A Study on Multi-signal Adaptive Sparse Representation Theory Based Algorithm Design and Applications with Its Inspired Global Optimization Methods PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download A Study on Multi-signal Adaptive Sparse Representation Theory Based Algorithm Design and Applications with Its Inspired Global Optimization Methods PDF full book. Access full book title A Study on Multi-signal Adaptive Sparse Representation Theory Based Algorithm Design and Applications with Its Inspired Global Optimization Methods by Lei Dai. Download full books in PDF and EPUB format.
Author: Lei Dai
Publisher:
ISBN:
Category : Adaptive signal processing
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Lei Dai
Publisher:
ISBN:
Category : Adaptive signal processing
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Michael Elad
Publisher: Springer Science & Business Media
ISBN: 1441970118
Category : Mathematics
Languages : en
Pages : 376
Get Book Here
Book Description
A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
Author: Ching-Hua Lee
Publisher:
ISBN:
Category :
Languages : en
Pages : 168
Get Book Here
Book Description
Sparsity has played an important role in numerous signal processing systems. By leveraging sparse representations of signals, many batch estimation algorithms and methods that are efficient, robust, and effective for practical engineering problems have been developed. However, gradient descent-based approaches that are less computationally expensive have become essential to the development of modern machine learning systems, especially the deep neural networks (DNNs). This dissertation examines how we can incorporate sparsity principles into gradient-based learning algorithms, in both signal processing and machine learning applications, for improved estimation and optimization performance. On the signal processing side, we study how to take advantage of sparsity in the system response for improving the convergence rate of the least mean square (LMS) family of adaptive filters, which are derived from using gradient descent on the mean square error objective function. Based on iterative reweighting sparse signal recovery (SSR) techniques, we propose a novel framework for deriving a class of sparsity-aware LMS algorithms by adopting an affine scaling transformation (AST) methodology in the algorithm design process. Sparsity-promoting LMS (SLMS) and Sparsity-promoting Normalized LMS (SNLMS) algorithms are introduced, which can take advantage of, though do not strictly enforce, the sparsity of the underlying system if it already exists for convergence speedup. In addition, the reweighting-AST framework is applied to the conjugate gradient (CG) class of adaptive algorithms, which in general demonstrate a much higher convergence rate than the LMS family. The resulting Sparsity-promoting CG (SCG) algorithm also demonstrates improved convergence characteristics for sparse system identification. Finally, the proposed algorithms are applied to the real-world problem of acoustic feedback reduction encountered in hearing aids. On the machine learning side, we investigate how to exploit the SSR techniques in gradient-based optimization algorithms for learning compact representations in nonlinear estimation tasks, especially with overparameterized models. In particular, the reweighting-AST framework is utilized in the context of estimating a regularized solution exhibiting some desired properties such as sparsity without having to incorporate a regularization penalty. The resulting algorithms in general have a weighted gradient term in the update equation where the weighting matrix provides certain implicit regularization capabilities. We start by establishing a general framework that can possibly extend to various regularizers and then focus on the sparsity regularization aspect. As notable applications of nonlinear model sparsification, we propose i) Sparsity-promoting Stochastic Gradient Descent (SSGD) algorithms for DNN compression and ii) Sparsity-promoting Kernel LMS (SKLMS) and Sparsity-promoting Kernel NLMS (SKNLMS) algorithms for dictionary pruning in kernel methods.
Author: Michael Elad
Publisher: Springer
ISBN: 9781441970121
Category : Mathematics
Languages : en
Pages : 376
Get Book Here
Book Description
A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
Author: Jean-Luc Starck
Publisher: Cambridge University Press
ISBN: 1316483304
Category : Computers
Languages : en
Pages : 449
Get Book Here
Book Description
This thoroughly updated new edition presents state-of-the-art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
Author: Stephane Mallat
Publisher: Elsevier
ISBN: 0080520839
Category : Computers
Languages : en
Pages : 663
Get Book Here
Book Description
This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and École Polytechnique in Paris. Provides a broad perspective on the principles and applications of transient signal processing with wavelets Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection, multifractal analysis, and time-varying frequency measurements Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet Content is accessible on several level of complexity, depending on the individual reader's needs New to the Second Edition Optical flow calculation and video compression algorithms Image models with bounded variation functions Bayes and Minimax theories for signal estimation 200 pages rewritten and most illustrations redrawn More problems and topics for a graduate course in wavelet signal processing, in engineering and applied mathematics
Author: Julien Mairal
Publisher: Now Publishers
ISBN: 9781680830088
Category : Computers
Languages : en
Pages : 216
Get Book Here
Book Description
Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
Author: Kutluyil Doğançay
Publisher: Academic Press
ISBN: 0080921159
Category : Technology & Engineering
Languages : en
Pages : 295
Get Book Here
Book Description
Partial-update adaptive signal processing algorithms not only permit significant complexity reduction in adaptive filter implementations, but can also improve adaptive filter performance in telecommunications applications. This book gives state-of-the-art methods for the design and development of partial-update adaptive signal processing algorithms for use in systems development.Partial-Update Adaptive Signal Processing provides a comprehensive coverage of key partial updating schemes, giving detailed information on the theory and applications of acoustic and network echo cancellation, channel equalization and multiuser detection. It also examines convergence and stability issues for partial update algorithms, providing detailed complexity analysis and a unifying treatment of partial-update techniques.Features:• Advanced analysis and design tools• Application examples illustrating the use of partial-update adaptive signal processing• MATLAB codes for developed algorithms This unique reference will be of interest to signal processing and communications engineers, researchers, R&D engineers and graduate students."This is a very systematic and methodical treatment of an adaptive signal processing topic, of particular significance in power limited applications such as in wireless communication systems and smart ad hoc sensor networks. I am very happy to have this book on my shelf, not to gather dust, but to be consulted and used in my own research and teaching activities" – Professor A. G. Constantinides, Imperial College, LondonAbout the author:Kutluyil Dogançay is an associate professor of Electrical Engineering at the University of South Australia. His research interests span statistical and adaptive signal processing and he serves as a consultant to defence and private industry. He was the Signal Processing and Communications Program Chair of IDC Conference 2007, and is currently chair of the IEEE South Australia Communications and Signal Processing Chapter. Advanced analysis and design tools Algorithm summaries in tabular format Case studies illustrate the application of partial update adaptive signal processing
Author: Thomas S. Alexander
Publisher: Springer Science & Business Media
ISBN: 1461249783
Category : Technology & Engineering
Languages : en
Pages : 189
Get Book Here
Book Description
The creation of the text really began in 1976 with the author being involved with a group of researchers at Stanford University and the Naval Ocean Systems Center, San Diego. At that time, adaptive techniques were more laboratory (and mental) curiosities than the accepted and pervasive categories of signal processing that they have become. Over the lasl 10 years, adaptive filters have become standard components in telephony, data communications, and signal detection and tracking systems. Their use and consumer acceptance will undoubtedly only increase in the future. The mathematical principles underlying adaptive signal processing were initially fascinating and were my first experience in seeing applied mathematics work for a paycheck. Since that time, the application of even more advanced mathematical techniques have kept the area of adaptive signal processing as exciting as those initial days. The text seeks to be a bridge between the open literature in the professional journals, which is usually quite concentrated, concise, and advanced, and the graduate classroom and research environment where underlying principles are often more important.
Author: Avishy Y. Carmi
Publisher: Springer Science & Business Media
ISBN: 364238398X
Category : Technology & Engineering
Languages : en
Pages : 505
Get Book Here
Book Description
This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing.
