Adapted Compressed Sensing for Effective Hardware Implementations, Mauro Mangia; Fabio Pareschi; Valerio Cambareri; R

Автор: Sied Mehdi Fakhraie; Kenneth C. Smith
Название: VLSI — Compatible Implementations for Artificial Neural Networks
ISBN: 1461378974 ISBN-13(EAN): 9781461378976
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book introduces several state-of-the-art VLSI implementations of artificial neural networks (ANNs). The work of Professors Fakhraie and Smith, presented in this book, is a welcome addition to the state-of-the-art and will greatly benefit researchers and students working in this area.

Автор: Avishy Y. Carmi; Lyudmila Mihaylova; Simon J. Gods
Название: Compressed Sensing & Sparse Filtering
ISBN: 366250894X ISBN-13(EAN): 9783662508947
Издательство: Springer
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Цена: 20896.00 р.
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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: Holger Boche; Giuseppe Caire; Robert Calderbank; M
Название: Compressed Sensing and Its Applications
ISBN: 331969801X ISBN-13(EAN): 9783319698014
Издательство: Springer
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Цена: 15372.00 р.
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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: Holger Boche; Robert Calderbank; Gitta Kutyniok; J
Название: Compressed Sensing and its Applications
ISBN: 3319160419 ISBN-13(EAN): 9783319160412
Издательство: Springer
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Цена: 13275.00 р.
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Автор: Amos R. Omondi; Jagath C. Rajapakse
Название: FPGA Implementations of Neural Networks
ISBN: 1441939423 ISBN-13(EAN): 9781441939425
Издательство: Springer
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Цена: 23508.00 р.
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Описание: This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption.

Автор: Iasson Karafyllis; Miroslav Krstic
Название: Predictor Feedback for Delay Systems: Implementations and Approximations
ISBN: 3319423770 ISBN-13(EAN): 9783319423777
Издательство: Springer
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Цена: 15372.00 р.
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Описание: Preview of Predictor Feedback and Delay Compensation.- Part I: Linear Systems Under Predictor Feedback.- Linear Systems with State Measurement.- Linear Systems with Output Measurement.- Part II: Nonlinear Systems Under Predictor Feedback.- Nonlinear Systems with State Measurement.- Nonlinear Systems with Output Measurement.- Application to the Chemostat.- Part III: Extensions of Predictor Feedback.- Systems Described by Integral Delay Equations.- Discrete-Time Systems.

Автор: Mohammad Rostami
Название: Compressed Sensing with Side Information on the Feasible Region
ISBN: 3319003658 ISBN-13(EAN): 9783319003658
Издательство: Springer
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Цена: 5589.00 р.
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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: Avishy Y. Carmi; Lyudmila Mihaylova; Simon J. Gods
Название: Compressed Sensing & Sparse Filtering
ISBN: 3642383971 ISBN-13(EAN): 9783642383977
Издательство: Springer
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Цена: 27950.00 р.
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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: Sied Mehdi Fakhraie; Kenneth C. Smith
Название: VLSI — Compatible Implementations for Artificial Neural Networks
ISBN: 0792398254 ISBN-13(EAN): 9780792398257
Издательство: Springer
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Цена: 23757.00 р.
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Описание: Introduces an approach to biologically-inspired and VLSI-compatible definition, simulation, and implementation of artificial neural networks. This book develops a set of guidelines for general hardware implementation of artificial neural networks. It provides a geometrical interpretation of the behavior of different variants of these networks.