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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

390
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...
390
Linear time-invariant Systems01:23

Linear time-invariant Systems

479
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...
479
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

132
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
132

You might also read

Related Articles

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

Sort by
Same author

Membrane bionic supramolecular nanomaterials for co-delivered of ribociclib and PEG10 siRNA for targeted therapy in breast cancer.

Cancer letters·2026
Same author

How surface curvature shapes water nanodroplets in air.

Journal of physics. Condensed matter : an Institute of Physics journal·2026
Same author

Superefficient optical frequency division referenced to μHz Schawlow-Townes-linewidth quantum noise-limited lasers.

Science advances·2026
Same author

Toward integrated security and monitoring: perception-embedded modulation for DCIs.

Optics letters·2026
Same author

Precise l-threonine-to-l-isoleucine pathway regulation for engineering high-efficiency whole-cell biocatalysts.

Synthetic and systems biotechnology·2026
Same author

Synergistic PNS-NAC Bioadhesive Microspheres for Enhanced Intestinal Permeability and Oral Bioavailability.

Recent advances in drug delivery and formulation·2026

Related Experiment Video

Updated: Sep 25, 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

9.1K

Adaptive time-delayed photonic reservoir computing based on Kalman-filter training.

Jiaoyang Jin, Ning Jiang, Yiqun Zhang

    Optics Express
    |April 27, 2022
    PubMed
    Summary

    This study introduces an adaptive photonic reservoir computing system trained with the Kalman filter algorithm. This novel approach significantly enhances time-series prediction and channel equalization performance compared to traditional methods.

    More Related Videos

    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
    09:23

    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

    Published on: May 30, 2014

    14.7K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.0K

    Related Experiment Videos

    Last Updated: Sep 25, 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

    9.1K
    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
    09:23

    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

    Published on: May 30, 2014

    14.7K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.0K

    Area of Science:

    • Photonics
    • Computational Science
    • Signal Processing

    Background:

    • Reservoir computing (RC) offers a powerful framework for complex system modeling.
    • Traditional RC training methods like least-squares (LS) have limitations in adaptive scenarios.
    • Photonic implementations of RC promise high-speed computation.

    Purpose of the Study:

    • To develop and evaluate an adaptive time-delayed photonic reservoir computing (RC) structure.
    • To enhance the performance of RC for time-series prediction and nonlinear channel equalization.
    • To investigate the impact of adaptive training using the Kalman filter (KF) algorithm.

    Main Methods:

    • Proposed an adaptive time-delayed photonic RC structure.
    • Employed the Kalman filter (KF) algorithm for adaptive training.
    • Utilized benchmark tasks: Santa Fe time-series prediction and nonlinear channel equalization.
    • Introduced a complex mask from a chaotic signal to further enhance performance.

    Main Results:

    • The adaptive KF training significantly improved prediction and equalization performance over LS training.
    • Incorporating a complex mask derived from chaotic signals further boosted RC performance.
    • The proposed RC system demonstrated superior equalization for parameter-variant wireless channels.

    Conclusions:

    • Adaptive Kalman filter training enhances photonic reservoir computing performance.
    • Complex masks from chaotic signals offer a route to further performance gains.
    • This work paves the way for adaptive photonic computing solutions.