High-power ultrafast laser amplification based on a non-polarization maintaining fiber chirped pulse amplifier is demonstrated. The active polarization control technology based on the root-mean-square propagation (RMS-prop) algorithm is employed to guarantee a linearly polarized output from the system. A maximum output power of 402.3 W at a repetition rate of 80 MHz is realized with a polarization extinction ratio (PER) of > 11.4 dB. In addition, the reliable operation of the system is verified by examining the stability and noise properties of the amplified laser. The M² factor of the laser beam at the highest output power is measured to be less than 1.15, indicating a diffraction-limited beam quality. Finally, the amplified laser pulse is temporally compressed to 755 fs with a highest average power of 273.8 W. This is the first time, to the best of our knowledge, that the active polarization control technology was introduced into the high-power ultrafast fiber amplifier.

Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.

利用高温固相法合成了Ca_9.15La_0.9（PO₄）₇∶0.05Eu²⁺，yMn²⁺和Ca_9.3La_0.8（PO₄）₇∶0.05Eu²⁺，yMn²⁺系列荧光粉。通过荧光粉的发射光谱和荧光衰减曲线证实Eu²⁺、Mn²⁺之间存在能量传递，且增大Mn²⁺的掺杂浓度，获得了从青光（绿光）到白光变化的荧光粉。材料的热稳定性显示Eu²⁺的两个不同颜色的发射峰表现出不同的温度猝灭行为。Ca_9.15La_0.9（PO₄）₇∶0.05Eu²⁺，0.35Mn²⁺的色坐标接近标准白光且色温与太阳光相近，光谱覆盖整个可见光区域。研究结果表明，材料在白光发光二极管方面有潜在的应用价值。