Physical Realization of a Quantum Game

Kowalski, A.M.; Plastino, A.; Casas, M.

doi:10.5772/54216

Cited by 2 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a matter of fact, the presence of trends, non-stationarities, etc., impact significantly the estimation process leading to biased estimates of α [10,11]. On the other hand, recently, the use of information theory quantifiers is playing an increasing role in the analysis of 1/f α signals [7,8,12].…”

Section: /Fmentioning

confidence: 99%

“…The density p j represents the probability that the energy of the fractal signal is located at scale j. Several information theoretic quantifiers have been defined using (9), examples include Shannon (called wavelet entropy) [8,26], Renyi and Tsallis q-entropies [12,13]. This article extends the Fisher's information concept to the wavelet domain using the density given in (9).…”

Section: Wavelet-based Probability Densitiesmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet Fisher’s Information Measure of 1=f α Signals

Ramírez-Pacheco

Torres-Roman

Rizo-Dominguez³

et al. 2011

Entropy

View full text Add to dashboard Cite

This article defines the concept of wavelet-based Fisher's information measure (wavelet FIM) and develops a closed-form expression of this measure for 1/f α signals.Wavelet Fisher's information measure characterizes the complexities associated to 1/f α signals and provides a powerful tool for their analysis. Theoretical and experimental studies demonstrate that this quantity is exponentially increasing for α > 1 (non-stationary signals) and almost constant for α < 1 (stationary signals). Potential applications of wavelet FIM are discussed in some detail and its power and robustness for the detection of structural breaks in the mean embedded in stationary fractional Gaussian noise signals studied.

show abstract

Section: /Fmentioning

confidence: 99%

Section: Wavelet-based Probability Densitiesmentioning

confidence: 99%

Wavelet Fisher’s Information Measure of 1=f α Signals

Ramírez-Pacheco

Torres-Roman

Rizo-Dominguez³

et al. 2011

Entropy

View full text Add to dashboard Cite

show abstract

“…A pmf obtained from the wavelet spectrum of scaling signals is given by the expression [9,[38][39][40][41] …”

Section: Wavelet Q-fisher Informationmentioning

confidence: 99%

Wavelet q-Fisher Information for Scaling Signal Analysis

Ramírez-Pacheco

Torres-Roman

Argaez-Xool

et al. 2012

Entropy

View full text Add to dashboard Cite

This article first introduces the concept of wavelet q-Fisher information and then derives a closed-form expression of this quantifier for scaling signals of parameter α. It is shown that this information measure appropriately describes the complexities of scaling signals and provides further analysis flexibility with the parameter q. In the limit of q → 1, wavelet q-Fisher information reduces to the standard wavelet Fisher information and for q > 2 it reverses its behavior. Experimental results on synthesized fGn signals validates the level-shift detection capabilities of wavelet q-Fisher information. A comparative study also shows that wavelet q-Fisher information locates structural changes in correlated and anti-correlated fGn signals in a way comparable with standard breakpoint location techniques but at a fraction of the time. Finally, the application of this quantifier to H.263 encoded video signals is presented.

show abstract

Physical Realization of a Quantum Game

Cited by 2 publications

References 36 publications

Wavelet Fisher’s Information Measure of 1=f α Signals

Wavelet Fisher’s Information Measure of 1=f α Signals

Wavelet q-Fisher Information for Scaling Signal Analysis

Contact Info

Product

Resources

About