FINAL_UTKARSH

说明：  this is matlab program uses GRNN neural network tocontrol the speed of Linear induction Motor Drive, both m-file and simulink file are includd

说明：  此例程可实现FMCW雷达数据处理得到距离。(This routine can realize the FMCW radar data processing to get the distance.)

这一个music 的DOA算法，可以说通俗易懂，结果也十分完美。我比较骄傲的一个。(music doa)

基础资料，我觉得对于初学者相当有用的。它是基于matlab环境开发的，可以在其中找到你具体想要处理的方面！(This is the fundamental material for the initial people who study the image processing. it is baed on Matlab code. People can find the specific knowledge which you want!)

关于kalman滤波的一个比较有代表性的例子，程序中把没有加高斯噪声和加过高斯噪声后进行滤波后的图形用不同颜色的曲线画在了一个图上，便于比较和理解(Kalman filter on a more representative example of the program to add Gaussian noise and processing not been filtered Gaussian noise after the graphic curves of different colors painted on a map, to facilitate comparison and understanding)

声纹识别，能够对说话人的声音进行识别，过程包括语音输入、预处理、特征提取、建立模型和匹配。(speaker Recognition)

algebraic reconstruction technique

这是ofdm的MATLAB仿真源程序，很好用(This is the MATLAB simulation ofdm source, useful)

this is a simple seed counter program.

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)