzing the energy distribution in signals.
Interestingly, Parseval's theorem also holds for the trigonometric form of the Fourier series, which...
560

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相关实验视频

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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关于基于数据驱动的信号分解的多尺度转换 entropy 的真正相关性

Meryem Jabloun1, Philippe Ravier1, Olivier Buttelli1

  • 1Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France.

Entropy (Basel, Switzerland)
|July 8, 2023
PubMed
概括

这项研究研究了非线性预处理如何影响时间序列分析的变量 (PE) 计算. 研究人员通过数据驱动的分解方法确定了潜在的解释问题,改进了多尺度PE分析.

科学领域:

  • 动态系统分析 动态系统分析
  • 时间序列复杂性 时间序列复杂性
  • 信息理论是信息理论.

背景情况:

  • 基于顺序模式的方法,如变量 (PE),对于分析动态系统非常有价值.
  • 多尺度顺序 (MPE) 变体通过结合预处理步骤来增强时间序列分析.
  • 预处理,特别是非线性方法对PE值的影响需要进一步描述.

研究的目的:

  • 扩展先前在线性预处理方面的工作,以非线性和数据驱动的分解方法用于MPE.
  • 识别和解决来自非线性预处理技术的解释PE值的潜在陷.
  • 提高MPE在复杂信号分析中的可靠性和可解释性.

主要方法:

  • 应用非线性预处理技术,包括经验模式分解 (EMD),变化模式分解 (VMD),单一光谱分析 (SSA) 和经验波形变换 (EWT).
  • 在模拟数据集 (高斯噪声,分数高斯过程,ARMA模型,合成sEMG) 和现实sEMG信号上计算的变 (PE) 和多尺度变 (MPE).
  • 分析了每个分解方法对PE值的影响,以确定解释挑战.

主要成果:

  • 非线性预处理,特别是数据驱动的分解,可以显著改变PE值,引入解释复杂性.
  • 发现特定的分解方法引入了影响计算PE的偏差或工件.
关键词:
数据驱动的分解电动肌谱学 电动肌谱学多尺度变量的.非线性过是一种非线性过.时间序列时间序列

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  • 该研究强调了了解预处理诱导的效应对于准确的MPE解释的必要性.
  • 结论:

    • 解释MPE值需要仔细考虑使用的非线性预处理方法.
    • 这项研究为MPE分析中的数据驱动分解技术的行为提供了关键的见解.
    • 这些发现有助于在分析复杂的时间序列数据 (包括生物医学信号) 时更强大,更准确地应用MPE.