fastica

matlab的命令大全，很详细，对各个命令的解释很全面，附带有例子(matlab command Daquan, very detailed, very comprehensive explanation of each command, with the example of)

The Gaussian blur is a type of image-blurring filter that uses a Gaussian function (which also expresses the normal distribution in statistics) for calculating the transformation to apply to each pixel in the image

COMPARISON OF MEAN AND MEDIAN FILTER METHOD

基于3GPP 25.996 的简化SCM信道模型，可用来生成基于SCM信道的仿真实时的信道增益的产生，到达角 给出了拉普拉斯分布 均匀分布与标准的20个子径的分布三种不同的形式。(Based on 3 GPP 25.996 simplified SCM channel model, based on SCM channel can be used to generate a simulation of real-time channel gain, arrival Angle gives the Laplace distribution uniform distribution and standard 20 is a diameter of three different forms of distribution)

SSBsc simulink model

MICA软件包，可在MATLAB 5或MATLAB 6环境下运行(MICA package, MATLAB 5 or in the MATLAB 6 environment running under)

关于BPSK的误码率的MATLAB仿真程序。是在高斯信道（AWGN)下的仿真，能够生成图像形式。(BPSK bit error rate on the MATLAB simulation program. In the Gaussian channel (AWGN) under the simulation, can generate the image forms.)

这是在AR模型的功率谱估计，对比经典谱估计有明显的优点，是一种新的估计。(This is the AR model of the power spectrum estimation, contrast classical spectrum estimation has obvious advantages, is a new estimate.)

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)

for set Plotting Map