这是关于小波变换的电子书，高清，最新版本，经典著作，英文版
2023-01-26 13:35:31
28.06MB
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这是关于小波的电子书，高清，最新版本，经典著作，英文版
2022-11-29 21:16:08
9.18MB
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自学小波分析的教材，起点低，习题丰富
目录
1.What is this book all about?
2. Mathematical Preliminary.
2.1 Linear Spaces.
2.2 Vectors and Vector Spaces.
2.3 Basis Functions, Orthogonality and Biothogonality.
2.4 Local Basis and Riesz Basis.
2.5 Discrete Linear Normed Space.
2.6 Approximation by Orthogonal Projection.
2.7 Matrix Algebra and Linear Transformation.
2.8 Digital Signals.
2.9 Exercises.
2.10 References.
3. Fourier Analysis.
3.1 Fourier Series.
3.2 Rectified Sine Wave.
3.3 Fourier Transform.
3.4 Properties of Fourier Transform.
3.5 Examples of Fourier Transform.
3.6 Poisson’s Sum and Partition of ZUnity.
3.7 Sampling Theorem.
3.8 Partial Sum and Gibb’s Phenomenon.
3.9 Fourier Analysis of Discrete-Time Signals.
3.10 Discrete Fourier Transform (DFT).
3.11 Exercise.
3.12 References.
4. Time-Frequency Analysis.
4.1 Window Function.
4.2 Short-Time Fourier Transform.
4.3 Discrete Short-Time Fourier Transform.
4.4 Discrete Gabor Representation.
4.5 Continuous Wavelet Transform.
4.6 Discrete Wavelet Transform.
4.7 Wavelet Series.
4.8 Interpretations of the Time-Frequency Plot.
4.9 Wigner-Ville Distribution.
4.10 Properties of Wigner-Ville Distribution.
4.11 Quadratic Superposition Principle.
4.12 Ambiguity Function.
4.13 Exercise.
4.14 Computer Programs.
4.15 References.
5. Multiresolution Anaylsis.
5.1 Multiresolution Spaces.
5.2 Orthogonal, Biothogonal, and Semiorthogonal Decomposition.
5.3 Two-Scale Relations.
5.4 Decomposition Relation.
5.5 Spline Functions and Properties.
5.6 Mapping a Function into MRA Space.
5.7 Exercise.
5.8 Computer Programs.
5.9 References.
6. Construction of Wavelets.
6.1 Necessary Ingredients for Wavelet Construction.
6.2 Construction of Semiorthogonal Spline Wavelets.
6.3 Construction of Orthonormal Wavelets.
6.4 Orthonormal Scaling Functions.
6.5 Construction of Biothogonal Wavelets.
6.6 Graphical Display of Wavelet.
6.7 Exercise.
6.8 Computer Programs.
6.9 References.
7. DWT and Filter Bank Algorithms.
7.1 Decimation and Interpolation.
7.2 Signal Representation in the Approximation Subspace.
7.3 Wavelet Decomposition Algorithm.
7.4 Reconstruction Algorithm.
7.5 Change of Bases.
7.6 Signal Reconstruction in Semiorthogonal Subspaces.
7.7 Examples.
7.8 Two-Channel Perfect Reconstruction Filter Bank.
7.9 Polyphase Representation for Filter Banks.
7.10 Comments on DWT and PR Filter Banks.
7.11 Exercise.
7.12 Computer Program.
7.13 References.
8. Special Topics in Wavelets and Algorithms.
8.1 Fast Integral Wavelet Transform.
8.2 Ridgelet Transform.
8.3 Curvelet Transform.
8.4 Complex Wavelets.
8.5 Lifting Wavelet transform.
8.6 References.
9. Digital Signal Processing Applications.
9.1 Wavelet Packet.
9.2 Wavelet-Packet Algorithms.
9.3 Thresholding.
9.4 Interference Suppression.
9.5 Faulty Bearing Signature Identification.
9.6 Two-Dimensional Wavelets and Wavelet Packets.
9.7 Edge Detection.
9.8 Image Compression.
9.9 Microcalcification Cluster Detection.
2022-05-15 18:22:29
4.49MB
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小波应用范例
【例 1-10】已知序列试绘制及
其Fourier变换的幅值图，并用变换后的数值求解逆变换，其中采样频率为。
我们知道，假定采样频率为 (即的采样间隔进行采样)，则该信号包含有和两种频率的波，其振幅均为1。
程序如下：
clf %清除图形框的内容
N=100;dt=1; %设置最大点数
n=0:N-1; t=n*dt; %给出时间序列
xn=cos(2*pi*0.24*t)+cos(2*pi*0.26*t); %给出原始信号的值序列
Xk=fft(xn,N); %对原始信号进行Fourier变换
magXk=abs(Xk);phaXk=angle(Xk); %求出Fourier变换的振幅和相位
subplot(2,2,1),plot(t,xn); xlabel('时间/s') %绘出原始信号
title('原始信号(N=100)');
xx=ifft(Xk,N); %Fourier逆变换
x=real(xx); %取变换后的实部，如做实验可以验证其虚部为零
subplot(2,2,2),plot(
2022-04-06 01:50:41
725KB
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随着计算机视觉的发展，机器人需要从图像序列中检测目标物体以进行自主导航。 为了识别目标，自主机器人的感知系统首先需要将图像分割为不重叠但有意义的区域，这些区域基于低级特征，例如颜色，纹理度量和形状等。作为重要组成部分，Gabor小波通常用作纹理由于它是人脑V1区域中单个细胞的空间感受野的数学近似值，因此具有一些特征。 这些Gabor纹理测度的问题是特征提取过程中卷积涉及的高计算成本。 为了部分解决该问题，在本文中，我们仔细研究了用于形成纹理特征的Gabor小波的行为，发现只有一小部分滤波器对识别过程有重要贡献。 实验结果表明，通过删除冗余滤波器，可以在更短的时间内获得更好的性能。
2021-12-26 13:21:17
402KB
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