engine

笔者费了很大的经历找到的matlab实例资料，希望大家好好利用，绝对经典的好资料哦(Programming with vc for the sample program, which contains many of the author s experience, I hope you make good use of)

说明：  使用MATCOM使VC与MATLAB混编的小例子(VC and MATLAB using the mixed MATCOM small example of)

OFDM仿真资料，以及一些仿真的程序源代码OFDM仿真资料，以及一些仿真的程序源代码OFDM仿真资料，以及一些仿真的程序源代码(OFDM simulation data, as well as some simulation program source code for OFDM simulation data, as well as some simulation program source code for OFDM simulation data, as well as some simulation program source code for)

GUI的使用实例，可以更好地学习！可以参照程序来做(Instance of the GUI to use, you can learn better! Can refer to the program to do )

matlab 匈牙利算法，百度搜到的，试了可以用(Hungarian algorithm matlab)

角点检测程序源码，使用的是HARRIS角点提取方法，效果很好。(Corner detection program source code, using extraction methods HARRIS corner, very good.)

由设计要求可知，wp<=0.2π，允许误差幅度小于1dB，ws>=0.3π内衰减应大于15dB。为了验证数字滤波器的实际滤波功能，设计程序时，把输入序列定为验证函数x（t）=4sin（100πt）以及x（t）=4sin（200πt）和x（t）=4sin（400πt）。又因为Ts=1.25ms，T=80ms，故采样点为64，即N=64，所以当M=6，可以同时调试并验证程序的滤波功能。(Seen by the design requirements, wp < = 0.2π, allow a margin of error of less than 1dB, ws> = 0.3π within attenuation greater than 15dB. In order to verify the actual filtering function of the digital filter design program, the input sequence as validation function x (t) = 4sin (100πt) and x (t) = 4sin (200πt) and x (t) = 4sin (400πt) . And because Ts = 1.25ms, T = 80ms, so the sampling points is 64, that is N = 64, so when M = 6, can debug and validate filtering program.)

支持向量机源程序代码，c++运行环境，matlab例程(SVM source code, c++ runtime environment, matlab routine)

matlab fir LPF least square error with zeroa weighted

LIBSVM 是台湾大学林智仁 (Chih-Jen Lin) 博士等开发设计的一个操作简单、易于使用、快速有效的通用 SVM 软件包，可以解决分类问题（包括 C- SVC 、n - SVC ）、回归问题（包括 e - SVR 、 n - SVR ）以及分布估计（ one-class-SVM ）等问题，提供了线性、多项式、径向基和 S 形函数四种常用的核函数供选择，可以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概率估计等。(LIBSVM is林智仁Taiwan University (Chih-Jen Lin) Dr. develop design a simple, easy to use, fast and effective generic SVM software package, can solve the classification problems (including the C-SVC, n- SVC), regression ( including e- SVR, n- SVR) as well as the distribution of estimates (one-class-SVM) and so on, provides a linear, polynomial, radial basis function and the S-shaped kernel function of four commonly used for selection, can effectively to solve a wide range of issues, cross-validation to choose the parameters of the imbalance in the weighted sample, multi-category probability estimation.)