LEACH-Test

该程序用于求解时间延迟t和嵌入维数m，该方法不同于自相关函数法和gp法，可以同时求解出两个量t和m的值，非常方面，在求解lyapunov指数时经常用到该方法(The procedure used to solve the time delay t and the embedding dimension m, the method is different from the self-correlation function gp method can solve the two quantities t and the value of m, the very aspect, often used to solve the lyapunov exponentthe method )

粒子群matlab程序，大家下载下来觉得好的话，一定要留言阿，包含粒子群优化，是用matlab编的。(PSO Matlab procedures, we feel good downloaded down, we must voice A, include PSO is using Matlab series.)

LDPC编码译码的Matlab代码，主要考察吞吐量随信噪比变化的关系。demo文件运行程序，其他文件为自己编的函数。(The LDPC codec Matlab code, mainly on the throughput changes with the signal-to-noise ratio. demo file to run the program, and other documents made themselves function.)

matlab编写的sierpinski地毯程序(sierpinski carpet written matlab program )

波束域降阶Root-MUSIC算法,在子阵分解降阶的基础上,进一步将多项式的求根阶次降到与源数目相同,提出了求解降阶多项式的方法。该方法最大程度地减少了求根多项式的阶次和求解多项式的次数,并保持了Root-MUSIC算法的优良性能。显著降低运算量是其最大优点,仿真结果验证了降阶算法的有效性,并比较了不同阶次波束域Root-MUSIC算法的估计性能。 (Beamspace Reduced Order Root-MUSIC algorithm, based on the reduced-order sub-array decomposition further order of the polynomial rooting down with the same number of sources, proposed to solve the reduced-order polynomial method. This method minimizes the number of polynomial order and solving polynomial rooting and maintain the excellent performance of Root-MUSIC algorithm. Significantly reduce the amount of computation is its biggest advantage, simulation results show the effectiveness of the reduced-order algorithm, and compare the performance of different estimates of the order beamspace Root-MUSIC algorithm.)

经典的灰度共生矩阵纹理计算方法，基于matlab平台实现(The classic method of calculating GLCM texture, based on matlab platform)

This function produces a numerical approximation to 2D Gabor function

说明：  matlab编程引擎(matlab programming engine)

第七章 IIR滤波器设计 数字滤波器：将输入序列通过一定运算，变换成输出序列的离散时间系统（数字网络）。 (Chapter 7 IIR filter design digital filters: input sequence is converted into the output sequence of discrete time system (Digital Network) through certain operations.)

MATLAB demo for paper "Visual Tracking with Online Multiple Instance Learning" by Boris Babenko, Ming-Hsuan Yang, Serge Belongie ,CVPR 2009, Miami, Florida. (MATLAB demo for paper "Visual Tracking with Online Multiple Instance Learning" by Boris Babenko, Ming-Hsuan Yang, Serge Belongie ,CVPR 2009, Miami, Florida.)