Related Experiment Video

Updated: Jun 19, 2026

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator
07:42

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Published on: December 15, 2021

Stability of soliton propagation with sliding-frequency guiding filters

P V Mamyshev, L F Mollenauer

    Optics Letters
    |October 27, 2009
    PubMed

    More Related Videos

    X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells10:16

    X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

    Published on: August 20, 2019

    Quasi-light Storage for Optical Data Packets07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    Related Concept Videos

    Stability01:28

    Stability

    The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
    The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
    Time and frequency -Domain Interpretation of Phase-lag Control01:21

    Time and frequency -Domain Interpretation of Phase-lag Control

    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 finite,...
    Time and frequency -Domain Interpretation of PI Control01:27

    Time and frequency -Domain Interpretation of PI Control

    Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
    Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires careful...
    Pole and System Stability01:24

    Pole and System Stability

    The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
    Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's response.
    Damped Oscillations01:07

    Damped Oscillations

    In the real world, oscillations seldom follow true simple harmonic motion. A system that continues its motion indefinitely without losing its amplitude is termed undamped. However, friction of some sort usually dampens the motion, so it fades away or needs more force to continue. For example, a guitar string stops oscillating a few seconds after being plucked. Similarly, one must continually push a swing to keep a child swinging on a playground.
    Although friction and other non-conservative...
    Linear Approximation in Frequency Domain01:26

    Linear Approximation in Frequency Domain

    Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
    In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.