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现代通信系统第一章 matlab程序源码(The first chapter of modern communication systems matlab program source)

EEG信号中眼动所带来的各种伪迹的自适应去除(artifacts removal for EEG)

本程序给出了多维卡尔曼滤波的仿真方法，很有参考价值哦！本程序给出了多维卡尔曼滤波的仿真方法，很有参考价值哦！(This procedure gives the track correlation of the simulation method, useful reference for Oh! This procedure gives the track correlation of the simulation method, useful reference for Oh!)

matlab,关于扑克牌识别的matlab程序，很不错(matlab, on identification cards matlab procedures, very good)

说明：  2代小波示意程序 2维小波变换经典程序 Daubechies小波基的构造 采用多孔trous算法(undecimated wavelet transform)实现小波变换 平移变换平移法(cycle_spinning)消除gibbs效应 提升法97经典程序 消失矩作用的程序 小波插值与小波构造 小波滤波器构造和消噪程序 小波谱分析mallat算法经典程序(2 Generation Wavelet indicated procedures classic 2-D wavelet transform procedures for Daubechies wavelet bases constructed using porous trous algorithm (undecimated wavelet transform) the realization of wavelet transform translational translational transform method (cycle_spinning) to eliminate Gibbs effect of lifting 97 vanishing moments the role of the classic procedure of wavelet interpolation procedure value structure of wavelet and wavelet de-noising filter structure and procedures of small Mallat algorithm for spectral analysis of the classical procedures)

说明：  非性多变量系统递归辨识算法，同时, 也对这些算法的估计一致性问题进行了讨论，具有效果还可以(Recurrent identification algorithms for non-linear multivariable systems are discussed. The estimation consistency of these algorithms is also discussed. The results show that the algorithm is effective and can be used to identify non-linear multivariable systems.)

Matlab 常用图像处理,包括图像相除、相乘、灰度线性变换、图像增强、降噪(matlab Common image processing, including image division, multiplication, gray linear transformation, image enhancement, noise reduction)

the third generation PP

支持向量机的matlab实例，对学习支持向量机有很大的帮助。(The matlab example of the support vector machine is of great help to the learning support vector machine.)

The main idea behind this book, and the incentive for writing it, is that strong connections exist between adaptive filtering and signal analysis, to the extent that it is not realistic—at least from an engineering point of view—to separate them. In order to understand adaptive filters well enough to design them properly and apply them successfully, a certain amount of knowledge of the analysis of the signals involved is indispensable. Conversely, several major analysis techniques become really efficient and useful in products only when they are designed and implemented in an adaptive fashion. This book is dedicated to the intricate relationships between these two areas. Moreover, this approach can lead to new ideas and new techniques in either field.