ID 原文 译文
18345 该文提出了一种基于核稀疏编码的自动检测方法，可以仅根据较短RR间期数据识别PAF发作。 An automatic detection method is proposed based on kernel sparse coding, which can identify PAF attacks based only on short RR interval data.
18346 该方法采用特殊几何结构来分析数据高维特性，通过计算协方差矩阵作为特征描述子，找到蕴含在数据中的黎曼流形结构； A specialgeometric structure is presented to analyze the high-dimensional characteristics of the data, and the covariancematrix is calculated as a feature descriptor to find the Riemannian manifold structure contained in the data;
18347 然后基于Log-Euclid框架，利用核方法将流形空间映射到高维可再生核希尔伯特空间，以获取更准确的稀疏表示来快速识别PAF。 Based on the Log-Euclidean framework, a manifold method is used to map the manifold space to a high-dimensional renewable kernel Hilbert space to obtain a more accurate sparse representation to identify quickly PAF.
18348 经麻省理工学院-贝斯以色列医院房颤数据库验证，获得98.71%的敏感性、98.43%的特异度和98.57%的总准确率。 After verification by the Massa-chusetts Institute of Technology-Beth Israel Hospital atrial fibrillation database, the sensitivity is 98.71%, the specificity is 98.43%, and the total accuracy rate is 98.57%.
18349 因此，该研究对检测短暂发作的PAF有实质性的改善，在临床监测和治疗方面显示出良好的潜力。 Therefore,this study has a substantial improvement in the detection of transient PAF and shows good potential forclinical monitoring and treatment.
18350 参数估计对雷达的目标检测和识别有着重要的意义。 Parameter estimation is very important for radar to detect and recognize targets.
18351 该文提出了一种基于期望最大化(EM)算法的捷变频联合正交频分复用(FA-OFDM)雷达高速多目标参数估计方法。 In this paper, a high speed multi-target parameter estimation method for Frequency Agility-Orthogonal Frequency Division Multiplexing(FA-OFDM) radar based on Expectation Maximization(EM) algorithm is proposed.
18352 首先，将窄带正交频分复用(OFDM)信号与传统捷变频雷达相结合，在每个脉冲宽度内同时发射多个载频随机跳变的子载波。 Firstly, apromising idea is to combine narrowband Orthogonal Frequency Division Multiplexing (OFDM) signals andfrequency agility, multiple subcarriers that frequency hopping randomly are simultaneously transmitted withineach pulse width.
18353 然后，对单个脉冲内所有子载波的回波进行脉冲压缩和稀疏重构处理，得到1维高分辨距离。 Then, all echoes of a single pulse are compressed and sparsely reconstructed to achieve 1-demension high range resolution.
18354 进一步地，将多个目标在不同脉冲时刻的高分辨距离信息构成观测数据，建立混合高斯模型。 Subsequently, the high resolution range of multiple targets at each pulse timeare obtained to constitute the observation data, and Gauss mixture model is established.