The realistic simulation of the cloud background has certain difficulty because of its appearance and irregular arbitrary distribution. In order to more realistically simulate cloud background, a multi-layer "cloud particle" superposition model is proposed in this paper based on the characteristics of real cloud background. First, characteristics of remote sensing of clouds under different distribution frequency are collected and the laws within them are analyzed. Second, the generation space of the "cloud particles" that will be generated is delimited based on the fractal theory. Third, cloud images at different frequency are generated based on the characteristic laws of the real cloud images. And finally, the multi-layer images are merged by linear superposition. The method used in this paper is also with controllable coverage, the remote sensing cloud images under different coverage, therefore, can be simulated.
The desorption of oxygen and carbon contamination are a key issue on improving the quantum efficiency of negative electron affinity GaAs-based photocathode during the preparation process. In this article, O-bonded and C-bonded absorption are executed in the calculation of pristine (100)-oriented GaAs photocathode of planar structure and nanowire structure. By analyzing the absorption energy, work function and dipole moments of different adsorption models, it is found that the adsorption of impurity atoms changed atomic and electronic structure of GaAs(100) pristine surface and affected the stability. The findings suggest that, oxygen impurities are more difficult to remove than carbon impurities due to more negative absorption energies especially in the surface layer. However, C-absorbed models may have bigger work function values than O-absorbed models in the most cases, which are not beneficial to the photoemission, and the phenomenon can be verified by the calculation results of surface dipole moments.
To improve the detection rate of small target in infrared image, this paper proposes an infrared small target detection algorithm based on the fusion of multiple saliency information, which combines local contrast measure (LCM), curvature filtering and motion saliency. Firstly, three saliency maps of the infrared image are calculated separately to prepare for the next advantages integration. Then, to improve the contrast of the target, the LCM saliency map and curvature saliency map are filtered according to the motion saliency value. Finally, the fusion weight is determined by the background suppression factor of the saliency map so that the fusion saliency map is obtained. Experimental results show that the proposed infrared small target algorithm outperforms other comparing methods in terms of detection capability.
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