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基于MATLAB的小波分析应用（第2版）

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基于MATLAB的小波分析应用（第2版）

Name: 基于MATLAB的小波分析应用（第2版）
Brand: Jingdong book
SKU: 3118066351
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《基于MATLAB的小波分析应用（第2版）》详细介绍了使用MATLAB小波工具箱进行信号处理、图像处理、机械故障诊断、数字水印以及语音信号处理等方面的方法和技巧，最后介绍了在Visual C++中使用MATLAB小波工具箱的方法。
与第一版相比，第二版采用了最新推出的MATLAB R2008小波分析工具箱，增加了对新增的小波函数、提升了小波功能和多信号小波的介绍，并且增加了MATLAB小波在数字水印、生物医学信号处理和矩阵方程求解等领域中的典型应用实例。
《基于MATLAB的小波分析应用（第2版）》可供信号处理、图像处理、机械故障诊断、数字水印、语音／生物医学信号处理以及矩阵方程求解等领域中使用MATLAB小波技术的工程技术人员借鉴，同时也是理工科各专业高年级本科生、研究生学习小波分析必不可少的参考书。

Catalogue

第1章小波分析基础
1.1 小波变换的由来
1.2 Haar小波
1.2.1 Haar小波简介
1.2.2 基于Haar小波的信号分解与重构算法
1.3 一维连续小波变换
1.4 离散小波变换
1.5 多分辨分析与小波构造
1.6 小波包分析

第2章 MATLAB小波工具箱简介
2.1 MATLAB小波工具箱的小波分析函数
2.2 自定义小波函数
2.2.1 准备添加一个新的小波函数
2.2.2 添加一个新的小波函数
2.2.3 使用新的小波函数
2.3 面向对象设计方法
2.3.1 小波工具箱中的主要对象
2.3.2 对象的应用

第3章小波图形用户接口
3.1 GUI启动
3.2 GUI主要特征
3.2.1 颜色设置
3.2.2 图形绘制的关联性
3.2.3 鼠标的使用
3.2.4 控制染色
3.2.5 控制颜色数目
3.2.6 控制染色模式
3.2.7 使用菜单
3.2.8 使用ViewAxes按钮
3.2.9 使用层次独立的阈值设置工具
3.3 一维小波分析
3.3.1 一维连续小波变换
3.3.2 一维连续复小波变换
3.3.3 一维离散小波变换
3.3.4 一维小波包变换
3.4 二维小波分析
3.4.1 二维离散小波变换
3.4.2 二维小波包变换
3.5 一维多信号小波分析
3.5.1 一维多信号分析
3.5.2 一维多变量去噪
3.5.3 多尺度主成分分析
3.6 小波构造
3.7 一维小波分析专用工具
3.7.1 一维平稳小波降噪
3.7.2 一维小波密度估计
3.7.3 一维回归估计
3.7.4 一维小波系数选择
3.7.5 一维FBM信号产生
3.8 二维小波分析专用工具
3.8.1 二维平稳小波降噪
3.8.2 二维小波系数选择
3.8.3 图像融合
3.9 小波显示
3.9.1 小波信息显示
3.9.2 小波包信息显示
3.10 延拓
3.10.1 信号延拓
3.10.2 图像延拓

第4章小波变换函数和小波函数
4.1 小波变换函数
4.1.1 函数biorfilt
4.1.2 函数centfrq
4.1.3 函数dyaddown
4.1.4 函数dyadup
4.1.5 函数intwave
4.1.6 函数orthfilt
4.1.7 函数qmf
4.1.8 函数scal2frq
4.1.9 函数wavefun
4.1.10 函数wavefun2
4.1.11 函数wavemngr
4.1.12 函数wfilters
4.1.13 函数wmaxlev
4.2 小波函数
4.2.1 函数biorwavf
4.2.2 函数cgauwavf
4.2.3 函数cmorwavf
4.2.4 函数coifwavf
4.2.5 函数dbaux
4.2.6 函数dbwavf
4.2.7 函数fbspwavf
4.2.8 函数gauswavf
4.2.9 函数mexihat
4.2.10 函数meyer
4.2.11 函数morlet
4.2.12 函数rbiowavf
4.2.13 函数shanwavf
4.2.14 函数symaux
4.2.15 函数symwavf

