cskmean

receiver operating characteristics

从零开始学习PSCAD与MATLAB的交互，有利于对着两个软件的相互转换，数据处理等(PSCAD started learning with MATLAB interaction, which is useful for the two software converts mutually, data processing, etc)

基于NLMS算法的回声消除，具有双端检测功能(Echo cancellation nlms Double-end detection)

Hearing test. It using music card to test left and right ear seperately. It contains graphic iterface[*.fig] and m-file. It works fine with 6.5 version of Matlab.

Controle vetorial do motor de induç ã o

五个经典matlab仿真实例，由易到难，初学者的学习法宝(Five classic matlab simulation examples, easy to difficult, for beginners to learn magic)

Least Mean Square (LMS) equalizer used in coherent receivers

本书使用简单易行的风格，一开始介绍了 MATLAB基础，你将学习到如何绘制图象，求解 代数方程及计算积分，也将学习到如何求解微分 方程，求 ODE 的数值解，使用特殊函数等。本书 含有几百个例子及其详细解答过程，章末带有习 题，书末还带最终测试题目，一句话，本书给予 你的是MATLAB精华。 本自学指导书提供了：  上手MATLAB的最快方法  几百个带有解答过程的工作实例  覆盖了MATLAB7  每章章末配有习题，提高学习质量， 查漏补缺。  书末附有最终测试题  为学习和工作节省时间提高效率 《MATLAB揭秘》对初学者很容易，对高手有挑战性，是通往精确计算的捷径。 (This book uses simple style, the beginning describes the MATLAB-based, you will learn how to draw images, solving Algebraic equations and calculating the integral will also learn how to solve differential Equation, find the numerical solution of ODE, the use of special functions and so on. Book Contains hundreds of examples and detailed answers to the process of learning with the end of Chapter Questions, end of the book is also the final test with the subject, in a word, give the book MATLAB is your essence. This book provides a self-study guide:  The fastest way to get started MATLAB  hundreds of examples of the work process with a solution  covered MATLAB7  chapter at the end of each chapter with exercises to improve the quality of learning, You may say.  end of the book with a final test questions  to study and work to save time and improve efficiency "MATLAB Secret" is very easy for beginners, challengin)

视频处理中让视频的前景和背景分离的matlab程序(Video processing--to realize the separation of foreground and background in a given video sequence )

自适应算法 rls 和原理和例子 带详解(Adaptive algorithm RLS and principles and examples with detailed)