MPC

说明：  Embedded Control Systems in C/C++: An Introduction for Software Developers Using MATLAB(Embedded Control Systems in C/C++: An Introduction for Software Developers Using MATLAB The author of this text illustrates how to implement control systems in your resource-limited embedded systems. Using C or C++, you will learn to design and test control systems to ensure a high level of performance and robustness)

基于matlab实现sift描述符程序，经测试可用，效果有待提高！(achieve sift descriptor， program has been tested and is available, the effect needs to be improved!)

设计一维纳滤波器 (1)产生三组观测数据：首先根据s(n)=a s(n-1)+w(n)产生信号s(n)，将其加噪（信噪比分别为20dB,10 dB，6 dB），得到观测数据x1(n), x2(n), x31(n). (2)估计xi(n)，i=1,2,3的AR模型参数。假设信号长度为L，AR模型阶数为N，分析实验结果，并讨论改变L,N对实验结果的影响。 (A Wiener filter design (1) to produce three sets of observations: first generating a signal s (n) according to s (n) = as (n-1)+w (n), to add noise (20dB SNR, respectively , 10 dB, 6 dB), to obtain observational data x1 (n), x2 (n), x31 (n). (2) estimate xi (n), i = AR model parameters 1,2,3. Assuming the signal length L, AR model of order N, analysis of experimental results and discuss the impact changing L, N of the experimental results.)

对图像实现骨架化操作、提取边界、执行图像开运算度、增强图像对比度并提取文本图像中的某些字符对象。(Implementation of the image skeleton operation, extraction of the border, the implementation of open computing degree images to enhance image contrast and to extract the text of some of the characters in the image object。)

二维经验模态分解，采用形态学求极值点，径向基神经网络拟合，速度较快。(Two-dimensional empirical mode decomposition, the use of morphology for extreme points, Radial Basis Function Neural Network Fitting, faster.)

这是使用systemgenerator的一个入门程序和范例使用matlab和system generator共同实现，并配有教学文档，清晰简单，易懂(This is an entry using systemgenerator procedures and examples using matlab and the system generator together to achieve, and with a teaching document, clear and simple and easy to understand)

泄漏电流的压缩，基于压缩传感理论和希尔伯特黄变换的改进(leakage compression based on CS and EMD)

matlab数字图像处理，包含m文件，产生高斯噪声或椒盐噪声然后降噪(matlab digital image processing, including m file, Gaussian noise or impulse noise and noise reduction)

HMM MATLAB ..speech recognize

Gist。 使用直方图的方式提取特征值里面有demo，修改一下可以直接使用，提取特征向量，使用机器学的方法来分类识别( Use the histogram to extract eigenvalues ​ ​ inside demo, change it can be directly used to extract feature vectors, using machine learning approach to classification)