ID	原文	译文
16405	在实际应用场景中使用过多EEG通道会带来诸多不便，因此研究如何选择EEG通道尤为重要。	Because of theinconvenience caused by using too many EEG channels in practical application scenarios, it is particularlyimportant to study how to choose EEG channels.
16406	该文针对嗅觉EEG信号分类中的通道选择问题，提出了一种新型的基于ReliefF-Pearson的嗅觉EEG通道选择算法。	In this paper, a new ReliefF-Pearson channel selectionalgorithm is proposed to solve the channel selection problem in the classification of olfactory EEG signals.
16407	该算法结合ReliefF的权值思想和Pearson系数的相关性原理对EEG通道进行选择。	Thealgorithm combines the weight idea of ReliefF and the correlation principle of Pearson coefficient to select EEGchannels.
16408	结果表明，与传统基于ReliefF的通道选择算法相比，该文所提算法在保证一定分类准确率的同时能够显著减少使用的通道数量，并且通道选择的结果不依赖人为经验和分类器。	Experimental results show that compared with the traditional ReliefF-based channel selectionalgorithm, the proposed algorithm could significantly reduce the number of channels used while ensuring acertain classification accuracy, and the result of channel selection does not depend on human experience andclassifiers.
16409	此外，使用该方法获取的通道，其空间分布与已有的嗅觉神经生理学位置相一致，进一步证实了该方法的科学性和有效性。	In addition, the spatial distribution of the selected channels is consistent with the existing olfactory neurophysiological position, which further confirms the scientificity and effectiveness of this method.
16410	该文所提算法为嗅觉EEG通道选择的研究提供了新思路。	The proposed method provides new idea for the research of olfactory EEG channel selection.
16411	为了有效抵抗水印图像的几何攻击，该文提出了一种基于Blob-Harris特征区域和非下采样轮廓波变换(NSCT)和伪Zernike矩的鲁棒水印算法。	To resist the geometric attack of watermarked images effectively, a robust watermarking algorithmbased on Blob-Harris feature region combined with NonSubsampled Contourlet Transform (NSCT) and pseudoZernike moment is proposed.
16412	首先原始图像进行两层非下采样Contourlet变换后提取其低频图像，	First, the original image is extracted from its low-frequency image after two-layerNSCT.
16413	然后利用Blob-Harris检测算子对低频图像进行特征点提取，	Then, Blob-Harris detection operator is used to extract the feature points of the low-frequency image.
16414	根据各个特征点的特征尺度确定其特征区域，优化筛选出稳定且互不重叠的特征区域并将其四周补零，得到稳定的互不重叠的方形特征区域作为水印嵌入区域，	The feature regions are determined according to the feature scale of each feature point, and the stable non-overlapping feature areas are optimized and filtered out and zero padding around them to obtain square featureareas as watermark embedding areas.