Dynamic Compressive Sensing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamic Compressive Sensing write by Muhammad Salman Asif. This book was released on 2013. Dynamic Compressive Sensing available in PDF, EPUB and Kindle. 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.
Design and Analysis of Dynamic Compressive Sensing in Distribution Grids
Design and Analysis of Dynamic Compressive Sensing in Distribution Grids - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Design and Analysis of Dynamic Compressive Sensing in Distribution Grids write by Hazhar Sufi Karimi. This book was released on 2020. Design and Analysis of Dynamic Compressive Sensing in Distribution Grids available in PDF, EPUB and Kindle. 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.
Compressed Sensing for Engineers
Compressed Sensing for Engineers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Compressed Sensing for Engineers write by Angshul Majumdar. This book was released on 2018-12-07. Compressed Sensing for Engineers available in PDF, EPUB and Kindle. 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
Data-Driven Science and Engineering
Data-Driven Science and Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Driven Science and Engineering write by Steven L. Brunton. This book was released on 2022-05-05. Data-Driven Science and Engineering available in PDF, EPUB and Kindle. A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Compressive Sensing for Wireless Networks
Compressive Sensing for Wireless Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Compressive Sensing for Wireless Networks write by Zhu Han. This book was released on 2013-06-06. Compressive Sensing for Wireless Networks available in PDF, EPUB and Kindle. This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.
