针对数字液滴聚合酶链式反应（droplet digital Polymerase Chain Reaction， ddPCR）核酸检测中数字聚合酶链式反应由液滴数量多、尺寸小、排列紧密、荧光强度不均匀而导致的液滴难以计数问题，提出一种图像处理方法，基于灰度遍历法采集图像的信息，通过微分分析法对液滴进行分类和计数，可准确采集ddPCR实验的图像信息。通过卷积算法去除图像中的噪声，使用灰度分布均衡化法增强图像的对比度。以灰度遍历的方式将图像在逐个阈值下二值化，并以几何条件为限制统计液滴数量。通过微分分析法对数据进行分析，得出荧光液滴与全部液滴的计数结果。在以人类gDNA（genomic DNA）为检测样本的ddPCR实验中，该算法的平均检测准确率为99.36%，与商用仪器算法和同类算法相比分别提高了2.24%，2.53%。该方法为ddPCR实验提供了可靠的检测结果，可更好地适用于ddPCR实验。

A modulation classification method in combination with partition-fractal and support-vector machine (SVM) learning methods is proposed to realize no prior recognition of the modulation mode in satellite laser communication systems. The effectiveness and accuracy of this method are verified under nine modulation modes and compared with other learning algorithms. The simulation results show when the signal-to-noise ratio (SNR) of the modulated signal is more than 8 dB, the classifier accuracy based on the proposed method can achieve more than 98%, especially when in binary phase shift keying and quadrature amplitude shift keying modes, and the classifier achieves 100% identification whatever the SNR changes to. In addition, the proposed method has strong scalability to achieve more modulation mode identification in the future.