ID 原文 译文
19085 与此同时，在对现有方法对比分析的基础上，提出了未来的重点发展方向。 At the same time, the existing methods are comparedand analyzed, the key development directions are put forward in the future.
19086 该文提出一种基于多通道调频连续波(FMCW)毫米波雷达的微动手势识别方法，并给出一种微动手势特征提取的最优雷达参数设计准则。 A micro-motion gesture recognition method based on multi-channel Frequency Modulated Continuous Wave (FMCW) millimeter wave radar is proposed, and an optimal radar parameter design criterion for feature extraction of micro-motion gestures is presented.
19087 通过对手部反射的雷达回波进行时频分析处理，估计目标的距离多普勒谱、距离谱、多普勒谱和水平方向角度谱。 The time-frequency analysis process is performed on the radar echo reflected by the hand, and the range Doppler spectrum, the range spectrum, the Doppler spectrum and the horizontal direction angle spectrum of the target are estimated.
19088 设计固定帧时间长度拼接的距离-多普勒-时间图特征，与距离-时间特征、多普勒-时间特征、水平方向角度-时间图特征和三者联合特征等，分别对7类微动手势进行表征。 Then the range-Doppler-time-map feature is designed, range-time-map feature, Doppler-time-map feature, horizontal-angle-time-map feature,and three-joint feature with fixed frame time length are used to characterize the 7 classes micro-motiongestures, respectively.
19089 根据手势运动过程振幅和速度差异，进行手势特征捕获和对齐。 And these gesture features are captured and aligned according to the difference in amplitude and speed of the gesture motion process.
19090 利用仅有5层的轻量化卷积神经网络对微动手势特征进行分类。 Then a five-layer lightweight convolutional neural networkis designed to classify the gesture features.
19091 实验结果表明，相较其他特征，设计的距离-多普勒-时间图特征能够更为准确地表征微动手势，且对未经训练的测试对象具有更好的泛化能力。 The experimental results show that, the range-Doppler-time-mapfeature designed in this paper characterizes the micro-motion gesture more accurately and has a better generalization ability for untrained test objects compared with other features.
19092 海量的生物医学图像蕴含着丰富的信息，模式识别算法能够从中挖掘规律并指导生物医学基础研究和临床应用。 Pattern recognition algorithms can discover valuable information from mass data of biomedical images as guide for basic research and clinical application.
19093 近年来，模式识别和机器学习理论和实践不断完善，尤其是深度学习的广泛研究和应用，促使人工智能、模式识别与生物医学的交叉研究成为了当前的前沿热点，相关的生物医学图像研究有了突破式的进展。 In recent years, with improvement of the theory and practice of pattern recognition and machine learning, especially the appearance and application of deep learning, the crossing researches among artificial intelligence, pattern recognition, and biomedicine become a hotspot, and achieve many breakthrough successes in related fields.
19094 该文首先简述模式识别的常用算法，然后总结了这些算法应用于荧光显微图像、组织病理图像、医疗影像等多种图像中的挑战性和国内外研究现状，最后对几个潜在研究方向进行了分析和展望。 This review introduces briefly the common framework and algorithms of image pattern recognition, summarizes the applications of these algorithms to biomedical image analysis including fluorescence microscopic images, histopathological images, and medical radiological images, and finally analyzes and prospect several potential research directions.