scholarly journals A generalized statistical complexity measure: Applications to quantum systems

2009 ◽  
Vol 50 (12) ◽  
pp. 123528 ◽  
Author(s):  
R. López-Ruiz ◽  
Á. Nagy ◽  
E. Romera ◽  
J. Sañudo
Keyword(s):  
Quantum Systems ◽  
Complexity Measure ◽  
Statistical Complexity

scholarly journals Structural Statistical Quantifiers and Thermal Features of Quantum Systems

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 19
Author(s):  
Flavia Pennini ◽  
Angelo Plastino ◽  
Angel Ricardo Plastino ◽  
Alberto Hernando
Keyword(s):  
Nuclear Physics ◽  
Quantum Systems ◽  
Fermion Model ◽  
Statistical Complexity ◽  
Thermal Features

This paper deals primarily with relatively novel thermal quantifiers called disequilibrium and statistical complexity, whose role is growing in different disciplines of physics and other sciences. These quantifiers are called L. Ruiz, Mancini, and Calvet (LMC) quantifiers, following the initials of the three authors who advanced them. We wish to establish information-theoretical bridges between LMC structural quantifiers and (1) Thermal Heisenberg uncertainties ΔxΔp (at temperature T); (2) A nuclear physics fermion model. Having achieved such purposes, we determine to what an extent our bridges can be extended to both the semi-classical and classical realms. In addition, we find a strict bound relating a special LMC structural quantifier to quantum uncertainties.

Intensive statistical complexity measure of pseudorandom number generators

2005 ◽  
Vol 356 (1) ◽  
pp. 133-138 ◽  
Author(s):  
H.A. Larrondo ◽  
C.M. González ◽  
M.T. Martín ◽  
A. Plastino ◽  
O.A. Rosso
Keyword(s):  
Complexity Measure ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generators ◽  
Statistical Complexity

GENERALIZED STATISTICAL COMPLEXITY MEASURE

2010 ◽  
Vol 20 (03) ◽  
pp. 775-785 ◽  
Author(s):  
OSVALDO A. ROSSO ◽  
LUCIANA DE MICCO ◽  
HILDA A. LARRONDO ◽  
MARÍA T. MARTÍN ◽  
ANGEL PLASTINO
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Probability Distribution ◽  
Metric Space ◽  
Fundamental Problem ◽  
Complexity Measure ◽  
Causal Effects ◽  
Probability Metric ◽  
Statistical Complexity ◽  
Selection Of

A generalized Statistical Complexity Measure (SCM) is a functional that characterizes the probability distribution P associated to the time series generated by a given dynamical system. It quantifies not only randomness but also the presence of correlational structures. We review here several fundamental issues in such a respect, namely, (a) the selection of the information measure [Formula: see text]; (b) the choice of the probability metric space and associated distance [Formula: see text]; (c) the question of defining the so-called generalized disequilibrium [Formula: see text]; (d) the adequate way of picking up the probability distribution P associated to a dynamical system or time series under study, which is indeed a fundamental problem. In this communication we show (point d) that sensible improvements in the final results can be expected if the underlying probability distribution is "extracted" via appropriate consideration regarding causal effects in the system's dynamics.

Statistical complexity measure of pseudorandom bit generators

2005 ◽  
Vol 354 ◽  
pp. 281-300 ◽  
Author(s):  
C.M. González ◽  
H.A. Larrondo ◽  
O.A. Rosso
Keyword(s):  
Complexity Measure ◽  
Statistical Complexity

COMPLEXITY MEASURE: A QUANTUM INFORMATION APPROACH

2012 ◽  
Vol 10 (04) ◽  
pp. 1250047 ◽  
Author(s):  
YURI CASSIO CAMPBELL-BORGES ◽  
JOSÉ ROBERTO CASTILHO PIQUEIRA
Keyword(s):  
Quantum Information ◽  
Quantum Systems ◽  
Complexity Measure ◽  
Entanglement Measure ◽  
Classical Information ◽  
Density Operators ◽  
The Past ◽  
Information Framework ◽  
General Tool ◽  
Do So

In the past decades, all of the efforts at quantifying systems complexity with a general tool has usually relied on using Shannon's classical information framework to address the disorder of the system through the Boltzmann–Gibbs–Shannon entropy, or one of its extensions. However, in recent years, there were some attempts to tackle the quantification of algorithmic complexities in quantum systems based on the Kolmogorov algorithmic complexity, obtaining some discrepant results against the classical approach. Therefore, an approach to the complexity measure is proposed here, using the quantum information formalism, taking advantage of the generality of the classical-based complexities, and being capable of expressing these systems' complexity on other framework than its algorithmic counterparts. To do so, the Shiner–Davison–Landsberg (SDL) complexity framework is considered jointly with linear entropy for the density operators representing the analyzed systems formalism along with the tangle for the entanglement measure. The proposed measure is then applied in a family of maximally entangled mixed state.

scholarly journals Feature Extraction of Ship-Radiated Noise Based on Intrinsic Time-Scale Decomposition and a Statistical Complexity Measure

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1079 ◽  
Author(s):  
Junxiong Wang ◽  
Zhe Chen
Keyword(s):  
Feature Extraction ◽  
Time Scale ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Complexity Measure ◽  
Real Time Processing ◽  
Radiated Noise ◽  
Intrinsic Time ◽  
Statistical Complexity ◽  
Time Scale Decomposition

Extracting effective features from ship-radiated noise is an important way to improve the detection and recognition performance of passive sonar. Complexity features of ship-radiated noise have attracted increasing amounts of attention. However, the traditional definition of complexity based on entropy (information stored in the system) is not accurate. To this end, a new statistical complexity measure is proposed in this paper based on spectrum entropy and disequilibrium. Since the spectrum features are unique to the class of the ship, our method can distinguish different ships according to their location in the two-dimensional plane composed of complexity and spectrum entropy (CSEP). To weaken the influence of ocean ambient noise, the intrinsic time-scale decomposition (ITD) is applied to preprocess the data in this study. The effectiveness of the proposed method is validated through a classification experiment of four types of marine vessels. The recognition rate of the ITD-CSEP methodology achieved 94%, which is much higher than that of traditional feature extraction methods. Moreover, the ITD-CSEP is fast and parameter free. Hence, the method can be applied in the real time processing practical applications.

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