fisher1111

说明：  mpsk的源代码 里面是一个txt文档下载后可以再matlab程序上直接运行(MPSK inside the source code is a txt file download matlab program can be run directly)

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. )

数学建模竞赛历年获奖论文，希望对大家有帮助！(Mathematical modeling contest winning papers over the years, want to help!)

zernike矩的源代码，非常不错，用matlab编的，值得下载(zernike moments of the source code, very good, with matlab code, it is worth downloading)

Advanced Mathematics and Mechanics Applications Using MATLAB, 3rd

Take an image window and place it in a squared window such that it s size is maximized without distortion.

说明：  matlab环境下 实现的3类分类的KNN算法(KNN algorithm to classify three classes developed by matlab)

ICA独立成分分析对信号进行特征提取，盲源分析保留有用信号通道(ICA independent component analysis for signal feature extraction and blind source analysis to preserve the desired signal channel)

粒子群优化算法（PSO）是一种基于种群的随机优化技术，由Eberhart和Kennedy于1995年提出。粒子群算法模仿昆虫、兽群、鸟群和鱼群等的群集行为，这些群体按照一种合作的方式寻找食物，群体中的每个成员通过学习它自身的经验和其他成员的经验来不断改变其搜索模式。 (Particle swarm optimization algorithm (PSO) is a population based stochastic optimization technique, put forward by Eberhart and Kennedy in 1995. Particle swarm optimization (pso) algorithm on insects, herds, flocks and schools of fish swarm behavior, and these groups to find food in a cooperative way, each member of the group itself by learning experience and the experience of the other members to continuously change its search pattern.)

SVM的实现，采用了SMO算法，用C语言结合matlab接口实现，可在matlab下运行。如果是64位的系统，可在matlab下重新同met.(SVM with SMO)