RSPe_NLFM_sin_Signal

这是加权网络中权重均匀分布，节点强度幂率分布的模型，可以画出度分步，强度分布图。(This is a weighted network, evenly distributed weight, power-law distribution of node strength of the model, you can draw the distribution of degrees and strength)

分别是一些MATLAB中有关图象处理的相关命令集(MATLAB were some relating to the relevance of image processing command set)

MATLAB软件中重要的函数的代码的源文件(An important function of MATLAB software, the code of the source file)

it is useful for my research work

运动目标跟踪程序-MATLAB源代码,利用camshift和meanshift实现运动目标的跟踪(Moving target tracking program - MATLAB source code, using camshift and meanshift to achieve tracking of moving targets)

说明：  共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcomings of the steepest descent method and avoids the need for the Newton method to store And the calculation of Hesse matrix and the inverse of the shortcomings, conjugate gradient method is not only to solve large-scale linear equations one of the most useful methods, but also solution for large-scale nonlinear optimization of one of the most effective algorithm. In a variety of optimization algorithms, conjugate gradient method is a very important one. The advantage is that the required storage capacity is small, with step convergence, high stability, and does not require any external parameters.)

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)

该文章主要是介绍滤波器的设计核心算法以及实例(This article mainly describes the design of filter core algorithm and example)

说明：  均匀线阵常规波束形成仿真 假设条件 窄带(单频)； 远场平面波(目标距离远大于阵长)； 无多途干扰； 信号无多普勒，无畸变。(Simulation of conventional beamforming of uniform linear array)

这个是基于matlab的二次型的最优化问题的课件，值得一看。(This is based on the matlab quadratic optimization problem of the courseware, worth a visit.)