Multipath diversity of precoded OFDM with linear equalization
|
|
- Diana Blair
- 5 years ago
- Views:
Transcription
1 Uvrsty of Wollogog Rsarch Ol Faculty of Iformatcs - aprs (Archv) Faculty of Egrg ad Iformato Sccs 8 ultpath dvrsty of prcodd OFD wth lar qualzato Xaojg uag Uvrsty of Wollogog, huag@uow.du.au ublcato Dtals X. uag, "ultpath dvrsty of prcodd OFD wth lar qualzato," IEEE Itratoal Cofrc o Commucatos, 8, pp Rsarch Ol s th op accss sttutoal rpostory for th Uvrsty of Wollogog. For furthr formato cotact th UOW Lbrary: rsarch-pubs@uow.du.au
2 ultpath dvrsty of prcodd OFD wth lar qualzato Abstract Ths papr sttls a cotrovrsy ovr th multpath dvrsty prformac of th prcodd orthogoal frqucy dvso multplg systm wth th lar qualzato. Through th asymptotcal aalyss of th bt rror rats wth trm systm paramtrs, a comprhsv udrstadg of th lar qualzato's bhavor th frqucy-slctv multpath fadg chals s gad. Compard wth th optmum mamum-lklhood dtcto, th dvrsty prformac of th lar qualzato ca b wll dscrbd by a asymptotcal sgal-to-os rato (SR) dgradato, whch rvals that th lar qualzato ca achv th mamum multpath dvrsty wth a opratoal SR rag but wll los dvrsty advatag as SR crass. Kywords ultpath, dvrsty, prcodd, OFD, lar, qualzato Dscpls hyscal Sccs ad athmatcs ublcato Dtals X. uag, "ultpath dvrsty of prcodd OFD wth lar qualzato," IEEE Itratoal Cofrc o Commucatos, 8, pp Ths cofrc papr s avalabl at Rsarch Ol:
3 Ths full tt papr was pr rvwd at th drcto of IEEE Commucatos Socty subjct mattr prts for publcato th ICC 8 procdgs. ultpath Dvrsty of rcodd OFD wth Lar Equalzato Xaojg uag School of Elctrcal, Computr ad Tlcommucatos Egrg Uvrsty of Wollogog Wollogog, Australa Abstract Ths papr sttls a cotrovrsy ovr th multpath dvrsty prformac of th prcodd orthogoal frqucy dvso multplg systm wth th lar qualzato. Through th asymptotcal aalyss of th bt rror rats wth trm systm paramtrs, a comprhsv udrstadg of th lar qualzato s bhavor th frqucy-slctv multpath fadg chals s gad. Compard wth th optmum mamum-lklhood dtcto, th dvrsty prformac of th lar qualzato ca b wll dscrbd by a asymptotcal sgal-to-os rato (SR) dgradato, whch rvals that th lar qualzato ca achv th mamum multpath dvrsty wth a opratoal SR rag but wll los dvrsty advatag as SR crass. Kywords orthogoal frqucy dvso multplg (OFD); multpath dvrsty; lar qulazato. I. ITRODUCTIO Orthogoal frqucy dvso multplg (OFD) provds a ffct mas to mtgat th trsymbol trfrc (ISI) causd by th chal multpath sprad ad joys th smpl frqucy doma chal qualzato va fast Fourr trasform (FFT). owvr, addto to th larg pak-to-avrag powr rato (AR) ad th cssty for complcatd frqucy sychrozato, covtoal OFD systms suffr from a major dsadvatag of poor dvrsty prformac frqucy-slctv fadg chals. Chal codg has b tradtoally usd to mprov th dvrsty across frqucy ad tm, ad rctly lar prcodg ad block spradg ar troducd for OFD systms to ga frqucy dvrsty [-4]. Though thr ar som varatos prformg prcodg, th prcodd OFD systms shar th sam prcpl of applyg a utary matr to a group of data symbols bfor subcarrr mappg. Sc th prcodd data symbol modulatd o a subcarrr s ow a lar combato of th orgal data symbols, f ay subcarrr prcs a dp fad aftr trasmttg ovr a frqucy-slctv multpath chal, th orgal data symbols ca b stll rcovrd from othr rcvd subcarrrs, so that th systm prformac s mprovd du to th crasd dvrsty ordr. Svral authors hav provd, usg a parws rror probablty (E) aalyss, that th prcodd OFD ca achv th mamum dvrsty advatag wth th mamuklhood (L) dtcto f th prcodg data group sz s largr tha or qual to th chal dvrsty ordr [,]. Sc th L dtcto s hghly computatoally complcatd, spcally wh th data group sz s larg, a lar qualzato, such as th mmum ma squard rror (SE) qualzato ad th zro-forcg (ZF) qualzato, followd by a hard dcso s prabl practc. owvr, rgardg th prformac of th prcodd OFD wth lar qualzato, thr s a cotrovrsy foud th ltratur. Tpdlloglu clams [3], basd o th E aalyss, that a prcodd OFD systm ca always achv