work

这篇代码介绍了一种阵列信号处理的一种算法，具有很大的作用。(This code describes an algorithm for an array signal processing, has a big role.)

图像的5/3小波变换的matlab代码。程序简短，易懂。(Image 5/3 wavelet transform matlab code. Program brief and easy to understand.)

本书简明扼要地介绍了时间序列及其相关领域的基本概念和基本理论，对ARMA序列预测、时间序列的统计分析、时间序列的时频分析和时间序列的小波变换等给出了有关分析计算方法，结合MATLAB编程应用，介绍了MATLAB时间序列分析有关函数的功能和用法，阐述了如何利用这些函数解决工程应用中的问题。(This book succinctly describes the time sequence and its related fields of basic concepts and theories of the ARMA series forecasting, time series statistical analysis, time series analysis and time-frequency wavelet transform of time series analysis and calculation are given about methods, combined with MATLAB programming applications, the introduction of time series analysis MATLAB functions about functions and usage, explains how to use these functions to solve engineering problems in the application.)

小波阈值去噪 软阈值去噪和硬阈值去噪的比较(Wavelet thresholding soft thresholding and hard thresholding comparison)

说明：  Gabor小波提取特征,然后用支持向量机作分类器，可以用于掌纹，人脸，指纹识别(Gabor wavelet is used to extract features, and then SVM is used as classifier. It can be used for palmprint, face recognition and fingerprint recognition)

MATLAB实现的高斯平滑滤波源代码，用了高斯函数的定义来做，附实例，效果很好(gauss filter by matlab)

具有提升分解结构的整数离散变换，可用于基于提升格式的小波变换设计。(Enhance the breakdown structure of the integers with the discrete transform can be used for wavelet transform based on lifting scheme design.)

用MATLAB编程实现实现滤波，与数字信号处理相关(Implementing filtering)

此为提供初学计算电磁学应用於色散介质的人，一维色散介质-Lorentz的程序(It is fortran code to provide a chance to learn the computational EM wave application of dispersive media for Lorentz model)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing