Research on Feature Extraction for Face Color and Shape Classification for TCM Observation

在国务院发布的“中医药发展战略规划纲要（2016—2030年）”的指导下，近年来，我国中医信息处理技术研究开始有了新的发展，中医四诊的客观化与智能化研究也引起了广泛的关注。面诊作为中医临床必察之项，即通过目视观察患者面部状态判断内部脏腑器官的病变情况，主要依赖于医生的主观定性诊断。本文将计算机视觉技术与传统面诊理论结合起来，使用信息技术辅助手段从定量角度对面诊进行客观化研究。 本文所研究的内容围绕面诊领域中人脸颜色和形状特征提取两方面展开，主要工作有以下两项： （1）提出了一种面色分类中基于多颜色空间融合的块均值特征提取方法。综合考虑HSI和Lab两种颜色空间模型对分割得到的面色块进行颜色...Under the guidance of"strategic plans for development of traditional Chinese medicine(2016-2030)"by the state council,in recent years, the information technology research of our country traditional Chinese medicine(TCM) is developing,and the objectification and intelligent study of TCM four diagnostic also cause wide attention. TCM observation is to diagnose patients’ illness by observing patients...学位：工程硕士院系专业：信息科学与技术学院_工程硕士(计算机技术)学号：3152014115330

Modulation Recognition Method of Non-cooperation Underwater Acoustic Communication Signals Using Principal Component Analysis

由于信道传输特性、信噪比低等因素的影响,非合作水声通信信号的调制识别极具挑战性。对信号功率谱、平方谱进行主分量分析,提取代表不同类型调制信号特有信息的主分量作为特征参数,从而降低特征参数维度、抑制噪声影响,并在此基础上设计一种基于人工神经网络的水声通信信号调制方式分类器。海上实录信号数据的识别实验结果表明了该方法的有效性。The modulation classification of the non-cooperation underwater acoustic communication signals is extremely challenging due to channel transmission characteristics and low signal-to-noise ratio. The principal component analysis( PCA) is used to analyze the power spectra and square spectrum features of signals,which is capable of extracting the principal components associated with different modulated signals as input vector,thus reducing the feature dimension and suppressing the influence of noise. An artificial neural network( ANN) classifier is proposed for modulation recognition. The experimental modulation classification results obtained from field signals in 4 different underwater acoustic channels show that the proposed modulation recognition method has good classification performance.国家自然科学基金项目(11274259、11574258

PKFSKC: PCA Based Key Frame Similarity Kernel Clustering Algorithm

针对基于内容的视频检索领域中,关键帧特征矩阵维度不同时的相似度计算问题,提出一种基于主成分分析的关键帧相似度核聚类检索算法。首先,针对任意具有不; 同数量关键帧的视频片段,提取特征向量并构造不同维度的特征矩阵。其次,基于PCA计算对特征矩阵进行SVD计算降维矩阵后,结合矩阵运算方法及核方法设; 计出一种视频关键帧相似度核聚类检索算法,并给出其加权改进形式。最后,通过测试视频标准库和人工视频片段的实验表明,该算法能更好地提视频高视频检索的; 效率。In the content-based video retrieval research, a PCA based key frame; similarity kernel clustering algorithm is proposed to calculate the; similarity of the feature matrix of video key frame with different; dimensions. Firstly, feature vectors and structure feature matrices with; different dimensions of any different video clip key frame are; extracted. Secondly, the dimension reduction matrix with SVD method; based on PCA algorithm is calculated, the key frame similarity kernel; clustering algorithm is proposed with the matrix calculation method and; the kernel method, and its improved weighted representation is proposed; as well. Finally, the simulation experiments on the standard test video; database and artificial video clip database show that the algorithm can; improve the efficiency of video retrieval.福建省软科学一般项目; 2016年虚拟现实技术与系统国家重点实验室立项; 厦门大学立项课题项

Research of Brain-Computer Interface Based on Motor Imagery Feature Extraction and Pattern Classification

近些年来，随着神经科学、信号处理、计算机科学等领域的不断地深入发展，作为这些学科的交叉学科——脑机接口，目前已成为了当前国际上科学研究的热门领域。脑机接口是一种不同于人脑神经与肢体肌肉组织的正常通道，而是通过解析人脑神经活动信息来与外界设备交流的通讯控制系统。由于脑电信号具有幅值微弱、非平稳、噪声干扰强等特点，导致该类信号特征提取与模式分类的复杂性以及构建在线脑机接口系统的困难。经过充分调研与研究，本文采用了对二分类与四分类脑机接口更加有效的特征提取方法，并构建一套基于运动想象的在线足球射门脑机接口系统。 本文试图从分析当前特征提取算法与模式分类算法对脑电信号适应性入手，通过优化脑电信号预处...In recent years, with the further development of neural science, signal processing, computer science and other fields, as the subject of interdisciplinary, brain computer interface is in its rapid development, and now has become a hot area of the current international scientific research. Brain-computer interface is a kind system of different from normal channel between one brain and body muscle t...学位：工学硕士院系专业：信息科学与技术学院_信号与信息处理学号：2332012115299