mamum multpath dvrsty through lar qualzato, whras ccloud cocluds [4], by asymptotcal rror prformac aalyss for oly ZF qualzato, that th lar qualzato dos ot ga ay dvrsty advatag for block sprad OFD (whch s also a prcodd OFD). I ths papr, a dffrt asymptotcal approach s tak to vstgat th dvrsty prformac of th lar qualzato for th prcodd OFD. W frst aalyz th bt rror rat (BER) of th lar qualzato udr two systm paramtrs,.., th data group sz ad th chal dvrsty ordr, assumg a quadratur phas shft kyg (QSK) subcarrr mappg. Th, w drv th asymptotcal prformac by puttg th two paramtrs to dffrt trm codtos. W also dtrm a prformac lowr boud of th L dtcto, so that th prformac of th lar qualzato ca b compard, ad th dvrsty advatag ca b assssd. Aftr dfg a asymptotcal SR dgradato, a comprhsv udrstadg of th lar qualzato s bhavor s gad, ad thus th cotrovrsy ovr th dvrsty prformac of th lar qualzato s sttld. Th rst of th papr s orgazd as follows. I Scto II, th prcodd OFD systm modls ar prstd. I Scto III, th BER of th lar qualzato s formulatd as a fucto of th data group sz ad th multpath dvrsty ordr. Scto IV s dvotd to th drvato of th closdform asymptotcal BER of th lar qualzato. Scto V provds th valuato rsults of th drvd asymptotcal BER ad dfs th asymptotcal SR dgradato. Fally, coclusos ar draw Scto VI. II. SYSTE AD CAEL ODELS Th prcodd OFD trasmttr basbad modl s show Fg. (a), whr a block of put data symbols ( ad ar chos as tgr powrs of for covc) s dotd as a squc [],,,,, or vctor. Ths rsarch s supportd by th Australa Rsarch Coucl Dscovry rojct D /8/$5. 8 IEEE 37
4 Ths full tt papr was pr rvwd at th drcto of IEEE Commucatos Socty subjct mattr prts for publcato th ICC 8 procdgs. Bfor prcodg, [] s frstly dvdd, through sral-toparalll covrso (S/), to groups of sz wth th th group,,,,, bg dotd as a vctor ( [ ], [ + ],, [ + ] ) T whr T [] dots th matr trasposto. S/ C Rmoval or Ovrlap-Add r[] U U U rcodg S/ Itrlavg FFT Y (a) IFFT y y[] /S C or Z r R ˆ ˆ [] (b) D-trlavg R R R Equalzato ad dtcto ˆ ˆ ˆ Fg.. rcodd OFD systm modls: (a) trasmttr ad (b) rcvr. Th prcodg procss s to apply a U, whch satsfs th proprty U U U /S utary matr U I, whr dots th matr trasposto ad compl-cojugato oprato ad I s th dtty matr of ordr, to ach vctor to produc a prcodd vctor U, ad thus ach lmt th prcodd vctor s a lar combato of th symbols vctor. To bttr plot frqucy dvrsty, th prcodd symbols ar prably mappd oto subcarrrs qually spacd across th trasmttd badwdth []. Ths mappg ca b mplmtd by prformg a block trlavg oprato amog th prcodd vctors ad th takg a IFFT of lgth o th trlavd vctor Y. Th rsultg vctor s dotd as y. Aftr paralll-to-sral covrso (/S), y s covrtd to a tm doma squc y [],,,,. To form a prcodd OFD symbol, thr a cyclc p (C) or a zropaddd (Z) suff of suffct lgth (logr tha th mamum chal multpath dlay L sampls) d to b addd to y [] to avod trfrc btw adjact prcodd OFD symbols ad tur th lar covoluto of th trasmttd sgal wth th chal mpuls rspos to a crcular o. Th prcodd OFD sgal s th trasmttd ovr a frqucy-slctv multpath fadg chal dscrbd by th dscrt chal mpuls rspos h [],,,, L, ad rcvd at th rcvr basbad. By rmovg th C or prformg a ovrlap-add oprato, -pot rcvd sampls r [],,,,, ar producd, whch ar rprstd as vctor r aftr S/, ad furthr trasformd to frqucy doma by FFT to yld a vctor R. Aftr dtrlavg, th dscrt-tm rcvd sgal ca b prssd th frqucy doma as R U + V,,,,, whr R ( [] [ ] [ ]) T R, R +,..., R + (3) s a vctor of lmts whch ar dcmatd from R by a dow-samplg factor startg from R [], th th lmt of R, dag( [], [ + ],..., [ ( ) + ] ) (4) s a dagoal matr wth dagoal lmts dcmatd from th chal dscrt frqucy rspos (th -pot dscrt Fourr trasform of h [] ) [] k, k,,,, by a dow-samplg factor startg from [], ad V s a zro-ma Gaussa os vctor wth covarac matr E{ V V } σ VI, whr E {} dots smbl avrag. It s also assumd that th data symbols ar mutually dpdt wth avrag sgal powr σ so that E{ } σ I. Fally, to rcovr ach trasmttd data vctor, qualzato ad