Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear time-invariant Systems01:23

Linear time-invariant Systems

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 calculated...
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from the...
Classification of Systems-II01:31

Classification of Systems-II

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,
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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.
Properties of DTFT I01:24

Properties of DTFT I

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.

PloS one·2026
Same author

Regulatory adaptation of an accessory gene controls fungal halotolerance and niche expansion.

The ISME journal·2026
Same author

Liuwei dihuang decoction attenuates intervertebral disc degeneration by inhibiting TRPA1-Mediated ferroptosis in endplate chondrocytes.

Frontiers in cell and developmental biology·2026
Same author

Factors of crisis and culture in international and Chinese death education research: a comparative bibliometric analysis.

Frontiers in medicine·2026
Same author

Histone Demethylase MoRph1 Regulates Fungal Development, Pathogenicity, and DNA Damage Repair in <i>Magnaporthe oryzae</i>.

Journal of fungi (Basel, Switzerland)·2026
Same author

The deubiquitinase USP35: from an oncogenic hub to a therapeutic target in human cancers.

Frontiers in oncology·2026

Related Experiment Video

Updated: May 29, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Analyzing inner and outer synchronization between two coupled discrete-time networks with time delays.

Weigang Sun, Rubin Wang, Weixiang Wang

    Cognitive Neurodynamics
    |September 3, 2011
    PubMed
    Summary

    This study explores synchronization in discrete-time networks with time delays, achieving asymptotic stability using Lyapunov stability theory and linear matrix inequality (LMI). The findings offer insights into coupled neural network dynamics.

    Keywords:
    Complex networksInner synchronizationOuter synchronization

    More Related Videos

    New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
    05:59

    New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

    Published on: October 6, 2023

    Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
    13:57

    Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

    Published on: July 1, 2015

    Related Experiment Videos

    Last Updated: May 29, 2026

    Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
    05:59

    New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

    Published on: October 6, 2023

    Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
    13:57

    Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

    Published on: July 1, 2015

    Area of Science:

    • Control theory
    • Network dynamics
    • Applied mathematics

    Background:

    • Discrete-time networks with time delays are prevalent in various scientific domains.
    • Understanding synchronization phenomena is crucial for analyzing complex network behavior.
    • Existing methods may not fully address the complexities of coupled networks with delays.

    Purpose of the Study:

    • To investigate inner and outer synchronization in two discrete-time networks with time delays.
    • To develop robust conditions for asymptotic stability in these networks.
    • To provide theoretical insights into the dynamics of coupled neural networks.

    Main Methods:

    • Lyapunov stability theory was employed to analyze system stability.
    • Linear matrix inequality (LMI) techniques were utilized to derive sufficient conditions.
    • Numerical simulations were performed to validate the theoretical results.

    Main Results:

    • Sufficient conditions for asymptotic stability of the discrete-time networks were established using LMI.
    • The derived conditions effectively guarantee both inner and outer synchronization.
    • Numerical examples demonstrated the practical applicability and effectiveness of the proposed methods.

    Conclusions:

    • The study successfully established conditions for asymptotic stability in discrete-time networks with time delays.
    • The theoretical framework provides a valuable tool for analyzing and controlling synchronized behaviors in complex networks.
    • The findings contribute to a deeper understanding of neural network dynamics and synchronization.