AR Simiu

这是华中科技大学提出的用于求解铣削加工动力学稳定性的完全离散法的两自由度程序(This is a two - degree - of - freedom program for solving the dynamic stability of milling)

DXP sch. lib for INDUCTANCE

信号与系统第二版清华大学出版社课后习题编程答案(Signals and Systems, Tsinghua University Press, second edition programming after-school exercise answer)

包含两个文件KMEAN.h,KMEAN.CPP,使用方法非常简单，将数据存成.dat文件，即可对其进行Ｋ均值聚类(Consists of two documents KMEAN.h, KMEAN.CPP, very simple to use, the data saved as. Dat file can be K-means clustering)

在matlab7.4基础上实现恒虚警检测,包括单元平均CFAR检测实例过程，杂波边缘虚警性能，GO-CFAR等(Achieve CFAR in matlab7.4 basis, including cell averaging CFAR detection instance procedure, clutter edge false alarm performance, GO-CFAR, etc.)

对一个曲面根据一定的规则将其划分进行分片，形成不同的区域(On a surface according to certain rules will be sub-divided pieces to form different regions)

DC-DC simulink建模，有初级的详细建模步骤，开环、闭环建模，包块了buck, boost， buck-boost的建模，以及瞬态分析(DC-DC buck，boost，buck-boost modeling on simulink, close loop and open loop modeling, transient analysis)

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)

实现了用匹配滤波器对多用户信号中的期望用户进行检测(Achieved with a matched filter on the multi-user signal detection in the hope that users)

to genrate a hisrogram in matlab