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We present a combined molecular dynamics (MD) and classical nucleation theory (CNT) approach to address many issues regarding the nucleation of inorganic aerosols. By taking parameters from MD simulations, we find the CNT predicts fairly reasonable free-energy profiles for the hygroscopic nucleation of aerosols. Moreover, we find that the ionization of sulfates can play a key role in stabilizing aqueous clusters and that both the size of the critical nucleus and the nucleation barrier can be significantly lowered by the H2SO4 and NH4HSO4, whereas the effect of NH3 on nucleation is negligible. NH4HSO4 provides stronger enhancement effect to aerosol formation than H2SO4. In view of the consistency between the theoretical prediction and experimental observation, the combination of MD simulation and CNT appears to be a valuable approach to gain deeper understanding of how aerosol nucleation is affected by different chemical species.

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Image registration is an important processing step in synthetic aperture radar (SAR) image applications, such as change detection and elevation extraction. The cross-correlation method is widely employed to find the matching points to realize image registration due to its effectiveness and simplicity. However, the large number of pixel operations and whole image sliding operations make it a computationally intensive problem, and it is difficult to adapt to the situation of increasing amount and volume of SAR images. GPU-based high-performance computing methods are usually used because of their high parallelism and efficiency. However, most of these methods do not maximally optimize the computing process according to the characteristics of GPU architecture nor do they reduce the calculation amount of the registration process. In this paper, a swarm-intelligent GPU parallel pixel-level registration is proposed, which takes into account not only the acceleration of the correlation operation but also the reduction of searching times. First, for each correlation operation, the GPU parallelization is systematically optimized, including parallel reduction, bank conflict prevention, and instruction optimization. Second, the particle swarm optimization (PSO) algorithm is implemented by GPU to efficiently search the matching points based on the cross-correlation coefficients. In the process of calculation, the CPU and GPU have zero-copy, which realizes the complete parallelization of the registration. The experimental results show that the method can achieve 40× speedup for a product-level SAR image.

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