Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford UniversityThe new edition of ...this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications.Features:* Balances presentation of the mathematics with applications to signal processing* Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox* Companion website for instructors and selected solutions and code available for studentsNew in this edition* Sparse signal representations in dictionaries* Compressive sensing, super-resolution and source separation* Geometric image processing with curvelets and bandlets* Wavelets for computer graphics with lifting on surfaces* Time-frequency audio processing and denoising* Image compression with JPEG-2000* New and updated exercisesA Wavelet Tour of Signal Processing: The Sparse Way, third edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering.Stephane Mallat is Professor in Applied Mathematics at École Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company.Companion website: A Numerical Tour of Signal Processing
Includes all the latest developments since the book was published in 1999, including itsapplication to JPEG 2000 and MPEG-4Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolboxBalances presentation of the mathematics with applications to signal processing
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.
First signals Bonner, John Tyler; Bonner, John Tyler
2000., 20090911, 2009, 2001, 2001-01-01
eBook
The enormous recent success of molecular developmental biology has yielded a vast amount of new information on the details of development. So much so that we risk losing sight of the underlying ...principles that apply to all development. To cut through this thicket, John Tyler Bonner ponders a moment in evolution when development was at its most basic--the moment when signaling between cells began. Although multicellularity arose numerous times, most of those events happened many millions of years ago. Many of the details of development that we see today, even in simple organisms, accrued over a long evolutionary timeline, and the initial events are obscured. The relatively uncomplicated and easy-to-grow cellular slime molds offer a unique opportunity to analyze development at a primitive stage and perhaps gain insight into how early multicellular development might have started.
Signal detection theory describes how an observer makes decisions about weak, uncertain, or ambiguous events or signals. It is widely applied in psychology, medicine, and other related fields. This ...book describes the theory, explains its mathematical basis, and shows how to separate the observer's sensitivity to a signal from his or her tendency to say “yes” or “no.” Both detection of an event and discrimination between two events are treated. Chapters 1-4 describe the basic form of the signal-detection model and how to use it; Chapters 5-7 extend the model to different procedures such as identification of a signal; Chapters 8-10 expand it to other methods and distributions; and Chapter 11 describes the statistical treatment of detection data.
The cGAS-STING signalling pathway has emerged as a key mediator of inflammation in the settings of infection, cellular stress and tissue damage. Underlying this broad involvement of the cGAS-STING ...pathway is its capacity to sense and regulate the cellular response towards microbial and host-derived DNAs, which serve as ubiquitous danger-associated molecules. Insights into the structural and molecular biology of the cGAS-STING pathway have enabled the development of selective small-molecule inhibitors with the potential to target the cGAS-STING axis in a number of inflammatory diseases in humans. Here, we outline the principal elements of the cGAS-STING signalling cascade and discuss the general mechanisms underlying the association of cGAS-STING activity with various autoinflammatory, autoimmune and degenerative diseases. Finally, we outline the chemical nature of recently developed cGAS and STING antagonists and summarize their potential clinical applications.
Sparse signal representation, analysis, and sensing have received a lot of attention in recent years from the signal processing, optimization, and learning communities. On one hand, learning ...overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. On the other hand, the framework of compressed sensing (CS) has shown that sparse signals can be recovered from far less samples than those required by the classical Shannon-Nyquist Theorem. The samples used in CS correspond to linear projections obtained by a sensing projection matrix. It has been shown that, for example, a nonadaptive random sampling matrix satisfies the fundamental theoretical requirements of CS, enjoying the additional benefit of universality. On the other hand, a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy or further reduce the necessary number of samples. In this paper, we introduce a framework for the joint design and optimization, from a set of training images, of the nonparametric dictionary and the sensing matrix. We show that this joint optimization outperforms both the use of random sensing matrices and those matrices that are optimized independently of the learning of the dictionary. Particular cases of the proposed framework include the optimization of the sensing matrix for a given dictionary as well as the optimization of the dictionary for a predefined sensing environment. The presentation of the framework and its efficient numerical optimization is complemented with numerous examples on classical image datasets.
Signals and datasets that arise in physical and engineering applications, as well as social, genetics, biomolecular, and many other domains, are becoming increasingly larger and more complex. In ...contrast to traditional time and image signals, data in these domains are supported by arbitrary graphs. Signal processing on graphs extends concepts and techniques from traditional signal processing to data indexed by generic graphs. This paper studies the concepts of low and high frequencies on graphs, and low-, high- and band-pass graph signals and graph filters. In traditional signal processing, these concepts are easily defined because of a natural frequency ordering that has a physical interpretation. For signals residing on graphs, in general, there is no obvious frequency ordering. We propose a definition of total variation for graph signals that naturally leads to a frequency ordering on graphs and defines low-, high-, and band-pass graph signals and filters. We study the design of graph filters with specified frequency response, and illustrate our approach with applications to sensor malfunction detection and data classification.
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