D2D

软判决译码方式，2PSK的维特比译码，MATLAB仿真(Soft-decision decoding methods, 2PSK Viterbi Decoding)

Programing the Finite Element Method with Matlab

参数辨识以及扩展卡尔曼滤波算法的SOC估算(Battery Model Parameter Estimation Using a Layered Technique)

试用Newton插值多项式计算f(1.13)的近似值(Trial of Newton interpolation approximation of polynomial computation f1.13 )

bayes code for matlab

vTools is a toolbox for Matlab 5.3 developed within the Department of Electrical Systems and Automation (DSEA) of the University of Pisa (Italy) with the aim to offering to the Matlab users (especially control engineers and control engineering students) a completely graphical toolbox for linear system analysis and robust control synthesis(vTools is a toolbox for Matlab 5.3 develope d within the Department of Electrical Systems a nd Automation (DSEA) of the University of Pisa ( Italy) with the aim to offering to the use of Matlab rs (especially control engineers and control e ngineering students) a completely graphical t oolbox for linear system analysis and robust co ntrol synthesis)

一个简单的数据统计软件，包括了计算方差等各种功能(A simple statistical software, including a variety of functions such as calculating the variance)

对二维图像进行消噪、融合、分割，对对比度，亮度等进行变换的编码源码(Two-dimensional image denoising, integration, segmentation, on the contrast, brightness, etc. Transform source code)

灰色神经网络的预测算法，订单需求预测神经网络实例(Grey neural network prediction algorithm, order demand forecasting)

这篇文章是盲信号处理，因为是英文文章，所以大家看内容就好了。(Principal component analysis (PCA) is a mainstay of modern data analysis- a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.)