東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Randomness and elements of decision ...

Borda, Monica.

Linked to FindBook

Google Book

Amazon

博客來

Randomness and elements of decision theory applied to signals

Record Type:	Electronic resources : Monograph/item
Title/Author:	Randomness and elements of decision theory applied to signals/ by Monica Borda ... [et al.].
other author:	Borda, Monica.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xvii, 242 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Decision making. -
Online resource:	https://doi.org/10.1007/978-3-030-90314-5
ISBN:	9783030903145

Randomness and elements of decision theory applied to signals
Randomness and elements of decision theory applied to signals[electronic resource] /by Monica Borda ... [et al.]. - Cham :Springer International Publishing :2021. - xvii, 242 p. :ill. (some col.), digital ;24 cm.

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:

517204
Decision making.

LC Class. No.: QA279.4

Dewey Class. No.: 519.542

Randomness and elements of decision theory applied to signals
LDR:02316nmm a2200337 a 4500 001 2262162
003 DE-He213
005 20211210112058.0
006 m d
007 cr nn 008maaau
008 220616s2021 sz s 0 eng d
020 $a 9783030903145 $q (electronic bk.)
020 $a 9783030903138 $q (paper)
024 7 $a 10.1007/978-3-030-90314-5 $2 doi
035 $a 978-3-030-90314-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.4
072 7 $a UYAM $2 bicssc
072 7 $a COM077000 $2 bisacsh
072 7 $a UYAM $2 thema
072 7 $a UFM $2 thema
082 0 4 $a 519.542 $2 23
090 $a QA279.4 $b .R194 2021
245 0 0 $a Randomness and elements of decision theory applied to signals $h [electronic resource] / $c by Monica Borda ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xvii, 242 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Decision making. $3 517204
650 0 $a Random variables. $3 646291
650 0 $a Signal processing $x Mathematics. $3 579697
650 1 4 $a Probability and Statistics in Computer Science. $3 891072
650 2 4 $a Operations Research/Decision Theory. $3 890895
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
700 1 $a Borda, Monica. $3 3538257
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-90314-5
950 $a Physics and Astronomy (SpringerNature-11651)