(origin)libsvm-mat-2.83-1

一个用MFC写的滤波器源代码，代码结构非常精巧，可以参考 功能：1 固定带通滤波 2 可变带通滤波 3 噪声删除 4 输入输出误差计算 实现比较简单，由于使用了Microsoft Foundation classes，代码流程很简单，但是实现的功能比较充分 Filter.h：头文件，定义了CFilterApp 类 Filter.cpp：利用MFC的函数实现了CFilterApp Filter.rc：资源文件，包括对话框和图标的说明(use a filter to write MFC source code, code structure is very delicate, can be functional : a fixed band-pass filtering two variable bandpass filter to delete four 3 noise input and output error calculation is relatively simple to achieve. As the use of Microsoft Foundation classes, code process is simple, but to achieve the more fully functional Filter.h : The first document, CFilterApp definition of the category Filter.cpp : MFC function realized CFilterApp Filter.rc : resource files, including dialog box and the note icon)

pid controller implementation using matlab

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说明：  利用matlab语言和软件采用粒子群优化算法解决电力系统无功优化问题(Using MATLAB language and software to solve reactive power optimization problem in power system by particle swarm optimization)

说明：  计算矩阵各点之间的距离，并选取合适的点，适用于大型矩阵的计算(Calculate the distance between the points of the matrix)

特征选择与提取 特征点 matlab应用 可用(Feature selection and extraction of feature points matlab applications available)

有限插分法求取泊松方程，非常经典啊，欢迎下载(Finite Difference Method to strike a Poisson equation, very classic ah, welcome to download)

多边形网格相交区域搜索,MATLAB源程序，含有若干示例(Polygon mesh intersection area search, MATLAB source, contains a number of examples)

产生右图所示图像f1(m,n)，其中图像大小为256×256，中间亮条为128 ×32，暗处=0，亮处=100。对其进行FFT： ① 同屏显示原图f1(m,n)和FFT(f1)的幅度谱图； ② 若令f2(m,n)=（-1）m+n f1(m,n)，重复以上过程，比较二者幅度 谱的异同，简述理由； ③ 若将f2(m,n)顺时针旋转90 度得到f3(m,n)，试显示FFT(f3)的幅 度谱，并与FFT(f2)的幅度谱进行比较； ④ 若将f1(m,n) 顺时针旋转90 度得到f4(m,n)，令f5(m,n)=f1(m,n)+f4(m,n)，试显 示FFT(f5)的幅度谱，并指出其与FFT(f1)和FFT(f4)的关系； ⑤ 若令f6(m,n)=f2(m,n)+f3(m,n)，试显示FFT(f6)的幅度谱，并指出其与FFT(f2)和 FFT(f3)的关系，比较FFT(f6)和FFT(f5)的幅度谱。(Generating an image f1 (m, n) shown in the figure, wherein the image size is 256 256, the intermediate light bar 128 32 0 = dark, bright Department 100. Its FFT: ① screen display picture f1 (m, n) and the FFT (f1) of the amplitude spectrum ② If so f2 (m, n) = (-1) m+n f1 (m, n), repeat The above process, comparing the amplitude spectrum of the similarities and differences between the two, brief reasons ③ If f2 (m, n) 90 degrees clockwise to get f3 (m, n), try to display FFT (f3) the amplitude spectrum and with the FFT (f2) comparing the amplitude spectrum ④ If f1 (m, n) obtained by 90 degrees clockwise f4 (m, n), so f5 (m, n) = f1 (m, n)+f4 (m, n ), try to display FFT (f5) amplitude spectrum, and pointed out its relationship with the FFT (f1) and FFT (f4) of ⑤ If so f6 (m, n) = f2 (m, n)+f3 (m, n) and try to display the FFT (f6) amplitude spectrum, and pointed out its relationship with the FFT (f2) and FFT (f3), comparing FFT (f6) and FFT (f5) amplitude spectrum.)

说明：  感知无线电中空闲频谱感知的双次协作检测算法仿真(double cooperative detection in spectrum holes)