ID 原文 译文
19025 仿真验证表明，应用于4通道14位TIADC系统，当输入信号为多频信号时，系统动态性能无杂散动态范围(SFDR)从48.6 dB提高到80.7 dB。 The simulation results show that when the input signal is a multi-frequency signal, the Spurious-Free Dynamic Range (SFDR) increases from 48.6 dB to 80.7 dB, after adopting the proposed time skew correction for a 4-channal 14-bit TIADC system.
19026 与传统基于前馈校准结构对比，可以将有效校准输入信号带宽从0.19提高到0.39，提高了校准算法的应用范围。 Compared with the traditional feedforward calibrationstructure based on correlation operation, the effective calibration input signal bandwidth can be increased from0.19 to 0.39, which greatly increases the application range of the calibration algorithm.
19027 随着工业物联网(IoT)、云计算等信息技术与工业控制系统(ICS)的整合，工业数据的安全正面临着极大风险。 With the integration of information technology such as industrial Internet of Things (IoT), cloudcomputing and Industrial Control System (ICS), the security of industrial data is at enormous risk.
19028 为了能在这样一个复杂的分布式环境中保护数据的机密性和完整性，该文采用基于属性的加密(ABE)算法，设计一种集数据加密、访问控制、解密外包、数据验证为一体的通信方案，同时具有密文长度恒定的特点。 In order to protect the confidentiality and integrity of data in such a complex distributed environment, a communication scheme is proposed based on Attribute-Based Encryption (ABE) algorithm, which integrates data encryption, access control, decryption outsourcing and data verification. In addition, it has the characteristics of constant ciphertext length.
19029 最后，从正确性、安全性和性能开销3个方面对方案进行详细的分析，并通过仿真验证得出该算法具有低解密开销的优势。 Finally, the scheme is analyzed in detail from three aspectsie correctness, security and performance overhead. The simulation results show that the algorithm has the advantage of low decryption overhead.
19030 样本数不足时，由采样协方差矩阵特征分解得到的噪声子空间偏离其真实值，使得多重信号分类(MUSIC)算法目标角度(DOA)估计性能下降。 For cases with small samples, the estimated noise subspace obtained from sample covariance matrix deviates from the true one, which results in MUltiple SIgnal Classification (MUSIC) Direction-Of-Arrival (DOA)estimation performance breakdown.
19031 为了解决这个问题，该文提出了一种迭代算法通过校正信号子空间来提高MUSIC算法性能。 To deal with this problem, an iterative algorithm is proposed to improve theMUSIC performance by modifying the signal subspace in this paper.
19032 该方法首先利用采样协方差矩阵特征分解得到的噪声子空间粗略估计目标角度； Firstly, the DOAs are roughly estimated based on the noise subspace obtained from sample covariance matrix.
19033 其次基于信源的稀疏性和导向矢量的低秩特性，由上一步得到的目标角度以及其邻域角度对应的导向矢量构造一个新的信号子空间； Then, considering the sparsity of signals and the low-rank property of steering matrix, a new signal subspace is got from the steering matrix consisting of estimated DOAs and their adjacent angles.
19034 最后通过解一个优化问题来校正信号子空间。 Finally, the signal subspace is modified by solving an optimizationproblem.