Download or read book Dynamic Compressive Sensing written by Muhammad Salman Asif and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents compressive sensing algorithms that utilize system dynamics in the sparse signal recovery process. These dynamics may arise due to a time-varying signal, streaming measurements, or an adaptive signal transform. Compressive sensing theory has shown that under certain conditions, a sparse signal can be recovered from a small number of linear, incoherent measurements. The recovery algorithms, however, for the most part are static: they focus on finding the solution for a fixed set of measurements, assuming a fixed (sparse) structure of the signal. In this thesis, we present a suite of sparse recovery algorithms that cater to various dynamical settings. The main contributions of this research can be classified into the following two categories: 1) Efficient algorithms for fast updating of L1-norm minimization problems in dynamical settings. 2) Efficient modeling of the signal dynamics to improve the reconstruction quality; in particular, we use inter-frame motion in videos to improve their reconstruction from compressed measurements. Dynamic L1 updating: We present homotopy-based algorithms for quickly updating the solution for various L1 problems whenever the system changes slightly. Our objective is to avoid solving an L1-norm minimization program from scratch; instead, we use information from an already solved L1 problem to quickly update the solution for a modified system. Our proposed updating schemes can incorporate time-varying signals, streaming measurements, iterative reweighting, and data-adaptive transforms. Classical signal processing methods, such as recursive least squares and the Kalman filters provide solutions for similar problems in the least squares framework, where each solution update requires a simple low-rank update. We use homotopy continuation for updating L1 problems, which requires a series of rank-one updates along the so-called homotopy path. Dynamic models in video: We present a compressive-sensing based framework for the recovery of a video sequence from incomplete, non-adaptive measurements. We use a linear dynamical system to describe the measurements and the temporal variations of the video sequence, where adjacent images are related to each other via inter-frame motion. Our goal is to recover a quality video sequence from the available set of compressed measurements, for which we exploit the spatial structure using sparse representations of individual images in a spatial transform and the temporal structure, exhibited by dependencies among neighboring images, using inter-frame motion. We discuss two problems in this work: low-complexity video compression and accelerated dynamic MRI. Even though the processes for recording compressed measurements are quite different in these two problems, the procedure for reconstructing the videos is very similar.
Download or read book Design and Analysis of Dynamic Compressive Sensing in Distribution Grids written by Hazhar Sufi Karimi and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The transition to a smart distribution grid is powered by enhanced sensing and advanced metering infrastructure that can provide situational awareness. However, aggregating data from spatially dispersed sensors/smart meters can present a significant challenge. Additionally, the lack of reliability in communication network used for aggregating this data, prevents its use for real time operations such as state estimation and control. With these challenges associated with measurement availability and accessibility, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing (CS) theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. The spatio-temporal correlation of loads and/or rooftop photovoltaic (PV) generation results in sparsity of distribution system states. In this dissertation, we first validate this system sparsity property and exploit it to develop two (direct/indirect) voltage state estimation strategies for a three-phase unbalanced distribution network. Secondly, we focus on addressing the challenge of sparse signal recovery from limited measurements while incorporating their temporal dependence. Specifically, we implement two recursive dynamic CS approaches namely, streaming modified weighted-L1 CS and Kalman filtered CS that reconstruct a sparse signal using the current underdetermined measurements and the prior information about the sparse signal and its support set. Using practical distribution system power measurements as a case study, we quantify, for the first time, the performance improvement achievable with such recursive techniques relative to batch algorithms. CS based signal recovery efforts typically assume that a limited number of measurements are available. However, in practice, due to communication network impairments, there is no guarantee that even this limited set of information might be available at the time of processing at the fusion/control center. Therefore, for the first time, we investigate the impact of intermittent measurement availability and random delays on recursive dynamic CS. Specifically, we quantify the error dynamics in both sparse signal estimation and support set estimation for a modified Kalman filter-CS based strategy in the presence of measurement losses. Using input-to-state stability analysis, we provide an upper bound for the expected covariance of the estimation error for a given rate of information loss. Next, we develop a modified CS algorithm that leverages apriori knowledge of signal correlation to project delayed measurements to the current signal recovery instant. We derive a new result quantifying the impact of errors in the apriori correlation model on signal recovery error. Lastly, we study the robustness of CS based state estimation to uncertainty in distribution network topology knowledge. Topology identification is a challenging problem in distribution systems in general and especially, when there are limited number of available measurements. We tackle this problem by jointly estimating the states and network topology via an integrated mixed integer nonlinear program formulation. By developing convex relaxations of the original formulation as well Markovian models for dynamic topology transitions, we illustrate the superior performance achieved in both state estimation and in topology identification. In summary, this dissertation offers the first comprehensive treatment of dynamic CS in smart distribution grids and can serve as the foundation of numerous follow-on efforts related to networked state estimation and control.
