ID 原文 译文
19155 首先依据混合高斯模型对非高斯背景建模，在此基础上系统研究了参数k与SFD的检测性能以及检测特性的关系，确定了k的最佳的取值，并指出SFD在检测性能达到最优的同时也具有恒虚警特性。 Firstly, the non-Gaussianbackground is modeled as a mixed Gaussian model. Based on this, the relationship between parameter k andSFD's performance and characteristics are systematically analyzed. It is pointed out that SFD will be aconstant false alarm detector when its detection performance is optimal.
19156 其次通过固定k值得到了一种新的非参量检测方法，较传统的匹配滤波性能有明显提升。 Secondly, a new non-parametric detector is proposed via fixing the parameter k, which has significant improvement over matched filter.
19157 最后进行仿真分析验证了SFD的有效性和优越性。 Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
19158 为了更好增强图像中的有用信息，改善图像视觉效果，该文提出了一种基于非局部多尺度分数阶微分图像增强算子(NMFD)。 In order to enhance the useful information in the image and improve the visual effect of the image, a Non-local Multi-scale Fractional Differential(NMFD) image enhancement operator is proposed.
19159 该算子首先将图像分成若干块子图像，计算每一块子图像的边缘强度系数、熵值和粗糙度等细节特征,将得到的特征数据在全局图像范围进行统一尺度的归一化， The operator divides the image into several sub-images and calculates the edge intensity coefficient, entropy value and roughness of each sub-image, and the obtained feature data are normalized in a unified scale in the global image range.
19160 然后对这些归一化的数据进行加权求和作为图像的非局部特征值，最后利用指数函数建立图像细节特征和分数阶微分算子阶次之间的非线性量化关系，在不同的图像子块区域，确定不同尺度的分数阶微分阶次，实现图像的非局部多尺度增强。 Then, the normalized data are weighted to be the non-local eigenvalues of the image. Finally, an exponential function is used to establish the non-linear quantization relationship between image detail features and the value of fractional order. Thus, the fractional order of different scales can be determined in differentimage sub-block regions, so that the non-local multi-scale image enhancement model is realized.
19161 非正交多址接入(NOMA)技术允许多个发送方共用同一个资源块，接收方通过连续干扰消除(SIC)解码出不同发送方的信息。 Non-Orthogonal Multiple Access (NOMA) serves multiple transmitters using the same resource block, and the receiver decodes the information from different transmitters through Successive Interference Cancellation (SIC).
19162 然而，目前针对NOMA系统的研究大多基于理想SIC的假设，而没有考虑非理想SIC对系统性能带来的影响。 However, most of the researches on NOMA systems are based on perfect SIC assumption,in which the impact of imperfect SIC on NOMA system is not considered.
19163 针对此问题，该文在非理想SIC的假设下，针对单小区上行NOMA系统提出一套性能分析框架。 Focusing on this problem, a framework is provided to analyze the performance of single-cell uplink NOMA system under the assumption of imperfect SIC.
19164 首先，采用二项式点过程(BPP)对上行NOMA系统中基站和用户设备的空间分布进行建模。 Firstly, the Binomial Point Process (BPP) is used to model the spatial distribution of basestation and user equipment in uplink NOMA system.