dtcto must b prformd o ach rcvd sgal vctor R. Dotg th stmatd data vctors as ˆ, th output data squc ˆ [],,,,, s th T T T obtad from vctor ˆ ( ˆ ˆ ) T aftr /S. ˆ Rgardg th frqucy-slctv multpath fadg chal, w us a ormalzd tappd dlay l modl ad assum a full multpath dvrsty of ordr L. That s, all chal tap coffcts h [],,,, L, ar dpdt ad dtcally dstrbutd (..d.) compl Gaussa radom varabls wth zro-ma ad varac. L III. BERS OVER ULTIAT FADIG CAELS A. BER Lowr Boud of L Dtcto To st up a bchmark for prformac comparso, w frst gv a BER lowr boud usg th L dtcto. Assumg prfct kowldg of th chal at th rcvr, th L stmat of th th data vctor ca b obtad by mmzg th quatty ( R Uˆ )( R Uˆ ),,,,, (5) through haust sarch from all possbl dat vctors ˆ. Followg a wll stablshd procdur, a lowr boud of th avrag BER for th L dtcto ovr frqucy-slctv multpath fadg chals s gv by 38
5 Ths full tt papr was pr rvwd at th drcto of IEEE Commucatos Socty subjct mattr prts for publcato th ICC 8 procdgs. [ + ], L Eh Q l (6) l whr E h{} dots th smbl avrag ovr all chal coffcts h [],,,, L, th Q-fucto s dfd as Q( ) t σ dt, ad s th put SR. To σ π V dscrb th mpact of th data group sz ad th multpath lgth L o th dtcto prformac, w hav dotd th BER lowr boud (6) as a fucto of ad L. B. BER of Lar Equalzato Lar qualzato s prabl practc sc t ca smply us a o-tap qualzr for ach subcarrr th frqucy doma. Lt C [] k dot th o-tap qualzr coffct to b appld to R [] k o subcarrr k ad C dag( C[], C[ + ],..., C[ ( ) + ] ) (7) dot a dagoal matr wth dagoal lmts C [ l + ], l,,,. Th lar qualzato ad dtcto procss ca b dscrbd as follows. Frst, applyg C to R producs a qualzd vctor C R. Scod, usg U to rmov prcodg ylds th dcso varabl vctor d U CR. Fally, a stmat of th trasmttd data vctor s obtad aftr hard dcso o d. Wh th SE crtro s usd,.., dsgg C so that th ma squard rror (SE) btw d ad ε E{ ( d )( d )} (8) s mmzd, th dagoal lmt C s foud to b [ ] [ l + ] C l + (9) [ l + ] + ad th avrag BER ca b valuatd as whr mms, L E Q h l [ l + ] + s th ormalzd mmum SE [5]. As s from (9), th SE qualzato rqurs th kowldg of th put SR. If ths kowldg s ot avalabl, th ZF qualzato ca b prformd by assumg o os s prst at th rcvr ad slctg th qualzr coffcts C[ l + ] to forc th ISI to zro. [ l + ] owvr, ths ZF qualzato hacs os powr wh th put SR s low ad causs dvdg-by-zro problm at ull subcarrrs. I practc, w ca dsg th lar qualzr coffcts accordg to a prdtrmd or stmatd rc SR as l + [ + ] C l [ ] [ + ] + l. Appartly, wh ths lar qualzato bcoms th SE qualzato, ad as t bcoms th ZF qualzato. Usg th qualzr coffcts dfd, th avrag BER for th lar qualzato ca b drvd as ( ), L Eh Q, L (3) whr, L [ ( )] [ ] ( [ ) ] + (4) s th output SR th dcso varabl ad. (5) l l + + ( [ ] ) IV. ASYTOTICAL BERS To show th rlatoshp btw th systm prformac ad th group sz as wll as th rlatoshp btw th systm prformac ad th chal dvrsty ordr, lt s work out two sts of asymptotcal BERs for th lar qualzato by asymptotcal aalyss. r, th word asymptotcal rs to th codtos wh som systm paramtrs tak o trm valus. Th frst st rprsts th BERs for dffrt block sz wh L. Th scod st dcats th BERs for a gv umbr of chal multpath L (rrd to as multpath dvrsty ordr) wth suffctly larg data block sz,..,. Wh both ad L approach fty, th asymptotcal BER wll dmostrat th bst dvrsty prformac that th lar qualzato could achv. A. Asymptotcal BER lowr bouds for L Dtcto For comparso purpos, w frst work out th asymptotcal BER lowr bouds for th L dtcto. To dtrm th asymptotcal BER lowr bouds udr codto L, w assum that th chal provds full multpath dvrsty,.., π j ( l + ) L. Sc [ l + ] h[], w hav { [ l + ] [ l ] } j E{ h[] h [ ]} E + π π ( l + ) j ( l + ) π j ( l l ), l l Ths mas that [ ]. (6), othrws l + bcoms a dpdt compl 39
