DAP_OSR

输入一个句子后，通过点击按钮，可以得到本句子的单词个数与句子的个数(Enter a sentence, by clicking on the button, you can get the number of words in this sentence and the number of sentences)

Atef Elsherbeni书第一章中一个一维FDTD仿真程序实例(A one dimensional FDTD simulation example in the FDTD textbook written by Atef Elsherbeni in the first chapter.)

说明：  KPCA降维处理，完整的运行程序，包括数据和参考文献(KPCA dimensionality reduction processing, complete operating procedures, including data and references)

BPSK modulation simulation,may be useful for you!

小波变换模极大值重建信号的matlab源程序，来源于胡广书的《现代信号处理教程》附属光盘(wavelet transform modulus maxima method wavelet transform modulus maximum method wavelet transform modulus maxima applying)

TSL ESPRIT GTD方法针对一维像的散射点提取(TSL ESPRIT GTDMethod for one-dimensional scattering point like extraction)

以Rossler方程!Duffing方程和Vanderpol方程为例,应用Matlab仿真工具进行模拟, 并对仿真结果作了简要说明和讨论,其中的示例对混沌研究和教学有一定的意义.(To Rossler equation! Duffing equation and the van der Pol equation as an example, the application of Matlab simulation tool for simulation, simulation results and a brief description and discussion, including examples of research and teaching on the chaos has a certain significance.)

我再其它论坛上下得模拟退火算法，希望对搞这方面研究的朋友有帮助(I would get up and down the other forums simulated annealing algorithm, we want to engage in research in this area to help a friend)

Using Over complete subband

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)