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Compressed Sensing for Engineers

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations;

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Compressed Sensing for Engineers

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations;

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Compressive Sensing for Wireless Communication

Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements;

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Compressed Sensing for Distributed Systems

This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has;

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Compressed Sensing and its Applications

that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as;

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Compressed Sensing in Li-Fi and Wi-Fi Networks

Compressed Sensing in Li-Fi and Wi-Fi Networks features coverage of the first applications in optical telecommunications and wireless;

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Compressed Sensing und Sparse Rekonstruktion bei Magnetic Particle Imaging

erstellt, indem die Systemantwort auf eine Punktprobe in jedem Voxel gemessen wird. Die Theorie des Compressed Sensing besagt, dass ein Signal;

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Reconstruction-Free Compressive Vision for Surveillance Applications

Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the;

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Reconstruction-Free Compressive Vision for Surveillance Applications

Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the;

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Compressed Sensing in Radar Signal Processing

highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it;

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Compressive Sensing for Urban Radar

. Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how;

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Compressive Sensing for Urban Radar

. Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how;

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Compressed Sensing

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics;

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Compressed Sensing for MRI

This work explores and extends the concept of applying compressed sensing to MRI. Asuccessful CS reconstruction requires incoherent;

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Image and Video Compression

the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most;

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Image and Video Compression

the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most;

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Numerical Linear Algebra: An Introduction

, domain decomposition methods, multipole expansions, hierarchical matrices and compressed sensing. The book provides rigorous mathematical proofs;

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Numerical Linear Algebra

, domain decomposition methods, multipole expansions, hierarchical matrices and compressed sensing. The book provides rigorous mathematical proofs;

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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic;

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Sampling: Theory and Applications: A Centennial Celebration of Claude Shannon

influence on the field. The topics covered include both theory and applications, such as: * Compressed sensing* Non-uniform and wave sampling* A-to;

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Compressed Sensing for Magnetic Resonance Image Reconstruction

aspects of mathematical forms. The book is intended for MRI engineers interested in the brass tacks of image formation; medical physicists;

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Sampling Theory

-invariant spaces and working through to the more recent fields of compressed sensing and sub-Nyquist sampling, the key concepts are addressed in a;

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Advanced Wireless Sensing Techniques for 5G Networks

of technologies in use in this area. It also provides new research topics and sensing techniques related to 5G networks for researchers.;

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Image Understanding Using Sparse Representations

, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the;

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In Vivo Glucose Sensing

In Vivo Glucose Sensing is a key reference for scientists and engineers working on the development of glucose sensing technologies for the;

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Compressive Sensing of Earth Observations

Future remote sensing systems will make extensive use of Compressive Sensing (CS) as it becomes more integrated into the system design with;

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Statistical Methods for Materials Science

applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.;

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