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相关概念视频

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

195
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
195
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

178
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
178
Classification of Systems-II01:31

Classification of Systems-II

132
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
132
Classification of Signals01:30

Classification of Signals

369
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
369
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

258
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...
258
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

315
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
315

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相关实验视频

Updated: May 22, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

在连续时间中无监督模型构建.

Jonathan J Park1, Zachary F Fisher2, Michael D Hunter2

  • 1Department of Psychology, University of California, Davis.

Structural equation modeling : a multidisciplinary journal
|May 19, 2025
PubMed
概括
此摘要是机器生成的。

我们介绍ct-gimme,这是分析动态网络的连续时间方法. 这种方法通过汇集主体信息和有效处理数据异质性来改进离散时间模型.

关键词:
连续时间连续时间.动态网络分析动态网络分析网络分析 网络分析国家空间建模 状态空间建模结构方程建模 结构方程建模

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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科学领域:

  • 心理网络分析 心理网络分析
  • 动态系统建模动态系统建模
  • 统计建模 统计建模

背景情况:

  • 传统的离散时间模型提供了直观的解释,但对于复杂的动态网络缺乏灵活性.
  • 连续时间模型提供了更大的灵活性,但对集团级网络分析的发展较少.

研究的目的:

  • 引入ct-gimme,这是集团代多重模型估计 (GIMME) 程序的连续时间延伸.
  • 为了使复杂的,高维的动态网络能够在多个主题中连续时间安装.

主要方法:

  • 开发了ct-gimme作为GIMME框架的持续时间调整.
  • 应用 ct-gimme 在连续时间中分析动态网络结构.

主要成果:

  • ct-gimme通过有效地跨主题汇集信息,优于标准的连续时间模型拟合.
  • 在处理样本内的异质性时,ct-gimme与组级离散时间匹配相比表现优越.

结论:

  • ct-gimme提供了一种灵活而强大的方法,用于在连续时间内分析动态网络.
  • 该方法通过利用组级信息和适应异质性来增强复杂,高维的网络数据的分析.