Self-Mixing Interferometry (SMI) is a promising interferometric measurement technology with unique system structure. It has an advantage that conventional two-beam interferometry do not have, i.e., movement directions of the target to be measured can be determined by a single-channel interferometric signal due to the existence of linewidth enhancement factor in lasers. However, movement directions are difficult to be determined when the optical feedback is weak. In this work, an algorithm is proposed for determination of SMI-based displacement directions based on Convolution Neural Network (CNN). We used Python language and the third-party libraries NumPy to complete numerical calculation as well as TensorFlow to establish the CNN. The simulation results shows that displacement directions are able to be determined with the accuracy higher than 94.8% when the optical feedback factor is low to 0.1.
Line-width enhancement factor (α) is a fundamental parameter of semiconductor lasers (SLs). In this paper, we propose a method for measuring α of SLs. The method is based on back-propagations neural network (BPNN) for all feedback regimes. MATLAB was used to carry out the numerical calculations and simulations of the BPNN. We used the training set and the test set to train the prediction model, and then used the predictive model to output the predicated value. The results of the BPNN model showed that the R2 value was 0.99994, and the results were following the requirement model. The accuracy of the method has been confirmed and tested by computer simulations, which show that the method can estimate α with a relative error less than 2.5%.
Damping vibration is one of the most common physical phenomena and an important research topic in the field of mechanical engineering. Self-mixing interferometry (SMI) is a non-destructive and non-contact optical sensing and measurement method. An SMI system commonly operates at weak or moderate feedback regime. The strong feedback regime is always avoided because of the possible instability in this regime. Recently, it has been demonstrated that if an SMI is stable in the strong feedback region, its input and output may maintain a linear relationship under proper operation conditions. In this paper, we proposed to apply an SMI system at strong feedback regime for measurement of damping vibration. The results show that an SMI system at strong feedback regime can achieve linear sensing even without need of extra SMI fringe processing, contributing to a new simple solution for measurement of damping vibration.
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