MATLAByuchekongzhi

可以按一定规律插入一些数字的一个很小的程序(Can insert a certain number of rules a small program)

用reweighted L1优化进行压缩感知的信号重建算法(Optimized by reweighted L1 signal is compressed sensing reconstruction algorithm)

matlab仿真作业，使用simulink，包括lorenz方程、卫星轨道、调制与解调(matlab simulation operations, the use simulink, including lorenz equation, satellite orbit, modulation and demodulation)

该源码是采用dspbuilder进行设计，目的是完成一个为随机m序列校验器，很有用看看就知道了(The source is dspbuilder design, the purpose is to complete a sequence of random m validator, useful look at the know)

Newton-Raphson算法 介绍 在科学计算和财经工程领域，许多数值算法都是通用的（至少在理论上是），可广泛地用于解决一类问题。一个大家熟悉的例子就是Newton-Raphson例程，它可用来寻找方程 f(x)=0的数值解。标准的数学表达式f(x)表示f是变量x的函数，其通常的表达形式为f(x,a,b,...)=0，f被定义为多于一个变量的函数。在这种情况下，Newton-Raphson算法试图把x以外的变量固定并作为参数，而寻找关于变量x的数值解。 由于Newton-Raphson算法需要知道被求解函数的确切表达，其传统实现方法是直接将代码嵌入到客户应用程序中。这就使得算法的实现代码经过针对不同被求解函数的少量修改后在客户程序中反复出现。 同许多其它数学例程一样，Newton-Raphson算法的具体实现是应该与特定用户无关的。并且，重复编码在任何情况下都应该尽量避免。我们很自然地会想到把该类例程作为库函数来实现，以使客户程序可以直接调用它们。但是，这种实现方式必然会涉及到如何将用户自定义函数（Newton-Raphson 例程需要调用该函数）封装成可以作为参数传递的形式。 (err)

Steepestdecent算法程序，matlab实例程序，适合初学者学习(Steepestdecent algorithm procedures, examples of Matlab procedures for beginners learning)

使用simulink对LMS算法进行多径仿真，在实验中验证LMS系统在多径环境下仍表现较好的收敛性和跟踪性能。(use Simulink LMS algorithm for multi-path simulation, In the experiment verified LMS system in the multi-path environment is still better performance convergence and tracking performance.)

这是一个关于模糊同步控制器的仿真程序，相关的参数以及模糊规则都可以在论文中找到，希望能对你们有帮助。(This is a fuzzy synchronization controller simulink, you can find the parameters in the paper. I think it is useful for you.)

Nonlinear model predictive Controller Impimetation

We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems.We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers.