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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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...
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Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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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.
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Classification of Systems-II01:31

Classification of Systems-II

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

BIBO stability of continuous and discrete -time systems

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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.
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Updated: May 22, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Unsupervised Model Construction in Continuous-Time.

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
Summary
This summary is machine-generated.

We introduce ct-gimme, a continuous-time method for analyzing dynamic networks. This approach improves upon discrete-time models by pooling subject information and handling data heterogeneity effectively.

Keywords:
Continuous-TimeDynamic Network AnalysisNetwork AnalysisState-Space ModelingStructural Equation Modeling

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Area of Science:

  • Psychological network analysis
  • Dynamic systems modeling
  • Statistical modeling

Background:

  • Traditional discrete-time models offer intuitive interpretations but lack flexibility for complex dynamic networks.
  • Continuous-time models provide greater flexibility but have been less developed for group-level network analysis.

Purpose of the Study:

  • To introduce ct-gimme, a continuous-time extension of the Group Iterative Multiple Model Estimation (GIMME) procedure.
  • To enable the fitting of complex, high-dimensional dynamic networks in continuous-time across multiple subjects.

Main Methods:

  • Developed ct-gimme as a continuous-time adaptation of the GIMME framework.
  • Applied ct-gimme to analyze dynamic network structures in continuous-time.

Main Results:

  • ct-gimme outperforms standard continuous-time model fitting by effectively pooling information across subjects.
  • ct-gimme demonstrates superior performance compared to group-level discrete-time fitting when dealing with within-sample heterogeneity.

Conclusions:

  • ct-gimme offers a flexible and powerful approach for analyzing dynamic networks in continuous-time.
  • The method enhances the analysis of complex, high-dimensional network data by leveraging group-level information and accommodating heterogeneity.