mfbox-1.06

说明：  对给定的数据，利用PCA求取数据中主分量(Given the data, using principal component in the PCA data to strike)

opencv的canny函数检测边缘的效果和matlab的不同 matlab的边缘更为细腻 (opencv canny edge effect and matlab function testing different- the edges of the matlab is more exquisite )

对噪声信号中的正弦信号，通过Pisarenko谐波分解方法、Music算法和Esprit算法进行频率估计，信号源是： 其中， ， ， ； 是高斯白噪声，方差为 。使用128个数据样本进行估计。 1、用三种算法进行频率估计，独立运行20次，记录各个方法的估计值，计算均值和方差； 2、增加噪声功率，观察和分析各种方法的性能。(Sinusoidal signal in the noise signal through the Pisarenko harmonic decomposition method, Music algorithms and Esprit frequency estimation algorithm, the signal source is: where,,, a Gaussian white noise, variance. 128 data samples used to estimate. 1, with three kinds of frequency estimation algorithm, run independently 20 times, record the estimated value of each method to calculate the mean and variance 2, the increase in noise power, observation and analysis of the performance of various methods.)

说明：  在matlab中开发了一种如何使算法收敛更快，精度更高的算法，效果明显(developed in the Matlab how a convergence algorithm faster and more precise algorithms, obviously)

matlab函数，专门计算列车车体横向sperling平稳性指标。调用方式：y=lridex(acc)，其中acc为测得的或仿真得到的车体横向加速度（不包括时间列）。(matlab function, specifically calculate the train body lateral sperling smooth indicators. The invocation: y = lridex (ACC), wherein acc obtained for the measured or simulation vehicle body lateral acceleration (not including the time column).)

FM demodulation signal

用MATLAB语言实现模糊控制算法，在300s处施加了一个干扰，运行结果理想(MATLAB language fuzzy control algorithm, applied in the 300s at a disturbance, the results satisfactory)

该程序的主要功能是实现典型相关分析算法。(The program' s main function is to achieve a canonical correlation analysis algorithms)

pca算法，有详细注释，通过光谱区分苹果酸甜度，对于想学习pca算法的初学者，有很大帮助(pca algorithm, detailed notes, by spectroscopic distinguish sour apple degree, want to learn pca algorithm for beginners, there are a great help)

通信课程设计 MATLAB下编程的 信号调制与解调相关程序等，希望与大家分享。(Communications curriculum design under MATLAB programming signal modulation and demodulation and other related procedures, I hope to share with you.)