ID	原文	译文
57148	通过分析不同时间段内物理层参数与包接收率之间的关系，选取接收信号强度指示均值、链路质量指示均值以及信噪比均值作为链路质量参数;采用依据包接收率划分的链路质量等级作为评价指标;	After analyzing the re- lationship between the physical parameters and the packet reception rate in different time periods，the re- ceived signal strength indicator mean，the link quality indicator mean and the signal to noise ratio mean are selected as the link quality parameters; The evaluation index is determined by the link quality levels divided by packet reception rate.
57149	基于超限快速决策树评估链路质量，采用基尼指数作为决策节点的启发式度量，并依据决策节点的高度改进决策节点寻找最优属性样本数的计算方法.	A link quality estimation model is constructed based on extremely fast decision tree，and Gini index is employed as heuristic measure of decision node; the computing method of sample number，with which decision nodes look for the best attributes，is improved in terms of the height of decision node.
57150	室内、走廊、停车场 3 种场景下的实验表明，与模糊逻辑、快速决策树、超限快速决策树等方法相比，提出的方法具有较好的评估准确率和更低的时间复杂度.	In scenarios of indoor，corridor and parking lot，the experiment shows that compared with fuzzy logic，very fast decision tree，the earlier extremely fast decision tree etc，the proposed method has better estimation accuracy and lower time complexity.
57151	针对低信噪比下语种识别正确率低的问题，提出了一种声道冲激响应频谱参数和 Teager 能量算子倒谱参数融合的识别方法.	Aiming at the problem of low accuracy of language identification under low signal to noise ratio， a fusion identification method is proposed，using spectral parameters of channel impulse response and Teager energy operators cepstral coefficients.
57152	根据语音中不同特征信息量分布特性，首先在特征提取前端引入低通滤波器滤除信号高频部分，并采用重采样方法降低采样率，再基于信号频谱提取声道冲激响应频谱参数，然后融合 Teager 能量算子倒谱参数，最后通过高斯混合通用背景模型进行语种识别验证.	Considering the distribution of different feature information in speech，a low-pass filter is introduced to filter out the high-frequency part of the signal in the front-end of feature extraction. The resampling method is used to reduce the rate. And then，the spectral parameters of channel impulse response of vocal tract are extracted，and fused with the Teager energy operators ceps- tral coefficients. Finally，a Gaussian mixture model-universal background model is used to perform the language identification.
57153	不同信噪比条件下性能测试表明，所提方法相对于基于单一的梅尔频率倒谱系数特征、单一的伽玛通频率倒谱系数特征和基于对数梅尔尺度滤波器组能量特征，在低信噪比下提升约 15 dB，显著提高了识别正确率.	Experiments under different signal to noise ratio conditions show that the pro- posed methold significantly improves the language identification accuracy with 15 dB gain at low signal to noise ratio compared with the single Mel frequency cepstrum coefficient feature，single Gammatone fre- quency cepstrum coefficient feature and log Mel-scale filter bank energies feature.
57154	为实现低信噪比环境下多种通信辐射源的高精度识别，提出了一种基于稳态循环谱特征的通信辐射源识别方法，利用循环谱频域截面谱对高斯噪声的强鲁棒性，提取不同辐射源成形滤波器间的本征差异进行识别.	In order to realize high-precision identification of multiple communication emitters in low sig- nal-to-noise ratio ( SNＲ) environment，a method of communication emitter identification based on steady- state cyclic spectrum characteristics is proposed. By using the strong robustness of cyclic spectrum＇s cross- sectional spectrum in frequency domain to Gaussian noise，the intrinsic differences between shaping filters of different emitters are extracted for identification.
57155	首先对接收到的稳态信号提取循环谱频域截面谱并利用主成分分析方法降维，之后分别采用皮尔逊相关系数法、概率神经网络、弗雷歇距离法等判决方法进行辐射源类别判决.	Specifically，the cyclic spectrum ＇s cross-sectional spectra in frequency domain are extracted from the received steady-state signals，and the dimensions are reduced by principal component analysis.
57156	仿真实验显示，该特征使用概率神经网络判决和皮尔逊相关系数法判决，显著优于传统循环频率域的切片特征，证明有一定应用价值.	Then the emitters’categories are determined by Pearson corre- lation coefficient method，probabilistic neural network and Fréchet distance method，etc. Simulation shows that the proposed feature is superior to the traditional slice feature in cyclic frequency domain by using probabilistic neural network and Pearson correlation coefficient，which proves that it has certain applica- tion value.
57157	社交关系在生活中扮演着重要角色，用户通常会受到其好友偏好的影响，更容易选择好友购买过的物品.	Social relationship plays an important role in life，and users are often affected by their friends’ preferences. It is easier for users to choose items that their friends have purchased.