BLDCM

说明：  心电信号滤波，数字信号处理课后试验，其中有源程序，频谱图，滤波图(ECG filtering, digital signal processing OSCAR test, which is the source, the spectrum, filtering plan)

emd源程序。emd源程序。emd源程序。emd源程序。emd源程序。(display the emd functions)

基于互相关matlAB图像配准代码，压缩文件包括源文件，GUI设计文件。程序能自动配准图像，能手动选择要配准的图像(Based on cross-correlation matlAB image registration code, source files, including compressed files, GUI design files. Automatic image registration procedures, registration can manually select the image you want to)

灰色预测模型称为CM模型，G为grey的第一个字母，M为model的第一个字母。GM（1，1）表示一阶的，一个变量的微分方程型预测模型。GM（1，1）是一阶单序列的线性动态模型，主要用于时间序列预测。 一、GM（1，1）建模 设有数列 共有 个观察值 对 作累加生成，得到新的数列 ，其元素 （5-1） 有： 对数列 ，可建立预测模型的白化形式方程， （5-2） 式中： ——为待估计参数。分别称为发展灰数和内生控制灰数。设 为待估计参数向量 则 按最小二乘法求解， 有： （5-3） 式中： （5-4） （5-5） 将（5-3）式求得的 代入（5-2）式，并解微分方程，有 （1，1）预测模型为： （5-6）(gray forecasting model called CM model, the G-gray of a letter, the M model for the first letter. GM (1,1), a band of a variable type of differential equation models. GM (1,1) is a sequence of single-band linear dynamic model, mainly for time series prediction. A GM (1,1) model series with a total value of observation for the cumulative production, to a new series of its elements (5-1) : The series, we can establish the prediction model albino form of equation (5-2) where :-- to question the estimated parameters. The development will be known as the gray and hygiene control within a few gray. Set parameters to be estimated according to Vector least squares method, are : (5-3) where : (5-4) (5-5) (5-3) that obtained in lieu of income (5-2)- and solutions differential equations, (1 1) Fore)

Peak power ratio reduction in OFDM systems

用Matlab软件搭出来的光伏电池模型，通过输入光照、温度、电压参数，可以实现光伏电池的一般特性。(Take out using Matlab software model of photovoltaic cells, by entering the light, temperature, voltage parameters, you can achieve the general characteristics of photovoltaic cells.)

FDTD code of a 2-D rectangular waveguide with Berenger s PML termination

Extracting MFCC features for audio files and removing silence them. Then calculating some statistical functions

Support vector machine is a machine learning method that is widely used for data analyzing and pattern recognizing. The algorithm was invented by Vladimir Vapnik and the current standard incarnation was proposed by Corinna Cortes and Vladimir Vapnik. This application note is to helping understand the concept of support vector machine and how to build a simple support vector machine using Matlab

传统信道化接收机，复信号，八通道八抽取,现代信号处理，多抽样率(Traditional channelized receiver, signal, channel eight extraction)