PLL_2nd

自己编写的产生小m序列的程序，希望对大家有所帮助(m sequence)

to subtract background

用于混沌时间预测一个很好的工具。可以求时间延时和时间窗。非常好用的一个预测工具(a very good software.which can make something more good.and make somebody know much)

Packet-dropfunction functionfunction function

Class Instance Source Code for Andriod.

功率谱估计的应用范围很广，在各学科和应用领域中受到了极大的重视。在《现代信号处理》课程中讲述了经典谱估计和现代谱估计这两大类谱估计方法；经典谱估计是基于傅立叶变换的，虽然具有运算效率高的优点，但是频谱分辨率低同时旁瓣泄漏严重，对长序列有着良好的估计。为了克服经典谱估计的缺点，人们开展了对现代谱估计方法的研究。现代谱估计是以随机过程的参数模型为基础的，有最大似然估计法、最大熵法、AR模型法、预测滤波器法。现代谱估计对短序列的估计精度高，同经典谱估计互为补充。在认真学习了现 代谱估计方法后，我选择了现代谱估计中的AR模型法的仿真作为题目。下面给出AR模型的相关理论和仿真实现。(Power Spectral Estimation of very extensive, in all disciplines and fields of application of a great deal of attention. The "modern signal processing" on the curriculum of classical and modern spectral estimation spectrum is estimated that the two types of spectrum estimation method; Classical spectrum estimation is based on Fourier transform, although high computing efficiency advantages, but also low-resolution spectrum Sidelobe serious leakage of long sequences have good estimates. In order to overcome the classic shortcomings of the spectrum estimation, there have been a pair of modern spectral estimation methods. Modern spectral estimation is the random process model parameters based on the maximum likelihood estimation, maximum entropy method, AR model, forecast filter. Spe)

802.15.4 MAC protocol source code

各类CS的仿真 包括BP OMP COSAMP SL0 FOCUSS MMV-FOCUSS(All types of CS simulation include BP OMP COSAMP SL0 FOCUSS MMV-FOCUSS)

学进一步了解分类器的设计概念，能够根据自己的设计对线性分类器有更深刻地认识，理解Fisher准则方法确定最佳线性分界面方法的原理，以及Lagrande乘子求解的原理。(school with a better understanding of classification of the design concept, According to the design of linear classification for a more profound understanding, Fisher understood the criteria to determine the best method of linear interface of the theory, Lagrande and multiplier for the principle.)

精度颇高的转子临界转速及不平和响应算法，主要采用了传递矩阵法(High precision of the rotor critical speed and the uneven and response algorithms, mainly in the transfer matrix method)