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Randomness and Elements of Decision ...
~
Terebes, Romulus.
Randomness and Elements of Decision Theory Applied to Signals
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Randomness and Elements of Decision Theory Applied to Signals/ by Monica Borda, Romulus Terebes, Raul Malutan, Ioana Ilea, Mihaela Cislariu, Andreia Miclea, Stefania Barburiceanu.
Author:
Borda, Monica.
other author:
Terebes, Romulus.
Description:
XVII, 242 p. 254 illus., 168 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Mathematical statistics. -
Online resource:
https://doi.org/10.1007/978-3-030-90314-5
ISBN:
9783030903145
Randomness and Elements of Decision Theory Applied to Signals
Borda, Monica.
Randomness and Elements of Decision Theory Applied to Signals
[electronic resource] /by Monica Borda, Romulus Terebes, Raul Malutan, Ioana Ilea, Mihaela Cislariu, Andreia Miclea, Stefania Barburiceanu. - 1st ed. 2021. - XVII, 242 p. 254 illus., 168 illus. in color.online resource.
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.
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.
ISBN: 9783030903145
Standard No.: 10.1007/978-3-030-90314-5doiSubjects--Topical Terms:
527941
Mathematical statistics.
LC Class. No.: QA276-280
Dewey Class. No.: 005.55
Randomness and Elements of Decision Theory Applied to Signals
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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.
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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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