Private communication with quantum cascade laser photonic chaos

Spitz, Olivier; Herdt, Andreas; Wu, Jiagui; Maisons, G.; Carras, Mathieu; Wong, Cynthia F.; Elsäßer, Wolfgang; Grillot, Frédéric

doi:10.1038/s41467-021-23527-9

Cited by 71 publications

(30 citation statements)

References 69 publications

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“…Genomics and proteomics on emergent properties, clonal fate, protein types [6]- [8]. Chemistry, physics, and material science on laser devices, ion energy, and photoelectrochemical power [9]- [11]. Environmental and earth science on climate and ocean dynamics [12]- [14].…”

Section: A Background Of Variable Dependence Detectionmentioning

confidence: 99%

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NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

et al. 2021

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For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences among variables, and so measures for that have been proposed. Correlation coefficient and its variants are most common, but they only detect a linear dependence (co-linearity) between two variables. Some recent measures can detect a nonlinear dependence (co-nonlinearity) by means of kernelization or segmentation. They are supposed to handle two variables only and open to discussion with regard to performance in detection and difficulty in setup. There is room for a novel measure based on Neural Networks (NNs), since usual NNs aim at prediction but not at variable dependence detection. For the high-performance detection of co-nonlinearities among multi variables, we propose a measure called NNR-GL based on Neural Network Regression (NNR) regularized by Group Lasso (GL). NNR-GL embodies the detection through multi-input single-output regression by NNR and regularization on the input layer by GL. NNR-GL then calculates how strong the detected co-nonlinearities are by unifying the regression performance and the weights on input variables. We conducted experiments using artificial data to examine the behaviors and fundamental effectiveness of NNR-GL. The performance was estimated by a comprehensive detection performance criterion (CDP-AUC in short), which is the mean of area under curves representing true positive and true negative detections. NNR-GL achieved the values of CDP-AUC from 0.7472 to 0.9681, where 0 means complete failure and 1 means complete success in detection. These values were consistently higher than those from 0.5972 to 0.9259 of the conventional measures for all the different conditions of dependence, data size, and noise rate. Consequently, the effectiveness and robustness of NNR-GL were clearly confirmed.

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Section: A Background Of Variable Dependence Detectionmentioning

confidence: 99%

“…We estimated the performances of NNR-GL and the competitive measures using Equ. (11). The number of trials T is 10.…”

Section: B Case Study Using Us-130 Hospital Datasetmentioning

confidence: 99%

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

et al. 2021

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“…For secure data transmission links, the utilization of hyperchaos with several positive Lyapunov exponents is highly desirable in contrast to chaos with only one positive Lyapunov exponent 26 , 27 . Recently, QCL’s hyperchaos was discovered and used to achieve the mid-infrared FSO communication based on the chaos synchronization technique 28 . This process successfully recovered a hidden message at a transmission rate of 0.5 Mbits/s with a few percent error that could be circumvented with the regular forward error correction.…”

Section: Introductionmentioning

confidence: 99%

Mid-infrared hyperchaos of interband cascade lasers

et al. 2022

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Chaos in nonlinear dynamical systems is featured with irregular appearance and with high sensitivity to initial conditions. Near-infrared light chaos based on semiconductor lasers has been extensively studied and has enabled various applications. Here, we report a fully-developed hyperchaos in the mid-infrared regime, which is produced from interband cascade lasers subject to the external optical feedback. Lyapunov spectrum analysis demonstrates that the chaos exhibits three positive Lyapunov exponents. Particularly, the chaotic signal covers a broad frequency range up to the GHz level, which is two to three orders of magnitude broader than existed mid-infrared chaos solutions. The interband cascade lasers produce either periodic oscillations or low-frequency fluctuations before bifurcating to hyperchaos. This hyperchaos source is valuable for developing long-reach secure optical communication links and remote chaotic Lidar systems, taking advantage of the high-transmission windows of the atmosphere in the mid-infrared regime.

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“…However, the route to chaos and the identification of the means for obtaining strong chaos are open for detailed study. A recent report shows that based on a QCL with external optical feedback, a generated mid-infrared low frequency chaotic oscillation was used to achieve 0.5 Mbit/s message private free-space optical communication [17]. To realize a much higher-speed message secure transmission, strong broadband chaos is essential.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics of Interband Cascade Laser Subjected to Optical Feedback

et al. 2021

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We present a theoretical study of the nonlinear dynamics of a long external cavity delayed optical feedback-induced interband cascade laser (ICL). Using the modified Lang–Kobayashi equations, we numerically investigate the effects of some key parameters on the first Hopf bifurcation point of ICL with optical feedback, such as the delay time (τf), pump current (I), linewidth enhancement factor (LEF), stage number (m) and feedback strength (fext). It is found that compared with τf, I, LEF and m have a significant effect on the stability of the ICL. Additionally, our results show that an ICL with few stage numbers subjected to external cavity optical feedback is more susceptible to exhibiting chaos. The chaos bandwidth dependences on m, I and fext are investigated, and 8 GHz bandwidth mid-infrared chaos is observed.

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Private communication with quantum cascade laser photonic chaos

Cited by 71 publications

References 69 publications

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

Mid-infrared hyperchaos of interband cascade lasers

Nonlinear Dynamics of Interband Cascade Laser Subjected to Optical Feedback

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