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基于时空梯度的多分辨率目标跟踪稳像算法，该方法可用于处理有前景目标的图像，效果很好(Space-time gradient based target tracking of multi-resolution image stabilization algorithm)

阵元间隔为半波长的均匀分布16元线阵，预成指向士arcsin（(2i-1)/16）(i=l，2，…，8)的16个均匀加权常规波束,远场有5个互不相关的目标源发射信号，每个源相对于基阵的方位为-40，-42，-5，30和33，且每个信号源到达基阵的信噪比相同，均为5DB，快拍数为1000.(Array element spacing of half wavelength for the uniform distribution of 16 per linear array, pre-point disabilities into arcsin ((2i-1)/16) (i = l, 2, ..., 8) of 16 evenly weighted conventional beam, far-field has 5 unrelated target the source of emission signals, each source array relative to the position for the-40,-42,-5,30 and 33, and each signal source to the array signal to noise ratio the same as Scientology, are 5DB, snapshots for 1000)

说明：  理发师睡眠问题，实验课的作业，各位可以参考，还是不错的(Barber sleep problems, experimental studies homework assignment, you can refer to, or good)

粒子群算法OptSwarm记录每个粒子的历史最优解（粒子历史最好的适应度）以及全部粒子搜索到的全局最优解。用Wg代表全局最优解，W.,1代表每个粒子的历史最优解。粒子群初始化阶段表OptSwarm的前N行与表ParSwarm中的相同，而Wg的值为表ParSwarm中适应度值的最大值对应的行。(Particle swarm algorithm OptSwarm record the history of each particle the optimal solution (particles history best fitness), and all the particles of the search to the global optimal solution. Use Wg represents the global optimal solution, w., 1 representative of each particle history to the solution. Particle swarm initial stage OptSwarm table before the line and the table ParSwarm N is same, and the value of the ParSwarm Wg for tables in the maximum fitness value corresponding line. )

matlab的fopen函数用法matlab中fopen函数在指定文件打开的实例(The fopen function matlab matlab use in fopen function to open an instance of the specified file)

本函数是一个通过设置阈值，检测基本通信水印的函数(This function is a through setting threshold value, the function of the basic communication watermark detection)

ESPRIT_MATLAB，适用于DOA估计，程序简单易懂，非常适合于刚入门的同学(ESPRIT Algorithm)

MUSIC AND VOICE SEPARATION FOR DIFFERENT PITCH. SUBMITTED PARTIAL CODE

Local Histogram Equilizaion, its input is the mask size and returns the equilized image buffer

使用支持向量机进行数据的分类预测，所需样本数据较少，且预测精度高，分类效果较好。(Using support vector machines for data classification prediction, requires less sample data, and predict high precision, classification better.)