Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Linear Approximation in Frequency Domain

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....

Linear Approximation in Time Domain

Linear Approximation in Time Domain

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,...

Convolution Properties I

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

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...

You might also read

Related Articles

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

Sort by

Same author

Real-Time and <i>In Situ</i> Monitoring of Pesticide Uptake and Transportation in Plants Using Surface-Enhanced Raman Scattering Nanosensors.

JACS Au·2026

Same author

Dual-channel hard negative sample generation for graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Experimental demonstration of 10Gbaud 64QAM SDM self-homodyne coherent optical transmission in U band with low-complexity sparsity-aware volterra equalization algorithm.

Optics express·2026

Same author

Boosting SERS Performance of TiO<sub>2</sub> Microspheres via Phytic Acid Modification: Application to Bisphenol A Detection in Consumer Products.

ACS applied materials & interfaces·2026

Same author

Cobalt-Catalyzed Asymmetric Cyclopropanation of Heteroaryl Alkenes with Homogeneous Zinc Carbenoids.

Journal of the American Chemical Society·2026

Same author

Ultra-wide bandwidth system optical fiber channel modeling based on Kolmogorov-Arnold network and physical constraints.

Optics express·2026

Search research articles

Related Experiment Video

Updated: May 14, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Perturbation dimensional reduction theory assisted low complexity convolutional neural network equalizer for optical

Lu Han, Yongjun Wang, Haipeng Yao

|April 12, 2025

Summary

This study introduces a novel data dimensionality reduction method and a complex-valued CNN equalizer to minimize complexity in optical communication systems. The approach significantly reduces feature units and equalizer complexity, enhancing transmission efficiency.

More Related Videos

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Related Experiment Videos

Last Updated: May 14, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

Optical Communications
Artificial Intelligence
Signal Processing

Background:

Modern optical communication systems require efficient equalization to mitigate nonlinear impairments.
Balancing high equalization performance with minimized system complexity is a key research challenge.

Purpose of the Study:

To propose a novel, low-complexity data dimensionality reduction (DR) method for neural network-based equalizers.
To develop an efficient complex-valued CNN (CvCNN) equalizer for optical fiber communication systems.
To reduce the complexity of neural network equalizers while maintaining performance.

Main Methods:

A DR method exploiting spatial symmetry of perturbation coefficients for low-complexity feature map construction.
Design of a CvCNN equalizer using single-channel complex-valued feature maps.
Numerical studies and experimental verification in a 20 GBaud PDM 64-QAM system.

Main Results:

The DR feature map achieved an 82.64% reduction in effective feature units.
The CvCNN equalizer reduced space and time complexity by 66.90% and 70.55%, respectively.
Performance was validated through Q-factor comparisons and complexity analyses.

Conclusions:

The proposed DR method and CvCNN equalizer offer a significant reduction in complexity for optical communication systems.
This approach effectively mitigates nonlinear impairments with improved efficiency.
The methodology provides a viable solution for developing low-complexity, high-performance equalizers.