LDPC-encdecPAWGN

利用SIMULINK 来实现16QAM的仿真，并分析QAM的系统性能。(16QAM using SIMULINK to realize the simulation, and analysis of system performance QAM.)

matlabx学习资料，是关于matlab编程的一个很好的参考(matlabx学习资料,是关于matlab编程的一... 计算机翻译: matlabx learning materials, is a very good about the Matlab programming reference)

已新测执行，可以实现算法编、解码算法，使用Matlab编写(The new test execution algorithm for encoding and decoding algorithm, using Matlab to write)

使用matlab分析传统光学系统加入三次相位掩模板之后的光学传递函数分析(Analysis using conventional optical systems matlab cubic phase mask after adding optical transfer function analysis)

二维FDTD程序，采用Mur吸收边界，实现了较好的吸收效果，可以用来与UPML吸收边界比较。(2D FDTD program, the Mur absorbing boundary to achieve a better absorption, can be used with UPML absorbing boundary.)

说明：  Matlab 是套應用於科學與工程領 域中數 值計算、分析與模擬的應用軟體，結合了 數 值分析、矩陣運算及繪圖等功能，功能強大、操作介面簡易 。在大學線性代數 及微積分課程中均可應用 Matlab 來 輔助學習。(Matlab 是??)

CPN网络，即对向传播网络，是将Kohonen特征映射网络与Grossberg基本竞争网络相结合，发挥各自特长的一种新型特征映射网络。(CPN network, the right to communication networks, Kohonen feature is the mapping of network and basic Grossberg competing networks combined play to their strong points of a new feature mapping network.)

一种基于matlab/simulink的风力力发电建模系统，里面有非常多实用的模型能直接使用 (A wind power generation modeling system based on matlab/simulink, there are very practical model can be used directly)

simple microprocessor that gives the greatest common divisor of 2 (4bit) numbers

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)