matlab_m

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. )

利用matlab实现语音输入、8bit PCM音频截短、滤波输出。用于将大于8bit的PCM语音截短为8bit的PCM语音。(Matlab realize the use of voice input, 8bit PCM audio truncated, filter output. Will be greater than for the PCM voice 8bit to 8bit truncated the PCM voice.)

线性规划问题(单纯形法、完整单纯形法与修正单纯形法)与整数规划问题(割平面法、分支定界法与0-1规划)的matlab源程序(matlab source programs of linear programming including simple method, complete simple method and modified simple method and integer programming including dividing plane method, branch and bound method and 0-1 programming method.)

this is matrix converter file with simulink

LinearRegressor這是一個經典的分類器，可以有效的預測出未分類的資料(it is common used for classification)

good projet for bbeginners ey useful

是一个基于遗传算法改进的模糊C均值算法，很不错的代码。(It is a improved fuzzy C-means algorithm based on genetic algorithm , a very good code. )

control with fezzy logic

This function is for optimization when you draw contour

基于Malvar窗时频谱的反Q滤波方法0479-8023(2012)03-0405-06(Seismic Inverse Q Filíering U sing Malvar Window based Time-Frequency Analysis)