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

Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.0K
Probability in Statistics01:14

Probability in Statistics

22.2K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
22.2K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

14.8K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
14.8K
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.1K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.1K
Sampling Plans01:23

Sampling Plans

896
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
896

You might also read

Related Articles

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

Sort by
Same author

[Scapular belt for the treatment of comminuted fractures of scapula].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2010
Same author

Manipulation of ordered nanostructures of protonated polyoxometalate through covalently bonded modification.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010
Same author

Developments in nonsteroidal antiandrogens targeting the androgen receptor.

ChemMedChem·2010
Same author

Dynamic presentation of immobilized ligands regulated through biomolecular recognition.

Journal of the American Chemical Society·2010
Same author

[Research on crop-weed discrimination using a field imaging spectrometer].

Guang pu xue yu guang pu fen xi = Guang pu·2010
Same author

A palladium/copper bimetallic catalytic system: dramatic improvement for Suzuki-Miyaura-type direct C-H arylation of azoles with arylboronic acids.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010

Related Experiment Video

Updated: Jan 17, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K

Mean clustering symbol decision mechanism based on statistical probability features.

Xue Tang, Bo Liu, Jianxin Ren

    Optics Express
    |September 23, 2025
    PubMed
    Summary

    A new Mean Clustering Symbol Decision (MCSD) mechanism improves symbol detection in probabilistically shaped (PS) systems. MCSD offers significant optical signal-to-noise ratio (OSNR) gains and extended transmission distances compared to traditional K-means clustering.

    More Related Videos

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.9K
    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
    05:12

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

    Published on: January 16, 2019

    11.9K

    Related Experiment Videos

    Last Updated: Jan 17, 2026

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.3K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.9K
    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
    05:12

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

    Published on: January 16, 2019

    11.9K

    Area of Science:

    • Optical communications
    • Digital signal processing
    • Machine learning for communications

    Background:

    • K-means clustering is widely used but struggles with local optima in probabilistically shaped (PS) systems.
    • Modulation nonlinear distortion and shaping strength can cause clustering difficulties in PS-M quadrature amplitude modulation (QAM) systems.

    Purpose of the Study:

    • To propose a novel Mean Clustering Symbol Decision (MCSD) mechanism for enhanced symbol detection accuracy in PS-M QAM systems.
    • To leverage statistical probability features for robust symbol decision making.

    Main Methods:

    • Developed a centroid update formula using mean clustering and probabilistic features.
    • Introduced probability weighting and regularization to address empty cluster issues.
    • Employed the Mahalanobis distance metric for constellation property tailoring.

    Main Results:

    • MCSD achieved a 1.9 dB optical signal-to-noise ratio (OSNR) gain over K-means under 28GBaud PS-64QAM (4.8 bits/symbol), enabling 672 km additional transmission.
    • Experimental results showed a 2.5 dB OSNR gain over K-means for PS-64QAM (5 bits/symbol).
    • MCSD overcame bit error rate bottlenecks and achieved a 0.2 bits/symbol entropy gain in shape strength variation tests, requiring fewer iterations.

    Conclusions:

    • The proposed MCSD mechanism significantly enhances symbol detection accuracy in PS-M QAM systems.
    • MCSD demonstrates superior performance over K-means, offering substantial OSNR gains and improved transmission capabilities.
    • MCSD provides a robust and efficient solution for symbol detection in advanced optical communication systems.