Time-Frequency Signal Analysis and Processing
Time-Frequency Signal Analysis and Processing

Author: Boualem Boashash

Publisher: Academic Press

Published: 2015-12-11

Total Pages: 1070

ISBN-13: 0123985250

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Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory, techniques and algorithms used for the analysis and processing of non-stationary signals, as found in a wide range of applications including telecommunications, radar, and biomedical engineering. This book gives the university researcher and R&D engineer insights into how to use TFSAP methods to develop and implement the engineering application systems they require. New to this edition: - New sections on Efficient and Fast Algorithms; a "Getting Started" chapter enabling readers to start using the algorithms on simulated and real examples with the TFSAP toolbox, compare the results with the ones presented in the book and then insert the algorithms in their own applications and adapt them as needed. - Two new chapters and twenty three new sections, including updated references. - New topics including: efficient algorithms for optimal TFDs (with source code), the enhanced spectrogram, time-frequency modelling, more mathematical foundations, the relationships between QTFDs and Wavelet Transforms, new advanced applications such as cognitive radio, watermarking, noise reduction in the time-frequency domain, algorithms for Time-Frequency Image Processing, and Time-Frequency applications in neuroscience (new chapter). - A comprehensive tutorial introduction to Time-Frequency Signal Analysis and Processing (TFSAP), accessible to anyone who has taken a first course in signals - Key advances in theory, methodology and algorithms, are concisely presented by some of the leading authorities on the respective topics - Applications written by leading researchers showing how to use TFSAP methods

Compressed Sensing & Sparse Filtering
Compressed Sensing & Sparse Filtering

Author: Avishy Y. Carmi

Publisher: Springer Science & Business Media

Published: 2013-09-13

Total Pages: 505

ISBN-13: 364238398X

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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.

Nonuniform Sampling
Nonuniform Sampling

Author: Farokh Marvasti

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 938

ISBN-13: 1461512298

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Our understanding of nature is often through nonuniform observations in space or time. In space, one normally observes the important features of an object, such as edges. The less important features are interpolated. History is a collection of important events that are nonuniformly spaced in time. Historians infer between events (interpolation) and politicians and stock market analysts forecast the future from past and present events (extrapolation). The 20 chapters of Nonuniform Sampling: Theory and Practice contain contributions by leading researchers in nonuniform and Shannon sampling, zero crossing, and interpolation theory. Its practical applications include NMR, seismology, speech and image coding, modulation and coding, optimal content, array processing, and digital filter design. It has a tutorial outlook for practising engineers and advanced students in science, engineering, and mathematics. It is also a useful reference for scientists and engineers working in the areas of medical imaging, geophysics, astronomy, biomedical engineering, computer graphics, digital filter design, speech and video processing, and phased array radar.

Information-Theoretic Methods in Data Science
Information-Theoretic Methods in Data Science

Author: Miguel R. D. Rodrigues

Publisher: Cambridge University Press

Published: 2021-04-08

Total Pages: 561

ISBN-13: 1108427138

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The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.

Sampling Theory
Sampling Theory

Author: Yonina C. Eldar

Publisher: Cambridge University Press

Published: 2015-04-09

Total Pages: 837

ISBN-13: 1107003393

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A comprehensive guide to sampling for engineers, covering the fundamental mathematical underpinnings together with practical engineering principles and applications.

Handbook of Mathematical Methods in Imaging
Handbook of Mathematical Methods in Imaging

Author: Otmar Scherzer

Publisher: Springer Science & Business Media

Published: 2010-11-23

Total Pages: 1626

ISBN-13: 0387929193

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The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

A Wavelet Tour of Signal Processing
A Wavelet Tour of Signal Processing

Author: Stephane Mallat

Publisher: Elsevier

Published: 1999-09-14

Total Pages: 663

ISBN-13: 0080520839

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This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and ÉcolePolytechnique in Paris. - Provides a broad perspective on the principles and applications of transient signal processing with wavelets - Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms - Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection, multifractal analysis, and time-varying frequency measurements - Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet - Content is accessible on several level of complexity, depending on the individual reader's needs New to the Second Edition - Optical flow calculation and video compression algorithms - Image models with bounded variation functions - Bayes and Minimax theories for signal estimation - 200 pages rewritten and most illustrations redrawn - More problems and topics for a graduate course in wavelet signal processing, in engineering and applied mathematics

Sparse Modeling
Sparse Modeling

Author: Irina Rish

Publisher: CRC Press

Published: 2014-12-01

Total Pages: 255

ISBN-13: 1439828695

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Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first present motivating examples and a high-level survey of key recent developments in sparse modeling. The book then describes optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art algorithms for finding sparse solutions. The authors go on to address a variety of sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. They also examine a particular class of sparse graphical models and cover dictionary learning and sparse matrix factorizations.

Mathematics in Image Processing
Mathematics in Image Processing

Author: Hong-Kai Zhao

Publisher: American Mathematical Soc.

Published: 2013-06-12

Total Pages: 258

ISBN-13: 0821898418

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The theme of the 2010 PCMI Summer School was Mathematics in Image Processing in a broad sense, including mathematical theory, analysis, computation algorithms and applications. In image processing, information needs to be processed, extracted and analyzed from visual content, such as photographs or videos. These demands include standard tasks such as compression and denoising, as well as high-level understanding and analysis, such as recognition and classification. Centered on the theme of mathematics in image processing, the summer school covered quite a wide spectrum of topics in this field. The summer school is particularly timely and exciting due to the very recent advances and developments in the mathematical theory and computational methods for sparse representation. This volume collects three self-contained lecture series. The topics are multi-resolution based wavelet frames and applications to image processing, sparse and redundant representation modeling of images and simulation of elasticity, biomechanics, and virtual surgery. Recent advances in image processing, compressed sensing and sparse representation are discussed.

New-generation SAR for Earth Environment Observation
New-generation SAR for Earth Environment Observation

Author: Huadong Guo

Publisher: Elsevier

Published: 2024-09-21

Total Pages: 349

ISBN-13: 0443153124

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How to reveal and fully use the information of "band, amplitude, polarization and phase" of electromagnetic wave is the key science and technology issues for SAR imaging mechanisms - information processing - parameter inversion – environmental change cognition, especially for new generation SAR with its capabilities of full polarimetric and interferometric information retrieval, 3D / 4D reconstruction, multi-based synergistic observation and ultra-high resolution data acquisition. Based on above key issues, three aspects of new generation SAR remote sensing are discussed in this book (1) The latest research status and development trend, the basic theories and methods of new generation SAR are discussed comprehensively (2) The new or original concepts, methods and typical applications of new generation SAR information processing and parameter inversion are systematically developed and created, it is the most advanced research achievement in the field of new generation SAR in the recent years (3) A new idea of SAR information integration processing and environmental parameter inversion is proposed, it represent a new SAR science application mode that has the capability to improve the SAR remote-sensing quantitative application level and promote the development of new theories and methodologies. - Presents key issues of new generation SAR remote sensing - Includes the latest research status and development trends and the basic theories and methods of new generation SAR - Presents new or original concepts, methods, and typical applications of new generation SAR information processing and parameter inversion - Covers a new idea of SAR information integration processing and environmental parameter inversion