finalMAIN

测试ADC的各个动态参数，如SNDR,THD,SFDR,ENOB,INL,DNL(ADC dynamic parameters of the various test)

星历数据读取文件，用来读取GPS获得的星历数据，以便计算。(Star calendar data reading files, to read the GPS get star calendar data, for the calculation.)

分别实现由MVDR和RLS实现了单波束形成（signal beamfroming using MVDR and RLS）(Realize, respectively, by the MVDR and RLS realize a single beamforming (signal beamfroming using MVDR and RLS))

这是关于“噪声调频信号”的仿真模型，已经编译好，直接运行即可(This is on the )

Simulation platform for uncoded MIMO-OFDM

实现了定子磁链定向的DQ坐标系下的双馈电机模型(Stator flux oriented to achieve the DQ coordinate system doubly-fed machine model)

Elman神经网络：Elman网络是 J. L. Elman于1990年首先针对语音处理问题而提出来的, 它是一种典型的局部回归网络( global feed for ward l ocal recurrent)。Elman网络可以看作是一个具有局部记忆单元和局部反馈连接的前向神经网络。Elman网络具有与多层前向网络相似的多层结构。它的主要结构是前馈连接, 包括输入层、 隐含层、 输出层, 其连接权可以进行学习修正 反馈连接由一组“结构 ” 单元构成,用来记忆前一时刻的输出值, 其连接权值是固定的。在这种网络中, 除了普通的隐含层外, 还有一个特别的隐含层,称为关联层 (或联系单元层 ) 该层从隐含层接收反馈信号, 每一个隐含层节点都有一个与之对应的关联层节点连接。关联层的作用是通过联接记忆将上一个时刻的隐层状态连同当前时刻的网络输入一起作为隐层的输入, 相当于状态反馈。隐层的传递函数仍为某种非线性函数, 一般为 Sigmoid函数, 输出层为线性函数, 关联层也为线性函数。(Elman neural network: Elman JL Elman network is first proposed for speech processing problems in 1990. It is a typical local regression network (global feed for ward l ocal recurrent). Elman network can be seen as having a forward neural network of local memory unit and local feedback connections. Elman network has a multi-layer multilayer structure similar to the network. Its main structure is a feedforward connection, comprising an input layer, a hidden layer and output layer, which can be connected right learning correction feedback connected by a set of " structural" units, the output value of the memory for the previous time, which connection weights are fixed. In such a network, in addition to ordinary hidden layer, there is a special hidden layer, called an associative layer (or contact layer unit) this layer receives feedback signals the hidden layer, each hidden layer node has an associated layer node corresponding connection. The role of the associated layer is fo)

This code simulates Shortest Path Routing protocol that resides in network layer of a Network Architecture

说明：  此程序能够处理流体力学中顶盖驱动流的动力学过程，并用程序形象的描述出来。(This program can handle lid-driven flow of fluid mechanics, the dynamic process, and describe the image with the program.)

八单元线阵的DOA估计的MUSIC算法实现。可以看出music方法分辨率较高。这里用的是36度方向下的weight,自己也可以尝试其他的天线weight值。 (eight modules Array DOA MUSIC algorithm. Music can be seen higher resolution method. Here is the direction of 36 degrees under the weight, they could also try to the other antenna weight values.)