Kehtarnavaz / Saki / Duran Anywhere-Anytime Signals and Systems Laboratory

From MATLAB to Smartphones, Second Edition
1. Auflage 2018
ISBN: 978-3-031-02539-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 260 Seiten, eBook

Reihe: Synthesis Lectures on Signal Processing

A typical undergraduate electrical engineering curriculum incorporates a signals and systems course. The widely used approach for the laboratory component of such courses involves the utilization of MATLAB to implement signals and systems concepts. This lecture series book presents a newly developed laboratory paradigm where MATLAB codes are made to run on smartphones, which most students already possess. This smartphone-based approach enables an anywhere-anytime platform for students to conduct signals and systems experiments. This book covers the laboratory experiments that are normally covered in signals and systems courses and discusses how to run MATLAB codes for these experiments on both Android and iOS smartphones, thus enabling a truly mobile laboratory environment for students to learn the implementation aspects of signals and systems concepts. A zipped file of the codes discussed in the book can be acquired via the website.

Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor in the Department of Electrical and Computer Engineering at the University of Texas at Dallas. His research areas include signal and image processing, real-time processing on embedded processors, deep learning, and machine learning. He has authored or co-authored more than 400 publications and 9 other books pertaining to signal and image processing, and regularly teaches the signals and systems laboratory course, for which this book is written. Dr. Kehtarnavaz is a Fellow of IEEE, a Fellow of SPIE, and a licensed Professional Engineer.Fatemeh Saki received her Ph.D. in Electrical Engineering from the University of Texas at Dallas in 2017. She is currently a Senior R&D Engineer at Qualcomm. Her research interests include signal and image processing, pattern recognition, and machine learning. Dr. Saki has authored or co-authored 20 publications in these areas.Adrian Duran received his M.S. in Electrical Engineering from the University of Texas at Dallas in 2018. He is currently a Signal Processing Analyst at Innovative Signal Analysis, Inc. His research interests are signal and image processing, pattern recognition, and machine learning.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Kehtarnavaz, Nasser

Saki, Fatemeh

Duran, Adrian

Inhaltsverzeichnis

Preface.- Introduction to MATLAB.- Software Development Tools.- From MATLAB Coder to Smartphone.- Linear Time-Invariant Systems and Convolution.- Fourier Series.- Continuous-Time Fourier Transform.- Digital Signals and Their Transforms.- Authors' Biographies.- Index.

Über Autor(innen)

Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor in the Department of Electrical and Computer Engineering at the University of Texas at Dallas. His research areas include signal and image processing, real-time processing on embedded processors, deep learning, and machine learning. He has authored or co-authored more than 400 publications and 9 other books pertaining to signal and image processing, and regularly teaches the signals and systems laboratory course, for which this book is written. Dr. Kehtarnavaz is a Fellow of IEEE, a Fellow of SPIE, and a licensed Professional Engineer.Fatemeh Saki received her Ph.D. in Electrical Engineering from the University of Texas at Dallas in 2017. She is currently a Senior R&D Engineer at Qualcomm. Her research interests include signal and image processing, pattern recognition, and machine learning. Dr. Saki has authored or co-authored 20 publications in these areas.Adrian Duran received his M.S. in Electrical Engineering from the University of Texas at Dallas in 2018. He is currently a Signal Processing Analyst at Innovative Signal Analysis, Inc. His research interests are signal and image processing, pattern recognition, and machine learning.