MATLABMfileuseDSP

Hankel法的过程辨识，利于用脉冲传递响应对系统的辨识，实例全过程。(Hankel identification process of law, in favor of using pulse delivery system to respond to the identification, examples of the entire process.)

matlab 实现图像的区域分块处理 区域函数。。(image processing)

pso pid的研究与控制，有源程序及simulink模型，分三个模块(pso pid)

算法是原始对偶算法，该方法充分的剖析了优化问题的结构，是求解二次规划问题的一个较好的算法。求解比采用matlab 的Quardprog求解速度快许多！(Algorithm is the original dual algorithm, this method is fully analyzed the optimization of structure, is to solve the quadratic programming problem of a good algorithm. To solve the fast Quardprog than using matlab to solve many!)

The following code implements a kernel Wiener Filter algorithm in MATLAB. The algorithm dependes on the eigenvalue decomposition, thus only a few thousand of data samples for training dataset is applicable so far.

1、分别生成两种信号（正弦波、方波、三角波、锯齿波、Sinc波形等）+噪声（高斯随机噪声、均匀噪声） 要求：频率：学号*100； 幅度：学号/10 相位：学号*pi/180 点数：学号*64 2、同时在一个图形中进行显示： 要求：标注X轴、Y轴、题目、颜色、样式、线型标注等。 3、进行信号分析及结果显示（对结果也要图形标注，同2） 要求：相关分析 卷积运算 求和运算 频谱分析（最大值、测频） 4、对上述原信号及信号分析结果分别存成EXCEL文件、文本文件和二进制文件，命名为名字+班级+学号+后缀(1, respectively, to generate two signals (sine wave, square wave, triangle wave, sawtooth wave, Sinc waveform, etc.)+ noise (Gaussian random noise, uniform noise) requirements: Frequency: No.* 100 margin: Student ID/10 phase: learning* pi/180 Points: No.* 642, in a graphical display: Requirements: X-axis label, Y axis, the title, color, style, linear marked. 3, signal analysis and results display (graphical results also marked with 2) requirements: analysis convolution summation spectrum analysis (maximum frequency measurement) 4, the original signal and the signal analysis results were kept into EXCEL files, text files and binary files, named for Name the+ class++ suffix number)

利用循环队列生成斐波那契数列的控制台程序！(Circular queue using a console program to generate Fibonacci number deed column!)

timing_recovery:The importance of the work is reflected in supporting the development of new technologies and their adaptation to existing infrastructures. From the economic point of view, the final product is more efficient, flexible it can be widely available and less expensive than existing similar products

MATLAB implementation for dense descriptor applied to wide baseline stereo DAISY

support vector training in matlab