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

Classification of Signals01:30

Classification of Signals

567
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
567
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

748
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
748
Reducing Line Loss01:18

Reducing Line Loss

180
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
180
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

120
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....
120

You might also read

Related Articles

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

Sort by
Same author

Uniform zinc oxide nanowire arrays grown on nonepitaxial surface with general orientation control.

Nano letters·2013
Same author

[American head and neck surgery progress of in 2012].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2013
Same author

A compact thermo-optical multimode-interference silicon-based 1 × 4 nano-photonic switch.

Optics express·2013
Same author

Experimental demonstration of 110-Gb/s unsynchronized band-multiplexed superchannel coherent optical OFDM/OQAM system.

Optics express·2013
Same author

Potentially functional variants of p14ARF are associated with HPV-positive oropharyngeal cancer patients and survival after definitive chemoradiotherapy.

Carcinogenesis·2013
Same author

Enhanced molecular transport in hierarchical silicalite-1.

Langmuir : the ACS journal of surfaces and colloids·2013

Related Experiment Video

Updated: Jul 31, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.7K

Ultra-low complexity random forest for optical fiber communications.

Chao Li, Yongjun Wang, Haipeng Yao

    Optics Express
    |May 8, 2023
    PubMed
    Summary

    This study introduces an efficient random forest equalizer for optical fiber communications. It achieves performance comparable to deep neural networks with reduced complexity, enhancing system efficiency.

    More Related Videos

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
    09:43

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

    Published on: March 20, 2017

    10.0K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    10.9K

    Related Experiment Videos

    Last Updated: Jul 31, 2025

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
    06:50

    O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

    Published on: November 8, 2019

    6.7K
    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
    09:43

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

    Published on: March 20, 2017

    10.0K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    10.9K

    Area of Science:

    • Optical Communications
    • Machine Learning
    • Signal Processing

    Background:

    • Channel equalization is crucial for maintaining signal integrity in high-speed optical fiber communication systems.
    • Traditional equalization methods face challenges with increasing data rates and modulation complexities.
    • Deep learning approaches show promise but often require significant computational resources.

    Purpose of the Study:

    • To develop and evaluate an efficient channel equalizer for optical fiber communication systems using a random forest algorithm.
    • To compare the performance of the random forest equalizer against deep neural networks.
    • To propose a novel two-step classification mechanism to further optimize equalization performance and reduce complexity.

    Main Methods:

    • An efficient equalizer based on the random forest algorithm was designed and implemented.
    • Experimental validation was conducted on a 120 Gb/s, 375 km, dual-polarization 64-quadrature amplitude modulation (QAM) optical fiber communication platform.
    • A two-step classification mechanism was proposed, dividing constellation points into regions for tailored compensation.

    Main Results:

    • The random forest equalizer demonstrated equalization performance on par with deep neural networks.
    • The proposed method achieved lower computational complexity compared to deep neural networks.
    • The two-step classification strategy further improved performance and reduced system complexity.

    Conclusions:

    • Random forest offers an efficient and effective solution for channel equalization in optical fiber communication systems.
    • The proposed two-step classification mechanism enhances the practical applicability of random forest equalizers.
    • This approach is suitable for real-world optical fiber communication systems due to its performance and efficiency.