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Author: Xiaojun Yuan
Publisher: Springer Nature
ISBN: 3030547620
Category : Technology & Engineering
Languages : en
Pages : 105
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Book Description
This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem). The book is divided into three parts. First, the authors introduce a turbo message passing algorithm termed denoising-based Turbo-CS (D-Turbo-CS). Second, the authors introduce a turbo message passing (TMP) algorithm for solving the ARM problem. Third, the authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed mixture. With this book, we wish to spur new researches on applying message passing to various inference problems. Provides an in depth look into turbo message passing algorithms for structured signal recovery Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message passing Shows applications in areas such as wireless communications and computer vision
Author: Xiaojun Yuan
Publisher: Springer Nature
ISBN: 3030547620
Category : Technology & Engineering
Languages : en
Pages : 105
Get Book Here
Book Description
This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem). The book is divided into three parts. First, the authors introduce a turbo message passing algorithm termed denoising-based Turbo-CS (D-Turbo-CS). Second, the authors introduce a turbo message passing (TMP) algorithm for solving the ARM problem. Third, the authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed mixture. With this book, we wish to spur new researches on applying message passing to various inference problems. Provides an in depth look into turbo message passing algorithms for structured signal recovery Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message passing Shows applications in areas such as wireless communications and computer vision
Author: Carmen Sippel
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Mohammad Ali Maleki
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 311
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Book Description
Compressed sensing refers to a growing body of techniques that `undersample' high-dimensional signals and yet recover them accurately. Such techniques make fewer measurements than traditional sampling theory demands: rather than sampling proportional to frequency bandwidth, they make only as many measurements as the underlying `information content' of those signals. However, as compared with traditional sampling theory, which can recover signals by applying simple linear reconstruction formulas, the task of signal recovery from reduced measurements requires nonlinear, and so far, relatively expensive reconstruction schemes. One popular class of reconstruction schemes uses linear programming (LP) methods; there is an elegant theory for such schemes promising large improvements over ordinary sampling rules in recovering sparse signals. However, solving the required LPs is substantially more expensive in applications than the linear reconstruction schemes that are now standard. In certain imaging problems, the signal to be acquired may be an image with $10^6$ pixels and the required LP would involve tens of thousands of constraints and millions of variables. Despite advances in the speed of LP, such methods are still dramatically more expensive to solve than we would like. In this thesis we focus on a class of low computational complexity algorithms known as iterative thresholding. We study them both theoretically and empirically. We will also introduce a new class of algorithms called approximate message passing or AMP. These schemes have several advantages over the classical thresholding approaches. First, they take advantage of the statistical properties of the problem to improve the convergence rate and predictability of the algorithm. Second, the nice properties of these algorithms enable us to make very accurate theoretical predictions on the asymptotic performance of LPs as well. It will be shown that more traditional techniques such as coherence and restricted isometry property are not able to make such precise predictions.
Author: Vladimir Poulkov
Publisher: Springer Nature
ISBN: 3030239764
Category : Computers
Languages : en
Pages : 444
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Book Description
This book constitutes the refereed post-conference proceedings of the Fourth International Conference on Future Access Enablers for Ubiquitous and Intelligent Infrastructures, FABULOUS 2019, held in Sofia, Bulgaria, in March 2019. This year’s conference topic covers Globalization through Advanced Digital Technologies – as the digitalization in all spheres of life has an impressive influence on communication and daily life in general. The 39 revised full papers were carefully reviewed and selected from 54 submissions. The main topics deal with: healthcare/wellness applications; IoT and sensor networks; IoT security in the digital transformation era; wireless communications and networks; virtual engineering and simulations.