6 Ths full tt papr was pr rvwd at th drcto of IEEE Commucatos Socty subjct mattr prts for publcato th ICC 8 procdgs. Gaussa varabl wth ut varac wh th chal has full dvrsty. Th, th smbl avrag (6) ca b prformd o [ l + ] drctly stad of h []. Dotg [ l + ] as l for covc, th asymptotcal BER lowr boud ca b valuatd as,, E Q l l E Q l (7) l whch s th BER wth dvrsty ordr (ot that th subscrpt l s gord sc th smbl avrags E Q l ar th sam for all ). Thus, t l s provd that gv a group sz th L dtcto ca oly achv dvrsty ordr v f th chal has ulmtd dvrsty. Th scod st of asymptotcal BER lowr bouds dcats th bst prformac for a gv multpath dvrsty ordr L but wth suffctly larg data block sz. I ths cas, w L hav [ ] π j l+ l + h[], ad thus l L L L [ l + ] h[] h[] h [ ] L [] l π ( + ) L π j l j ( l + ) [] h π ( ) π j j l ( ) l h, for L. (8) As, w hav (, L) (, ) L Eh Q h L L Eh Q h (9) whch s th BER wth dvrsty ordr L. Wh both ad h L l ad L, w hav l. Thor, from thr (7) or (9) th asymptotcal BER lowr boud of th L dtcto bcoms ( ) Q( ), whch s bst prformac a prcodd OFD systm could achv. W r to t as th mamum dvrsty prformac. It s th sam as th BER Gaussa chal wthout fadg. B. Asymptotcal BERs for Lar Equalzato Followg th sam procdur as that w usd for drvg (7), th frst st of th asymptotcal BERs (.., L ) ca b prssd as (, ) E { Q( (, ) )} whr (, ) has th sam prsso as (4) but th subscrpt s gord ad l l + ( ) l + ( l ). Th scod st of th asymptotcal BERs s rachd as udr a gv multpath dvrsty ordr L, ad ca b prssd as (, L) E h { Q( (, L) )} whr (, L) has th sam prsso as (4) but ( ) ( ) ( ) π π π ω ( ) j dω + (3) ω π d (4) ω ( ) j + jω whr ( ) s th Fourr trasform of h []. Wh both ad L, a closd-form prsso of th bst prformac that th lar qualzato could offr ca b drvd as (, ) Q ( (, ) ), whr (, ) has th sam prsso as (4) but ρ ( ) d ρ + ρ (5) ( ) ( ) ρ dρ (6) ( ρ + ) whch ca b drvd from ad rspctvly. For ampl, to drv (5), w otc that l s chsquar-dstrbutd wth two dgrs of frdom ad probablty dstrbuto fucto (pdf) ρ. As, th avrag ovr l ca b rplacd by smbl l l + ρ avrag d ρ + ρ. V. ASYTOTICAL BER AD SR DEGRADATIO Comparg th mamum dvrsty prformac of th L dtcto wth th asymptotcal BERs of th lar qualzato drvd abov, w ar ow abl to ga a full udrstadg of th dvrsty prformac of th lar qualzato. Fg. shows th (, ) curvs as fuctos of E b, by rplacg th put SR E b for QSK, whr E b Dfg a spcal fucto ( E ) ( ) altratvly valuatd by ( ) t t dt, (5) ad (6) ca b E for ad. 3
7 Ths full tt papr was pr rvwd at th drcto of IEEE Commucatos Socty subjct mattr prts for publcato th ICC 8 procdgs. s th sgal rgy pr bt ad s th os powr spctral dsty. Th rc ormalzd SR s st to ( E mms,,, 3, 4 db rspctvly. (, ) ad (, ) b ), whch s th prformac lowr boud of th lar qualzato, ar also dsplayd for comparso purpos. lowr tha th rc SR th dgradato s almost a costat, whch mpls th sam slop o th BER curv. Oc th SR gos byod th opratoal rag, th dgradato crass rapdly, whch dcats th loss of dvrsty. As th rc SR crass, th opratoal rag bcoms wdr but th SR dgradato crass too. 5 BER (E b / ) db db db 3 db 4 db SR dgradato (db) 5 (E b / ) db db db 3 db 4 db E / (db) b Fg.. Asymptotcal BERs of prcodd OFD wth lar qualzato udr dffrt rc ormalzd SRs (sold ls). Th asymptotcal BERs for L dtcto (dotd l) ad SE qualzato (dashd l) ar also dsplayd. As w kow, th dvrsty ordr dscrbs how fast th BER dcrass as th SR crass,.., t s rlatd to th slop of th BER curv. From Fg. w s that a BER curv of th lar qualzato for a gv rc SR has smlar slop to that of th mamum dvrsty BER curv for SRs blow th rc SR. As th SR crass byod th rc SR, th BER curv gradually gos flat. Ths obsrvato dcats that th lar qualzato achvs th sam dvrsty ordr as th L dtcto for SRs lowr tha th rc SR (though th BER tslf s wors tha that of th L dtcto) but th dvrsty s lost wh th SR s hghr tha th rc SR. W r to th SRs blow th rc SR as th opratoal SR rag. Furthrmor, w otc that th asymptotcal BERs (, ) ad (, ) ad (, ) ar valuatd by th sam Q-fucto but wth SR rspctvly. Thus th prformac dgradato ca b mor ffctly dscrbd by th SR dgradato. W call ths SR dgradato as th asymptotcal SR dgradato, whch s dfd as (, ) D log [ ] ( + [ ) ]( ) [ ] log. Fg. 3 shows th asymptotcal SR