ID 原文 译文
20805 数值实验表明，与传统重构算法相比，所提算法提高了重构成功率、降低了高概率重构所需的通道数，而且重构性能总体上随压缩次数增加而提高。 Numerical experiments show that, compared with the traditional recovery algorithm, the proposal can improve the recovery success rate, and reduce the channel number required for high-probability recovery. Furthermore,in general, the recovery performance improves with the rise of compression times.
20806 为了对抗恶意代码的沙箱规避行为，提高恶意代码的分析效率，该文提出基于代码进化的恶意代码沙箱规避检测技术。 In order to resist the malware sandbox evasion behavior, improve the efficiency of malware analysis, acode-evolution-based sandbox evasion technique for detecting the malware behavior is proposed.
20807 提取恶意代码的静态语义信息和动态运行时信息，利用沙箱规避行为在代码进化过程中所产生的动静态语义上的差异，设计了基于相似度差异的判定算法。 The approach can effectively accomplish the detection and identification of malware by first extracting the static and dynamic features of malware software and then differentiating the variations of such features during code evolution using sandbox evasion techniques.
20808 在7个实际恶意家族中共检测出240个具有沙箱规避行为的恶意样本，相比于JOE分析系统，准确率提高了12.5%，同时将误报率降低到1%，其验证了该文方法的正确性和有效性。 With the proposed algorithm, 240 malware samples with sandbox-bypassing behaviors can be uncovered successfully from 7 malware families. Compared with the JOE analysis system, the proposed algorithm improves the accuracy by 12.5% and reduces the false positive to 1%, which validates the proposed correctness and effectiveness.
20809 无源被动定位是入侵者检测、环境监测以及智能交通等应用的关键问题之一。 Device-free passive localization is a key issue of the intruder detection, environmental monitoring, andintelligent transportation.
20810 现有的无源被动定位方法可通过信道状态信息获取多个维度上的测量信息，但是现有方案未能充分挖掘多个信道上的频率分集以提高定位性能。 The existing device-free passive localization method can obtain the multidimensional measurement information by channel state information, but the existing scheme can not fully exploit the frequency diversity on multiple channels to improve the localization performance.
20811 该文提出一种基于多维测量信息的压缩感知多目标无源被动定位算法，在压缩感知框架下利用多维测量信息的频率分集提高定位精度和鲁棒性。 This paper proposes a Compressive Sensing (CS) based multi-target device-free passive localization algorithm using multidimensional measurement information. It takes advantage of the frequency diversity of multidimensional measurement information to improve the accuracy and robustness of localization results under the CS framework.
20812 根据鞍面模型建立无源字典，将多目标无源被动定位问题建模成多测量向量联合稀疏恢复问题，并利用多维稀疏贝叶斯学习算法估计目标位置向量。 The dictionary is built according to the saddle surface model, and the multi-target device-free passive localization problem is modeled as a joint sparse recovery problem based on multiple measurement vectors. The target location vector is estimated based on the multiple sparse Bayesian learning algorithm.
20813 仿真结果表明，该算法能有效利用多维测量信息提高定位性能。 Simulation results indicate that the proposed algorithm can make full use of the multidimensional measurement information to improve the localization performance.
20814 针对频谱监测系统中被监测信号无法控制并且没有任何先验知识，只能通过对信号被动监测，即接收与处理信号来估计信号源位置的要求， The signal source position can only be estimated by passive monitoring of the signal in terms of thatthe signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge.