ID 原文 译文
19295 该文通过选取恰当的定义集，构造了有限域(p为奇素数)上的一类四重和六重线性码，利用高斯和确定了码的重量分布，并编写Magma程序进行了验证。 A class of linear codes with four-weight and six-weight over finite field  (p is an odd prime) is constructed by a proper selection of the defining set. The explicit weight distribution is obtained using Gauss sums, and some examples from Magma program to illustrate the validity of the conclusions are provided.
19296 结果表明，构造的码中存在关于Singleton界的几乎最佳码。 The results show that these codes include almost optimal codes with respect to Singleton bound.
19297 针对大规模无线传感器网络(WSN)中节点难以定位的问题，该文提出一种基于改进牛顿法的分布式定位算法。 A distributed algorithm based on modified Newton method is proposed to solve the nodes localization problem in large scale Wireless Sensor Network(WSN).
19298 该算法包括网络划分和分布式算法。 The algorithm includes network partitioning anddistributed algorithm.
19299 首先，根据节点位置和节点之间直接相连的距离信息，将无线传感器网络划分为若干个重叠的子区域，并将子区域的定位问题归结为无约束优化问题，每个子区域可以独立计算； Firstly, the network is divided into several overlapping subregions according to the nodes positions and the distance information between the sensors. The localization problem of subregions is formulated into an unconstrained optimization problem and each subregion can be calculated independently.
19300 然后，使用分布式算法估计子区域中的节点位置并进行局部融合。 Then distributed algorithm is used to determine nodes positions in subregions and merge the subregions.
19301 实验结果表明，与已有算法相比，该算法具有良好的扩展性，在大规模网络中定位精度更高，能满足大规模无线传感器网络中节点定位需求。 Simulation results indicate that the proposed algorithm is superior to the existing algorithms in terms ofaccuracy in large scale network, which can meet the needs of nodes localization in large scale network.
19302 针对物理环境下旁路分析技术对电路中规模较小的硬件木马检出率低的问题，该文引入边界Fisher分析(MFA)方法，并提出一种基于压缩边界Fisher分析(CMFA)的硬件木马检测方法。 Against the problem of low detection rate to detect small hardware Trojan by side-channel inphysical environment, the Marginal Fisher Analysis (MFA) is introduced. On the basis, a hardware Trojandetection method based on Compression Marginal Fisher Analysis (CMFA) is proposed.
19303 通过减小样本的同类近邻样本与该样本以及类中心之间距离和增大类中心的同类近邻样本与异类样本之间距离的方式，构建投影空间，发现原始功耗旁路信号中的差异特征，实现硬件木马检测。 The projection space is constructed by reducing the distance between the sample and its same neighbor samples, and the distance between the same neighbor samples and the center of the same kind, and increasing the distance between the same neighbor samples of the center and the sample in different kind. Thus, the difference in the original datais found without any assumptions about data distribution, and the detection of hardware Trojan is achieved.
19304 AES加密电路中的硬件木马检测实验表明，该方法具有比已有检测方法更高的检测精度，能够检测出占原始电路规模0.04%的硬件木马。 The hardware Trojan detection experiment in AES encryption circuit shows that this method can effectively distinguish the statistical difference in side-channel signal between reference chip and Trojan chip and detect the hardware Trojan whose scale is 0.04% of the original circuit.