tftb-0.1

matlab code of ecg in this we can see how we ecg to r-r interval

国外经典教材的中文译本，清华大学黄开支翻译的。个人觉得是所见过的最经典的(Overseas Chinese translation of the classic textbook, Tsinghua University, Huang expenditure translation. Personally feel is the most classic ever seen)

一个简单的matlab程序。作用是用快速傅里叶变换（FFT），对心电信号去基线漂移。传统的去基线漂移方法没有提到FFt，只不过效果的确不太好。(Translate From: Chinese To: English ChineseEnglishSpanishTranslate text or webpage 一个简单的matlab程序。作用是用快速傅里叶变换（FFT），对心电信号去基线漂移。传统的去基线漂移方法没有提到FFt，只不过效果的确不太好。 Allow phonetic typingType text or a website address or translate a document. Cancel Yīgè jiǎndān de matlab chéngxù. Zuòyòng shì yòng kuàisù fu lǐ yè biànhuàn (FFT), duì xīn diàn xìnhào qù jīxiàn piāoyí. Chuántǒng de qù jīxiàn piāoyí fāngfǎ méiyǒu tí dào FFt, zhǐ bùguò xiàoguǒ díquè bù tài hǎo.EnglishSpanishArabicAlpha A simple matlab program. The role of the fast Fourier transform (FFT), the ECG baseline drift. To baseline drift did not mention FFt, only the results have not been very good.)

cordic算法实现特征值分解，非常实用，代码可以作为参考(cordic eigenvalue decomposition algorithm, very practical, code can be used as a reference)

Press Numerica Methods in Engineering with MATLAB 2005

利用Matlab来设计计算土石坝的浸润线等(Matlab to design calculation of embankment dams saturation line)

是一个小波降噪的程序，里边包括了小波降噪的实例，有三种方法：默认阈值去噪；软硬阈值的去噪；强制性去噪。(Is a wavelet noise reduction program, including the inside of the Wavelet Transform instance, there are three ways: the default threshold denoising hard and soft threshold de-noising mandatory de-noising.)

SVM分类器，用于对多维采样点进行分类。可根据类别数修改分类器，我们的模式识别作业。(SVM classifier, multi-dimensional sampling points used for classification. Can be modified according to the number of classification categories, and our pattern recognition operation.)

RichTextBox带行号可滚动 RichTextBox with line numbers can scroll(RichTextBox with line numbers can scroll)

In this report, we devise a methodology for identifying the species of an iris amongst 3 based on 4 distinctive features. We first cover the constitution of the data set and input patterns. We then determine the layout and structure of the neural network we will use for the classification. We continue by testing the network in real life conditions. We conclude with a review of the methods used, and how they could be improved.