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Measurement and Instrumentation Theory and Application 3rd Edition PDF

Measurement and Instrumentation: Theory and Application, Third Edition, introduces undergraduate engineering students to measurement principles and the range of sensors and instruments used for measuring physical variables.

Providing the most balanced coverage of measurement theory/technologies and instrumentation, this clearly and comprehensively written text arms students and recently graduated engineers with the knowledge and tools to design and build measurement systems for virtually any engineering application.

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ABOUT THE AUTHOR:

Dr. Morris Retired senior lecturer in the Department of Automatic Control & Systems Engineering at the University of Sheffield. He has taught the undergraduate course in measurement and instrumentation for nearly 30 years, as well as undergraduate courses in robot technology, engineering design and laboratory skills, and graduate level courses in robot control, modeling and measurement for quality assurance. He is the author of eight books and more than 130 research papers in the fields of measurement and instrumentation and robot control.

Dr. Langari is a professor in the Department of Mechanical Engineering at Texas A&M University and head of the Department of Engineering Technology and Industrial Distribution. He earned bachelor’s, master’s and doctoral degrees from the University of California, Berkeley. He has held research positions at NASA Ames Research Center, Rockwell International Science Center, United Technologies Research Center, as well as the U.S. Air Force Research Laboratory. Langari’s expertise is in the area of computational intelligence with application to mechatronic systems and industrial automation. He has played a significant role in the development of theoretical foundations of fuzzy logic control and its applications to problems in mechanical engineering. His work on stability of fuzzy control systems is widely recognized as pioneering the use of nonlinear systems analysis techniques to fuzzy logic.

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