TFBSSpack

心电信号检测及其前期处理，心电信号检测及其前期处理(ECG testing and pre-processing, ECG detection and pre-processing)

Energy calculation for compression and denoising

实现MIT数据库ECG信号的读取，并进行QRS波形的检测(ECG signal MIT database achieved the reading, and for detection of QRS wave)

DESCRIPTION: This Matlab algorithm performs many tasks associated with power system analysis. First, it has the ability to perform both fully coupled and dc power flow analysis. Second, it has the ability to create measurements (with user-defined error) to be used as input for state estimation. Third, it has the ability to perform state estimation - allowing the user to examine how the placement of measurement devices as well as magnitude of distibution of measurement error affect the discrepency between power flow and state estimation results. In essence, it provides the user with a complete package for the study of both power flow and state estimation in n-bus power systems.

Bit plane slicing using MATLAB

Linear algebra problems!

matlab下的adaptfilt，用于自适应滤波器，滤波信号堪比输入无噪音信号(matlab under adaptfilt, for the adaptive filter, filtering the signal comparable to the input signal without noise)

说明：  使用Matlab编写的逆谐波程序。该程序对于不同的参数进行了实验。当图像噪声不同时，选用不同的参数。参数的正确选择很重要。本程序给出了不同噪声比较适合的参数选择(The use of Matlab procedures for the preparation of the inverse harmonic. The program parameters for different experiments. When the image noise is not at the same time, choosing different parameters. Correct choice of parameters is very important. This procedure gives more suitable for different noise parameters)

demo Learning-Vector-Quantization

机器学习中的垃圾邮件分类程序，用matlab做的。从以下链接下载垃圾邮件数据（spam data）：(数据已下载，放在spambase.zip) http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html 该数据包含57个邮件信息相关的变量，每条邮件可以被分类为垃圾邮件（Y=1）和非垃圾邮件（Y=0）。输出Y的值在文件中每一列的末尾。练习的目标是要预测电子邮件是否为垃圾邮件。 (Machine Learning spam classification procedures, using matlab to do. Data (spam data) from the following link to download the junk mail: (data has been downloaded, put spambase.zip) http://www-stat.stanford.edu/ ~ tibs/ElemStatLearn/index.html The data includes 57 e-mail messages related variables, each message can be classified as spam (Y = 1) and non-spam (Y = 0). Y value of the output end of each column in the file. The goal is to predict exercise email is spam.)