LEADER 00976nam0-22002651i-450- 001 990001263980403321 035 $a000126398 035 $aFED01000126398 035 $a(Aleph)000126398FED01 035 $a000126398 100 $a20000920d1960----km-y0itay50------ba 101 0 $aeng 200 1 $aApproximation formulae for generalized hypergeometric functions for large values of the parameters with applications to expansion theorems for the function$fby Knottnerus 210 $aGroningen$cWolters$d1960 610 0 $aFunzioni ipergeometriche 700 1$aKnottnerus,$bUbbo Johannes$f<1916- >$057970 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001263980403321 952 $a5-C-24$b13501$fMA1 959 $aMA1 962 $a33CXX 996 $aApproximation formulae for generalized hypergeometric functions for large values of the parameters with applications to expansion theorems for the function$9381123 997 $aUNINA DB $aING01 LEADER 01952oam 2200637 a 450 001 9911006787303321 005 20240104112008.0 010 $a1-61344-854-6 035 $a(CKB)2430000000037482 035 $a(CtWfDGI)bke00025992 035 $a(SSID)ssj0000384484 035 $a(PQKBManifestationID)12137318 035 $a(PQKBTitleCode)TC0000384484 035 $a(PQKBWorkID)10342903 035 $a(PQKB)10414922 035 $a(EXLCZ)992430000000037482 100 $a20081001d2008 uy 0 101 0 $aeng 135 $aurzn|||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLean production$b[electronic resource] $eimplementing a world-class system /$fJohn Black 205 $a1st ed. 210 $aNew York $cIndustrial Press, Inc.$dc2008 300 $aTitle from title screen. 320 $aIncludes bibliographical references and index. 531 $aLEAN PRODUCTION 606 $aProduction management 606 $aProduction control 606 $aIndustrial productivity 606 $aProduction management 606 $aProduction control 606 $aIndustrial productivity 606 $aIndustrial & Management Engineering$2HILCC 606 $aMechanical Engineering$2HILCC 606 $aEngineering & Applied Sciences$2HILCC 608 $aElectronic books.$2lcsh 615 0$aProduction management. 615 0$aProduction control. 615 0$aIndustrial productivity. 615 0$aProduction management. 615 0$aProduction control. 615 0$aIndustrial productivity. 615 7$aIndustrial & Management Engineering 615 7$aMechanical Engineering 615 7$aEngineering & Applied Sciences 676 $a658.5 700 $aBlack$b John R$0191375 712 02$aBooks24x7, Inc. 801 0$bCtWfDGI 801 1$bCtWfDGI 906 $aBOOK 912 $a9911006787303321 996 $aLean production$94393161 997 $aUNINA LEADER 04486nam 22006495 450 001 9910299588003321 005 20250612140242.0 010 $a981-10-6511-X 024 7 $a10.1007/978-981-10-6511-8 035 $a(CKB)4100000000586862 035 $a(DE-He213)978-981-10-6511-8 035 $a(MiAaPQ)EBC5061517 035 $a(PPN)20453111X 035 $a(EXLCZ)994100000000586862 100 $a20170927d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMusicality of Human Brain through Fractal Analytics /$fby Dipak Ghosh, Ranjan Sengupta, Shankha Sanyal, Archi Banerjee 205 $a1st ed. 2018. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2018. 215 $a1 online resource (XVII, 232 p. 119 illus., 111 illus. in color.) 225 1 $aSignals and Communication Technology,$x1860-4870 311 08$a981-10-6510-1 320 $aIncludes bibliographical references. 327 $aIntroduction -- Methodology -- Emotions from Hindustani Classical Music: An EEG based study with evidence of neural hysteresis -- Musical perception and visual imagery: Do musicians visualize while performing? -- Tanpura drone and brain dynamics: How a simple acoustic signal affects brain rhythms -- In search of universality of music: effect of cross cultural instrumental clips -- Gestalt phenomenon in music: which frequencies do we hear? -- Quantification of musical emotion with neural jitter-shimmer: novel study with hindustani music -- An approach to encapsulate improvisation in hindustani classical music -- Ambiguity in hindustani classical music: development of an automated algorithm to asses ambiguity -- Computing the pitch of an EEG signal: a new paradigm in analysis of bio-signals -- Epilogue. 330 $aThis book provides a comprehensive overview of how fractal analytics can lead to the extraction of interesting features from the complex electroencephalograph (EEG) signals generated by Hindustani classical music. It particularly focuses on how the brain responses to the emotional attributes of Hindustani classical music that have been long been a source of discussion for musicologists and psychologists. Using robust scientific techniques that are capable of looking into the most intricate dynamics of the complex EEG signals, it deciphers the human brain?s response to different ragas of Hindustani classical music, shedding new light on what happens inside the performer?s brain when they are mentally composing the imagery of a particular raga. It also explores the much- debated issue in the musical fraternity of whether there are any universal cues in music that make it identifiable for people throughout the world, and if so, what are the neural correlates associated with the universalcues? This book is of interest to researchers and scholars of music and the brain, nonlinear science, music cognition, music signal processing and music information retrieval. 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