dgradato as a fucto of th ormalzd SR E b, E b wth,, 3, 4 db rspctvly. W s that wh th SR s E / (db) b Fg. 3. Asymptotcal SR dgradatos for lar qualzato udr dffrt rc ormalzd SRs (sold ls). Th asymptotcal SR dgradato for SE qualzato (dashd l) rprsts a lowr boud. VI. COCLUSIOS W hav show that a practcal lar qualzato achvs th mamum multpath dvrsty for th SRs wth a opratoal rag whch s dtrmd by th rc SR usd to dsg th lar qualzato coffcts. owvr, oc th SR gos across th opratoal thrshold (.., th rc SR), th dvrsty advatag s lost. If th SR s kow at th rcvr, th SE qualzato wll offr th lowr boud prformac. Th drvd closd-form asymptotcal BER ad SR dgradato prssos ot oly provd a ffct way to valuat th pottal prformac that th lar qualzato could achv but also srv as a usful tool for optmal rcvr dsg. REFERECES [] Z. Wag ad G. B. Gaaks, Larly prcodd or codd OFD agast wrlss chal fads?, rocdgs of Sgal rocssg Advacs Wrlss Commucatos Workshop, Taoyua, Tawa, arch 3,, pp [] Z. Lu, Y. X, ad G. B. Gaaks, Lar costllato prcodg for OFD wth mamum multpath dvrsty ad codg gas, IEEE Trasactos o Commucatos, Vol. 5, o. 3, arch 3, pp [3] C. Tpdlloglu, amum multpath dvrsty wth lar qualzato prcodd OFD systms, IEEE Trasactos o Iformato Thory, Vol. 5, o., auary 4, pp [4]. L. ccloud, Aalyss ad dsg of short block OFD spradg matrcs for us o multpath fadg chals, IEEE Trasactos o Commucatos, Vol. 53, o. 4, Aprl 5, pp [5] X. uag, Dvrsty prformac of prcodd OFD wth SE qualzato, prstd at th 7 Itratoal Symposum o Commucatos ad Iformato Tchologs (ISCIT7), Sydy, Australa, 6-9 Octobr, 7. 3
Estimation Theory. Chapter 4
Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,
More information3.4 Properties of the Stress Tensor
cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato
More informationReliability of time dependent stress-strength system for various distributions
IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,
More informationBER Analysis of Optical Wireless Signals through Lognormal Fading Channels with Perfect CSI
7th tratoal Cofrc o Tlcommucatos BER Aalyss of Optcal Wrlss Sgals through ogormal Fadg Chals wth rfct CS Hassa Morad, Maryam Falahpour, Hazm H. Rfa Elctrcal ad Computr Egrg Uvrsty of Olahoma Tulsa, OK,
More informationFILTER BANK MULTICARRIER WITH LAPPED TRANSFORMS
FILTER BANK ULTICARRIER WITH LAPPED TRANSFORS aurc Bllagr, CNA Davd attra, aro Tada, Uv.Napol arch 5 Obctvs A multcarrr approach to mprov o OFD for futur wrlss systms - asychroous mult-usr accss - spctral
More informationIntroduction to logistic regression
Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data
More informationBinary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit
(c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,
More informationChannel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto {t-asano,
School of Iformato Scc Chal Capacty 009 - Cours - Iformato Thory - Ttsuo Asao ad Tad matsumoto Emal: {t-asao matumoto}@jast.ac.jp Japa Advacd Isttut of Scc ad Tchology Asahda - Nom Ishkawa 93-9 Japa http://www.jast.ac.jp
More informationTotal Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are
Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,
More informationUnbalanced Panel Data Models
Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr
More informationOn Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data
saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord
More informationMath Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)
Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts
More informationLECTURE 6 TRANSFORMATION OF RANDOM VARIABLES
LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt
More informationSecond Handout: The Measurement of Income Inequality: Basic Concepts
Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart
More informationMachine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider
Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.
More informationMODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f
MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu
More informationBayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution
Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt
More informationLecture 1: Empirical economic relations
Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.
More informationIntroduction to logistic regression
Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots
More informationThe real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.
Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /
More informationRepeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.
Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms
More informationThe R Package PK for Basic Pharmacokinetics
Wolfsggr, h R Pacag PK St 6 h R Pacag PK for Basc Pharmacotcs Mart J. Wolfsggr Dpartmt of Bostatstcs, Baxtr AG, Va, Austra Addrss of th author: Mart J. Wolfsggr Dpartmt of Bostatstcs Baxtr AG Wagramr Straß
More informationSuzan Mahmoud Mohammed Faculty of science, Helwan University
Europa Joural of Statstcs ad Probablty Vol.3, No., pp.4-37, Ju 015 Publshd by Europa Ctr for Rsarch Trag ad Dvlopmt UK (www.ajourals.org ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN WEIBULL DISTRIBUTION
More informationOrdinary Least Squares at advanced level
Ordary Last Squars at advacd lvl. Rvw of th two-varat cas wth algbra OLS s th fudamtal tchqu for lar rgrssos. You should by ow b awar of th two-varat cas ad th usual drvatos. I ths txt w ar gog to rvw
More informationDepartment of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis
Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..
More informationRound-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform
Appl. Sc. 4, 4, 99-7; do:.339/app499 Artcl OPEN ACCESS appld sccs ISSN 76-347 www.mdp.com/joural/applsc Roud-Off Nos of Multplcatv FIR Fltrs Implmtd o a FPGA Platform Ja-Jacqus Vadbussch, *, Ptr L ad Joa
More informationCounting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.
Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w
More informationA COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY
Colloquum Bomtrcum 44 04 09 7 COMPISON OF SEVEL ESS FO EQULIY OF COEFFICIENS IN QUDIC EGESSION MODELS UNDE HEEOSCEDSICIY Małgorzata Szczpa Dorota Domagała Dpartmt of ppld Mathmatcs ad Computr Scc Uvrsty
More informationIndependent Domination in Line Graphs
Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG
More informationCourse 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:
Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght
More informationA Stochastic Approximation Iterative Least Squares Estimation Procedure
Joural of Al Azhar Uvrst-Gaza Natural Sccs, 00, : 35-54 A Stochastc Appromato Itratv Last Squars Estmato Procdur Shahaz Ezald Abu- Qamar Dpartmt of Appld Statstcs Facult of Ecoomcs ad Admstrato Sccs Al-Azhar
More informationGroup Consensus of Second-Order Multi-agent Networks with Multiple Time Delays
Itratoal Cofrc o Appld Mathmatcs, Smulato ad Modllg (AMSM 6) Group Cossus of Scod-Ordr Mult-agt Ntworks wth Multpl Tm Dlays Laghao J* ad Xyu Zhao Chogqg Ky Laboratory of Computatoal Itllgc, Chogqg Uvrsty
More informationThe probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1
Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th
More informationTolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data
Itratoal Rfrd Joural of Egrg ad Scc (IRJES) ISSN (Ol) 319-183X, (Prt) 319-181 Volum, Issu 10 (Octobr 013), PP. 6-30 Tolrac Itrval for Expotatd Expotal Dstrbuto Basd o Groupd Data C. S. Kaad 1, D. T. Shr
More informationNumerical Method: Finite difference scheme
Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from
More informationNotation for Mixed Models for Finite Populations
30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p
More informationASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES
AYMPTOTIC AD TOLERACE D-MODELLIG I ELATODYAMIC OF CERTAI THI-WALLED TRUCTURE B. MICHALAK Cz. WOŹIAK Dpartmt of tructural Mchacs Lodz Uvrsty of Tchology Al. Poltrchk 6 90-94 Łódź Polad Th objct of aalyss
More informationResearch on the Massive Data Classification Method in Large Scale Computer Information Management huangyun
Itratoa Crc o Automato, Mchaca Cotro ad Computatoa Egrg (AMCCE 05) Rsarch o th Massv Data Cassfcato Mthod Larg Sca Computr Iformato Maagmt huagyu Chogqg ctroc grg Carr Acadmy, Chogqg 4733, Cha Kywords:
More informationOn the Design of Finite-State Shaping Encoders for Partial-Response Channels
O th Dsg of Ft-Stat Shapg Ecodrs for Partal-Rspos Chals Josph B. Soraga Qualcomm, Ic. 5775 Morhous Drv, Sa Dgo, CA 922 Emal: jsoraga@qualcomm.com Paul H. Sgl Ctr for Magtc Rcordg Rsarch Uvrsty of Calfora,
More informationAlmost all Cayley Graphs Are Hamiltonian
Acta Mathmatca Sca, Nw Srs 199, Vol1, No, pp 151 155 Almost all Cayly Graphs Ar Hamltoa Mg Jxag & Huag Qogxag Abstract It has b cocturd that thr s a hamltoa cycl vry ft coctd Cayly graph I spt of th dffculty
More informationLine Matching Algorithm for Localization of Mobile Robot Using Distance Data from Structured-light Image 1
Advacd Scc ad Tchoogy Lttrs Vo.86 (Ubqutous Scc ad Egrg 015), pp.37-4 http://dx.do.org/10.1457/ast.015.86.08 L Matchg Agorthm for Locazato of Mob Robot Usg Dstac Data from Structurd-ght Imag 1 Soocho Km
More informationThis is a repository copy of Estimation of generalised frequency response functions.
