ID 原文 译文
17275 该文构造的测量矩阵的相关性小于已有文献构造的测量矩阵的相关性。 The correlation of the measurement matrix constructed in thispaper is smaller than that of the existing constructions in the literature.
17276 模拟仿真结果表明，该文构造的测量矩阵与同等条件下的随机高斯矩阵相比，可以更好地恢复稀疏信号； The simulation results show that themeasurement matrix constructed in this paper can recover the sparse signal better than the random Gaussianmatrix under the same conditions.
17277 所构造的矩阵还可以应用于信道估计以及2维图像的重构。 The proposed matrix can also be applied to channel estimation andreconstruction of two-dimensional images.
17278 针对脑-机接口(BCI)研究中采用单一特征对运动想象脑电信号(EEG)识别率不高的问题，该文提出一种结合脑功能网络和样本熵的特征提取方法。 For the low recognition rate of motor imagery ElectroEncephaloGram (EEG) signals using singlefeature in Brain-Computer Interface (BCI) research, a feature extraction method combining brain functionnetwork and sample entropy is proposed.
17279 根据事件相关同步/去同步(ERS/ERD)现象以及皮层与肢体运动想象间的对侧映射机制，选取小波包变换消噪重构后的 节律脑电信号， According to the neural mechanism appearing in Event RelatedSynchronization/Event Related Desynchronization (ERS/ERD) phenomenon and the contralateral mappingmechanism between cortex and limb motor imagery, the m rhythm is denoised by wavelet packet transform.
17280 用左侧27个通道、右侧27个通道分别对左半球脑区和右半球脑区构建脑功能网络，计算网络的平均节点度和平均聚集系数作为运动想象的脑功能网络特征，并结合C3, C4通道 节律的样本熵构筑分布性和指向性相结合的特征向量。 The brain function network is constructed for left hemispherical brain region and right hemispherical brainregion by m rhythm of 27 left channels and 27 right channels respectively. The mean node degree and the meanclustering coefficient are calculated as the brain function network characteristics, and the feature vectorscombining the distribution and directivity are constructed by the sample entropy of C3 and C4 channels withthe m rhythm.
17281 选用支持向量机(SVM)对左右手运动想象脑电信号进行分类，结果表明基于脑功能网络和样本熵的特征提取方法能够实现更优的分类效果，分类准确率最高可达90.27%。 The Support Vector Machine (SVM) is used to classify the left hand and right hand motorimagery EEG signals. The results show that the feature extraction method based on brain function network andsample entropy achieves better classification result, and the highest classification rate reached 90.27%.
17282 针对同步跳频(FH)网台分选问题，该文提出一种基于时频域单源点检测的欠定盲源分离(UBSS)分选算法。 Considering the problem of synchronous Frequency Hopping(FH) network station sorting, an underdetermined Blind Source Separation(UBSS) algorithm based on time-frequency domain single source point detection is proposed.
17283 该算法首先对观测信号时频变换，利用自适应阈值去噪算法消除时频矩阵背景噪声，增加算法抗噪性能， Firstly, the algorithm performs time-frequency transform on the observed signal,and uses adaptive threshold denoising algorithm to eliminate the background noise of the time-frequencymatrix. It can increase the algorithm anti-noise performance.
17284 然后根据信号绝对方位差算法进行单源点检测，有效保证单源点的充分稀疏性，并通过改进的模糊值聚类算法完成混合矩阵和2维波达方向估计，降低噪声和样本集分布差异对聚类结果的影响，提高估计精度。 Then, single source point detection is performedaccording to the absolute azimuth difference of the signal. It can effectively ensure the sufficient sparsity of asingle source point. The hybrid matrix estimation is completed by the improved fuzzy C value clusteringalgorithm. It can reduce the influence of noise and sample set distribution differences and improve theestimation accuracy.