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Entropy02:39

Entropy

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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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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
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Exponential Fourier series01:24

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In audio signal processing, the exponential Fourier series plays a crucial role in sound synthesis, allowing complex sounds to be broken down into simpler sinusoidal components. This decomposition process is fundamental in analyzing and reconstructing musical notes and other audio signals. The exponential Fourier series expresses periodic signals as the sum of complex exponentials at both positive and negative harmonic frequencies, providing a powerful tool for signal analysis.
Euler's identity...
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Entropy Change in Reversible Processes01:10

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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The Second Law of Thermodynamics01:14

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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
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Exponential and Sinusoidal Signals01:18

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The exponential function is crucial for characterizing waveforms that rise and decay rapidly. This continuous-time exponential function is defined using exponential terms with constants α and A. When both constants are real, the function is represented as,
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Updated: Jul 7, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Simplicial complex entropy for time series analysis.

Lev Guzmán-Vargas1, Alvaro Zabaleta-Ortega2, Aldo Guzmán-Sáenz3

  • 1Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, 07340, Mexico City, Mexico. lguzmanv@ipn.mx.

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|December 20, 2023
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Summary

This study introduces a new entropy measure, simplicial complex approximate entropy, to better characterize complex system dynamics. The method effectively quantifies irregularity in random and chaotic time series and differentiates physiological signals.

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Area of Science:

  • Complexity Science
  • Time Series Analysis
  • Nonlinear Dynamics

Background:

  • Complex systems in nature exhibit dynamic behaviors requiring robust analysis methods.
  • Existing time series irregularity measures struggle with stochastic and chaotic dynamics.
  • Novel approaches are needed for detailed characterization of complex system dynamics.

Purpose of the Study:

  • To introduce a new entropy measure, simplicial complex approximate entropy (SCAE).
  • To evaluate SCAE's capability in characterizing irregularity in various complex dynamics.
  • To demonstrate SCAE's potential in differentiating physiological states and chaotic systems.

Main Methods:

  • Developed simplicial complex approximate entropy based on conditional probability of simplicial complex elements.
  • Applied SCAE to simulated random sequences and low-dimensional chaotic dynamics.
  • Utilized SCAE to analyze cardiac interbeat sequences and coupled chaotic maps.

Main Results:

  • SCAE provides a wide range of values, revealing details missed by standard methods.
  • The method successfully quantifies irregularity in random and chaotic time series.
  • SCAE consistently differentiates cardiac signals from healthy and heart failure patients and identifies changes in coupled chaotic maps.

Conclusions:

  • Simplicial complex approximate entropy offers enhanced characterization of complex system dynamics.
  • The revealed structures by simplicial complexes are crucial for detailed analysis.
  • This approach shows promise for diverse applications in analyzing complex systems.