matlab_6_x

使用matlab编程，实现文件数据游程编码、（7，4）线性码、模拟二元传输信道(Using matlab programming, file data length coding, (7,4) linear code, binary transmission channel simulation)

基于matlab的彩色图像分割，方法是K-means聚类分割(Matlab-based color image segmentation method is K-means clustering segmentation)

主要分析了ofdm系统中空时频分组编码的性能，并比较了st－ofdm与sft－ofdm(major analysis of the OFDM system at the frequency of hollow block coding performance, and compared st-OFDM and sft-OFDM)

it will help the beginners to understand digital image processing using matlab

matlab作图程序总汇，相当的好，包含matlab 源程序与课件(matlab plot summary procedure, rather good, and courseware that contains the source code matlab)

说明：  基于RLS算法的自适应滤波器的MATLAB程序，有5个程序！！！(RLS adaptive filter algorithm based on the MATLAB program, there are five programs! ! !)

GPS信号P码生成算法设计及仿真实现,介绍P码生成原理和MATLAB仿真实现(Code generation algorithm for GPS signal P Design and Simulation, introduced the principle P and MATLAB simulation code generation to achieve)

语音信号的盲信号分离算法研究与应用的matlab程序代码(Blind signal separation algorithm research and application of speech signal matlab code)

鲁棒控制与鲁棒控制器设计: 1、线性二次型 Gauss 控制 2、鲁棒控制问题的一般描述 3、鲁棒控制器的计算机辅助设计 4、新鲁棒控制工具箱及应用 5、分数阶控制系统分析与设计(Robust control and robust controller design: linear quadratic Gauss control, a general description of the robust control problem, the computer-aided design of robust controller 4 new Robust Control Toolbox and Application 5 Fractional Control System Analysis and Design)

denoise In BayesShrink[5] we determine the threshold for each subband assuming a Generalized Gaussian Distribution(GGD) . The GGD is given by GG¾ X ¯ (x) = C(¾ X ¯ )exp¡ [® (¾ X ¯ )jxj]¯ (6) ¡ 1 < x < 1 ¯ > 0, where ® (¾ X ¯ ) = ¾ ¡ 1 X [ ¡ (3=¯ ) ¡ (1=¯ ) ]1=2 and C(¾ X ¯ ) = ¯ ¢ ® (¾ X ¯ ) 2¡ ( 1 ¯ ) and ¡ (t) = R1 0 e¡ uut¡ 1du. The parameter ¾ X is the standard deviation and ¯ is the shape parameter It has been observed[5] that with a shape parameter ¯ ranging from 0.5 to 1, we can describe the the distribution of coefficients in a subband for a large set of natural images.Assuming such a distribution for the wavelet coefficients, we empirically estimate ¯ and ¾ X for each subband and try to find the threshold T which minimizes the Bayesian Risk, i.e, the expected value of the mean square error.