随着故障分析在Matlab的Simulink的功率流的研究

此matlab程序，用来检测并定位人脸及人眼。采用的算法是肤色的颜色分隔。附件中的文件包括 eyematch.m, eyematch2.m, face.m, findeye.m,skin.m, k001.JPG等等(This matlab procedures used to detect and locate human faces and the human eye. Algorithm used in the color of skin are separated. Attachment of documents including eyematch.m, eyematch2.m, face.m, findeye.m, skin.m, k001.JPG and so on)

digital map which is realized by processing matrix data in matlab toolbox(digital map which is realized by processin g matrix data in Matlab toolbox)

将整个空间进行划分，变成无数的小空间，其中小空间的长度可变。(The entire space is divided into numerous small space, wherein the variable length of the small space.)

有效观察OAM的相位模式，可自由设定拓扑荷数(Observing the phase mode of OAM effectively, the topological charge can be set freely.)

用模糊预测混沌时间序列预测方法的matlab程序，很好用(Mackey-Glass Time Series Forecasting using Method 1 Single Stage Fuzzy Forecaster)

利用matlab写成的窄带噪声发生，应用小区域方差对比，程序简单，自己编的5种调制信号，分数阶傅里叶变换计算方面,LDPC码的完整的编译码，实现了对10个数字音的识别程。

MATLAB7的基础性教程，里面内容很全，非常适合新手使用(MATLAB7 basic tutorial, which are very informative, very suitable for new users)

声音信号的功率谱分析。。。。。。。。。。。。。。(Spectral analysis of the power of the sound signal.)

单点采用能量检测法，局限性大，于是采用多点分布式合作检测(Single point of using energy detection method, the limitations of large, so the detection of multi-point distributed cooperation)

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)