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.

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QGIS中可被python调用的算法名称列表。比如QGIS中vector下图片数据的差分合并，可在python控制台，应用台调用。程序代码见博客。

CPU 调度算法 先到先得 (FCFS) 最短作业优先 (SJF) 优先算法 (Prio) 最后期限调度算法抢先优先级算法 (P-Prio) 最短剩余时间优先算法 (SRTF) 循环算法 (RR) 通过单击“添加”按钮，将创建默认作业对象。 “保存”按钮用于保存对作业详细信息所做的更改。 您可以通过单击“删除”按钮删除作业。 选择算法并设置好作业详细信息后，单击“运行”以求解和计算周转时间和等待时间。 甘特图也将显示在顶部。

Actually, this is the lecture notes for CS762 in the University of Waterloo, it is awesome. The prof. is Biedl Therese.

【2018新书】Nature-Inspired Algorithms and Applied Optimization（自然算法与应用优化）

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