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Wavelet Theory and Its Applications

Posted By: AvaxGenius
Wavelet Theory and Its Applications

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.

Wavelets in Neuroscience (Repost)

Posted By: AvaxGenius
Wavelets in Neuroscience (Repost)

Wavelets in Neuroscience by Alexander E. Hramov, Alexey A. Koronovskii, Valeri A. Makarov, Alexey N. Pavlov, Evgenia Sitnikova
English | PDF(True) | 2015 | 331 Pages | ISBN : 3662438496 | 13.24 MB

This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade.

Wavelets in Neuroscience

Posted By: AvaxGenius
Wavelets in Neuroscience

Wavelets in Neuroscience by Alexander E. Hramov
English | PDF | 2021 | 397 Pages | ISBN : 3030759911 | 13.2 MB

This book illustrates how modern mathematical wavelet transform techniques offer fresh insights into the complex behavior of neural systems at different levels: from the microscopic dynamics of individual cells to the macroscopic behavior of large neural networks. It also demonstrates how and where wavelet-based mathematical tools can provide an advantage over classical approaches used in neuroscience. The authors well describe single neuron and populational neural recordings.