ID 原文 译文
23135 首先算法建立以阵列稀疏为目标函数以及方向调制信号不同性能要求为约束的非凸优化问题； Firstly, a nonconvex optimization problem is formulated associated with some basic metrics of DM signal.
23136 然后针对这个非凸问题，给出了两种不同的求解方案：一种基于迭代加权1l算法，但稀疏算法得到的结果可能存在阵元间距小于半个波长的情况； Secondly, two different solutions are presented: one is based on Iterative Reweighted l-norm1 (IRL) resulting in a superdirective array with the interelement spacing less than half-wavelength;
23137 另一种基于混合整数规划，确保稀疏算法得到的阵元间距至少为半个波长； the other is based on Mixed Integer Programming (MIP) resulting in a nonsuperdirective array with the interelement spacing more than half-wavelength.
23138 最后在混合整数规划算法的基础上建立以方向调制信号功率利用率为目标的优化问题，优化稀疏阵列方向调制信号发射机的功率利用率。 Finally, the power efficiency of DM transmitter is optimized based on MIP algorithm.
23139 仿真结果表明，相比于与现有的基于均匀等间距直线阵列的方向调制信号综合算法，所提算法在方向调制信号的安全性能、方向调制信号发射机的功率利用率以及阵列的稀疏程度之间具有良好的设计灵活度。 Simulation results show that the proposed synthesis method provides greater flexibility of controlling the security performance, power efficiency and sparse level, while at the same time the number of excitations is less than the uniformly spaced linear array in the benchmark problems.
23140 针对基于分块匹配的行人再识别中对分块的规则和大小缺乏指导，以及不同分块间的区分度差异问题，该文提出基于显著度融合的自适应分块行人再识别方法。 In this paper, an adaptive block person re-identification method based on saliency fusion is proposed to solve the problems of the lack of guidance on the rule and size of block in the block matching-based person re-identification, and the differentiation degree between different blocks.
23141 首先，利用启发式思想确定初始聚类中心，并根据图像内容自动确定分块的大小和数目。 Firstly, the heuristic idea is used to determine the initial clustering center, and the size and number of blocks are determined automatically according to the image content.
23142 然后，利用归一化部分曲线下面积计算各块的图像间显著度，利用结构化支持向量机学习各块的图像内显著度，并融合两类显著度得到各块的权重作为匹配得分融合的依据。 Then, the intra-image salience of each block is calculated using the Area Under the normalized partial Curve (pAUC), the intra-image salience of each block is learned by structured SVM, and the weights of each block are fused as the base of matching Score fusion.
23143 实验证明，在常用的行人再识别数据集上，该方法能取得较好的识别结果。 Experiments show that this method can achieve better recognition results on the commonly used person re-identification data sets.
23144 为了实现含有复杂背景和弱边界图像的快速准确分割，传统的水平集常采用重新初始化的方法，但是这种方法存在计算量大、分割不准确等问题。 In order to achieve fast and accurate segmentation of images with complicated background and weak boundaries, the re-initialization method is often adopted in the traditional level set function. However, this method has many problems such as large computation and inaccurate segmentation.