Автор: Avishy Y. Carmi; Lyudmila Mihaylova; Simon J. Gods Название: Compressed Sensing & Sparse Filtering ISBN: 366250894X ISBN-13(EAN): 9783662508947 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents fundamental concepts, methods and algorithms able to cope with undersampled data. It introduces the concept of compressive sampling, which is also called compressed sensing.
Автор: Mauro Mangia; Fabio Pareschi; Valerio Cambareri; R Название: Adapted Compressed Sensing for Effective Hardware Implementations ISBN: 3319870653 ISBN-13(EAN): 9783319870656 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Поставка под заказ.
Описание: This book describes algorithmic methods and hardware implementations that aim to help realize the promise of Compressed Sensing (CS), namely the ability to reconstruct high-dimensional signals from a properly chosen low-dimensional “portrait”. The authors describe a design flow and some low-resource physical realizations of sensing systems based on CS. They highlight the pros and cons of several design choices from a pragmatic point of view, and show how a lightweight and mild but effective form of adaptation to the target signals can be the key to consistent resource saving. The basic principle of the devised design flow can be applied to almost any CS-based sensing system, including analog-to-information converters, and has been proven to fit an extremely diverse set of applications. Many practical aspects required to put a CS-based sensing system to work are also addressed, including saturation, quantization, and leakage phenomena.
Автор: Matteo Testa; Diego Valsesia; Tiziano Bianchi; Enr Название: Compressed Sensing for Privacy-Preserving Data Processing ISBN: 9811322783 ISBN-13(EAN): 9789811322785 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The objective of this book is to provide the reader with a comprehensive survey of the topic compressed sensing in information retrieval and signal detection with privacy preserving functionality without compromising the performance of the embedding in terms of accuracy or computational efficiency. The reader is guided in exploring the topic by first establishing a shared knowledge about compressed sensing and how it is used nowadays. Then, clear models and definitions for its use as a cryptosystem and a privacy-preserving embedding are laid down, before tackling state-of-the-art results for both applications. The reader will conclude the book having learned that the current results in terms of security of compressed techniques allow it to be a very promising solution to many practical problems of interest. The book caters to a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in security, cryptography and privacy in information retrieval systems. Accompanying software is made available on the authors’ website to reproduce the experiments and techniques presented in the book. The only background required to the reader is a good knowledge of linear algebra, probability and information theory.
Название: Compressed sensing and its applications ISBN: 3319730738 ISBN-13(EAN): 9783319730738 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include:
Quantized compressed sensingClassificationMachine learningOracle inequalitiesNon-convex optimizationImage reconstructionStatistical learning theory
This volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.
Автор: Holger Boche; Robert Calderbank; Gitta Kutyniok; J Название: Compressed Sensing and its Applications ISBN: 3319160419 ISBN-13(EAN): 9783319160412 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses compressive sensing in the presence of side information. Interesting instances of Compressive Sensing (CS) can occur when, apart from sparsity, side information is available about the source signals.
Автор: Holger Boche; Giuseppe Caire; Robert Calderbank; M Название: Compressed Sensing and Its Applications ISBN: 331969801X ISBN-13(EAN): 9783319698014 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing.
Автор: Avishy Y. Carmi; Lyudmila Mihaylova; Simon J. Gods Название: Compressed Sensing & Sparse Filtering ISBN: 3642383971 ISBN-13(EAN): 9783642383977 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents fundamental concepts, methods and algorithms able to cope with undersampled data. It introduces the concept of compressive sampling, which is also called compressed sensing.
Автор: Mauro Mangia; Fabio Pareschi; Valerio Cambareri; R Название: Adapted Compressed Sensing for Effective Hardware Implementations ISBN: 3319613723 ISBN-13(EAN): 9783319613727 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes algorithmic methods and hardware implementations that aim to help realize the promise of Compressed Sensing (CS), namely the ability to reconstruct high-dimensional signals from a properly chosen low-dimensional "portrait".
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