P
Pedro Carpena
Researcher at University of Málaga
Publications - 83
Citations - 5093
Pedro Carpena is an academic researcher from University of Málaga. The author has contributed to research in topics: Genome & Detrended fluctuation analysis. The author has an hindex of 28, co-authored 80 publications receiving 4706 citations. Previous affiliations of Pedro Carpena include University of Oxford & Boston University.
Papers
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Effect of trends on detrended fluctuation analysis.
TL;DR: It is shown how to use DFA appropriately to minimize the effects of trends, how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation, or if the crossover is due to a trend without any transition in the dynamical properties of the noise.
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On the determination of the critical micelle concentration by the pyrene 1:3 ratio method
TL;DR: In this paper, a simple and accurate approach to the treatment of pyrene 1:3 ratio data in the context of critical micelle concentration determination in surfactant solutions is established.
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Problems Associated with the Treatment of Conductivity−Concentration Data in Surfactant Solutions: Simulations and Experiments
TL;DR: In this article, the authors proposed a new approach to analyze the conductivity-concentration data of ionic surfactant solutions, in the context of the determination of micellization parameters such as critical micelle concentration and degree of counterion dissociation.
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Analysis of symbolic sequences using the Jensen-Shannon divergence.
Ivo Grosse,Ivo Grosse,Pedro Bernaola-Galván,Pedro Bernaola-Galván,Pedro Carpena,Pedro Carpena,Ramón Román-Roldán,José L. Oliver,H. Eugene Stanley +8 more
TL;DR: A segmentation method is presented that is able to segment a nonstationary symbolic sequence into stationary subsequences, and is applied to DNA sequences, which are known to be non stationary on a wide range of different length scales.
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Effect of nonlinear filters on detrended fluctuation analysis.
TL;DR: This work investigates how various linear and nonlinear transformations affect the correlation and scaling properties of a signal, using the detrended fluctuation analysis (DFA) which has been shown to accurately quantify power-law correlations in nonstationary signals.