第5章小波变换与信号处理
5.1 信号分解
5.1.1 信号的连续小波分解
5.1.2 信号的离散小波分解
5.1.3 信号的平稳小波分解
5.1.4 信号的小波包分解
5.2 信号重构
5.2.1 信号的离散小波重构
5.2.2 信号的平稳小波重构
5.2.3 信号的小波包重构
5.3 信号去噪
5.3.1 信号的闽值去噪
5.3.2 小波去噪函数
5.3.3 信号去噪实例
5.4 信号压缩
5.4.1 信号压缩简述
5.4.2 信号压缩实例
5.5 信号分析
5.5.1 分离信号的不同成分
5.5.2 识别某一频率区间上的信号
5.5.3 识别信号的发展趋势
5.6 信号检测
5.6.1 检测信号的自相似性
5.6.2 信号奇异性检测
5.7 信号延拓
5.7.1 信号延拓函数
5.7.2 信号延拓实例

第6章小波变换与图像处理
6.1 图像分解
6.1.1 图像的小波分解
6.1.2 图像的平稳小波分解
6.1.3 二维小波包分解
6.2 图像重构
6.2.1 图像的小波分解重构
6.2.2 图像的平稳小波重构
6.2.3 图像的小波包分解重构
6.3 图像去噪
6.3.1 图像阈值去噪函数
6.3.2 图像阈值去噪实例
6.4 图像压缩
6.4.1 图像压缩概述
6.4.2 图像压缩实例
6.5 图像增强
6.6 图像融合
6.6.1 图像融合概述
6.6.2 图像融合实例
6.7 图像延拓

第7章小波变换与机械故障诊断
7.1 机械状态监测中的非平稳信号
7.2 发动机故障诊断
7.2.1 故障机理分析
7.2.2 故障检测方法
7.3 齿轮故障诊断
……
第8章小波变换与数字水印
第9章小波变换与语音/生物医学信号处理
第10章小波变换与矩阵方程求解
第11章提升小波变换及应用
第12章多信号小波分析及应用
第13章在VC环境中使用小波工具箱
附录小波分析工具箱函数
参考文献

Book Abstract

自从1822年傅里叶发表“热传导解析理论”以来，傅里叶变换一直是传统的信号处理的基本方法。傅里叶变换的基本思想是将信号分解成许多不同频率的正弦波的叠加，即将信号从时间域转换到频率域。
傅里叶变换能够满足大多数应用的需求，但是由于傅里叶变换在转换时丢掉了时间信息，因此无法对某一时间段的频域信息或者某一频率段所对应的时间信息进行分析。即傅里叶变换具有最高的频域分辨率，但不具有时域分辨率。
傅里叶变换的这种特性在分析非平稳性信号时，表现出严重的不足。然而实际中的信号均包含大量的非平稳成分，例如偏移、趋势、突变等，它们往往反映了信号的重要特征。因此需要寻求同时具有时间分辨率和频域分辨力的分析方法。
为了研究信号在局部时间的频域特征，1946年Gabor提出了著名的Gabor变换，之后发展成为短时傅里叶变换。其基本思想是对信号加窗，然后对窗内的信号进行傅里叶变换，因此它可以反映出信号的局部特性。

Introduction

目前小波分析在许多工程领域中都得到了广泛的应用，成为科技工作者经常使用的工具之一。MATLAB作为一种高性能的数值计算和可视化软件，经过各个领域专家的共同努力，现已包含信号处理、图像处理、通信、小波分析、系统辨识、优化以及控制系统等不同应用领域的工具箱.本书以MATLABR2008中的小波分析工具箱为蓝本，结合工程实际中的各应用领域，由浅入深地讲解了如何应用MATLAB来实现小波分析。
本书各章内容安排如下：
第1章简要介绍了小波变换的基本理论。
第2章对MATLAB小波工具箱进行了简要介绍。
第3章介绍了MATLAB小波工具箱的图形用户接口。
第4章介绍了常用的小波变换函数和小波函数。
第5章讲述了MATLAB小波在信号处理中的应用，主要介绍了MATLAB小波在信号分解、信号重构、信号去噪、信号压缩、信号分析、信号检测和信号延拓7个方面的应用。
第6章讲述了MATLAB小波在图像处理中的应用，从图像分解、图像重构、图像去噪、图像压缩、图像增强、图像融合以及图像延拓等方面介绍了MATLAB小波在图像处理中的应用。

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