优化非对称正切相位掩模实现无相干成像系统焦外不变调制传递函数

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"优化的不对称正切相位掩模在非相干成像系统中实现焦外不变调制传递函数" 这篇研究论文探讨了波前编码技术在扩展光学系统的景深中的应用。波前编码是一种光学-数字混合成像技术,其核心是设计适当的相位掩模,以在整个大范围的焦外条件下保持恒定的成像特性。文章中提出了一种新的相位掩模,采用了正切函数,以丰富现有的奇对称相位掩模类型。 在传统的光学成像系统中,景深通常受限,导致近焦和远焦区域的图像清晰度降低。通过波前编码,可以克服这一限制,使系统能在较大的景深范围内保持较好的成像质量。作者Van Nhu Le、Shouqian Chen和Zhigang Fan通过引入这种基于正切函数的不对称相位掩模,旨在改善现有相位掩模的设计,特别是针对奇对称类型的掩模。 论文对比分析了正切相位掩模与立方掩模、改进的-1对数掩模、改进的-2对数掩模以及正弦掩模的性能。通过对这些掩模的比较评估,研究发现正切相位掩模在扩展景深方面表现出优越的性能。这表明,采用正切函数设计的相位掩模有可能成为一种更有效的工具,用于优化非相干成像系统的深度感知和图像质量。 该研究对于光学成像领域的工程设计具有重要意义,为未来开发更先进的光学系统提供了新的思路。通过优化相位掩模,不仅能够提高成像质量,还能扩大适用范围,适应更复杂的成像环境,如在不稳定的光学条件下或对动态场景的捕捉。 这篇论文揭示了正切函数在波前编码中的潜力,并且证明了其在增加非相干成像系统景深方面的优势。这一创新设计可能对未来光学成像技术的进一步发展和应用产生积极影响。

Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

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