Research of Underwater Acoustic Communication Signal Modulation Recognition and Its Implementation

随着海洋开发、海洋权益维护、国防安全等领域信息获取和处理需求的不断提升，对水声通信信号的调制样式的自动识别研究成为重要的研究课题。然而目前无线领域常用的通信信号(BPSK，QPSK，MFSK，OFDM)的调制识别方法往往需要较多的调制参数作为先验知识（如精确载波频率、初始相位、符号速率）。由于水声信道的随机时-空-频变、窄带高噪、多途效应及多普勒频移等特性，使得上述这些先验知识在水声信号调制方式未知的情况下很难得到，因此，非合作水声通信信号的自动识别极具挑战性。 信号频谱是对信号在频域方面的一种描述，不同调制方式的信号，在频域上表现为不同的形式。信号功率谱及其二次方谱可以较好的反映出MFSK...With rapid development of ocean exploitation, maritime rights protection and other marine information acquiring and processing related fields, the modulation recognition of underwater acoustic communication has become an important research topic. At present, the modulation recognition method of commonly used communication signal in wireless field, such as based on instantaneous features, based on ...学位：理学硕士院系专业：海洋与地球学院_海洋物理学号：2232013115144

Research on Epileptic EEG Signal Classification Based on Particle Swarm Optimization and RBF Neural Network

癫痫是一种危害人类健康的常见病和多发病之一，随时随地地发作给患者身心健康造成很大影响，在很多国家已经成为神经系统很受重视的高发疾病。脑电图是常用于辅助检测癫痫的一种重要手段，但是癫痫患者的脑电图不总是显示异常，所以依靠观察脑电图进行癫痫脑电识别依然存在问题，而且经过研究发现癫痫具有较强的随机性、非平稳性和非线性等特点，对癫痫疾病相关的研究带来较大的困扰。因此如何有效地提取脑电特征来表征癫痫脑电特征的信息，是进行癫痫诊断的首要问题。 针对癫痫脑电信号具有的随机性、非平稳性以及非线性等特点，本文提出了混合特征提取，将时域方法和非线性分析方法混合提取特征，然后采用粒子群优化算法进行优化选择，最后利...Epilepsy is one of the common diseases and frequently-occurring diseases which endangers the health of human beings and gets more attention in many countries. EEG is an important method used to detect the epileptic. However the EEG of epileptics don’t show abnormal all the time, it still exists problem to recognize the epileptic depending on the EEG only. Moreover, researchers have found that the ...学位：工学硕士院系专业：软件学院_计算机科学与技术学号：2432014115239

基于栈自编码器的图像分类器

图像分类问题包含两个重要的部分:特征提取器和分类器.多年来研究人员一直将精力投入到特征表示中,对于分类器却仅进行局部调参.基于一个性能优异的分类器与特征表示对图像分类系统同等重要的思想,提出了基于卷积特征的栈自编码器(stacked autoencoder on convolutional feature maps,SACF)的分类系统,并在数据集CUB-200和VGGflower上进行了实验,对比了SACF与基于卷积特征和多层感知机的卷积神经网络(CNN)分类系统的分类效果,实验结果表明SACF具有更优的分类效果.国家自然科学基金(61572409,61571188,61202143);;福建省自然科学基金(2013J05100);;中国乌龙茶产业福建省2011协同创新中心项目(闽教科[2015]75号);;福建省教育厅A类科技项目(JA13317

Research on Monitoring Behavior of Brittle Material Removal Based on State Signal feature Extraction

为了实现脆性材料的高效低损伤加工，本文对脆性材料加工过程状态特征以及磨粒磨损规律进行了较为系统的研究。本文在不同划刻参数条件下采集了声发射信号和力信号，利用提取的特征对典型光学脆性材料去除过程进行识别与监测，主要研究内容如下： 1.在四轴精密机床的基础上，搭建了脆性材料划刻实验平台。对BK7玻璃进行了压痕实验，实验中的信号特征变化大致分为三个不同阶段：在施加的载荷很小时，材料塑性变形，并未产生突发式信号；当载荷持续加载，应力超过断裂极限，产生侧向裂纹和中位裂纹，此时伴随着持续的突发式信号，幅值有一定上涨，频谱成分丰富；卸载过程中，中位裂纹闭合，残余应力使塑性变形区域产生侧向裂纹，此时有部分突...In order to achieve high and low damage processing of brittle materials, this paper studies the state characteristics of brittle materials and the wear law of abrasive grains. In this paper, acoustic emission signals and force signals are collected under different scoring parameters. The extraction process of typical optical brittle materials is identified and monitored by using the extracted feat...学位：工程硕士院系专业：航空航天学院_工程硕士(机械工程)学号：1992014115290