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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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Related Experiment Video

Updated: Oct 6, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Permutation Entropy of Weakly Noise-Affected Signals.

Leonardo Ricci1,2, Antonio Politi3

  • 1Department of Physics, University of Trento, 38123 Trento, Italy.

Entropy (Basel, Switzerland)
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

This study quantifies how observational noise impacts permutation entropy in chaotic signals. A multifractal analysis helps reconstruct noiseless entropy, though effectiveness varies across chaotic systems.

Keywords:
entropymultifractal analysisordinal patterns

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Area of Science:

  • Nonlinear Dynamics
  • Chaos Theory
  • Information Theory

Background:

  • Permutation entropy quantifies complexity in time series data.
  • Observational noise can obscure underlying dynamics in chaotic systems.
  • Understanding noise effects is crucial for accurate complexity measures.

Purpose of the Study:

  • To analyze the effect of observational noise on permutation entropy in deterministic chaotic signals.
  • To investigate the scaling of entropy increase with noise amplitude and window length.
  • To develop a method for reconstructing noiseless permutation entropy.

Main Methods:

  • Analysis of permutation entropy in noisy chaotic signals.
  • Investigation of scaling laws for entropy increase.
  • Multifractal analysis to identify noise-induced symbolic sequences.
  • Reconstruction of noiseless permutation entropy.

Main Results:

  • Identified scaling dependence of permutation entropy increase on noise amplitude and window length.
  • Multifractal analysis revealed emergence of poorly populated symbolic sequences due to noise.
  • Successful reconstruction of noiseless permutation entropy for Hénon and tent maps.
  • Limited effectiveness of the reconstruction method for hyperchaotic systems.

Conclusions:

  • Observational noise significantly alters permutation entropy in chaotic systems.
  • The proposed multifractal-based reconstruction method shows promise but has limitations for complex hyperchaos.
  • Further research is needed to understand and mitigate noise effects in hyperchaotic signal analysis.