HDB3codingEqulizion

用基于拉格朗日函数Hesse矩阵的SQP方法求解约束优化问题(matlab: SQP based on modification Hesse matrix)

cooperative communication

Multiscale performance

阐述vs2008如何调用matalb2009b生成的DLL的文档，自己摸索的经验，供大家参考(Vs2008 described how to call matalb2009b generated DLL in the document, find their own way of experience, for your reference)

基于Mtalab实现的指纹特征提取的实例(Mtalab realize fingerprint-based feature extraction examples)

qpsk经典小程序，调通了的程序，欢迎大家下载哦(qpsk classic applets, tune talked procedures, welcome to download oh)

一个关于多目标优化算法的程序，并有说明资料(A multi-objective optimization algorithm on the program, and descriptive information)

AWGN信道环境下，信道编码的性能分析（包括误码率BER和开销即运行时间），运用了Hamming Code Golay Code Circle Code and Converlute Code(channel encode including Hamming Code Golay Code Circle Code and Converlute Code in AWGN channel)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)

OFDM的仿真程序，经过瑞利多径的仿真。其中子载波数为4.(OFDM simulation procedure, after the simulation of multipath Rayleigh. Subcarriers of which the number of 4.)