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Multidimensional Discrete Unitary Transforms: Representation: Partitioning, and Algorithms: Signal Processing and Communications

Autor Artyom M. Grigoryan, Sos S. Agaian
en Limba Engleză Hardback – 31 iul 2003
This reference presents a more efficient, flexible, and manageable approach to unitary transform calculation and examines novel concepts in the design, classification, and management of fast algorithms for different transforms in one-, two-, and multidimensional cases. Illustrating methods to construct new unitary transforms for best algorithm selection and development in real-world applications, the book contains a wide range of examples to compare the efficacy of different algorithms in a variety of one-, two-, and three-dimensional cases. Multidimensional Discrete Unitary Transforms builds progressively from simple representative cases to higher levels of generalization.

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Specificații

ISBN-13: 9780824745967
ISBN-10: 0824745965
Pagini: 544
Dimensiuni: 178 x 254 x 30 mm
Greutate: 1.16 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria Signal Processing and Communications


Public țintă

Professional

Notă biografică

Artyom M. Grigoryan, Sos S. Agaian

Cuprins

Series Introduction, Preface, 1: Basic Concepts and Notation, I: Tensor Representation of Multidimensional Signals, II: Analysis and effective computing procedures, III: Applications of Paired Transformations, Index

Descriere

This reference presents a more efficient, flexible, and manageable approach to unitary transform calculation and examines novel concepts in the design, classification, and management of fast algorithms for different transforms in one-, two-, and multidimensional cases. Illustrating methods to construct new unitary transforms for best algorithm selection and development in real-world applications, the book contains a wide range of examples to compare the efficacy of different algorithms in a variety of one-, two-, and three-dimensional cases. Multidimensional Discrete Unitary Transforms builds progressively from simple representative cases to higher levels of generalization.