lecroy_sparq_datasheet

基于MATLAB语言之心电信号处理的文件(MATLAB language-based ECG processing)

说明：  自编：张贤达《数字信号处理》习题4.26的MATLAB程序。加入了一个GUI界面。 在MATLAB下运行MDSP。 要求MATLAB7以上版本，否则GUI无法运行(self : Automation, "Digital Signal Processing" Exercise 4.26 MATLAB. Adding a GUI interface. MATLAB running MDSP. MATLAB7 version of the above requirements, otherwise unable to run GUI)

FFt code of 8 point in matlab

预测反褶积matlab编码,适合初学者学习和使用(predictive deconvlution)

本文在介绍MIMO无线通信系统模型的基础上，通过对比分析几种常用空时编码方法，选定空时分组码对信号进行编码并建立瑞利衰落信道模型，通过对两发一收及两发两收系统的仿真，研究接收天线数对系统接收信号可靠性的影响。之后进一步扩展到不同发射矩阵条件下研究多发一收系统的频谱利用率及误符号率，分析不同天线数目，不同发射矩阵条件下空时正交分组码检测算法的性能。(This paper introduced the MIMO wireless communication system model, through the comparative analysis of several commonly used method of space-time coding, grouping code for the selected space-time signal is encoded and establish Rayleigh fading channel model, through two rounds one and two rounds of closing Simulation of two closed systems, research receiving antennas to receive signals affect the reliability of the system. After further extended to study the conditions under different emission matrix of multiple spectral efficiency and symbol error rate of a closed system, analyze the different number of antennas, the performance under different emission matrix orthogonal space-time block codes condition detection algorithm.)

这是"Matlab从入门到精通"一书的全部源代码程序！有兴趣的人员可以自己跑跑看看结果！(This is the " Matlab from the entry to the master," a book full source code program! Interested persons may look at the result that running their own!)

关于指纹识别算法的实现，简单可行，是正确的(Simulated annealing algorithm matlab program, verify that is correct)

最基本的粒子群算法实现的数据聚类，超简单，觉得对初学者有用(The most basic particle swarm data clustering algorithm, super-simple, feel useful for beginners)

关于matlab数据预处理的课件ppt，以及一些个人的整理，比如matlab某些函数的应用。(About the matlab data preprocessing courseware ppt, as well as some personal finishing, such as some matlab function.)

fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm format: [u,sig,t,iter] = fit_mix_gaussian( X,M ) input: X - input samples, Nx1 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian sig - fitted standard deviation for each gaussian t - probability of each gaussian in the complete distribution iter- number of iterations done by the function