medfilt3

matlab仿真出光栅函数的菲涅尔衍射，其中有四副图，原图，二维衍射图样，函数图象和三维衍射图样(matlab simulation of the Fresnel diffraction grating function, including four plans, artwork, two-dimensional diffraction patterns, functions, images and three-dimensional diffraction pattern)

说明：  《MATLAB 7.0编程基础基础》源程序，适合初学者使用，第3章(&quot MATLAB 7.0 programming foundation base&quot source, suitable for beginners, Chapter 3)

说明：  Matlab教学用，适合初学者，描述最基本的指令及应用(Matlab teaching for beginners, the basic description and application instructions)

使用hamming和随机线性码对数据进行编解码，在二进制堆成信道上进行传输，比较仿真与理论误码率的图线(Use hamming and random linear codes for data encoding and decoding, piled in binary transmission channel, comparing simulation and theoretical BER plot)

多目标跟踪时的逻辑航迹起始算法，用于跟踪航迹起始，仿真效果不错(Logical starting algorithm of multi-target tracking )

其中给出了多种调制方式的调制及解调程序，对初学者有一定帮助。(Which gives a wide range of modulation of the modulation and demodulation process, has a certain extent, help for beginners.)

该 程序是将经验模态函数分解为特征模态函数的一种有效方法(emd is emperical module decomposition it can decompose the signal into characteristic signals and it is an effective method.)

Correlation between interferences

采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波(2D median filtering function medfilt2 right by the Salt and Pepper noise image filtering)

Source separation problems in digital signal processing are those which are used to separate useful signal a mix of signals of interest, other signals and noise. The typical example of a source separation problem is the cocktail party problem, where many number of peoples are talking simultaneously in a room and a listener is trying to follow one of the discussions. The human brain can separate the voices of different people (source separation) using neurons in the auditory region. But source separation using digital signals is difficult task with the available techniques. Several approaches have been proposed for solving this issue but most of the techniques are still in development stages. Some of the successfully tested approaches are principal components analysis (PCA) and Independent components analysis (ICA). Both the approaches work well when there are no delays or echoes present