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David G.Lowe的SIFT算子在Matlab环境下的实现，尺度不变的特征提取，并对两幅图片的特征点进行匹配，运行前要把siftWin32文件放在Matlab的win目录下，具体请参照README。默认为输入为灰度图像，要处理彩色图片请先转灰度rgb2gray() (The realization of SIFT operator David G.Lowe in the Matlab environment, the scale invariant feature extraction, matching and pictures of the two feature points, the siftWin32 file in the Matlab win directory to run before, please refer to the specific README. The default is the input for the gray image to color image processing, please turn gray by rgb2gray ())

matlab code for Subaxis - Subplot

一个QPSK调制解调的程序，在Matlab环境下实现。(A QPSK modulation and demodulation process in Matlab environment to achieve.)

FFT 基2变换源程序，matlab测试与系统函数变换结果一致(Radix-2 FFT transform source code, matlab test results are consistent with the system function transformation)

实现suzuki信道模型的仿真,同时包含大尺度、小尺度衰落(to simulate suzuki channel model，include large scale fading and small scale fading)

倒立摆的编程，单级倒立摆，完美运行，比较好的程序(daolibai kongzhi )

说明：  采用多相滤波结构，可利用多个阶数较低的滤波来实现原本阶数较高的滤波，而且每个分支滤波器处理的数据速率仅为原数据速率的I/D，这为工程上高速率实时信号处理提供了实现途径。(By using polyphase filter structure, the original higher order filtering can be achieved by using multiple lower order filters, and the data rate processed by each branch filter is only I/D of the original data rate, which provides a way to realize high-speed real-time signal processing in engineering.)

说明：  lower的原版关于sift算法的资料，很难找的哦。本文非常详细的介绍了sift算法，并与常用的边缘检测算法作了比较，是学习sift算法的好资料。(lower of the original information on the sift algorithm, it is difficult to find, oh. This very detailed introduction to the sift algorithm, and with commonly used edge detection algorithms are compared, is the learning algorithms sift the good information.)

matlab程序画图，常见分形图，function cantor(A,B) c=1 p=0 figure hold on plot([A(2) B(2)],[A(1) B(1)], r* ) while norm(A(1,:)-B(1,:))>=1 C=[] D=[] for i=1:size(A,1) a=A(i,:)+(B(i,:)-A(i,:))/3 b=A(i,:)+2*(B(i,:)-A(i,:))/3 C=[C A(i,:) b] D=[D a B(i,:)] end A=C B=D p=p+1 (matlab program drawing, common fractal images function cantor(A,B) c=1 p=0 figure hold on plot([A(2) B(2)],[A(1) B(1)], r* ) while norm(A(1,:)-B(1,:))>=1 C=[] D=[] for i=1:size(A,1) a=A(i,:)+(B(i,:)-A(i,:))/3 b=A(i,:)+2*(B(i,:)-A(i,:))/3 C=[C A(i,:) b] D=[D a B(i,:)] end A=C B=D p=p+1 )

saving an image to a binary file.