kalman_introduction

MATLAB中常用的程序，包括硬阀值、软阀值、全局阀值，小包波去噪等等，希望对初学者有用。(Procedures commonly used in MATLAB, including the hard threshold and soft threshold, global threshold, wave packet de-noising, etc., in the hope that useful for beginners.)

说明：  几个改进EMD算法的matlab代码，包括旋转EMD，间歇EMD。(Several improvements in the EMD algorithm matlab code, including rotating EMD, Intermittent EMD.)

compressive sensing theory

controlled Half wave rectifier circuit with inductive load and gives positive half cycle and block negative half cycle.

用粒子群算法求解车间调度问题，包含甘特图的制作程序(Particle swarm optimization algorithm for job shop scheduling problem, including Gantt chart)

Plots the polar spectrum of acceleration,velocity and displacement

针对焊接过程光谱的去噪问题进行分析，利用matlab编写去噪程序(Analyze the welding process for de-noising issue of the spectrum, the use of de-noising procedure matlab prepared)

Horn_Schunck_Optical_Flow_Method，值得学习(Horn_Schunck_Optical_Flow_Method, worth learning)

本论文的主要工作在于引入了一种新的特征提取方法----独立分量分析。独立分量分析的根本原理是通过分析多维观测数据间的高阶统计相关性，找出相互独立的隐含信息成分，完成分量间高阶冗余的去除及独立信源的提取(In this paper, the major work is the introduction of a new method of feature extraction independent component analysis. Independent component analysis of the fundamental principle is that through the analysis of multi-dimensional data between the relevance of higher-order statistics, independent of each other to identify the implied message composition, the completion of inter-component higher-order removal of redundant and independent source extraction)

he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/abs/cs.CV/0401017. The file extractForeground.m contains a sample function that will perform a complete foreground segmentation for static camera video. It uses a number of parameters, which are described in the documentation. An attempt was made to choose sensible default values, but they may need to be adjusted for some videos. More generally, the implementation in extractForeground.m is only a suggestion of how the graph-based segmentation might be used. The same approach can be applied with other (perhaps time-adaptive) background models. The key step is at line 113 of the code, once the deviation array has been created. The graph-cut segmentation can be applied similarly to a deviation matrix computed in any other way.