moxing

一种常用的线性搜索技术，快速精确希望对大家有所帮助。(A common linear search technology, quickly and accurately hoping help you.)

JPEG 仿真 在Matlab7 上运行，效果最佳(The code of JPEG.If some one want using it,please download。)

计算方法中，拉格朗日插值法的源代码。在最后，绘出了拉格朗日插值法的图像和原图像的比较。原创。(Calculation method, Lagrange interpolation source code. In the end, draw a comparison image and the original image Lagrange interpolation. Original.)

STC跟踪算法，matlab源码，可实现对框定目标的有效稳定跟踪(STC tracking algorithm, matlab source code, can achieve the goal of effective framing stable tracking)

matlab自编函数库，含有大量常用函数。。。。。。。。。。。。。。。。(self matlab function library containing a large number of commonly used functions. . . . . . . . . . . . . . . .)

matlab code for compressed sensing

基于数字地球的车载卫星导航定位的设计与实现(Digital Earth s satellite navigation and positioning the vehicle design and implementation)

说明：  用MATLAB生成bpsk信号，并通过AWGN信道的仿真(MATLAB bpsk signal generation, and through the AWGN channel simulation)

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

blind source seperation