Vanishing Moments analyzes how various American authors have reified class through their writing, from the first influx of industrialism in the 1850s to the end of the Great Depression in the early 1940s. Eric Schocket uses this history to document America’s long engagement with the problem of class stratification and demonstrates how deeply America’s desire to deny the presence of class has marked even its most labor-conscious cultural texts. Schocket offers careful readings of works by Herman Melville, Rebecca Harding Davis, William Dean Howells, Jack London, T. S. Eliot, Gertrude Stein, Muriel Rukeyser, and Langston Hughes, among others, and explores how these authors worked to try to heal the rift between the classes. He considers the challenges writers faced before the Civil War in developing a language of class amidst the predominant concerns about race and slavery; how early literary realists dealt with the threat of class insurrection; how writers at the turn of the century attempted to span the divide between the classes by going undercover as workers; how early modernists used working-class characters and idioms to shape their aesthetic experiments; and how leftists in the 1930s struggled to develop an adequate model to connect class and literature. Vanishing Moments’ unique combination of a broad historical scope and in-depth readings makes it an essential book for scholars and students of American literature and culture, as well as for political scientists, economists, and humanists. Eric Schocket is Associate Professor of American Literature at Hampshire College. “An important book containing many brilliant arguments—hard-hitting and original. Schocket demonstrates a sophisticated acquaintance with issues within the working-class studies movement.” --Barbara Foley, Rutgers University

Vanishing Moments analyzes how various American authors have reified class through their writing, from the first influx of industrialism in the 1850s to the end of the Great Depression in the early 1940s. Eric Schocket uses this history to document America’s long engagement with the problem of class stratification and demonstrates how deeply America’s desire to deny the presence of class has marked even its most labor-conscious cultural texts. Schocket offers careful readings of works by Herman Melville, Rebecca Harding Davis, William Dean Howells, Jack London, T. S. Eliot, Gertrude Stein, Muriel Rukeyser, and Langston Hughes, among others, and explores how these authors worked to try to heal the rift between the classes. He considers the challenges writers faced before the Civil War in developing a language of class amidst the predominant concerns about race and slavery; how early literary realists dealt with the threat of class insurrection; how writers at the turn of the century attempted to span the divide between the classes by going undercover as workers; how early modernists used working-class characters and idioms to shape their aesthetic experiments; and how leftists in the 1930s struggled to develop an adequate model to connect class and literature. Vanishing Moments’ unique combination of a broad historical scope and in-depth readings makes it an essential book for scholars and students of American literature and culture, as well as for political scientists, economists, and humanists. Eric Schocket is Associate Professor of American Literature at Hampshire College. “An important book containing many brilliant arguments—hard-hitting and original. Schocket demonstrates a sophisticated acquaintance with issues within the working-class studies movement.” --Barbara Foley, Rutgers University

These notes introduce the central concepts surrounding wavelets and their applications. By focusing on the essential ideas and arguments, the authors enable readers to get to the heart of the matter as quickly as possible. A list of references guides readers interested in further study to the appropriate places in the literature for detailed proofs and real applications. The authors begin with the notion of time-frequency analysis, present the multiresolution analysis and basic wavelet construction, introduce the many friends, relatives, and mutations of wavelets, and finally give a selection of applications. This book is suitable for beginning graduate students and above. A preliminary chapter containing some of the prerequisite concepts and definitions is included for reference.

Presents a review of image denoising algorithms with practical MATLAB implementation guidance Digital Image Denoising in MATLAB provides a comprehensive treatment of digital image denoising, containing a variety of techniques with applications in high-quality photo enhancement as well as multi-dimensional signal processing problems such as array signal processing, radar signal estimation and detection, and more. Offering systematic guidance on image denoising in theories and in practice through MATLAB, this hands-on guide includes practical examples, chapter summaries, analytical and programming problems, computer simulations, and source codes for all algorithms discussed in the book. The book explains denoising algorithms including linear and nonlinear filtering, Wiener filtering, spatially adaptive and multi-channel processing, transform and wavelet domains processing, singular value decomposition, and various low variance optimization and low rank processing techniques. Throughout the text, the authors address the theory, analysis, and implementation of the denoising algorithms to help readers solve their image processing problems and develop their own solutions. Explains how the quality of an image can be quantified in MATLAB Discusses what constitutes a “naturally looking” image in subjective and analytical terms Presents denoising techniques for a wide range of digital image processing applications Describes the use of denoising as a pre-processing tool for various signal processing applications or big data analysis Requires only a fundamental knowledge of digital signal processing Includes access to a companion website with source codes, exercises, and additional resources Digital Image Denoising in MATLAB is an excellent textbook for undergraduate courses in digital image processing, recognition, and statistical signal processing, and a highly useful reference for researchers and engineers working with digital images, digital video, and other applications requiring denoising techniques.

