VF-for-DSN

基于独立成分分析的多声源分离程序包，涵盖了大部分独立成分分析的函数,经验证能够有效解决多声源分离问题。(Based on independent component analysis of multi-source separation package, covering most of the functions of independent component analysis.)

说明：  MATLAB语言常用算法程序集。第6章 矩阵特征值计算(MATLAB language commonly used algorithm for assembly. Chapter 6 matrix eigenvalue calculation)

constancy gui using white patch

大斜视条带合成孔径雷达仿真程序，包括回波仿真(Large squint synthetic aperture radar simulation program, including echo simulation)

Matlab语言编写的2维粒子群算法进行函数最优化的源代码，并附有AVI视频演示！(2-D particle swarm optimization source code for optimization (with the AVI video!))

source code for Viterbi Algorithm.

This file has the codes which calculate tha characteristics of yagi-yuda antenna.

Representation of a Dalvik class, which is basically a set of members (fields or methods) along with a few more pieces of information.

自己编程实现的MATLAB gui界面，能实现对.edit框中输入数值的加1，减一，乘2，除2，等相关操作。源码见压缩包。(The MATLAB gui own programming interface, can achieve. Edit box, enter the value plus 1, minus one, multiplied by two, in addition to 2, and other related operations. See the source tarball.)

A Novel Image Reconstruction Algorithm Based on Concatenated Dictionary This paper presents a novel method for image reconstruction, which decomposites the image by concatenating a redundant dictionary of several bases and then reconstruct the image efficiently by means of Matching Pursuit algorithm. The proposed method constructs the concatenated dictionary with cosine bases, wavelet bases and contourlet bases, which will lead to a better approximation of the original image. The experimental results show that the proposed algorithm can greatly reduce the computational complexity and generate a better reconstruction effect compared with previous methods.(A Novel Image Reconstruction Algorithm Based on Concatenated Dictionary This paper presents a novel method for image reconstruction, which decomposites the image by concatenating a redundant dictionary of several bases and then reconstruct the image efficiently by means of Matching Pursuit algorithm. The proposed method constructs the concatenated dictionary with cosine bases, wavelet bases and contourlet bases, which will lead to a better approximation of the original image. The experimental results show that the proposed algorithm can greatly reduce the computational complexity and generate a better reconstruction effect compared with previous methods.)