Matlab-Csharp

FCM算法的matlab实现，经典的FCM算法，使用IRIS数据(FCM algorithm matlab realize, classic FCM algorithm, the use of IRIS data)

Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal)

some examples to learn basic c++

NICAM（丽音）系统编解码Matlab源程序(NICAM encoder and decoder)

本程序使用Shuey方程完成三层界面的正演模拟 子波使用雷克子波(This program uses the Shuey equation, a three-forward simulation interface using Leike Zi wave wavelet)

对于学习混沌及设计混沌系统 可以用来参考学习用(For learning the chaotic and design of chaotic systems can be used to refer to learning with)

说明：  此程序描述了QPSK调制解调的过程，包括成形与匹配滤波，最后给出解调误码率。(This program describes the process of QPSK modulation and demodulation, including shaping and matching filtering, and finally gives the demodulation error rate.)

说明：  巴特沃思和契比雪夫滤波器，有wav文件以及输出的wav文件，使用带通滤波器，保留了300-3300Hz的音频，可以广泛使用在音频滤波方面(Butterworth and Chebyshev filter, wav files and output wav file, the use of band-pass filters, retained the 300-3300Hz audio can be widely used in audio filtering)

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)

说明：  8 psk modulation matlab code without error coding