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Related Concept Videos

Ranks01:02

Ranks

272
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
272
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

319
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
319
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

296
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
296

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Information Complexity Ranking: A New Method of Ranking Images by Algorithmic Complexity.

Thomas Chambon1, Jean-Loup Guillaume1, Jeanne Lallement2

  • 1Laboratoire Informatique, Image et Interaction (L3i), La Rochelle University, 23 Avenue Albert Einstein, 17000 La Rochelle, France.

Entropy (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

A new Information Complexity Ranking (ICR) method predicts visual complexity using Kolmogorov complexity and normalized compression distance. ICR effectively ranks image simplicity, outperforming many existing algorithms.

Keywords:
Kolmogorov complexityalgorithmic information theoryinformation complexitysimilarity complexity

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Area of Science:

  • Computer Science
  • Information Theory
  • Human-Computer Interaction

Background:

  • Predicting visual complexity is crucial for user interface design but remains underexplored.
  • Existing methods lack a comprehensive approach to intrinsic and relational complexity.

Purpose of the Study:

  • To introduce a novel method, Information Complexity Ranking (ICR), for ranking visual information complexity.
  • To evaluate ICR's performance against established algorithms using diverse image datasets.

Main Methods:

  • Developed ICR, integrating Kolmogorov complexity (intrinsic algorithmic complexity) and normalized compression distance (NCD) for object similarity.
  • Validated ICR on 7200 randomly generated images (text, digits, colored dots).
  • Tested ICR on 1400 categorized images, comparing results with five state-of-the-art complexity algorithms.

Main Results:

  • ICR demonstrated superior performance in certain image categories compared to existing algorithms.
  • The method showed competitive results across various categories, outperforming the majority of state-of-the-art approaches.
  • ICR's efficiency decreased for images with high semantic content.

Conclusions:

  • Information Complexity Ranking (ICR) offers a promising approach to quantifying visual complexity.
  • The method effectively balances intrinsic and relational complexity measures.
  • Further refinement is needed for complex, semantically rich visual information.