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Related Concept Videos

Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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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.
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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.
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Upsampling01:22

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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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Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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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. 
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A Singular Value Decomposition based Maximal Poisson-disk Sampling for adaptive Digital Elevation Model

Xingquan Wu1, Zhiwei Li1, Hongyuan Zhang1

  • 1China Energy Engineering Group Gansu Electronic Power Design Institute Co. Ltd, Lanzhou, China.

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|September 2, 2020
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Summary
This summary is machine-generated.

This study simplifies Digital Elevation Models (DEM) using an improved Maximal Poisson-disk Sampling (MPS) method. The new approach efficiently samples terrain data, concentrating points in complex areas for better accuracy and reduced computational cost.

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Area of Science:

  • Geoinformatics
  • Geomorphometry
  • Computational Geometry

Background:

  • Digital Elevation Models (DEM) require simplification for efficient representation while preserving accuracy.
  • Traditional Maximal Poisson-disk Sampling (MPS) offers hyper-uniform distribution but faces challenges with complex geodesic computations and high memory demands for DEM adaptive sampling.
  • Existing methods struggle with computational complexity and memory requirements in DEM simplification.

Purpose of the Study:

  • To propose an efficient and accurate method for simplifying Digital Elevation Models (DEM).
  • To develop an extension of Maximal Poisson-disk Sampling (MPS) that accounts for terrain heterogeneity.
  • To reduce the computational complexity and memory footprint associated with DEM simplification.

Main Methods:

  • An extended Maximal Poisson-disk Sampling (MPS) method is proposed, utilizing eigenvalue distribution for quasi-random point selection from DEM nodes.
  • Local terrain complexity is quantified using an index, and the sampling disk radius is inversely proportional to this complexity.
  • This approach implicitly incorporates geodesic metric parameters, concentrating samples in rugged terrain.

Main Results:

  • The proposed method achieves DEM simplification by adaptively sampling points based on local terrain complexity.
  • Results demonstrate comparable or superior performance to geodesic distance-based Poisson disk sampling methods.
  • Significant acceleration of the sampling process and reduction in memory costs were observed.

Conclusions:

  • The developed MPS-based method provides an effective solution for DEM simplification, balancing accuracy and efficiency.
  • The approach successfully addresses the limitations of traditional methods by reducing computational and memory overhead.
  • This technique offers a practical advancement for handling large-scale DEM data.