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相关概念视频

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

238
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]...
238
Time-Series Graph00:54

Time-Series Graph

4.3K
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...
4.3K
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

281
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...
281
Linear time-invariant Systems01:23

Linear time-invariant Systems

229
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
229
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

201
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
201
State Space Representation01:27

State Space Representation

178
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
178

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相关实验视频

Updated: Jun 15, 2025

Basics of Multivariate Analysis in Neuroimaging Data
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对多变量时间序列的离散表示学习.

Marzieh Ajirak1, Immanuel Elbau1, Nili Solomonov1

  • 1Weill Cornell Medicine, Cornell University, New York, NY, USA.

Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)
|June 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的深度学习方法,用于使用高斯过程在多变量时间序列中进行离散表示学习. 该方法提高了可解释性,并提高了fMRI数据的分类准确性.

关键词:
贝叶斯的推理 贝叶斯的推理斯过程是高斯过程.可解释的离散表示.多变量时间序列.

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Last Updated: Jun 15, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Cross-Modal Multivariate Pattern Analysis
13:51

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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科学领域:

  • 机器学习 机器学习
  • 时间序列分析时间序列分析
  • 计算神经科学是一种神经科学.

背景情况:

  • 多变量时间序列分析经常面临高维度和可解释性的挑战.
  • 深度学习模型因非区分性问题而难以结合离散的潜在变量.
  • 高斯过程为时间序列建模提供了一个有价值的概率框架.

研究的目的:

  • 开发一种新的深度学习架构,用于在多变量时间序列中进行离散表示学习.
  • 通过学习低维嵌入和离散隐藏状态来提高时间序列数据的可解释性.
  • 为了提高复杂时间序列数据集的分类性能,例如fMRI数据.

主要方法:

  • 在集成离散潜变量时使用了Gumbel-softmax重组参数化技巧来处理非可区分性.
  • 通过可学习的潜伏空间离散,开发了一个联合集群和嵌入框架.
  • 在深度学习架构中使用高斯过程来进行时间序列建模.

主要成果:

  • 成功实现了对多变量时间序列的嵌入和离散潜态的联合学习.
  • 通过减少维度和识别不同的潜在状态,证明了增强的解释性.
  • 在合成和现实世界fMRI数据集上取得了改进的分类结果.

结论:

  • 拟议的离散表示学习方法有效地解决了对时间序列的深度学习的挑战.
  • 该模型为复杂的时间序列数据提供了更易于解释的表示.
  • 该方法显示了神经成像和其他涉及高维时间序列数据的领域的应用的巨大潜力.