卷积盲源分离（2 source 基于能量相关排序）

列出了matlaba最常用的工具箱函数，包括最小二乘函数，样条函数等等(Lists the most commonly used matlaba toolbox function, including least-squares function, and so on spline function)

关于线性打靶法的GUI文件,函数方程用shootingfun.m文件(linear shooting method on the GUI, function equation shootingfun.m documents)

信道估计算法及实现,matlab编写(Channel estimation algorithm and realize, matlab prepared)

基于matlab三角波的写法及改进的载波设计。(Based on the wording of the triangular wave Matlab and improved carrier design。)

一个简单的在matlab上实现logistic方程，并花出图像。(A simple implementation in matlab on logistic equation, and the flower images.)

这是一本介绍MAtlab基础和编程的书籍，对于初学者来说是一本极好的教程。(It is a basic introduction of MAtlab and matlab programming. For the matlab beginners,it s indeed an excellent tutorial.)

说明：  小波插值（全小波插值，小波双线性插值，小波双三次插值及其改进）放大图像，提高图像的分辨率；对插值图像做质量评价（峰值信噪比PSNR；均方信噪比SNR）(Wavelet interpolation (full wavelet interpolation, wavelet bilinear interpolation, cubic interpolation and improved method) to enlarge the image and improve image resolution assesse the interpolation image (peak signal to noise ratio PSNR mean square signal to noise ratio SNR))

这5个小程序分别为：用Monte Carlo随机法圆柱相交体的体积；用刘徽割圆法计算pi；梯形法.辛普森法计算圆周率；蒲丰掷针实验程序；线性函数的迭代(This five small programs were: using Monte Carlo random method cylinder intersects the volume cyclotomic method with Liu Hui pi trapezoidal method Simpson method to calculate pi Buffon throw needles experimental procedures linear function of iteration)

实现Po计算电磁散射单站计算，对于一般的目标可进行简单的计算。(it can be used to calculate the monostatic RCS of the object in matlab)

“Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。(In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.)