Self-mixing interferometry is an interferometric technique based on the self-mixing effect. Its unique structure and operating principle allow part of the light emitted by the laser to be reflected back into the laser cavity via a remote target, producing optical output power modulations and displayed as interference waveforms. The shape of these waveforms is determined by the optical feedback factor and the linewidth enhancement factor, which also affect the behavior and measurement performance of the self-mixing interferometric system. To address the need for optical measurements in the medium feedback case, this paper proposes a method to accurately estimate the optical feedback factor and linewidth enhancement factor using the non-dominated sorting genetic algorithm II (NSGA-II). The effectiveness and robustness of the method are verified by simulation and experimental results, and preliminary tests show that it can achieve an accuracy of 0.35% and 0.56% in estimating the optical feedback factor and linewidth enhancement factor. The method has potential for practical engineering applications and promotes the development of self-mixing interferometry.
Self-mixing interferometry (SMI) is superior to other laser interferometry methods due to its simplicity and compactness. However, SMI signals are often complex and difficult to process due to interference in the form of variations in the effective reflectivity of the target, noisy signals, complex signal shapes, and other dependencies. Deep neural networks have been a very popular area of research in computer artificial intelligence in recent years, allowing more implicit features to be uncovered than traditional shallow machine learning. It has been shown that convolutional and back-propagation neural networks can be used for SMI signal processing. There are also studies that have used machine learning genetic algorithms for absolute distance measurement. Based on the above, this study used convolutional neural networks to form a deep neural network for absolute distance measurement based on SMI technology. We first trained the deep convolutional neural network at different feedback strengths. The results of the Convolutional Neural Network (CNN) model showed a coefficient of determination of 0.9987. which is consistent with the required model. The trained network was then used to estimate absolute distances with and without the addition of noise. The comparison proves that the proposed method is noise-proof and has high adaptability for measurements under different conditions.
Semiconductor laser (SL) with optical feedback presents rich nonlinear dynamics, e.g., Period-One Oscillation (POO), quasi-periodic oscillation, and chaotic states. It is of significance to study the state boundary between different dynamic states for an SL with optical feedback from the perspective of both suppressing and using these dynamics, especially the boundary of POO state. This paper reveals the POO boundary of an SL with Dual Optical Feedback (DOF). The modified Lang-Kobayashi equations were first numerically solved to develop a bifurcation diagram to determine the boundary between POO and other states. Then the POO-DOF boundary was compared with that of an SL with single optical feedback. Moreover, the effect of system parameters on the POO-DOF boundaries is researched. The results obtained are helpful to promote the development of the applications of POO dynamics.
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