001441369 000__ 03712cam\a2200649\i\4500
001441369 001__ 1441369
001441369 003__ OCoLC
001441369 005__ 20230309004735.0
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001441369 008__ 211218s2021\\\\sz\a\\\\ob\\\\000\0\eng\d
001441369 019__ $$a1288465189$$a1288557540$$a1288636384$$a1288664071$$a1294351423
001441369 020__ $$a9783030903145$$q(electronic bk.)
001441369 020__ $$a3030903141$$q(electronic bk.)
001441369 020__ $$z9783030903138
001441369 020__ $$z3030903133
001441369 0247_ $$a10.1007/978-3-030-90314-5$$2doi
001441369 035__ $$aSP(OCoLC)1289372331
001441369 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCF$$dOCLCO$$dDCT$$dOCLCO$$dOCLCQ$$dUKAHL$$dOCLCQ
001441369 049__ $$aISEA
001441369 050_4 $$aQA279.4$$b.B67 2021
001441369 08204 $$a519.5/42$$223
001441369 1001_ $$aBorda, Monica,$$eauthor.
001441369 24510 $$aRandomness and elements of decision theory applied to signals /$$cMonica Borda, Romulus Terebes, Raul Malutan, Ioana Ilea, Mihaela Cislariu, Andreia Miclea, Stefania Barburiceanu.
001441369 264_1 $$aCham :$$bSpringer,$$c[2021]
001441369 264_4 $$c©2021
001441369 300__ $$a1 online resource (251 pages) :$$billustrations (some color)
001441369 336__ $$atext$$btxt$$2rdacontent
001441369 337__ $$acomputer$$bc$$2rdamedia
001441369 338__ $$aonline resource$$bcr$$2rdacarrier
001441369 347__ $$atext file
001441369 347__ $$bPDF
001441369 504__ $$aIncludes bibliographical references.
001441369 5050_ $$aIntroduction 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.
001441369 506__ $$aAccess limited to authorized users.
001441369 520__ $$aThis 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.
001441369 588__ $$aDescription based on print version record.
001441369 650_0 $$aDecision making.
001441369 650_0 $$aRandom variables.
001441369 650_0 $$aSignal processing$$xMathematics.
001441369 650_6 $$aPrise de décision.
001441369 650_6 $$aVariables aléatoires.
001441369 650_6 $$aTraitement du signal$$xMathématiques.
001441369 655_0 $$aElectronic books.
001441369 7001_ $$aTerebes, Romulus,$$eauthor.
001441369 7001_ $$aMalutan, Raul,$$eauthor.
001441369 7001_ $$aIlea, Ioana,$$eauthor.
001441369 7001_ $$aCislariu, Mihaela,$$eauthor.
001441369 7001_ $$aMiclea, Andreia,$$eauthor.
001441369 7001_ $$aBarburiceanu, Stefania,$$eauthor.
001441369 77608 $$iPrint version:$$aBorda, Monica.$$tRandomness and Elements of Decision Theory Applied to Signals.$$dCham : Springer International Publishing AG, ©2022$$z9783030903138
001441369 852__ $$bebk
001441369 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-90314-5$$zOnline Access$$91397441.1
001441369 909CO $$ooai:library.usi.edu:1441369$$pGLOBAL_SET
001441369 980__ $$aBIB
001441369 980__ $$aEBOOK
001441369 982__ $$aEbook
001441369 983__ $$aOnline
001441369 994__ $$a92$$bISE