Power Enhancement and Spot Homogenization Design for Arrayed Semiconductor Lasers.

Improving the spot brightness and uniformity of arrangement of the array laser is conducive to ensuring the beam quality of the fiber laser. Based on the light tracing principle, the optical model performance of two common fiber lasers was first analyzed. Then, a novel rotationally polarized optical model with high power and spot uniformity was designed and optimized on the basis of the aforementioned analysis. The results of the evaluation metrics of the multi-indicator optical model show that the spot uniformity of our proposed model improved by 24.03%, the power improved by 0.55%, and the maximum light distance was shortened from 120 mm to 82.58 mm. Further, the results of the coupling tolerance analysis of the optical elements show that the total coupling efficiency was 89.04%. The coupling power and tolerance relationships did not produce degradation compared with the traditional model. Extensive comparative results show that the designed rotationally polarized optical path model can effectively improve the optical coupling efficiency and spot uniformity of arrayed semiconductor lasers.

Hierarchical attention-guided multiscale aggregation network for infrared small target detection.

All man-made flying objects in the sky, ships in the ocean can be regarded as small infrared targets, and the method of tracking them has been received widespread attention in recent years. In search of a further efficient method for infrared small target recognition, we propose a hierarchical attention-guided multiscale aggregation network (HAMANet) in this thesis. The proposed HAMANet mainly consists of a compound guide multilayer perceptron (CG-MLP) block embedded in the backbone net, a spatial-interactive attention module (SiAM), a pixel-interactive attention module (PiAM) and a contextual fusion module (CFM). The CG-MLP marked the width-axis, height-axis, and channel-axis, which can result in a better segmentation effect while reducing computational complexity. SiAM improves global semantic information exchange by increasing the connections between different channels, while PiAM changes the extraction of local key information features by enhancing information exchange at the pixel level. CFM fuses low-level positional information and high-level channel information of the target through coding to improve network stability and target feature utilization. Compared with other state-of-the-art methods on public infrared small target datasets, the results show that our proposed HAMANet has high detection accuracy and a low false-alarm rate.