Optimized properties of chaos from a laser diode

Bouchez, Guillaume; Malica, Tushar; Wolfersberger, Delphine; Sciamanna, Marc

doi:10.1103/physreve.103.042207

Cited by 16 publications

(2 citation statements)

References 34 publications

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“…As an example, for ultra-fast optical random number generation [43][44][45], it is crucial to generate optical chaotic signals that are as uncorrelated and as "pseudorandom" as possible, for which its deterministic nature is hidden by noise-like properties. Many different setups have been proposed to generate such broad-band, high-entropy optical signals [46][47][48][49][50]. Our algorithm allows an automatic comparison of the strength of the correlations and the level of randomness of signals generated by different setups.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Temporal Correlations in Time Series Using Permutation Entropy, Ordinal Probabilities and Machine Learning

Boaretto

Budzinski

Rossi

et al. 2021

Entropy

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Time series analysis comprises a wide repertoire of methods for extracting information from data sets. Despite great advances in time series analysis, identifying and quantifying the strength of nonlinear temporal correlations remain a challenge. We have recently proposed a new method based on training a machine learning algorithm to predict the temporal correlation parameter, α, of flicker noise (FN) time series. The algorithm is trained using as input features the probabilities of ordinal patterns computed from FN time series, xαFN(t), generated with different values of α. Then, the ordinal probabilities computed from the time series of interest, x(t), are used as input features to the trained algorithm and that returns a value, αe, that contains meaningful information about the temporal correlations present in x(t). We have also shown that the difference, Ω, of the permutation entropy (PE) of the time series of interest, x(t), and the PE of a FN time series generated with α=αe, xαeFN(t), allows the identification of the underlying determinism in x(t). Here, we apply our methodology to different datasets and analyze how αe and Ω correlate with well-known quantifiers of chaos and complexity. We also discuss the limitations for identifying determinism in highly chaotic time series and in periodic time series contaminated by noise. The open source algorithm is available on Github.

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Section: Discussionmentioning

confidence: 99%

Evaluating Temporal Correlations in Time Series Using Permutation Entropy, Ordinal Probabilities and Machine Learning

Boaretto

Budzinski

Rossi

et al. 2021

Entropy

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“…Theoretical model. The experimental system is simulated using the Lang-Kobayashi model modified for phase-conjugate feedback as described in 17,45,46 .…”

Section: Chaos Bandwidth (β)mentioning

confidence: 99%

High-frequency chaotic bursts in laser diode with optical-feedback

et al. 2022

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The diversity of observed nonlinear dynamics in laser diodes subjected to optical feedback shows promise as an excellent candidate for chaos-based commercial applications. Thus, works in the last decade have primarily focused on system performances, geometric configurations, and balancing their trade-offs. We demonstrate an optical feedback system operating on phase-conjugate feedback exhibiting state-of-the-art chaos bandwidth values reaching ≈ 30 GHz. We report numerous high-frequency, spatiotemporally complex, chaotic dynamics undocumented in the past four decades. We highlight the underlying physics involving a three-tier temporal interaction mechanism between laser relaxation oscillations, phase-conjugate feedback induced external cavity modes, and chaotic bursts repeating each delay time in the extended cavity. We show supporting real-time high-definition system outputs captured by modern large bandwidth oscilloscopes. The presented work shows to our knowledge, the highest bandwidth and complexity entropy to-date in an optical chaos from a single laser, thereby proving the unnecessary need for further complexity using cascading lasers.

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Prediction of Chaotic Intensities and Dynamics for an Optically Injected Semiconductor Laser Using Reservoir Computing

Sheng

et al. 2022

2022 Asia Communications and Photonics Conference (ACP)

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Optimized properties of chaos from a laser diode

Cited by 16 publications

References 34 publications

Evaluating Temporal Correlations in Time Series Using Permutation Entropy, Ordinal Probabilities and Machine Learning

Evaluating Temporal Correlations in Time Series Using Permutation Entropy, Ordinal Probabilities and Machine Learning

High-frequency chaotic bursts in laser diode with optical-feedback

Prediction of Chaotic Intensities and Dynamics for an Optically Injected Semiconductor Laser Using Reservoir Computing

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