Multiscale Signal Analysis And Modeling
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Multiscale Signal Analysis And Modeling
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Author : Xiaoping Shen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-09-18
Multiscale Signal Analysis And Modeling written by Xiaoping Shen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-18 with Technology & Engineering categories.
Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory.
Multiscale Signal Analysis And Modeling
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Author :
language : en
Publisher: Springer
Release Date : 2012-09-19
Multiscale Signal Analysis And Modeling written by and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-19 with categories.
Generative Signal Processing Through Multilayer Multiscale Wavelet Models
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Author : Jieqian He
language : en
Publisher:
Release Date : 2021
Generative Signal Processing Through Multilayer Multiscale Wavelet Models written by Jieqian He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Electronic dissertations categories.
Wavelet analysis and deep learning are two popular fields for signal processing. The scattering transform from wavelet analysis is a recently proposed mathematical model for convolution neural networks. Signals with repeated patterns can be analyzed using the statistics from such models. Specifically, signals from certain classes can be recovered from related statistics. We first focus on recovering 1D deterministic dirac signals from multiscale statistics. We prove a dirac signal can be recovered from multiscale statistics up to a translation and reflection. Then we switch to a stochastic version, modeled using Poisson point processes, and prove wavelet statistics at small scales capture the intensity parameter of Poisson point processes. We also design a scattering generative adversarial network (GAN) to generate new Poisson point samples from statistics of multiple given samples. Next we consider texture images. We successfully synthesize new textures given one sample from the texture class through multiscale, multilayer wavelet models. Finally, we analyze and prove why the multiscale multilayer model is essential for signal recovery, especially natural texture images.
A Stochastic Modeling Approach To Multiscale Signal Processing
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Author : Kenneth Chien-ko Chou
language : en
Publisher:
Release Date : 1991
A Stochastic Modeling Approach To Multiscale Signal Processing written by Kenneth Chien-ko Chou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Signal processing categories.
Multiscale Analysis Of Complex Time Series
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Author : Jianbo Gao
language : en
Publisher: John Wiley & Sons
Release Date : 2007-12-04
Multiscale Analysis Of Complex Time Series written by Jianbo Gao and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-04 with Mathematics categories.
The only integrative approach to chaos and random fractal theory Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts necessary for researchers to fully understand the ever-expanding literature and apply novel methods to effectively solve their signal processing problems. Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner. Adopting a data-driven approach, the book covers: DNA sequence analysis EEG analysis Heart rate variability analysis Neural information processing Network traffic modeling Economic time series analysis And more Additionally, the book illustrates almost every concept presented through applications and a dedicated Web site is available with source codes written in various languages, including Java, Fortran, C, and MATLAB, together with some simulated and experimental data. The only modern treatment of signal processing with chaos and random fractals unified, this is an essential book for researchers and graduate students in electrical engineering, computer science, bioengineering, and many other fields.
Higher Dimensional Signal Processing Via Multiscale Geometric Analysis
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Author :
language : en
Publisher:
Release Date : 2010
Higher Dimensional Signal Processing Via Multiscale Geometric Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
This project pursued a general theory for complex-valued multiscale signal and image modeling, processing, and analysis that is matched to singularity-rich data. Higher-dimensional signals that feature geometric manifold structures were of particular interest in developing theory and a practical toolset for analysis and processing. We pursued a three-pronged approach in creating new multiscale transforms, new geometric statistical models, and new manifold-based signal representations. The results of our research include (1) the Dual Tree Quaternion Wavelet, an efficient transform and analysis tool that features near shift invariance and linear computational complexity; (2) a geometric hidden Markov tree wavelet model, which accounts for geometric regularity by capturing the dependencies between complex wavelet coefficients along a contour; and (3) surflet representations of signal discontinuities with near optimal rate-distortion performance. These new tools have led to significant performance gains immediately applicable to a number of important Navy-relevant applications, including target detection and classification, image segmentation and fusion, and computer network traffic modeling.
Multiscale Statistical Models For Signal And Image Processing
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Author :
language : en
Publisher:
Release Date : 2004
Multiscale Statistical Models For Signal And Image Processing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
We are developing a general theory for multi scale signal and image modeling, processing, and analysis that matched to singularity-rich data, such as transients and images with edges. Using a linguistic analogy, our model can be interpreted as grammars that constrain the wavelet vocabulary. Our investigation focuses on probabilistic graph models (tree-based hidden Markov models) that can accurately, realistically, and efficiently represent singularity structure in the wavelet domain. Grammar design is being guided by a detailed study of the final structure of singularities using Besov spaces and multifractal analysis.
A Stochastic Modelling Approach To Multiscale Signal Processing
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Author : Kenneth Chien-ko Chou
language : en
Publisher:
Release Date : 1991
A Stochastic Modelling Approach To Multiscale Signal Processing written by Kenneth Chien-ko Chou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Multiscale Analysis Modeling And Processing Of Higher Dimensional Geometric Data
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Author :
language : en
Publisher:
Release Date : 2007
Multiscale Analysis Modeling And Processing Of Higher Dimensional Geometric Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
The wavelet transform has emerged over the last decade as a powerful new tool for statistical signal processing. The wavelet domain provides a natural setting for many applications involving real-world signals and images, especially those rich in singularities (edges, ridges, and other transients). In this project, we extended wavelet transform modeling and processing algorithms to handle multidimensional signals that are smooth save for singularities along lower-dimensional manifolds. The key building block is a new quaternion wavelet transform (QWT) that generalizes the complex wavelet transform to higher dimensions using a multidimensional Hilbert transform. The QWT has a quaternion magnitude-phase representation that encodes image shifts in an absolute (x, y)-coordinate system and thus provides a theoretical framework for analyzing the phase behavior of 2-D image shifts. We conducted a thorough analysis of the QWT phase around edge regions and thereby developed efficient multiscale edge localization and flow/motion estimation algorithms for image registration based on the QWT phase.
Multiscale Statistical Signal Processing
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Author : Michèle Basseville
language : en
Publisher:
Release Date : 1989
Multiscale Statistical Signal Processing written by Michèle Basseville and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Kalman filtering categories.