The Brillouin Optical Time-Domain Analyzer assisted by the AdaBoost Algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form of a decision tree, forming several base classifiers and combining them into a strong classifier. Based on the pseudo-Voigt curve training set with noise, the performance of the AdaBoost Algorithm is studied, and the influence of different signal-to-noise ratio (SNR), frequency range, and frequency step is also studied. Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.

Achieving high-quality reconstructions of images is the focus of research in image compressed sensing. Group sparse representation improves the quality of reconstructed images by exploiting the non-local similarity of images; however, block-matching and dictionary learning in the image group construction process leads to a long reconstruction time and artifacts in the reconstructed images. To solve the above problems, a joint regularized image reconstruction model based on group sparse representation (GSR-JR) is proposed. A group sparse coefficients regularization term ensures the sparsity of the group coefficients and reduces the complexity of the model. The group sparse residual regularization term introduces the prior information of the image to improve the quality of the reconstructed image. The alternating direction multiplier method and iterative thresholding algorithm are applied to solve the optimization problem. Simulation experiments confirm that the optimized GSR-JR model is superior to other advanced image reconstruction models in reconstructed image quality and visual effects. When the sensing rate is 0.1, compared to the group sparse residual constraint with a nonlocal prior (GSRC-NLR) model, the gain of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) is up to 4.86 dB and 0.1189, respectively.

It is of great significance to construct deterministic measurement matrices with good practical characteristics in Compressed Sensing (CS), including good reconstruction performance, low memory cost and low computing resources. Low-density-parity check (LDPC) codes and CS codes can be closely related. This paper presents a method of constructing compressed sensing measurement matrices based on quasi-cyclic (QC) LDPC codes and arithmetic sequence sets. The cyclic shift factor in each submatrix of QC-LDPC is determined by arithmetic sequence sets. Compared with random matrices, the proposed method has great advantages because it is generated based on a cyclic shift matrix, which requires less storage memory and lower computing resources. Because the restricted isometric property (RIP) is difficult to verify, mutual coherence and girth are used as computationally tractable indicators to evaluate the measurement matrix reconstruction performance. Compared with several typical matrices, the proposed measurement matrix has the minimum mutual coherence and superior reconstruction capability of CS signal according to one-dimensional (1D) signals and two-dimensional (2D) image simulation results. When the sampling rate is 0.2, the maximum (minimum) gain of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) is up to 2.89 dB (0.33 dB) and 0.031 (0.016) while using 10 test images. Meanwhile, the reconstruction time is reduced by nearly half.

We explored the evolution and stability of solitons formed by a Gaussian beam excited on-site in periodic photonic moiré lattices controlled by an angle with saturable self-focusing nonlinearity medium of lower applied bias, which makes use of the square lattices as a reference. When we introduce the rotation angle θ ( θ ∈ ( 0 , π / 2 ) ), which satisfies the Pythagorean theorem and is commensurable, it will create a new lattice structure by rotating and superimposing two square lattices of the same period, which we called periodic photonic moiré lattices. Its energy band structure is special, which includes the flat band, and even opens the higher band-gap compared with square lattices. On the other hand, when we reduce the applied bias of the crystal, periodic photonic moiré lattices can still suppress the diffraction behaviour of Gaussian beam effectively and realize light localization by contrast with square lattices. Moreover, we proved the stability of the solitons formed by the Gaussian beams excited on-site in periodic photonic moiré lattices and revealed that the solitons could keep stable, but their stability regimes depend on the rotation angle shrinks and belong to the different bandgap. The discovery of the photonic moiré lattice provides a fresh approach for suppressing the diffraction behaviour of light and reduces the requirement for nonlinearity, which is beneficial for the diversity of our crystal material selection, and also provides a new way to manufacture high-efficiency optoelectronic devices.