Author: Radu Balan
Publisher: Birkhäuser
ISBN: 3319201883
Category : Mathematics
Languages : en
Pages : 440
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Book Description
This volume consists of contributions spanning a wide spectrum of harmonic analysis and its applications written by speakers at the February Fourier Talks from 2002 – 2013. Containing cutting-edge results by an impressive array of mathematicians, engineers and scientists in academia, industry and government, it will be an excellent reference for graduate students, researchers and professionals in pure and applied mathematics, physics and engineering. Topics covered include: Special Topics in Harmonic Analysis Applications and Algorithms in the Physical Sciences Gabor Theory RADAR and Communications: Design, Theory, and Applications The February Fourier Talks are held annually at the Norbert Wiener Center for Harmonic Analysis and Applications. Located at the University of Maryland, College Park, the Norbert Wiener Center provides a state-of- the-art research venue for the broad emerging area of mathematical engineering.
Author: Jeremy Paul Vila
Publisher:
ISBN:
Category :
Languages : en
Pages : 168
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Book Description
In recent years, there have been massive increases in both the dimensionality and sample sizes of data due to ever-increasing consumer demand coupled with relatively inexpensive sensing technologies. These high-dimensional datasets bring challenges such as complexity, along with numerous opportunities. Though many signals of interest live in a high-dimensional ambient space, they often have a much smaller inherent dimensionality which, if leveraged, lead to improved recoveries. For example, the notion of sparsity is a requisite in the compressive sensing (CS) field, which allows for accurate signal reconstruction from sub-Nyquist sampled measurements given certain conditions. When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution is apriori known, then one could use computationally efficient approximate message passing (AMP) techniques that yield approximate minimum MSE (MMSE) estimates or critical points to the maximum a posteriori (MAP) estimation problem. In practice, though, the distribution is unknown, motivating the use of robust, convex algorithms such as LASSO-which is nearly minimax optimal-at the cost of significantly larger MSE for non-least- favorable distributions. As an alternative, this dissertation focuses on empirical-Bayesian techniques that simultaneously learn the underlying signal distribution using the expectation-maximization (EM) algorithm while recovering the signal. These techniques are well-justified in the high-dimensional setting since, in the large system limit under specific problem conditions, the MMSE version of AMP's posteriors converge to the true posteriors and a generalization of the resulting EM procedure yields consistent parameter estimates. Furthermore, in many practical applications, we can exploit additional signal structure beyond simple sparsity for improved MSE. In this dissertation, we investigate signals that are non-negative, obey linear equality constraints, and exhibit amplitude correlation/structured sparsity across its elements. To perform statistical inference on these structured signals, we first demonstrate how to incorporate these structures into our Bayesian model, then employ a technique called "turbo" approximate message passing on the underlying factor graph. Specifically, we partition the factor graph into the Markov and generalized linear model subgraphs, the latter of which can be efficiently implemented using approximate message passing methods, and combine the subgraphs using a "turbo" message passing approach. Numerical experiments on the compressive sensing and hyperspectral unmixing applications con- firm the state-of-the-art performance of our approach, in both reconstruction error and runtime, on both synthetic and real-world datasets.
Author: Bane Vasic
Publisher: CRC Press
ISBN: 0203490312
Category : Computers
Languages : en
Pages : 742
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Book Description
Implementing new architectures and designs for the magnetic recording read channel have been pushed to the limits of modern integrated circuit manufacturing technology. This book reviews advanced coding and signal processing techniques and architectures for magnetic recording systems. Beginning with the basic principles, it examines read/write operations, data organization, head positioning, sensing, timing recovery, data detection, and error correction. It also provides an in-depth treatment of all recording channel subsystems inside a read channel and hard disk drive controller. The final section reviews new trends in coding, particularly emerging codes for recording channels.
Author: David J. C. MacKay
Publisher: Cambridge University Press
ISBN: 9780521642989
Category : Computers
Languages : en
Pages : 694
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Book Description
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Author: Yonina C. Eldar
Publisher: Cambridge University Press
ISBN: 1108832989
Category : Computers
Languages : en
Pages : 559
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Book Description
Discover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 886
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Book Description
