matlab实例 lms 算法

DFT IDFT stands for discrete time fourier transform and inverse fast fourier transform

说明：  运用龙格库塔算法求解朗之万方程，进而实现随机共振系统(The use of Runge-Kutta algorithm for solving Langevin equation, and then the realization of stochastic resonance systems)

This is the source code about OFDM under the condition of QPSK and perfect compensation.

实现DBF运算的具体的仿真程序，可帮助新人参考 (the simulated program to implement DBF )

数字语音是信号的一种，我们处理数字语音信号，也就是对一种信号的处理，那信号是什么呢？ 信号是传递信息的函数。离散时间信号——序列——可以用图形来表示。 按信号特点的不同，信号可表示成一个或几个独立变量的函数。例如，图像信号就是空间位置（二元变量）的亮度函数。一维变量可以是时间，也可以是其他参量，习惯上将其看成时间。 (Digital Voice is a signal that we deal with digital voice signal, that is, the processing of a signal, that signal what is it? Signal is a function of transmitting information. Discrete-time signal- sequence- can be used to indicate the graphics. According to the different signal characteristics, signal or can be expressed as a function of several independent variables. For example, the image signal is the spatial location (binary variables) of the brightness function. Variables can be one-dimensional time and other parameters can be, habits will be time to treat it as a.)

Tlestrar dirctory ifrmaotion5

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)

LQR for active suspension system _ Matlab code

Long Term Evolution (LTE) receiver coding

地震数据速度库转换程序，matlab编写，可以实现不同速度类型之间的转换(Seismic data speed database conversion program。matlab。Conversion between different speed types)