numberRecognition

matlab新增寻找极值点程序，较老的版本中没有次函数(matlab find new extreme point of procedure, not the older version of the second function)

说明：  dijkstra算法求最短路径及路径标号的matlab实现(dijkstra，shortest path)

图论中KMB算法，用于解决加权节点的Steiner树的问题。(KMB graph theory algorithms for solving weighted Steiner tree problem nodes.)

应用ADI方法对海洋原始方程组数值离散，再在理想湖泊的假设前提下，结合物理海洋学，有限差分法，利用MATLAB数学软件，进行三维数值模拟，以期对海洋数值模式做出最基础的实现，并从动力机制上分析，验证其模拟结果的近似准确性。( ADI method is applied to Marine primitive equations, numerical discrete, again under the ideal assumptions of lakes, combining with the physical oceanography, finite difference method, using mathematical software MATLAB, the 3-d numerical simulation of the ocean numerical patterns in order to make the most basic implementation, and from the analysis on dynamic mechanism, simulation results verify its approximation accuracy. )

二维码扫描应用demo，精选ios编程学习源码，很好的参考资料。(Two dimensional bar code scanning application demo, select learning IOS programming source code, a good reference.)

说明：  matlab实现dfig并网，亲测可以运行出结果。(MATLAB realizes DFIG grid connection, and pro-test can run the results.)

wcdma simulation without rake receiver.

this expalins gray pridiction algo for localisation method...........................

Complete simulation in matlab of loss of a signal, diffraction, Friss, and measure of Fresnell zones

Locally-Linear Embedding (LLE)[9] was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems. LLE also begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describe the point as a linear combination of its neighbors. Finally, it uses an eigenvector-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model.