LEADER 01103nam0 22002771i 450 001 UON00146758 005 20231205102904.437 100 $a20020107d1963 |0itac50 ba 101 $ahin 102 $aIN 105 $a|||| 1|||| 200 1 $aUse Himalaya para bhagao$fRupachanda Parika 210 $aJodhapura$cPrabhata Prakashana$d1963 215 $a32 p.$d19 cm 606 $aLETTERATURA HINDI$xRAJASTHAN$xPOESIA$3UONC004393$2FI 620 $aIN$dJodhpur$3UONL001106 686 $aSI VI CC$cSUBCONT. INDIANO - LETTERATURA HINDI - MODERNA E CONTEMPORANEA - POESIA$2A 700 1$aPARIKA$bRupachanda$3UONV088170$0672149 712 $aPrabhata Prakashana$3UONV247532$4650 790 1$aPARIKA, Rupacanda$zPARIKA, Rupachanda$3UONV088171 801 $aIT$bSOL$c20240220$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00146758 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI SI VI CC 724 N $eSI SA 49951 5 724 N 996 $aUse Himalaya para bhagao$91277298 997 $aUNIOR LEADER 02590nam 2200553Ia 450 001 9910739440403321 005 20200520144314.0 010 $a1-299-33715-5 010 $a1-4614-6292-4 024 7 $a10.1007/978-1-4614-6292-7 035 $a(OCoLC)844056224 035 $a(MiFhGG)GVRL6YAI 035 $a(CKB)2670000000336419 035 $a(MiAaPQ)EBC1106350 035 $a(EXLCZ)992670000000336419 100 $a20130219d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aSystem identification using regular and quantized observations $eapplications of large deviations principles /$fQi He, Le Yi Wang, G. George Yin 205 $a1st ed. 2013. 210 $aDordrecht $cSpringer$d2013 215 $a1 online resource (xii, 95 pages) $cillustrations (some color) 225 0$aSpringerBriefs in mathematics 300 $aDescription based upon print version of record. 311 $a1-4614-6291-6 320 $aIncludes bibliographical references and index. 327 $aIntroduction and Overview.- System Identification: Formulation.- Large Deviations: An Introduction.- LDP under I.I.D. Noises.- LDP under Mixing Noises.- Applications to Battery Diagnosis.- Applications to Medical Signal Processing.-Applications to Electric Machines -- Remarks and Conclusion -- References -- Index. 330 $aThis brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular.  By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications. 410 0$aSpringerBriefs in mathematics. 606 $aPiezoelectric materials 606 $aPiezoelectricity 606 $aPyroelectricity 615 0$aPiezoelectric materials. 615 0$aPiezoelectricity. 615 0$aPyroelectricity. 676 $a537.2446 700 $aHe$b Qi$0885026 701 $aWang$b Le Yi$01755450 701 $aYin$b G. George$061947 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739440403321 996 $aSystem identification using regular and quantized observations$94192230 997 $aUNINA