EEG-power-spectrum-estimation-

说明：  matlab读取nc格式的插件，直接放入matlab的toolbox文件夹下，保存路径可调用函数(Matlab reads the plug-in in NC format and directly puts it into the toolbox folder of MATLAB. The saved path can call the function)

针对盲模拟调制信号的自动识别算法，利用了盲信号识别中常用的gamma、ap、dp、P等参数，并附送dsb、lsb、usb、am、fm信号的生成程序(Automatic analogue modulation recognition using MATLAB. There is also a function to generate dsb,lsb,usb,am,fm signal)

该工程实现对单载波系统QPSK的频偏估计算法。调相信号在接收机相干解调时需要同频同相的参考载波，而发射机和接收机之间往往存在频率偏差，为了消除这种误差，必须在软件层面实现。使用了QPSK四相调制的信号特点进行载波频偏估计，经验证，效果较好。(This project implements frequency offset estimation algorithm for single carrier system QPSK. The phase modulation signal needs the same frequency reference carrier in the coherent demodulation of the receiver, and the frequency deviation often exists between the transmitter and the receiver. In order to eliminate this error, it must be implemented on the software level. The carrier frequency offset estimation is carried out by using the signal characteristics of QPSK four phase modulation.)

TO PLOT THE RAYLEIGH DISTRIBUTION PLOT

这个程序是DSP TMS320F2812与主机通过USB口进行通讯的c语言程序(This procedure is DSP TMS320F2812 and host USB port to communicate through the c language program)

基于matlab的原模图仿真代码程序，有需要的可以在这上面改进(Simulation code program of original model graph based on MATLAB)

单相并网逆变器代码，PI控制加电网电压前馈控制，LCL滤波器(Single-phase grid-connected inverter code, plus feedforward control PI control grid voltage, LCL filter)

说明：  载波同步仿真，使用的是反馈补偿法，接收信号经补偿矩阵，误差提取，环路滤波，相位累加器后反馈回补偿矩阵，稳定后估计相位趋近相位差，存在相位模糊情况。(the simulation of the carrier synchronization)

说明：  ofdm 信道估计 16qam MMSE估计 对比了块状导频和梳状导频的区别(ofdm channel 16qam MMSE compare with block pilot and comb pilot)

说明：  序贯滤波程序： 卡尔曼滤波算法（Kalman filtering）一种利用线性系统状态方程，通过系统输入输出观测数据，对系统状态进行最优估计的算法。由于观测数据中包括系统中的噪声和干扰的影响，所以最优估计也可看作是滤波过程。 　　序贯算法又叫序贯相似性检测算法，是指图像匹配技术是根据已知的图像模块(模板图)在另一幅图像(搜索图)中寻找相应或相近模块的过程，它是计算机视觉和模式识别中的基本手段。已在卫星遥感、空间飞行器的自动导航、机器人视觉、气象云图分析及医学x射线图片处理等许多领域中得到了广泛的应用。研究表明，图像匹配的速度主要取决于匹配算法的搜索策略。 　　(Sequential filtering procedures: Kalman filtering (Kalman filtering) is an algorithm that uses the state equation of a linear system to estimate the optimal state of the system through the input and output observation data of the system. Because the observation data include the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process.)