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 toolboxNew 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. - Includes all the latest developments since the book was published in 1999, including its application to JPEG 2000 and MPEG-4 - Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox - Balances presentation of the mathematics with applications to signal processing

The refereed post-proceedings of the International Conference on Computational Intelligence and Security are presented in this volume. The 116 papers were submitted to two rounds of careful review. Papers cover bio-inspired computing, evolutionary computation, learning systems and multi-agents, cryptography, information processing and intrusion detection, systems and security, image and signal processing, and pattern recognition.

The limited coverage of data analysis and statistics offered in most undergraduate and graduate analytical chemistry courses is usually focused on practical aspects of univariate methods. Drawing in real-world examples, Practical Guide to Chemometrics, Second Edition offers an accessible introduction to application-oriented multivariate meth

#1 NEW YORK TIMES BESTSELLER ONE OF BARACK OBAMA'S FAVORITE BOOKS OF THE YEAR NAMED A BEST BOOK OF 2020 BY THE NEW YORK TIMES * THE WASHINGTON POST * NPR * PEOPLE * TIME MAGAZINE* VANITY FAIR * GLAMOUR 2021 WOMEN'S PRIZE FINALIST “Bennett’s tone and style recalls James Baldwin and Jacqueline Woodson, but it’s especially reminiscent of Toni Morrison’s 1970 debut novel, The Bluest Eye.” —Kiley Reid, Wall Street Journal “A story of absolute, universal timelessness …For any era, it's an accomplished, affecting novel. For this moment, it's piercing, subtly wending its way toward questions about who we are and who we want to be….” – Entertainment Weekly From The New York Times-bestselling author of The Mothers, a stunning new novel about twin sisters, inseparable as children, who ultimately choose to live in two very different worlds, one black and one white. The Vignes twin sisters will always be identical. But after growing up together in a small, southern black community and running away at age sixteen, it's not just the shape of their daily lives that is different as adults, it's everything: their families, their communities, their racial identities. Many years later, one sister lives with her black daughter in the same southern town she once tried to escape. The other secretly passes for white, and her white husband knows nothing of her past. Still, even separated by so many miles and just as many lies, the fates of the twins remain intertwined. What will happen to the next generation, when their own daughters' storylines intersect? Weaving together multiple strands and generations of this family, from the Deep South to California, from the 1950s to the 1990s, Brit Bennett produces a story that is at once a riveting, emotional family story and a brilliant exploration of the American history of passing. Looking well beyond issues of race, The Vanishing Half considers the lasting influence of the past as it shapes a person's decisions, desires, and expectations, and explores some of the multiple reasons and realms in which people sometimes feel pulled to live as something other than their origins. As with her New York Times-bestselling debut The Mothers, Brit Bennett offers an engrossing page-turner about family and relationships that is immersive and provocative, compassionate and wise.

"Digital signal transforms are of a fundamental value in digital signal and image processing. Their role is manifold. Transforms selected appropriately enable substantial compressing signals and images for storage and transmission. No signal recovery, image reconstruction and restoration task can be efficiently solved without using digital signal transforms. Transforms are successfully used for logic design and digital data encryption. Fast transforms are the main tools for acceleration of computations in digital signal and image processing. The volume collects in one book most recent developments in the theory and practice of the design and usage of transforms in digital signal and image processing. It emerged from the series of reports published by Tampere International Centre for Signal Processing, Tampere University of Technology. For the volume, all contributions are appropriately updated to represent the state of the art in the field and to cover the most recent developments in different aspects of the theory and applications of transforms. The book consists of two parts that represent two major directions in the field: development of new transforms and development of transform based signal and image processing algorithms. The first part contains four chapters devoted to recent advances in transforms for image compression and switching and logic design and to new fast transforms for digital holography and tomography. In the second part, advanced transform based signal and image algorithms are considered: signal and image local adaptive restoration methods and two complementing families of signal and image re-sampling algorithms, fast transform based discrete sinc-interpolation and spline theory based ones."--Publisher.

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