Blind_recognition_STBC

在详细介绍各种命令的同时,向读者介绍了MATLAB在高等数学,线性代数,图形图像处理等方面的应用和外部接口.(In detail the various commands at the same time, introduced to readers MATLAB in higher mathematics, linear algebra, graphics, image processing and other applications and external interfaces.)

标准粒子群算法PSO，matlab实现，处理彩图或灰度图(Standard particle swarm optimization PSO)

与左右手运动想象相关程序，涉及burg算法，功率谱估计(With the left and right hand movement imagination related procedures involving burg algorithm, power spectrum estimates)

SNOPT 大型非线性规划软件windows系统32位 64位均有(SNOPT Nonlinear Program )

航位推算代码，内含三种常见的平滑处理方法代码 内含部分数据(Dead reckoning code, containing three common smoothing method code)

时间序列数据分析中的梅林变换工具，数据包传送源码程序，wolf 方法计算李雅普诺夫指数。( Time series data analysis Mellin transform tool, Data packet transfer source program, wolf calculated Lyapunov exponent.)

基于s-function的DFIG模型，输入dq轴电压、转差、驱动转矩，输出dq轴电流、电机转速(s-function model of DFIG based input dq-axis voltage, slip, drive torque, output current dq-axis motor speed)

Description All the filters have needed neighbor data the current pixel in the image,here this function(res_window=cover_window(how many neighbor value for row ,how many neighbor value for column ,input image) produce the result( res_window(row,col,total size)) input inp_img=2d data(row * col) w=cover size w=[3 3] = 3x3 cover pixel generation output o_img=result 3d o_img(w(1),w(2),r*col) if inp_img=[1 2 3 4 5 6 ] w=[3 3] Soloution it will construct new image new_img=[0 0 0 0 0 0 1 2 3 0 0 4 5 6 0 0 0 0 0 0] result o_img(:,:,1)=[0 0 0 0 1 2 0 4 5] o_img(:,:,2)=[0 0 0 1 2 3 4 5 6] finally o_img(1:3,1:3,6) example a=imread( pears.png ) b=rgb2gray(a) c=cover_window( b,[3 3]) 3x3 window construction

在matlab开发平台上，关于gui界面设计的参考资料，包含很多应用案例。(On matlab development platform, about gui interface design reference, contains many application cases.)

Carrier-phase synchronization can be approached in a general manner by estimating the multiplicative distortion (MD) to which a baseband received signal in an RF or coherent optical transmission system is subjected. This paper presents a unified modeling and estimation of the MD in finite-alphabet digital communication systems. A simple form of MD is the camer phase exp GO) which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. We assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly-varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme.