MATLAB7ImageProcessing

Here is a code for image restoration after compression. Using this you can calculate system parameters like PSNR, BER, CR

matlab绘制图形，绘制圆形双心圆以及圆环。(matlab draw graphics, draw the circular double heart round ring)

异步电机矢量控制仿真模型，采用间接磁场定向控制策略，电机模型没有采用matlab自带电机，而是根据两相旋转坐标系下的电机方程自己搭建了一个电机模型。(Induction motor vector control simulation model, using the indirect field oriented control strategy, the motor model comes with no motor using matlab, but according to the two-phase rotating coordinate system of the motor equations built himself a motor model.)

BPSK在AWGN信道下,不同的采样率下不同的EB/NO的误码率仿真(failed to translate)

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)

good document for modeling LIM

阈值变换，小波变换，从前人的基础上改进变换，使得信号噪声压制的很低。(Threshold transform, wavelet transform, improve the conversion of people on the basis of the past, so that the signal noise suppression is very low.)

MATLAB数据处理教程，专门针对初学者的讲解，方便易懂(MATLAB data processing tutorial explaining specifically for beginners, easy to understand)

说明：  基于EM算法，可以估计在混合高斯分布下的三个参数(EM expection)

说明：  dfig有功无功解耦控制matlab模型(Matlab model of DFIG active and reactive decoupling control)