Hardcover. Master the basic concepts and methodologies of digital signal processing with this systematic introduction, without the need for an extensive mathematical background. The authors lead the reader through the fundamental mathematical principles underlying the operation of key signal processing techniques, providing simple arguments and cases rather than detailed general proofs. Coverage of practical implementation, discussion of the limitations of particular methods and plentiful MATLAB illustrations allow readers to better connect theory and practice. A focus on algorithms that are of theoretical importance or useful in real-world applications ensures that students cover material relevant to engineering practice, and equips students and practitioners alike with the basic principles necessary to apply DSP techniques to a variety of applications. Chapters include worked examples, problems and computer experiments, helping students to absorb the material they have just read. Lecture slides for all figures and solutions to the numerous problems are available to instructors. The basic concepts and methodologies of DSP are introduced, leading students through the fundamental mathematical principles underlying signal processing. Blending theory with practical implementation, outlining the limitations of digital signal processing, and with a focus on MATLAB, students and practitioners are shown how to better connect theory and practice. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N� de ref. del art�culo 9780521110020

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Detalles bibliogr�ficos

T�tulo: Applied Digital Signal Processing (Hardcover)
Editor: Cambridge University Press, Cambridge
Fecha de publicaci�n: 2011
Idioma: Ingl�s
ISBN 10: 0521110025
ISBN 13: 9780521110020
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

Master the basic concepts and methodologies of digital signal processing with this systematic introduction, without the need for an extensive mathematical background. The authors lead the reader through the fundamental mathematical principles underlying the operation of key signal processing techniques, providing simple arguments and cases rather than detailed general proofs. Coverage of practical implementation, discussion of the limitations of particular methods and plentiful MATLAB illustrations allow readers to better connect theory and practice. A focus on algorithms that are of theoretical importance or useful in real-world applications ensures that students cover material relevant to engineering practice, and equips students and practitioners alike with the basic principles necessary to apply DSP techniques to a variety of applications. Chapters include worked examples, problems and computer experiments, helping students to absorb the material they have just read. Lecture slides for all figures and solutions to the numerous problems are available to instructors.

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Dimitris G. Manolakis is currently a Member of Technical Staff at MIT Lincoln Laboratory in Lexington, Massachusetts. Prior to this he was a Principal Member of Research Staff at Riverside Research Institute. Since receiving his Ph.D. in Electrical Engineering from the University of Athens in 1981, he has taught at various institutions including Northeastern University, Boston College, and Worcester Polytechnic Institute and co-authored two textbooks on signal processing. His research experience and interests include the areas of digital signal processing, adaptive filtering, array processing, pattern recognition, remote sensing and radar systems.

Vinay K. Ingle is currently an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, where he has worked since 1981 after receiving his Ph.D. in Electrical and Computer Engineering from Rensselaer Polytechnic Institute. He has taught both undergraduate and graduate courses in many diverse areas including systems, signal/image processing, communications and control theory, and has co-authored several textbooks on signal processing. He has broad research experience in the areas of signal and image processing, stochastic processes and estimation theory. Currently he is actively involved in hyperspectral imaging and signal processing.

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