HW9

图像配准算法SIFT，matlab源程序，可运行(Image registration algorithm the SIFT, matlab source code can be run)

9MW风电场simulink仿真模型，可直接运行(9MW wind farm simulink simulation model can be run directly)

一些关于matlab的书，，，很有用的，，(Some of the book about matlab,,, very useful,)

一、 本程序采用16QAM调制方式，对一串2进制信源进行调制，采用（7，4）循环码对信源做信道编码，用升余弦滚降函数进行基带调制，再调到高频信道；在信道上加入高斯白噪声，运用匹配滤波器解调，画出解调星座图，运用最小欧氏距离译码判决，再对信号进行信道解码，画出采用信道编码技术和不采用信道编码技术的误比特率图。(First, this program uses 16QAM modulation mode, for a string of two binary source is modulated, using (7,4) cyclic code of the source-channel coding to do with a raised cosine roll-off function for base-band modulation, and then transferred to a high frequency channel in the channel by adding Gaussian white noise, the use of matched filter demodulation, demodulation constellation plot, using the minimum Euclidean distance decoding decision, and then right channel signals, decoding, channel coding techniques used to draw and non- channel coding techniques using bit error rate plans.)

粒子滤波的基本仿真程序。设计EKF，UKF，PF 三个算法。已经实现了，还算好用。(Particle filter simulation program. Design of the EKF, UKF, PF algorithm. Has achieved a fairly easy to use.)

凝汽器稳定工况下，换热平衡下的相关计算。(Condenser stable condition, the relevant calculations of the heat balance)

Its a little matlab example for recognizing Numbers. It shows a in a good way how Pattern recognizen work.

This Matlab code calculates the Rand Index and Adjusted rand Index between two clustering groups. The two input clusters has to be in the form of a structure as shown in the example mat file ( A and B ) attached in the folder. The code calculates the contingency table among the cluster and then calculates the Rand Index and Adjusted Rand Index. Using the value of the indices one can know about the similarity of the clustering group

MATLAB 主要函数指令表 MATLAB 主要函数指令表(The main function of MATLAB MATLAB command table form the main function of instruction)

support vector classification machine ％ soft margin ％ uses "kernel.m" ％ ％ xtrain: (Ltrain,N) with Ltrain: number of points N: dimension ％ ytrain: (Ltrain,1) containing class labels (-1 or +1) ％ xrun: (Lrun,N) with Lrun: number of points N: dimension ％ atrain: alpha coefficients (from svcm_train on xtrain and ytrain) ％ btrain: offest coefficient (from svcm_train on xtrain and ytrain) ％ ％ ypred: predicted y (Lrun,1) containing class labels (-1 or +1) ％ margin: (signed) separation from the separating hyperplane (Lrun,1(support vector machine classification% s oft margin% uses "kernel.m"%% xtrain : (Ltrain, N) with Ltrain : number of points N : % ytrain dimension : (Ltrain. 1) containing class labels (-1 or 1)% xrun : (Lrun, N) with Lrun : number of points N : % atrain dimension : alpha coefficients (from svcm_train on xtrain and ytrain)% btrain : offest coefficient (from svcm_train on xtrain and ytrain)%% ypred : predicted y (Lrun. 1) containing class labels (-1 or 1)% margin : (signed) separation from the separating hyper plane (Lrun, 1)