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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI;

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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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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 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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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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MRI

MRI: Essentials for Innovative Technologies describes novel methods to improve magnetic resonance imaging (MRI) beyond its current;

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MRI

MRI: Essentials for Innovative Technologies describes novel methods to improve magnetic resonance imaging (MRI) beyond its current;

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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 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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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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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 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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Compressed Sensing

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

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Quantitative Magnetic Resonance Imaging

mapping are performed How fast imaging approaches including parallel imaging, compressed sensing, and Magnetic Resonance

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Regularized Image Reconstruction in Parallel MRI with MATLAB

of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features;

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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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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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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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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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Statistical Methods for Materials Science: The Data Science of Microstructure Characterization

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

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Computing in Communication Networks

software, such as Mininet and Docker. It shows how and where to place disruptive techniques, such as machine learning, compressed sensing, or;

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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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