Randomness and Elements of Decision Theory Applied to Signals
Springer International Publishing (Verlag)
978-3-030-90313-8 (ISBN)
This book offers an overview on the main modern important topics in random variables, random processes, and decision theory for solving real-world problems. After an introduction to concepts of statistics and signals, the book introduces many essential applications to signal processing like denoising, texture classification, histogram equalization, deep learning, or feature extraction.
The book uses MATLAB algorithms to demonstrate the implementation of the theory to real systems. This makes the contents of the book relevant to students and professionals who need a quick introduction but practical introduction how to deal with random signals and processes
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Monica BORDA received the Ph.D. degree from "Politehnica" University of Bucharest, Romania, in 1987. She has held faculty positions at the Technical University of Cluj-Napoca (TUC-N), Romania, where she is an Advisor for Ph.D. candidates since 2000. She is a Professor of Information Theory and Coding, Cryptography and Genomic Signal Processing with the Department of Communications, Faculty of Electronics, Telecommunications and Information Technology, TUC-N. She is also the Director of the Data Processing and Security Research Center, TUC-N. She has conducted research in coding theory, nonlinear signal and image processing, image watermarking, genomic signal processing and computer vision having authored and coauthored more than 100 research papers in referred national and international journals and conference proceedings. She is the author and coauthor of five books. Her research interests are in the areas of information theory and coding, signal, image, and genomic signal processing.
Romulus TEREBE was born in Livada, Romania. He received the BSc degree in electronics and telecommunications from the Technical University of Cluj-Napoca (TUC-N), Cluj-Napoca, Romania, in 1994 and the Ph.D. degree from the University of Bordeaux 1, Talence, France, and TUC-N (co-advised Ph.D. thesis), in 2004. The subject of his Ph.D. thesis dealt with partial-derivative-equation-based image processing techniques. Dr. Terebes is currently an IEEE Signal Processing society member and professor with the Department of Communications, Faculty of Electronics, Telecommunications and Information Technology, TUC-N. His research interests are in the area of image processing and computer vision. His publication list includes 48 articles and proceeding papers indexed in the Web of Science, the Core Collection, 41 being also IEEE Xplore indexed.
Raul MALU AN received the B.Sc. degree in Telecommunications from Technical University of Cluj Napoca, Romania in 2004 and the PhD degree in Electronics and Telecommunications from Technical University of Cluj Napoca, Romania and in Informatics from Polytechnic University of Madrid, Spain in 2010. He is currently an Associate Professor with the Communications Department from Technical University of Cluj Napoca, Romania. He has authored more than 40 research articles published in ISI indexed journals, as book chapters and in peer-reviewed conferences. He is the co-author of 2 Romanian Patents. His research interests include high-order statistics, genomic processing, image processing, bioinformatics.
Ioana ILEA received in 2017 the PhD degree in Electronics and Telecommunications from the Technical University of Cluj-Napoca, Romania and in Automation, Computer-Integrated Manufacturing, Signal and Image, Cognitive Engineering from the University of Bordeaux, France. She is currently Assistant Professor with the Communications Department of the Technical University of Cluj-Napoca, Romania. Her research interests include signal and image processing, statistical modeling, and machine learning.
Mihaela CÎ LARIU received the B.Sc. degree in Electronics from Technical University of Cluj-Napoca, Romania in 2008 and the PhD degree in Electronics and Telecommunications from Technical University of Cluj-Napoca, Romania in 2011. She is currently an Assistant Professor with the Communications Department from Technical University of Cluj-Napoca, Romania. Her current research interests include computational intelligence, artificial intelligence, image restoration, motion analysis, image classification, machine learning and computer vision.
tefania BARBURICEANU was born in Sinaia, Romania in 1993. She received a BSc in electronics and telecommunications in 2016 from the Technical University of Cluj-Napoca, Romania and a MSc in image and signal processing in 2018 from the University of Bordeaux, France, and the Technical University of Cluj-Napoca (double diploma). She is currently
Introduction in Matlab.- Random variables.- Probability distributions.- Joint random variables.- Random processes.- Binary pseudo-noise sequence generator.- Markov processes.- Noise in telecommunication systems.- Decision systems in noisy transmission channels.- Audio signals denoising using Independent Component Analysis.- Texture classification based on statistical models.- Histogram equalization.- PCM and DPCM.- NN and kNN supervised classification algorithms.- Supervised deep learning classification algorithms.- Texture feature extraction and classification using the Local Binary Patterns operator.
"The book follows a tutorial style, with a good listing of various formulae and equations, without developing the theory or including proofs but instead presenting numerous solved problems and MATLAB code." (Paparao Kavalipati, Computing Reviews, September 21, 2023)
“The book follows a tutorial style, with a good listing of various formulae and equations, without developing the theory or including proofs but instead presenting numerous solved problems and MATLAB code.” (Paparao Kavalipati, Computing Reviews, September 21, 2023)
| Erscheinungsdatum | 12.12.2021 |
|---|---|
| Zusatzinfo | XVII, 242 p. 254 illus., 168 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 547 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | convolutional neural network • Decision Systems • denoising • Histogram Equalization • Joint random variables • kNN supervised classification • Markov Processes • probability distributions • pseudo-noise sequence generator • Pulse code modulation • Random prcoesses • Random signals • random variables |
| ISBN-10 | 3-030-90313-3 / 3030903133 |
| ISBN-13 | 978-3-030-90313-8 / 9783030903138 |
| Zustand | Neuware |
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