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差分法比较曲线的形状相似度，接着筛选出有用曲线求相位差，给了原始数据，可以直接运行(Difference Method for curve shape similarity, then filter out the demand curve useful in phase, to the raw data, can run)

用Matlab进行曲面的插值和拟合，来进行数值优化(Matlab surface interpolation and fitting, for numerical optimization)

关于Matlab的编程，由浅入深，很好的参考书，(On Matlab programming, Deep good reference book,)

图像去噪123456789 matlab(failed to translate)

关于信源编码和信道编码的源程序，信道编码包括NRZ码及其频谱分析，信道编码是关于RS码的分析(About the source code of the source coding and channel coding, channel coding NRZ code and its spectrum analysis, channel coding on the analysis of RS code)

说明：  压缩感知的基于正交化重构算法,该算法用matlab实现，(Orthogonalization compressed sensing reconstruction algorithm, the algorithm using matlab,)

合成孔径雷达（SAR)目标成像仿真，粒子图像分割及匹配均为自行编制的子例程，isodata 迭代自组织的数据分析，时间序列数据分析中的梅林变换工具，一些自适应信号处理的算法，关于神经网络控制，IMC-PID是利用内模控制原理来对PID参数进行计算。( Synthetic Aperture Radar (SAR) imaging simulation target, Particle image segmentation and matching subroutines themselves are prepared, Isodata iterative self-organizing data analysis, Time series data analysis Mellin transform tool, Some adaptive signal processing algorithms, On neural network control, The IMC- PID is using the internal model control principle for PID parameters is calculated.)

去噪为仿真信号建立； 去噪1为eemd去噪； 去噪2为形态滤波器； 去噪3为小波去噪，全局阈值，给定阈值（软阈值，硬阈值）； 去噪4为实际去噪(2 for morphological filter denoising Denoising 3 as the wavelet denoising, global threshold, the given threshold (soft threshold and hard threshold) 4 for practical denoising denoising)

最新台湾中央大学HHT程序；包含一个实例，EMD工具和EEMD工具；可以求瞬时频率(A few examples of how to use these programs are given, with a given dataset “gsta.dat”, which is the annual mean global surface temperature anomaly. In “gsta.dat”, the first column is the time and the second is the corresponding data value.)

OTSU Gray-level image segmentation using Otsu s method. Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern. 9:62-66 1979). Thresholds are computed to maximize a separability criterion of the resultant classes in gray levels. OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the corresponding Iseg is therefore a binary image. The pixel values for Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ... [Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability criterion within the range [0 1]. Zero is obtained only with images having less than n gray level, whereas one (optimal value) is obtained only with n-valued images.