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In this work, the use of an asymmetric feedback technique for improving the beam quality of a broad-area diode laser is investigated using numerical simulations. A mirror stripe is placed in the external cavity to select lateral mode and provide asymmetric feedback. The width and the position of the mirror stripe are optimized to improve the beam quality. The simulation results show a good beam quality of 0.7 • FWHM and M 2 value of 2.69.

Abstract-High-brightness laser diode technology is progressing rapidly in response to competitive and evolving markets. The large volume resonators required for high-power, high-brightness operation makes their beam parameters and brightness sensitive to thermal-and carrier-induced lensing and also to multimode operation. Power and beam quality are no longer the only concerns for the design of high-brightness lasers. The increased demand for these technologies is accompanied by new performance requirements, including a wider range of wavelengths, direct electrical modulation, spectral purity and stability, and phase-locking techniques for coherent beam combining. This paper explores some of the next-generation technologies being pursued, while illustrating the growing importance of simulation and design tools. The paper begins by investigating the brightness limitations of broadarea laser diodes, including the use of asymmetric feedback to improve the modal discrimination. Next, tapered lasers are considered, with an emphasis on emerging device technologies for applications requiring electrical modulation and high spectral brightness.

Deep learning-based object detection in remote sensing images is an important yet challenging task due to a series of difficulties, such as complex geometry scene, dense target quantity, and large variant in object distributions and scales. Moreover, algorithm designers also have to make a trade-off between model’s complexity and accuracy to meet the real-world deployment requirements. To deal with these challenges, we proposed a lightweight YOLO-like object detector with the ability to detect objects in remote sensing images with high speed and high accuracy. The detector is constructed with efficient channel attention layers to improve the channel information sensitivity. Differential evolution was also developed to automatically find the optimal anchor configurations to address issue of large variant in object scales. Comprehensive experiment results show that the proposed network outperforms state-of-the-art lightweight models by 5.13% and 3.58% in accuracy on the RSOD and DIOR dataset, respectively. The deployed model on an NVIDIA Jetson Xavier NX embedded board can achieve a detection speed of 58 FPS with less than 10W power consumption, which makes the proposed detector very suitable for low-cost low-power remote sensing application scenarios.

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