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

Properties of Fourier Transform II01:24

Properties of Fourier Transform II

203
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
203
Properties of Fourier series I01:20

Properties of Fourier series I

293
The Fourier series is a powerful tool in signal processing and communications, allowing periodic signals to be expressed as sums of sine and cosine functions. A foundational property of the Fourier series is linearity. If we consider two periodic signals, their linear combination results in a new signal whose Fourier coefficients are simply the corresponding linear combinations of the original signals' coefficients. This property is crucial in applications like frequency modulation (FM)...
293
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

88
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
88
Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

80
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
80
Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

2.1K
The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
2.1K
Sampling Theorem01:15

Sampling Theorem

324
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
324

You might also read

Related Articles

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

Sort by
Same author

Resetting without resetting: An alternate strategy to experimentally verify optimal mean first passage time under stochastic resetting.

Physical review. E·2026
Same author

Learning the bistable cortical dynamics of the sleep-onset period.

PLoS computational biology·2026
Same author

Dynamics of Marangoni-driven elliptical Janus particles.

Soft matter·2026
Same author

Emergent thermalization thresholds in unitary dynamics of inhomogeneously disordered quantum systems.

Physical review. E·2026
Same author

Designing logic gates using active particles.

Physical review. E·2026
Same author

Confinement-induced intermittent motion of a camphor-infused paper disk.

Physical review. E·2026
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

9.0K

Synchronization through frequency shuffling.

Manaoj Aravind1, Vaibhav Pachaulee1, Mrinal Sarkar2

  • 1Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.

Physical Review. E
|June 22, 2024
PubMed
Summary
This summary is machine-generated.

Frequent shuffling of oscillator frequencies advances synchrony in coupled networks. This provides a new method for controlling synchronization, even with limited resources.

More Related Videos

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

6.0K
Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

12.8K

Related Experiment Videos

Last Updated: Jun 23, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

9.0K
Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

6.0K
Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

12.8K

Area of Science:

  • Complex Systems
  • Nonlinear Dynamics
  • Network Science

Background:

  • Many natural and engineered systems are modeled as networks of coupled nonlinear oscillators.
  • In natural systems, oscillator frequencies often change over time, a phenomenon known as temporal heterogeneity.

Purpose of the Study:

  • To investigate the impact of temporal frequency heterogeneity on coupled oscillator networks.
  • To explore a novel strategy for inducing and controlling synchrony in oscillator networks.

Main Methods:

  • Utilized the Kuramoto model to analyze coupled oscillator networks.
  • Repeatedly shuffled intrinsic frequencies among oscillators at random or regular time intervals.
  • Performed analytical derivations and experimental validation using Wien Bridge oscillators.

Main Results:

  • Frequent shuffling of intrinsic frequencies led to an earlier onset of synchrony.
  • Synchrony was achieved at lower coupling strengths when frequencies were shuffled more often.
  • Demonstrated the effectiveness of frequency shuffling as a synchronization control strategy.

Conclusions:

  • Temporal heterogeneity, specifically frequency shuffling, can be leveraged to control synchrony in coupled oscillator networks.
  • This approach offers a resource-efficient method for inducing and managing synchronization phenomena.
  • Findings are supported by both theoretical analysis and experimental results.