ID 原文 译文
20595 在此基础上，首先对接收的1周期组合码序列进行重叠分段， Firstly, the received one pseudo codeperiod is overlapped segmentation based on the estimated pseudo code period.
20596 其次优化筛选出学习系数，对每段数据向量作为RBF神经网络的输入信号并进行有监督地调节， Secondly, the learning coefficientis optimized selection and each segment of date vector as an input signal to the RBF neural networks tosupervised adjustment.
20597 最后对每段数据向量多次输入并反复训练权值向量就可以恢复原组合码序列。 Finally, through the continuous input signal, it can restore the original combinationcode sequence according to the convergent weight vectors.
20598 仿真结果表明，利用2次谱可以在低信噪比下估计出伪码周期； Simulation results show that the pseudo code periodcan be estimated using the secondary power spectrum under low Signal-to-Noise Ratio (SNR).
20599 在误码率低于1%的情况下，所提出的RBF神经网络相比于反向传播(BP)神经网络与Sanger神经网络，信噪比分别提高1 dB和3 dB，并且在同等条件下所需的数据组数较少。 Compared with the Back Propagation (BP) neural networks and the Sanger neural networks, the proposed RBF neural networks improve the SNR by 1 dB and 3 dB respectively and the number of data groups required is less through RBF neural networks under the same condition.
20600 随着网络信息系统的发展，网络病毒扩散方式及免疫策略成为网络安全领域研究的热点之一。 With the development of network information system, virus propagation and immunization strategy become one of the hot topics in the field of network security.
20601 该文研究了一类新型混合攻击病毒，并根据其特点将这类病毒定义为“去二存一”型病毒。通过分析新型病毒的攻击方式，构建了“去二存一”混合机制下病毒的SEIQRS信息扩散模型。 In this paper, a new virus with hybrid attacking is introduced, which can attack network in two modes. One is to attack and infect the network nodes directly,and the another is to hide itself in the nodes by hiding its viral characteristic. According to its characteristics, this type of virus is defined as “Two-go and One-live” and the corresponding virus propagation model is established.
20602 在此基础上，求解对应系统的平衡点，并运用Routh-Hurwitz判据分析了系统基本再生数R0及其对系统稳定性的影响。 Moreover, the stability of the system is studied by solving the equilibrium points and analyzing the basic reproduction number R0.
20603 最后，仿真验证了模型的有效性和稳定性。 Numerical simulations are presented to verify effectiveness and stability of the novel model.
20604 在超高频射频识别(UHF RFID)系统近场(NF)密集标签应用中，由于微带标签天线的结构特点，传统线圈间互阻抗表达式在预估系统频率偏移等互耦效应问题方面误差较大，精确性不够。 In Near-Field (NF) applications of Ultra-High-Frequency Radio Frequency IDentification (UHFRFID) systems, due to the structural characteristics of the microstrip tag, the traditional inter-coil mutual impedance expression has a large error in the estimation of the mutual coupling effect such as the frequency shift of the prediction system, and the accuracy is not enough.