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Sensoer code is realted to loacalization that is written in matlab programming..

说明：  非常好用的程序，里面包括EMD分解和HHT谱分析的程序，可以很好地处理非线性数据(Very useful procedure, which include EMD and HHT spectrum decomposition process, can well handle nonlinear data)

对一维扰动信号进行数学形态学滤波程序，开闭操作函数均基于matlab编写(One-dimensional disturbance signal filtering procedures based on mathematical morphology , opening and closing operation functions are developed based on matlab )

图像信号，处理，比较流行的粒子滤波，对遥感影像处理有借鉴意义(PARTICL)

这是一个基于GUI系统。 GUI是在命令行调用通过键入“ui_start”。 GUI是自我解释性。 工具提示是由于在蓝颜色。将光标定位需要这些详细的说明。主要文件 “runsim.m”。在这些模拟的单变量的选择是没有明确给出。这是因为单输入单输出的结果为特定 调制类型显示在每一个用于比较的阴谋。(This is a GUI based system. The GUI is invoked from the command line by typing "ui_start". The GUI is self explanatory. Tool tips are given in blue color. The cursor needs to be positioned on these for detailed explanation. The main file is "runsim.m". In these simulations the SISO option is not explicitly given. This is because the SISO result for a particular type of modulation is shown in every plot for purposes of comparison. This software has been tested on MATLAB version 6.0 and above with signal processing and communication toolbox options.)

说明：  滤掉低频段和高频段频率，分波段滤波，过滤掉噪声干扰，绘制功率谱密度曲线(Filter out the low and high frequency bands, filter in different bands, filter out the noise interference, and draw the power spectral density curve.)

rgb color component analysis for image pprocessing porpese

通过M atlab 模拟了随机产生的一定概率分布的信号序列, 叠加高斯噪声后利用最大后验概率(MAP)检 测到的误码率, 做出BER~ SNR曲线, 并与理论计算得到的曲线进行比较, 两者吻合.(M atlab simulated by a certain probability distribution of random signal sequence, use of superposition of Gaussian noise is the maximum a posteriori (MAP) detection to the error rate, making BER ~ SNR curves obtained with the theoretical curve of the combined fit.)

常用的一些图像处理Matlab源代码,比如数字图像的傅立叶变换，二维离散余弦变换的图像压缩，采用灰度变换的方法增强图像的对比度，直方图均匀化，图像滤波等(Some commonly used image processing Matlab source code, such as digital image Fourier transform two-dimensional discrete cosine transform image compression method using gray transform image contrast enhancement, histogram equalization, image filtering, etc.)

neural network pro.output learning