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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

822
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
822
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

816
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...
816
Upsampling01:22

Upsampling

684
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
684
Aliasing01:18

Aliasing

747
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
747
Second Order systems II01:18

Second Order systems II

459
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
459
Sampling Theorem01:15

Sampling Theorem

1.5K
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.
1.5K

You might also read

Related Articles

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

Sort by
Same author

Real-time motion trajectory training and prediction using reservoir computing for intelligent sensing equipment.

The Review of scientific instruments·2025
Same author

Reservoir computing-based digital signal equalizer for equivalent-time sampling.

The Review of scientific instruments·2023
Same journal

Erratum: "Highly versatile, two-color setup for high-order harmonic generation using spatial light modulators" [Rev. Sci. Instrum. 95, 073002 (2024)].

The Review of scientific instruments·2026
Same journal

Thermal correction method for accurate performance evaluation of micro-thermoelectric coolers.

The Review of scientific instruments·2026
Same journal

Correcting the energy-dependent asymmetry in low-energy muon spin rotation.

The Review of scientific instruments·2026
Same journal

Fiber-integrated acousto-optic-modulator-based phase-controlled Rydberg atomic electrometer.

The Review of scientific instruments·2026
Same journal

A top-loading point-contact spectroscopy probe with in-situ sample exchange for dilution refrigerators.

The Review of scientific instruments·2026
Same journal

Investigation of plasma characteristics in a developed large-diameter, low-aspect ratio, radio frequency plasma source with a flat spiral antenna.

The Review of scientific instruments·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

9.1K

High-order modulation signals equalization based on reservoir computing under equivalent-time sampling.

Chunhui Yan1, Yongjie Guo1, Xidong Wang1

  • 1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.

The Review of Scientific Instruments
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial intelligence equalizer using reservoir computing (RC) to improve digital signal analysis. The RC equalizer effectively compensates for signal distortions, significantly enhancing waveform quality and data transmission reliability.

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

15.1K
Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

11.4K

Related Experiment Videos

Last Updated: Mar 19, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

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

15.1K
Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

11.4K

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Digital signal testing with sampling oscilloscopes faces challenges in equalizing frequency components due to sampling point discontinuities.
  • Traditional high-pass filtering equalizers are unsuitable for equivalent-time sampling data.

Purpose of the Study:

  • To introduce an artificial intelligence equalizer based on reservoir computing (RC) for processing equivalent-time sampling data.
  • To compensate for low-response, high-frequency components in high-order modulation signals.

Main Methods:

  • An RC equalizer was developed for the in-phase and quadrature channels of optical quadrature phase shift keying (QPSK) signals.
  • The RC model was trained to process equivalent-time sampling data, compensating for signal distortions.

Main Results:

  • The RC equalized waveform quality significantly improved compared to the input.
  • Signal constellation diagram convergence was enhanced.
  • At a 15 dB signal-to-noise ratio (SNR), error vector magnitude (EVM) decreased from 17.35% to 1.75%.

Conclusions:

  • The RC equalizer demonstrates robustness and effectiveness in improving signal quality.
  • The equalizer significantly reduces bit error rate (BER) across various SNRs, showing potential for reliable high-speed optical communication.