Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Compressed sensing (Telecommunication) --- Biosensing Techniques --- Medical innovations. --- Innovations, Medical --- Medicine --- Medical technology --- Technological innovations --- Compressive sensing (Telecommunication) --- Sensing, Compressed (Telecommunication) --- Sparse sampling (Telecommunication) --- Signal processing --- Innovations

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.

Signal processing. --- Wavelets (Mathematics). --- Signal processing --- Wavelets (Mathematics) --- Compressed sensing (Telecommunication) --- Compressive sensing (Telecommunication) --- Sensing, Compressed (Telecommunication) --- Sparse sampling (Telecommunication) --- Wavelet analysis --- Harmonic analysis --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- telecommunicatie

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways. Matlab and IDL code accompany these methods and applications to reproduce the experiments and illustrate the reasoning and methodology of the research are available for download at the associated web site.

Transformations (Mathematics) --- Signal processing. --- Image processing. --- Sparse matrices. --- Wavelets (Mathematics) --- Wavelet analysis --- Harmonic analysis --- Spare matrix techniques --- Matrices --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Algorithms --- Differential invariants --- Geometry, Differential --- Compressed sensing (Telecommunication) --- Compressive sensing (Telecommunication) --- Sensing, Compressed (Telecommunication) --- Sparse sampling (Telecommunication) --- Signal processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The objective of this 5th edition of the book, as with the prior editions, is to teach through images a practical approach to magnetic resonance (MR) physics and image quality. Unlike other texts covering this topic, the focus is on clinical images rather than equations. A practical approach to MR physics is developed through images, emphasizing knowledge of fundamentals important to achieve high image quality. Pulse diagrams are also included, which many at first find difficult to understand. Readers are encouraged to glance at these as they go through the text. With time and repetition, as a reader progresses through the book, the value of these and the knowledge thus available will become evident (and the diagrams themselves easier to understand). The text is organized into concise chapters, each discussing an important point relevant to clinical MR and illustrated largely with images from routine patient exams. The topics covered encompass the breadth of the field, from imaging basics and pulse sequences to advanced topics including contrast-enhanced MR angiography, spectroscopy, perfusion and advanced parallel imaging/data sparsity techniques. Discussion of the latest hardware and software innovations, for example next generation low field MR, deep learning, MR-PET, 7 T, interventional MR, 4D flow, CAIPIRINHA, spiral techniques, radial acquisition, simultaneous multislice, compressed sensing and MR fingerprinting, is included because these topics are critical to current clinical practice as well as to future advances. Included in the fifth edition are a large number of new topics, keeping the text up to date in this increasingly complex field. The text has also been thoroughly revised to include additional relevant clinical images, to improve the clarity of descriptions, and to increase the depth of content. The book is highly recommended for radiologists, physicists, and technologists interested in the background of image acquisition used in standard as well as specialized clinical settings. .

Magnetic resonance imaging. --- Medical radiology. --- Compressed sensing (Telecommunication) --- Compressive sensing (Telecommunication) --- Sensing, Compressed (Telecommunication) --- Sparse sampling (Telecommunication) --- Signal processing --- Clinical radiology --- Radiology, Medical --- Radiology (Medicine) --- Medical physics --- Clinical magnetic resonance imaging --- Diagnostic magnetic resonance imaging --- Functional magnetic resonance imaging --- Imaging, Magnetic resonance --- Medical magnetic resonance imaging --- MR imaging --- MRI (Magnetic resonance imaging) --- NMR imaging --- Nuclear magnetic resonance --- Nuclear magnetic resonance imaging --- Cross-sectional imaging --- Diagnostic imaging --- Diagnostic use --- Radiology. --- Medical physics. --- Neurology. --- Nervous system --- Medical Physics. --- Neurosurgery. --- Surgery. --- Nerves --- Neurosurgery --- Medicine --- Neuropsychiatry --- Health physics --- Health radiation physics --- Medical radiation physics --- Radiotherapy physics --- Radiation therapy physics --- Biophysics --- Physics --- Radiological physics --- Radiation --- Surgery --- Diseases

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Reconstructing or approximating objects from seemingly incomplete information is a frequent challenge in mathematics, science, and engineering. A multitude of tools designed to recover hidden information are based on Shannon’s classical sampling theorem, a central pillar of Sampling Theory. The growing need to efficiently obtain precise and tailored digital representations of complex objects and phenomena requires the maturation of available tools in Sampling Theory as well as the development of complementary, novel mathematical theories. Today, research themes such as Compressed Sensing and Frame Theory re-energize the broad area of Sampling Theory. This volume illustrates the renaissance that the area of Sampling Theory is currently experiencing. It touches upon trendsetting areas such as Compressed Sensing, Finite Frames, Parametric Partial Differential Equations, Quantization, Finite Rate of Innovation, System Theory, as well as sampling in Geometry and Algebraic Topology.

Mathematical Theory --- Computer Science --- Engineering & Applied Sciences --- Mathematics --- Physical Sciences & Mathematics --- Mathematical statistics --- Compressed sensing (Telecommunication) --- Compressive sensing (Telecommunication) --- Sensing, Compressed (Telecommunication) --- Sparse sampling (Telecommunication) --- Mathematics. --- Approximation theory. --- Functions of complex variables. --- Information theory. --- Applied mathematics. --- Engineering mathematics. --- Information and Communication, Circuits. --- Signal, Image and Speech Processing. --- Approximations and Expansions. --- Appl.Mathematics/Computational Methods of Engineering. --- Functions of a Complex Variable. --- Signal processing --- Mathematical and Computational Engineering. --- Complex variables --- Elliptic functions --- Functions of real variables --- Engineering --- Engineering analysis --- Mathematical analysis --- Math --- Science --- Signal processing. --- Image processing. --- Speech processing systems. --- Theory of approximation --- Functional analysis --- Functions --- Polynomials --- Chebyshev systems --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Communication theory --- Communication --- Cybernetics