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相关概念视频

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

183
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
183
Time-Series Graph00:54

Time-Series Graph

4.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.2K
Discrete Fourier Transform01:15

Discrete Fourier Transform

187
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...
187
Properties of DTFT I01:24

Properties of DTFT I

317
In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
317
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

239
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
239
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

222
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
222

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相关实验视频

Updated: May 10, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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使用多分形时间序列分析技术探索单词相邻网络.

Jakub Dec1, Michał Dolina1, Stanisław Drożdż1,2

  • 1Faculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Kraków, Poland.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用网络映射和多分位分析分析语言结构的新方法. 这项研究揭示了文学中复杂的文本组织,为语言学和网络科学提供了新的见解.

关键词:
聚类系数的聚类系数复杂的网络复杂的网络.多个尺度的相关性.量化语言学的语言学时间序列时间序列顶点可观测的可观测物

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相关实验视频

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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科学领域:

  • 量化语言学 量化语言学
  • 网络科学 网络科学
  • 复杂系统分析 复杂系统分析

背景情况:

  • 传统的语言分析往往忽视了文本中的复杂结构模式.
  • 了解语言的复杂组织对于计算机语言学和网络理论的进步至关重要.

研究的目的:

  • 通过将单词相邻网络映射到时间序列,引入一种探索语言网络的新方法.
  • 应用多分形分析技术来发现文本数据中复杂的结构模式.
  • 研究句号对语言网络分析的影响.

主要方法:

  • 将单词相邻网络映射到时间序列数据.
  • 从网络属性 (集群系数,节点度) 衍生出来的时间序列应用多分片分析.
  • 使用易斯·卡罗尔的"爱丽丝在奇迹国"的案例研究,有和没有标点符号.

主要成果:

  • 来自聚类系数的时间序列表现出多分形特征,表明固有的文本复杂性.
  • 统计验证证实了多分形属性是真实的,而不是假的.
  • 整合标点改变了扩展到非统一的多分形形式;节点度分析显示的复杂性较小.

结论:

  • 提出的方法为定量语言学和网络科学提供了一个新的视角.
  • 对语言网络的多分法分析揭示了对文本结构的深入洞察.
  • 这项研究强调了标点在文本组织中的重要而复杂的作用.