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N� de ref. del art�culo 1337620351-11-1

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Detalles bibliogr�ficos

T�tulo: Fundamentals of Logic Design, Enhanced Edition
Editor: Cengage Learning (edition 7)
Fecha de publicaci�n: 2020
Idioma: Ingl�s
ISBN 10: 1337620351
ISBN 13: 9781337620352
Encuadernaci�n: Hardcover
Edici�n: 7.
Estado: Good

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Sinopsis

Master the principles of logic design with the exceptional balance of theory and application found in Roth/Kinney/John's FUNDAMENTALS OF LOGIC DESIGN, ENHANCED, 7th Edition. This edition introduces you to today's latest advances. The authors have carefully developed a clear presentation that introduces the fundamental concepts of logic design without overwhelming you with the mathematics of switching theory. Twenty engaging, easy-to-follow study units present basic concepts, such as Boolean algebra, logic gate design, flip-flops and state machines. You learn to design counters, adders, sequence detectors and simple digital systems. After mastering the basics, you progress to modern design techniques using programmable logic devices as well as VHDL hardware description language.

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Charles Roth is Professor Emeritus in Electrical and Computer Engineering at the University of Texas at Austin, where he taught Digital Design for more than four decades. In addition to this successful book, Dr. Roth has co-authored DIGITAL SYSTEMS DESIGN USING VHDL and DIGITAL SYSTEMS DESIGN USING VERILOG.

Larry L. Kinney is a Professor and Director of Undergraduate Studies at the University of Minnesota. He received his Ph.D. in Electrical Engineering from the University of Iowa. His research has focused on digital system and digital computer design, specifically concurrent error detection techniques, testing of logic and design, distributed computer systems, computer architectures, error detecting/correcting codes and applications of microprocessors.

Eugene B. John is a Professor in Electrical and Computer Engineering at the University of Texas at Austin. His areas of interest include VLSI design, computer architecture, energy efficient computing, energy efficient hardware for machine learning and artificial intelligence and hardware security.

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