Selective-Harmonic-Elimination

基于HHT方法的模拟调频信号的hilbert谱分析(HHT-based method of analog FM signal hilbert spectral analysis)

关于信道容量及误码率，里面M文件较多，大家自己去看看吧(On channel capacity and error rate, which M file more, we go and see their own)

KPCA程序，可用于数据降维，特征提取，用起来比较简单(KPCA procedure can be used for data dimensionality reduction, feature extraction, using relatively simple)

a code which export a letter/array to serial port

控制台程序，实现matlab中xcorr2（）函数，实现二维相关运算(xcorr2())

人体舌苔的光谱学研究。。运用光谱法获得表面反射光谱数据。(spectroscopic study of human tongue)

three motor simulation work in matlab algorithm

BCH，LPDC,walsh编解码源程序代码，在视频传输中应用广泛(BCH ，the LPDC ，walsh codec source code, widely used in video transmission)

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)

约翰 列侬的轻音乐代码 真的很好听的哦 感受一下matlab的强大功能(John Lennon Easy Listening code really good to hear feel matlab powerful)