The time-delay problem, which is introduced by the response time of hardware for correction, is a critical and non-ignorable problem of adaptive optics (AO) systems. It will result in significant wavefront correction errors while turbulence changes severely or system responses slowly. Predictive AO is proposed to alleviate the time-delay problem for more accurate and stable corrections in the real time-varying atmosphere. However, the existing prediction approaches either lack the ability to extract non-linear temporal features, or overlook the authenticity of spatial features during prediction, leading to poor robustness in generalization. Here, we propose a mixed graph neural network (MGNN) for spatiotemporal wavefront prediction. The MGNN introduces the Zernike polynomial and takes its inherent covariance matrix as physical constraints. It takes advantage of conventional convolutional layers and graph convolutional layers for temporal feature catch and spatial feature analysis, respectively. In particular, the graph constraints from the covariance matrix and the weight learning of the transformation matrix promote the establishment of a realistic internal spatial pattern from limited data. Furthermore, its prediction accuracy and robustness to varying unknown turbulences, including the generalization from simulation to experiment, are all discussed and verified. In experimental verification, the MGNN trained with simulated data can achieve an approximate effect of that trained with real turbulence. By comparing it with two conventional methods, the demonstrated performance of the proposed method is superior to the conventional AO in terms of root mean square error (RMS). With the prediction of the MGNN, the mean and standard deviation of RMS in the conventional AO are reduced by 54.2% and 58.6% at most, respectively. The stable prediction performance makes it suitable for wavefront predictive correction in astronomical observation, laser communication, and microscopic imaging.

对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据，机载多光谱激光雷达(multispectral light detection and ranging，MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类，但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此，提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index，NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云，获取兼具空间位置及其多光谱信息的单一点云数据；然后利用空间邻域增长下的地面滤波算法分离地面和非地面点；接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数，并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类；最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性，实验结果表明：算法的平均总体精度和Kappa系数分别可达90.17%和0.861，可有效实现城区MS-LiDAR数据的三维土地覆盖分类；分步处理的方式更有利于针对具体的分离目标的特点设计简单且有效的规则，算法设计更简单、复杂度低；NDRI可为其他机器学习算法的显著性特征的设计和选择提供理论支撑。

In an acousto-optic modulator, the electrode shape plays an important role in performance, since it affects the distribution of the acoustic field. The acousto-optic modulator based on the conventional rectangular electrode has the problems of low energy efficiency and small modulation bandwidth due to an imperfect acoustic field. In this paper, a new serrated periodic electrode has been proposed for using acousto-optic modulator transducers. The proposed electrode has the following advantages. By using serrated periodic electrodes to suppress the sidelobes, the collimation of the acoustic field in the direction perpendicular to the light incidence is improved. This makes the acousto-optic modulator have a stable diffraction efficiency fluctuation and high energy efficiency. In addition, the electrode has a large divergence angle in the direction of light incidence, so a large bandwidth can be obtained. The simulations and experiments demonstrate that the serrated periodic electrode has an increased bandwidth and high energy efficiency.

The quantum multimode of correlated fields is essential for future quantum-correlated imaging. Here we investigate multimode properties theoretically and experimentally for the parametric amplified multiwave mixing process. The multimode behavior of the signals in our system stems from spatial phase mismatching caused by frequency resonant linewidth. In the spatial domain, we observe the emission rings with an uneven distribution of photon intensity in the parametric amplified four-wave mixing process, suggesting different spatial modes. The symmetrical distribution of spatial spots indicates the spatial correlation between the Stokes and anti-Stokes signals. While in the frequency domain, the multimode character is reflected as multiple peaks splitting in the signals’ spectrum. A novelty in our experiment, the number of multimodes both in the spatial and frequency domains can be controlled by dressing lasers by modifying the nonlinear susceptibility. Finally, we extend the multimode properties to the multiwave mixing process. The results can be applied in quantum imaging.