Wavelet Analysis – Applications with Wolfram Language
Free Download Wavelet Analysis – Applications with Wolfram Language
Released 1/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Beginner | Genre: eLearning | Language: English + srt | Duration: 51m | Size: 121 MB
This course presents examples from a variety of wavelet analysis applications in the Wolfram Language, including financial time series, edge detection and denoising of images, thresholding, image and data compression, and image fusion. Familiarity with Fourier transforms and data smoothing methods is recommended for this class. Learn to analyze a time series using wavelets for detecting discontinuities, isolating peaks and inspecting nonstationary behavior; apply wavelet analysis to financial data; detect edges and discontinuities in images and other two-dimensional data; reduce noise in images by removing higher-frequency components; and more.


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Wavelet Analysis – Concepts with Wolfram Language
Free Download Wavelet Analysis – Concepts with Wolfram Language
Released 1/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 49m | Size: 106 MB
Wavelets decompose a signal into approximations and details at different scales, making them useful for applications such as data compression, detecting features and removing noise from signals. This course from Wolfram Research explains some of the theory behind continuous, discrete, and stationary wavelet transforms and demonstrates how the Wolfram Language and its built-in functions can be used to construct, compute, visualize, and analyze wavelet transforms and related functions.


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Wavelet Theory and Its Applications
Free Download Wavelet Theory and Its Applications by Randy K. Young
English | PDF | 1993 | 233 Pages | ISBN : 079239271X | 23.5 MB
The continuous wavelet transform has deep mathematical roots in the work of Alberto P. Calderon. His seminal paper on complex method of interpolation and intermediate spaces provided the main tool for describing function spaces and their approximation properties. The Calderon identities allow one to give integral representations of many natural operators by using simple pieces of such operators, which are more suited for analysis. These pieces, which are essentially spectral projections, can be chosen in clever ways and have proved to be of tremendous utility in various problems of numerical analysis, multidimensional signal processing, video data compression, and reconstruction of high resolution images and high quality speech. A proliferation of research papers and a couple of books, written in English (there is an earlier book written in French), have emerged on the subject. These books, so far, are written by specialists for specialists, with a heavy mathematical flavor, which is characteristic of the Calderon-Zygmund theory and related research of Duffin-Schaeffer, Daubechies, Grossman, Meyer, Morlet, Chui, and others. Randy Young's monograph is geared more towards practitioners and even non-specialists, who want and, probably, should be cognizant of the exciting proven as well as potential benefits which have either already emerged or are likely to emerge from wavelet theory.


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