UID:
almahu_9949227821102882
Format:
XVII, 242 p. 254 illus., 168 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030903145
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030903138
Additional Edition:
Printed edition: ISBN 9783030903152
Additional Edition:
Printed edition: ISBN 9783030903169
Language:
English
DOI:
10.1007/978-3-030-90314-5
URL:
https://doi.org/10.1007/978-3-030-90314-5
URL:
Volltext
(URL des Erstveröffentlichers)
