MODAL

Program of publication "Fast Channel Shortening with Polynomial Weighting Functions" sub-pg_of_fig5and6

another progaram that has been in matlab

Threshold choice of non-coherent detection of on=-off keying (OOK) signal

说明：  遗传算法在参数优化中的应用，用来选择最优的参数值(Application of Genetic Algorithm optimization parameters used to the optimal parameter values)

冈萨雷斯数字图像处理全书源代码，包含.m文件以及函数，应用matlab编写(Gonzalez Digital Image Processing book source code, including .m files and functions, applications written in matlab)

一篇关于Insar相位展开的文章，对面分析和各种经典算法和最前沿的算法比较(An article on the Insar phase of the article, the opposite analysis and a variety of classical algorithms and the most cutting-edge algorithm)

说明：  介绍广义软件人的概念及其发展趋势，“软件人”不仅具有无形、静态“软件人”的特征和功能，而且具有拟人外部和内部动态特征和功能，具有拟人的动态特性与行为。(Generalized Soft Man)

期望最大化算法的ppt，很不错，个人用了，很受益啊。。。(EM algorithm)

将doubly stochastic matrix分解成一个排列矩阵的集合，并构造其概率分布(The doubly stochastic matrix is ​ ​ decomposed into a set of permutation matrix，at the same time，its probability distribution is constructed.)

MATLAB 牛顿下山法的一个小程序，原理就不详细说了。因牛顿迭代法受初值选取的限制，为防止迭代发散，对迭代过程再附加一项要求:|f(x(k+1))|<|f(x(k))|,将牛顿法迭代的结果:x(k+1) =x(k)-f(x(k))/f (x(k))和前一近似值x(k)适当加权平均做为新的改进值:x(k+1)=u*x(k+1) +(1-u)*x(k),其中u(0<> 迭代时u取1开始，逐次减半计算，直至附加要求符合为止。实例计算中x(k)=x(0)不变，只更新u和x(k+1)，直至:|f(x(k+1))|<|f(x(k))|（即|f(x(1))|<|f(x(0))|)，然后更新下山因子为u=1,继续以牛顿法迭代。(MATLAB Newton-down a small program, the principle is not explained in detail. Newton iteration by the initial value of the selected limit, in order to prevent additional iterative divergence, the iterative process a request: | f (x (k+1)) | < | f (x (k)) | Newton iteration Results: x (k+1) ' = X (k)-f (x (k))/f' (x (k)) and a previous approximation x (k), appropriately weighted average as a new improved value: x (k+1 ) = u* x (k+1) ' + (1-u)* x (k), where u (0 < u> iteration take successive calculated at 50 until the additional requirements found so far. examples of calculation of X ( k) = x (0) unchanged profile u and x (k+1, hungry) until: | f (x (k+1)) | < | f (x (k)) | (i.e. | f (x (1)) | < | f (x (0)) |), then profile downhill factor for u = 1, to continue to Newton iteration.)