nuttallwindowFFT

this is a programm for Matlab for calculating dimension correlation and integral correlation

对经过分块算法隐藏的图像，进行信息提取，此算法适用于二值图像(After a block algorithm for the hidden images, the information extraction, this method is applicable to binary images)

希尔伯特-黄变换的matlab代码，仅供参考(Hilbert-Huang Transform)

数字基带传输系统的MATLAB仿真实现 function [sampl,re_sampl]=system_1(A,F,P,D,snr,m,N) 输入变量A ,F,P分别为输入信号的幅度、频率和相位，D为量化电平数，snr 为信道信噪比，N为D/A转换时的内插点数；输出变量sampl为抽样后的输入 信号，re_sampl为恢复出的输入信号。 数字基带传输系统的MATLAB仿真实现 [sampl,quant,pcm]=a_d_1(A,F,P,D) [changed_ami]=signal_encod_1(pcm) [ami_after_channel]=channel_1(changed_ami,snr) [adjudged_ami]=adjudg_1(ami_after_channel,m) re_pcm=signal_decod_1(adjudged_ami) [re_voltag,re_sampl,re_sampl1]=d_a_1(re_pcm,sampl,D,N)(Digital baseband transmission system implementation MATLAB simulation function [sampl, re_sampl] = system_1 (A, F, P, D, snr, m, N) input variables A, F, P, respectively, for the input signal amplitude, frequency and phase , D to quantify the number of electric-ping, snr for the channel signal to noise ratio, N to D/A converter when the interpolation points output variables for the sample after Sampl input signal, re_sampl for the restoration of the input signal. Digital baseband transmission system simulation MATLAB implementation [sampl, quant, pcm] = a_d_1 (A, F, P, D) [changed_ami] = signal_encod_1 (pcm) [ami_after_channel] = channel_1 (changed_ami, snr) [adjudged_ami] = adjudg_1 (ami_after_channel, m) re_pcm = signal_decod_1 (adjudged_ami) [re_voltag, re_sampl, re_sampl1] = d_a_1 (re_pcm, sampl, D, N))

It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

labview与matlab实现的混合编程实例。mathscirpt编写的程序(LabVIEW and Matlab the mixed programming examples achieve. the written in mathscirpt program)

一种无线通信网中的用户移动模型，用来描述仿真中小区内分布的用户及其移动规律。(User moves in a wireless communication network model, used to describe the simulation of small and medium-sized district distribution of users and their movement law.)

When x is a matrix, a wavelet transform is performed on all rows of x. ％ - To create filters + shifts: see makeWTfilter.m and mirrorshifts.m ％ - The forward transform is in FWT_BIO.m(When x is a matrix, a wavelet transform is performed on all rows of x.- To create filters+ Shifts: see makeWTfilter.m and mirrorshifts.m- The forward transform is in FWT_BIO.m)

matlab遗传算法程序 GATOOLS 遗传算法资源 GA(GA matlab)