Download or read book Compressed Sensing for Engineers written by Angshul Majumdar and published by CRC Press. This book was released on 2018-12-07 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) and has also helped reduce the health hazard in X-Ray Computed CT. This book is a valuable resource suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra. Covers fundamental concepts of compressed sensing Makes subject matter accessible for engineers of various levels Focuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learning Includes MATLAB examples for further development
Download or read book Compressive Sensing for Wireless Networks written by Zhu Han and published by Cambridge University Press. This book was released on 2013-06-06 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.
Download or read book Compressive Sensing Based Dynamic Spectrum Access for Interference Networks written by Dennis Wieruch and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Spectrum Access Using Compressive Sensing Algorithms written by Michael Olaoluwa Odeyomi and published by . This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Mathematical Introduction to Compressive Sensing written by Simon Foucart and published by Springer Science & Business Media. This book was released on 2013-08-13 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.
Download or read book Compressive Sensing for the Photonic Mixer Device written by Miguel Heredia Conde and published by Springer. This book was released on 2017-04-18 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Miguel Heredia Conde aims at finding novel ways to fit the valuable mathematical results of the Compressive Sensing (CS) theory to the specific case of the Photonic Mixer Device (PMD).To this end, methods are presented that take profit of the sparsity of the signals gathered by PMD sensors. In his research, the author reveals that CS enables outstanding tradeoffs between sensing effort and depth error reduction or resolution enhancement.
Download or read book Compressive Sensing for Wireless Communication written by Radha Sankararajan and published by CRC Press. This book was released on 2022-09-01 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements. CS can be applied to wireless communication to enhance its capabilities. As this technology is proliferating, it is possible to explore its need and benefits for emerging applicationsCompressive Sensing for Wireless Communication provides:• A clear insight into the basics of compressed sensing• A thorough exploration of applying CS to audio, image and computer vision• Different dimensions of applying CS in Cognitive radio networks• CS in wireless sensor network for spatial compression and projection• Real world problems/projects that can be implemented and tested• Efficient methods to sample and reconstruct the images in resource constrained WMSN environmentThis book provides the details of CS and its associated applications in a thorough manner. It lays a direction for students and new engineers and prepares them for developing new tasks within the field of CS. It is an indispensable companion for practicing engineers who wish to learn about the emerging areas of interest.
Download or read book Sparse Representations and Compressive Sensing for Imaging and Vision written by Vishal M. Patel and published by Springer Science & Business Media. This book was released on 2013-02-11 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal. These measurements are usually much smaller than the number of samples that define the signal. From these small numbers of measurements, the signal is then reconstructed by non-linear procedure. Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways. In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.
Download or read book Compressive Sensing in Healthcare written by Mahdi Khosravy and published by Academic Press. This book was released on 2020-05-18 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressive Sensing in Healthcare, part of the Advances in Ubiquitous Sensing Applications for Healthcare series gives a review on compressive sensing techniques in a practical way, also presenting deterministic compressive sensing techniques that can be used in the field. The focus of the book is on healthcare applications for this technology. It is intended for both the creators of this technology and the end users of these products. The content includes the use of EEG and ECG, plus hardware and software requirements for building projects. Body area networks and body sensor networks are explored. Provides a toolbox for compressive sensing in health, presenting both mathematical and coding information Presents an intuitive introduction to compressive sensing, including MATLAB tutorials Covers applications of compressive sensing in health care
Download or read book Compressed Sensing Sparse Filtering written by Avishy Y. Carmi and published by Springer Science & Business Media. This book was released on 2013-09-13 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Compressed Sensing for Magnetic Resonance Image Reconstruction written by Angshul Majumdar and published by Cambridge University Press. This book was released on 2015-02-26 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Discusses different ways to use existing mathematical techniques to solve compressed sensing problems"--Provided by publisher.
Download or read book Global Oceans 2020 Singapore U S Gulf Coast written by IEEE Staff and published by . This book was released on 2020-10-05 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: To promote awareness, understanding, advancement and application of ocean engineering and marine technology This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources
Download or read book Compressed Sensing for Engineers written by Angshul Majumdar and published by CRC Press. This book was released on 2018-12-07 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) and has also helped reduce the health hazard in X-Ray Computed CT. This book is a valuable resource suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra. Covers fundamental concepts of compressed sensing Makes subject matter accessible for engineers of various levels Focuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learning Includes MATLAB examples for further development
Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Download or read book Handbook of Research on Software Defined and Cognitive Radio Technologies for Dynamic Spectrum Management written by Kaabouch, Naima and published by IGI Global. This book was released on 2014-10-31 with total page 1064 pages. Available in PDF, EPUB and Kindle. Book excerpt: The inadequate use of wireless spectrum resources has recently motivated researchers and practitioners to look for new ways to improve resource efficiency. As a result, new cognitive radio technologies have been proposed as an effective solution. The Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management examines the emerging technologies being used to overcome radio spectrum scarcity. Providing timely and comprehensive coverage on topics pertaining to channel estimation, spectrum sensing, communication security, frequency hopping, and smart antennas, this research work is essential for use by educators, industrialists, and graduate students, as well as academicians researching in the field.
