ID 原文 译文
20285 利用差族与互补序列之间的联系，首先推导出高斯整数互补序列存在的充分条件，进而直接构造了阶数为2的高斯整数互补序列。 Based on the relationship between periodic complementary sequences and difference families, the sufficient condition of the existence of Gaussian integer periodic complementary sequences is proposed at first, then Gaussian integer periodic complementary sequences with degree 2 are constructed directly.
20286 为进一步增加高斯整数互补序列数目，又利用映射方法构造了阶数为4的高斯整数互补序列。 To extend thenumber of Gaussian integer complementary sequences, Gaussian integer complementary sequences with degree4 are constructed based on mappings.
20287 同传统的2元互补序列相比，高斯整数互补序列的存在数目很多，因此该文方法可以为通信系统提供大量的互补序列。 Compared with binary complementary sequences, there are more Gaussian integer complementary sequences, as a result, the presented methods will propose an abundance of complementary sequences for communication systems.
20288 随着雷达信号的日益复杂，从实数序列中提取特征变得越来越困难，但当它们表示成符号序列时，通常能更容易地挖掘出有效的特征参数。 With the increasing complexity of radar signals, it is more and more difficult to extract features ofthe real sequences, but when they are transformed to a symbol sequence, it is usually easier to mine the effective feature parameters.
20289 因此，该文提出一种基于多尺度信息熵(MSIE)的雷达信号识别方法。 Therefore, a radar signal recognition method based on Multi-Scale Information Entropy (MSIE) is proposed.
20290 首先通过符号聚合近似(SAX)算法在不同字符集尺度下将雷达信号转换为符号化序列； Firstly, the radar signal is transformed into symbolic sequence by Symbolic Aggregate approXimation (SAX) algorithm under different character number scales.
20291 然后联合各符号序列的信息熵值，组成MSIE特征向量； Then, the information entropy of each symbol sequence is combined to form the MSIE feature vector.
20292 最后，使用k邻近算法(k-NN)作为分类器实现雷达信号的分类识别。 Finally, the k-Nearest Neighbor(k-NN) is used as a classifier to realize the classification and identification of radar signals.
20293 通过仿真6种典型的雷达信号进行验证，结果表明该方法在信噪比(SNR)为5 dB时，不同雷达信号的识别正确率大于90%，并且优于传统的基于复杂度特征(盒维数和稀疏性)的识别方法。 The simulation results of 6 typical radar signals show that using the proposed method the correct recognition rate of different radar signals is greater than 90% when Signal to Noise Ratio (SNR) is 5 dB, and better performance can be obtained compared with the traditional identification method based on complexity characteristics (box-dimension and sparseness).
20294 图像质量评价研究的目标在于模拟人类视觉系统对图像质量的感知过程，构建与主观评价结果尽可能一致的客观评价算法。 The goal of Image Quality Assessment (IQA) research is to simulate the Human Visual System’s(HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result.