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Downsampling01:20

Downsampling

159
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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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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Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Related Experiment Video

Updated: Jul 7, 2025

A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis
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A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images.

Borut Žalik1, Damjan Strnad1, David Podgorelec1

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, SI-2000 Maribor, Slovenia.

Entropy (Basel, Switzerland)
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

A new string transformation technique, Move with Interleaving (MwI), effectively reduces information entropy in 2D raster images. The Hilbert arrangement within MwI achieved the best compression results among tested methods.

Keywords:
Hilbert space filling curvealgorithmcomputer scienceinformation entropystring transformation

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Area of Science:

  • Computer Science
  • Data Compression
  • Image Processing

Background:

  • String transformation techniques are crucial for data compression.
  • Existing methods like Burrows-Wheeler Transform (BWT) are widely used.
  • Novel approaches are needed to improve compression efficiency for image data.

Purpose of the Study:

  • To introduce and evaluate a new string transformation technique named Move with Interleaving (MwI).
  • To compare MwI's performance against established methods for 2D raster image data.

Main Methods:

  • Developed the Move with Interleaving (MwI) string transformation technique.
  • Implemented four distinct 2D-to-1D data arrangement methods: scan-line, left-right, strip-based, and Hilbert.
  • Conducted experiments on 32 benchmark greyscale raster images of varying resolutions.

Main Results:

  • The proposed MwI transformation significantly reduces information entropy in greyscale raster images.
  • MwI demonstrated comparable entropy reduction to the combination of Burrows-Wheeler Transform (BWT) and Move-To-Front (MTF) or Inversion Frequencies (IF).
  • The Hilbert arrangement within MwI yielded superior results compared to other tested arrangements and transformations.

Conclusions:

  • Move with Interleaving (MwI) is a promising new technique for string transformation and data compression.
  • The Hilbert arrangement is particularly effective when used with MwI for image data.
  • MwI offers a competitive alternative to existing compression pre-processing techniques.