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Related Concept Videos

Sampling Theorem01:15

Sampling Theorem

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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.
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Sampling Plans01:23

Sampling Plans

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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...
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Upsampling01:22

Upsampling

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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...
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Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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.
Systematic sampling is one of the simplest methods...
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Optimization of multiple sampling for solving network boundary specification problem.

Ruochen Zhang1

  • 1School of Economics and Management, Xi'an Shiyou University, Xi'an, 710065, Shaanxi Province, China. zrc@xsyu.edu.cn.

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Summary

Optimizing sampling methods is key to analyzing network structures. This study introduces a mathematical model and a memetic algorithm to improve sample representativeness and address missing data issues in network analysis.

Keywords:
AlgorithmsBoundary specificationOptimizationSocial networks

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Area of Science:

  • Social network analysis
  • Data science
  • Network science

Background:

  • Missing data due to boundary specification significantly impacts network structure analysis.
  • Optimal sampling strategy design is critical for effective network investigations.
  • Existing methods may not adequately address boundary specification challenges in surveys.

Purpose of the Study:

  • To address the boundary specification problem in multiple independent surveys.
  • To propose a mathematical model for optimizing sampling strategies.
  • To develop and evaluate a memetic algorithm for maximizing sample representativeness.

Main Methods:

  • Development of a mathematical model for optimizing sampling strategies in independent surveys.
  • Implementation of a memetic algorithm designed to enhance sample representativeness.
  • Experimental validation using Zachary's Karate Club network and migrant worker networks.

Main Results:

  • The proposed mathematical model effectively optimizes sampling strategies.
  • The memetic algorithm demonstrates high effectiveness and efficiency in maximizing sample representativeness.
  • Experimental results confirm the practical utility and performance of the developed algorithm.

Conclusions:

  • The developed mathematical model and memetic algorithm offer a robust solution to the boundary specification problem in network analysis.
  • The findings provide a valuable tool for improving the accuracy and reliability of network structure investigations.
  • The study highlights the social implications of optimal sampling methods in real-world network contexts.