Time-delay signatures in multi-transverse mode VCSELs subject to double-cavity polarization-rotated optical feedback

Lin, Hong; Khurram, Aliza; Hong, Yanhua

doi:10.1016/j.optcom.2016.05.044

Cited by 6 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the idea of associating permutations with finite-dimensional partitions of a time series allows the application of this method to time series of arbitrary nature. These remarks agree with the fact that the method of Bandt and Pompe is already widely spread over the scientific community [8,9,10,11,12,13,14,15,16,17].…”

Section: Introductionsupporting

confidence: 85%

Characterization of time series via Rényi complexity–entropy curves

Jauregui

Zunino

Lenzi

et al. 2018

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

show abstract

Section: Introductionsupporting

confidence: 85%

Characterization of time series via Rényi complexity–entropy curves

Jauregui

Zunino

Lenzi

et al. 2018

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

show abstract

“…Here, in order to quantitatively evaluate the TDS, the ACF and the MI are introduced. The former has been mostly adopted to analyze TDS for its simplicity and fast calculation [8][9][10][11], and the latter, which is based on the probability analysis, is also widely used for TDS identification [12,19]. Figure 4 shows the time series, power spectrum, ACF, and MI for the chaotic output of the FBGF-VCSEL under different feedback strengths of ξ f 0.06 (row 1), 0.22 (row 2) and 0.50 (row 3).…”

Section: Resultsmentioning

confidence: 99%

“…VCSELs subject to suitable optical feedback can generate high Kaplan-Yorke dimension optical chaotic output and then apply to optical-chaos-based communication [1,2,7]. However, the chaotic output from external cavity feedback VCSELs (ECF-VCSELs) usually retains a time-delay signature (TDS) originated from the optical round trip between the VCSEL and the external feedback mirror [8][9][10][11][12][13][14], which may provide some possibilities to reconstruct the system by adopting time-series analysis techniques for delayed systems [15][16][17] and then threaten the system security. As a result, the TDS suppression has been a research hot spot in the field related to chaos generation and applications based on delayed VCSELs in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Priyadarshi et al experimentally demonstrated the TDS concealment in VCSELs subject to VPOF [9]. By introducing another feedback cavity, Lin and co-workers experimentally verified that the TDS in a single transverse and multi-transverse mode VCSEL with double-cavity polarization-rotated feedback can be effectively suppressed [10,11]. Elsonbaty et al theoretically investigated the TDS of chaos in a hybrid optical and electro-optic feedback VCSEL and successfully achieved TDS of intensity and phase simultaneously suppressed chaotic carriers [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental investigation on the time-delay signature of chaotic output from a 1550 nm VCSEL subject to FBG feedback

Zhong

Xia

2016

Photon. Res.

View full text Add to dashboard Cite

The time-delay signature (TDS) of chaos output in a 1550 nm vertical-cavity surface-emitting laser (VCSEL) subject to fiber Bragg grating (FBG) feedback is investigated experimentally. Autocorrelation function (ACF) and mutual information (MI) are used for quantitatively identifying the TDS of chaos. For various bias currents, the TDS evolution with the feedback strength is different, as the FBG provides wavelength-selective feedback. Furthermore, based on the TDS map of the FBG feedback VCSEL (FBGF-VCSEL) in the parameter space of feedback strength and bias current, the optimal TDS suppression regions, where the dominant polarization mode of FBGF-VCSEL locates at the edge of the main lobe of FBG reflection spectrum, have been determined. Finally, for comparative purpose, the TDS of chaos in mirror feedback VCSEL (MF-VCSEL) also has been presented, and the results show that an FBGF-VCSEL possesses better TDS suppression performance than an MF-VCSEL.

show abstract

“…To overcome this problem, Bandt and Pompe [6] have introduced a complexity measure based on the comparison of neighboring values that can be easily applied to any time series. The application of this technique (the permutation entropy) is widely spread over the scientific community [7][8][9][10][11][12][13] mainly because of its simplicity and ability to distinguish among regular, chaotic and random time series. Regarding the particular issue of distinguishing between chaotic and stochastic processes, Rosso et al [14] have shown that the permutation entropy alone is not enough for accomplishing this task.…”

Section: Introductionmentioning

confidence: 99%

Characterizing time series via complexity-entropy curves

et al. 2017

View full text Add to dashboard Cite

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q-complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

show abstract

Time-delay signatures in multi-transverse mode VCSELs subject to double-cavity polarization-rotated optical feedback

Cited by 6 publications

References 44 publications

Characterization of time series via Rényi complexity–entropy curves

Characterization of time series via Rényi complexity–entropy curves

Experimental investigation on the time-delay signature of chaotic output from a 1550 nm VCSEL subject to FBG feedback

Characterizing time series via complexity-entropy curves

Contact Info

Product

Resources

About