Download Free On Multi Resolution Signal Decomposition Techniques Book in PDF and EPUB Free Download. You can read online On Multi Resolution Signal Decomposition Techniques and write the review.

This book provides an in-depth, integrated, and up-to-date exposition of the topic of signal decomposition techniques. Application areas of these techniques include speech and image processing, machine vision, information engineering, High-Definition Television, and telecommunications. The book will serve as the major reference for those entering the field, instructors teaching some or all of the topics in an advanced graduate course and researchers needing to consult an authoritative source. n The first book to give a unified and coherent exposition of multiresolutional signal decomposition techniques n Classroom tested textbook clearly describes the commonalities among three key methods-transform coding, and wavelet transforms n Gives comparative performance evaluations of many proposed techniques
Signals play an important role in our day-to-day life. We frequently come across signals carrying information in the shape of speech, music, picture and video signals. A signal is a function of independent variables such as time, distance, position, temperature, pressure etc. Main objective of signal processing is concerned with the mathematical representation of signal and the algorithmic operation carried out to extract the information present in it. Application of B-Spline and wavelet tools has been discussed in texture classification in this book. We have introduced a new wavelet decomposition technique using fast recursive generalised IIR filters. We have also proposed Oriented Laplacian Pyramid (OLP) using generalised B- spline filters. This book is mainly useful to students/ researchers who are working in the areas of signal/image processing.
The purpose of this thesis is to apply the wavelet transform WT to multiresolution structures for analyzing the information content of images based on multiresolution signal decomposition of the wavelet representation. The advantage of the wavelet transform is the fact that it uses different building blocks than the Fourier's sines and cosines and can also work around any gaps in the data. The wavelet block has start and end points and is a right tool for analyzing nonstationary signals. The wavelet transform is related to wavelets, a scaling function and an input signal. From Haar scaling and wavelets, the wavelet transform system was built by using multiresolution signal decomposition. Since Daubechies' scaling and wavelets contain very unique characteristics, which can compress signals having constant or linear components, they were chosen to build both 1-D and 2-D wavelet transforms. In this thesis, three test signals were carefully selected to be used for comparing the efficiencies of data compression between the wavelet and the Fourier transform. By visually inspecting the results, a wavelet reconstructed signal shows better resolution than the same Fourier reconstructed signal under the same compression ratio. The process of signal decomposition and reconstruction is described as follows: A signal is first broken down into its low and high frequency components. The part that contains the low frequency components contains most of the information, is again decomposed into low and high parts. The coarsest signal is kept in the last stage of the lowpass filter operation. It is obtained through a pyramidal algorithm based on convolutions with quadrature mirror filters. Finally, two specific applications (scaling up and image classification) of wavelet analysis are presented for the case of forested landscapes in the Pacific Northwest, U.S.A. The NMSE (normalized mean square error) is used to quantify the amount of information change with image scaling up. To relate changes in ecological function with changes in ecological pattern and information content which occurs in the process of data compression using the wavelet, a simple classification is performed. Thus, changes in information which occur in scaling-up (i.e. the change in forest pattern which results from filtering using the wavelet) are related to changes in ecological function. It is hoped that the results of the study will contribute to issues concerning data compression using satellite imagery to monitor forest health and develop understanding for scaling problems in ecology.
This book is intended for use in the teaching of graduate and senior undergraduate courses on multiresolution signal and geometry processing in the engineering and related disciplines. It has been used for several years for teaching purposes in the Department of Electrical and Computer Engineering at the University of Victoria and has been well received by students. This book provides a comprehensive introduction to multiresolution signal and geometry processing, with a focus on both theory and applications. The book has two main components, corresponding to multiresolution processing in the contexts of: 1) signal processing and 2) geometry processing. The signal-processing component of the book studies one-dimensional and multi-dimensional multirate systems, considering multirate structures such as sampling-rate converters, filter banks, and transmultiplexers. A particularly strong emphasis is placed on filter banks. Univariate and multivariate wavelet systems are examined, with the biorthogonal and orthonormal cases both being considered. The relationship between filter banks and wavelet systems is established. Several applications of filter banks and wavelets in signal processing are covered, including signal coding, image compression, and noise reduction. For readers interested in image compression, a detailed overview of the JPEG-2000 standard is also provided. Some other applications of multirate systems are considered, such as transmultiplexers for communication systems (e.g., multicarrier modulation). The geometry-processing component of the book studies subdivision surfaces and subdivision wavelets. Some mathematical background relating to geometry processing is provided, including topics such as homogeneous coordinate transformations, manifolds, surface representations, and polygon meshes. Several subdivision schemes are examined in detail, including the Loop, Kobbelt sqrt(3), and Catmull-Clark methods. The application of subdivision surfaces in computer graphics is considered. A detailed introduction to functional analysis is provided, for those who would like a deeper understanding of the mathematics underlying wavelets and filter banks. For those who are interested in software applications of the material covered in the book, appendices are included that introduce the CGAL and OpenGL libraries. Also, an appendix on the SPL library (which was developed for use with this book) is included. Throughout the book, many worked-through examples are provided. Problem sets are also provided for each major topic covered.
This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges.The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD.The book also provides a platform for researchers to develop the HHT method further and to identify more applications.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
The first book to provide a detailed discussion of the application of wavelets in wireless communications, this is an invaluable source of information for graduate students, researchers, and telecommunications engineers, managers and strategists. It overviews applications, explains how to design new wavelets and compares wavelet technology with existing OFDM technology. • Addresses the applications and challenges of wavelet technology for a range of wireless communication domains • Aids in the understanding of Wavelet Packet Modulation and compares it with OFDM • Includes tutorials on convex optimisation, spectral factorisation and the design of wavelets • Explains design methods for new wavelet technologies for wireless communications, addressing many challenges, such as peak-to-average power ratio reduction, interference mitigation, reduction of sensitivity to time, frequency and phase offsets, and efficient usage of wireless resources • Describes the application of wavelet radio in spectrum sensing of cognitive radio systems.
This work results from a selection of the contributions presented in the mini symposium “Applications of Multiresolution Analysis with “Wavelets”, presented at the ICIAM 19, the International Congress on Industrial and Applied Mathematics held at Valencia, Spain, in July 2019. The presented developments and applications cover different areas, including filtering, signal analysis for damage detection, time series analysis, solutions to boundary value problems and fractional calculus. This bunch of examples highlights the importance of multiresolution analysis to face problems in several and varied disciplines. The book is addressed to researchers in the field.