Double-FrequencyUnipolarSPWM_2015b

Its an model for buck converter input volatge is around 96volt and out put voltage is around 12Volts. typically used in telecommunication industries.

经典的香农采样定理认为,为了不失真地恢复模拟信号,采样频率应该不小于奈奎斯特频率(即模拟信号 频谱中的最高频率)的两倍.但是其中除了利用到信号是有限带宽的假设外,没利用任何的其它先验信息.采集到 的数据存在很大程度的冗余.Donoho等人提出的压缩感知方法(Compressed Sensing或Compressive Sampling, CS)充分运用了大部分信号在预知的一组基上可以稀疏表示这一先验信息,利用随机投影实现了在远低于奈奎斯 特频率的采样频率下对压缩数据的直接采集.该方法不仅为降低采样频率提供了一种新思路,也为其它科学领域 的研究提供了新的契机.该文综述性地阐述了压缩感知方法的基本原理,给出了其中的一些约束问题和估计方法, 并介绍压缩感知理论的相关问题———矩阵填充,最后讨论了其未来可能的应用前景. (According to the conventional Shannon s sampling theorem,in order to represent the analog signal,the sampling rate should not be less than twice the Nyquist sampling rate.Howev- er,this theorem only makes use of the bandwidth information.As a result,the collected data contain many redundant information.The recently proposed sampling method,compressed sens- ing or compressive sampling(CS),can collect compressed data at the sampling rate much lower than that needed in Shannon s sampling theorem by exploring the compressibility of the signal. This paper presents a review on the basic theory of CS.Some of the restrictions and recovery methods in CS are also discussed.Finally,some potential applications based on CS are presented. )

MATLAB常用函数参考,各种函数命令，操作指南。(MATLAB function reference, a variety of command functions, operation guide.)

用遗传算法解决TSP问题，还有用CPLEX解决(Solving TSP problem by genetic algorithm)

说明：  matlab常见的几个算法，有人脸识别， 车牌号识别等。(matlab several common algorithms, there is face recognition, license plate number recognition.)

An AutoRegressive Moving Average Spectral Analysis toolbox for use with Matlab.(An AutoRegressive Moving Average Spectra l Analysis toolbox for use with Matlab.)

说明：  自己的一点小资料，很乱，设计一些例子，svm的，之类的(Little information, messy, design examples, svm, like the)

introduction to matlab part 3 and examples

SQP method 的matlab程序代码(programming in sqp method of matlab )

适用于雷达方面学习的初学者熟悉相关参数设置了解相关线性调频信号离散化，匹配滤波的产生方法等一系列基本问题。比较时域和频域不通滤波方法的区别(Beginners apply to radar aspects of learning to understand familiar with the parameters related to the chirp signal discrete, matched filtering to produce a series of basic questions like. Comparison of time domain and frequency domain filtering method fails difference)