hs s a rpostory copy of Estmato of gralsd frqucy rspos fuctos. Wht Ros Rsarch Ol URL for ths papr: http://prts.whtros.ac.uk/74654/ Moograph: L, L.M. ad Bllgs, S.A. 9 Estmato of gralsd frqucy rspos fuctos.
More informationConsistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach
ISSN 168-8 Joural of Statstcs Volum 16, 9,. 1-11 Cosstcy of th Mamum Lklhood Estmator Logstc Rgrsso Modl: A Dffrt Aroach Abstract Mamuur Rashd 1 ad Nama Shfa hs artcl vstgats th cosstcy of mamum lklhood
More informationNote on the Computation of Sample Size for Ratio Sampling
Not o th Computato of Sampl Sz for ato Samplg alr LMa, Ph.D., PF Forst sourcs Maagmt Uvrst of B.C. acouvr, BC, CANADA Sptmbr, 999 Backgroud ato samplg s commol usd to rduc cofdc trvals for a varabl of
More informationDifferent types of Domination in Intuitionistic Fuzzy Graph
Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,
More informationA Measure of Inaccuracy between Two Fuzzy Sets
LGRN DEMY OF SENES YERNETS ND NFORMTON TEHNOLOGES Volum No 2 Sofa 20 Masur of accuracy btw Two Fuzzy Sts Rajkumar Vrma hu Dv Sharma Dpartmt of Mathmatcs Jayp sttut of formato Tchoy (Dmd vrsty) Noda (.P.)
More informationWeights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )
Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also
More informationChapter 6. pn-junction diode: I-V characteristics
Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc
More informationDr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23
BIO53 Bosascs Lcur 04: Cral Lm Thorm ad Thr Dsrbuos Drvd from h Normal Dsrbuo Dr. Juchao a Cr of Bophyscs ad Compuaoal Bology Fall 06 906 3 Iroduco I hs lcur w wll alk abou ma cocps as lsd blow, pcd valu
More informationEstimation of the Present Values of Life Annuities for the Different Actuarial Models
h Scod Itratoal Symposum o Stochastc Modls Rlablty Egrg, Lf Scc ad Opratos Maagmt Estmato of th Prst Valus of Lf Auts for th Dffrt Actuaral Modls Gady M Koshk, Oaa V Guba omsk Stat Uvrsty Dpartmt of Appld
More informationCOMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES
COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld
More informationUsing Nonlinear Filter for Adaptive Blind Channel Equalization
HAMDRZA BAKHSH Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Us oar Ftr or Adaptv Bd Cha quazato MOHAMMAD POOYA Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Abstract: trsybo trrc
More informationMB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH
Yugoslav Joural of Opratos Rsarch 6 (06), Numbr, 89-98 DOI: 0.98/YJOR405906B MB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH Suma BHADRA Rsarch Scholar Dpartmt of Mathmatcs IIEST, Shbpur
More informationERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**
ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults
More informationEstimation of Population Variance Using a Generalized Double Sampling Estimator
r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst
More informationAotomorphic Functions And Fermat s Last Theorem(4)
otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral
More informationChapter Discrete Fourier Transform
haptr.4 Dscrt Fourr Trasform Itroducto Rcad th xpota form of Fourr srs s Equatos 8 ad from haptr., wt f t 8, h.. T w t f t dt T Wh th abov tgra ca b usd to comput, h.., t s mor prfrab to hav a dscrtzd
More informationChiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since
56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,
More informationThe Role of Branch-Correlation for an MC-CDMA System Combining with Coherent Diversity over Frequency Selective Channels
WEA RAACIO o COMMUICAIO a-hg La, Joy Iog-Zog Ch, Chh W Lou, I. Ma Huag h Rol of Brach-Corrlato for a MC-CDMA yst Cog wth Cohrt Dvrsty ovr Frqucy lctv Chals a-hg La, *Joy Iog-Zog Ch, Chh W Lou, ad I Ma
More informationsignal amplification; design of digital logic; memory circuits
hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll
More informationCorrelation in tree The (ferromagnetic) Ising model
5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.
More informationChapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University
Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod
More informationSuperbosonization meets Free Probability
Suprbosoato mts Fr Probablty M Zrbaur jot wor wth S Madt Eulr Symposum St Ptrsburg Ju 3 009 Itroducto From momts to cumulats Larg- charactrstc fucto by fr probablty Suprbosoato Applcato to dsordrd scattrg
More informationPosition Control of 2-Link SCARA Robot by using Internal Model Control
Mmors of th Faculty of Er, Okayama Uvrsty, Vol, pp 9-, Jauary 9 Posto Cotrol of -Lk SCARA Robot by us Itral Modl Cotrol Shya AKAMASU Dvso of Elctroc ad Iformato Systm Er Graduat School of Natural Scc ad
More informationLinear Prediction Analysis of Speech Sounds
Lr Prdcto Alyss of Sch Souds Brl Ch 4 frcs: X Hug t l So Lgug Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus sch rcogto rocdgs of th I Stbr 993 5-47 Lr Prdctv
More informationDesign of Functionally Graded Structures in Topology Optimization
EgOpt 2008 - Itratoal Cofrc o Egrg Optmzato Ro d Jaro, Brazl, 0-05 Ju 2008. Dsg of Fuctoally Gradd Structurs Topology Optmzato Sylva R. M. d Almda, Glauco H. Paulo 2, Emlo C. N. Slva 3 Uvrsdad Fdral d
More informationOdd Generalized Exponential Flexible Weibull Extension Distribution
Odd Gralzd Epotal Flbl Wbull Etso Dstrbuto Abdlfattah Mustafa Mathmatcs Dpartmt Faculty of Scc Masoura Uvrsty Masoura Egypt abdlfatah mustafa@yahoo.com Bh S. El-Dsouy Mathmatcs Dpartmt Faculty of Scc Masoura
More informationLinear-Quadratic-Gaussian Optimization of Urban Transportation Network with Application to Sofia Traffic Optimization
BULGARIAN ACADEMY OF SCIENCES CYBERNEICS AND INFORMAION ECHNOLOGIES Volum 16 No 3 Sofa 216 Prt ISSN: 1311-972; Ol ISSN: 1314-481 DOI: 1.1515/cat-216-41 Lar-Quadratc-Gaussa Optmzato of Urba rasportato Ntwork
More information' 1.00, has the form of a rhomb with
Problm I Rflcto ad rfracto of lght A A trstg prsm Th ma scto of a glass prsm stuatd ar ' has th form of a rhomb wth A th yllow bam of moochromatc lght propagatg towards th prsm paralll wth th dagoal AC
More informationLearning from Data with Information Theoretic Criteria II
Larg from Data th Iformato Thortc Crtra II Jos C. Prcp, Ph.D. Dstgushd Profssor of Elctrcal ad Bomdcal Egrg ad BllSouth Profssor Computatoal uroegrg Laborator Uvrst of Florda http://.cl.ufl.du prcp@cl.ufl.du
More informationBAYESIAN ANALYSIS OF THE SIMPLE LINEAR REGRESSION WITH MEASUREMENT ERRORS
BAYESIAN ANALYSIS OF THE SIMPLE LINEAR REGRESSION WITH MEASUREMENT ERRORS Marta Yuk BABA Frado Atoo MOALA ABSTRACT: Usually th classcal approach to mak frc lar rgrsso modl assums that th dpdt varabl dos
More informationEntropy Equation for a Control Volume
Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S
More informationDTFT Properties. Example - Determine the DTFT Y ( e ) of n. Let. We can therefore write. From Table 3.1, the DTFT of x[n] is given by 1
DTFT Proprtis Exampl - Dtrmi th DTFT Y of y α µ, α < Lt x α µ, α < W ca thrfor writ y x x From Tabl 3., th DTFT of x is giv by ω X ω α ω Copyright, S. K. Mitra Copyright, S. K. Mitra DTFT Proprtis DTFT
More informationOn the Possible Coding Principles of DNA & I Ching
Sctfc GOD Joural May 015 Volum 6 Issu 4 pp. 161-166 Hu, H. & Wu, M., O th Possbl Codg Prcpls of DNA & I Chg 161 O th Possbl Codg Prcpls of DNA & I Chg Hupg Hu * & Maox Wu Rvw Artcl ABSTRACT I ths rvw artcl,
More informationTransforms that are commonly used are separable
Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )
More informationIn 1991 Fermat s Last Theorem Has Been Proved
I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral
More informationCHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS
CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS 3. INTRODUCTION Th Ivrs Expoal dsrbuo was roducd by Kllr ad Kamah (98) ad has b sudd ad dscussd as a lfm modl. If a radom varabl
More informationInformation Theoretic Upper Bound on the Capacity of Wireless Backhaul Networks
Iformato Thortc Uppr Boud o th Capacty of Wrlss Bachaul Ntwors Harprt S Dhllo ad Guspp Car Abstract W drv a formato thortc uppr boud o th capacty of a wrlss bachaul twor modld as a classcal radom xtdd
More informationDISCUSSION PAPER SERIES
ISCUSSIO PAPER SERIES scusso papr o. 166 Th optmal choc of tral dcso-makg structurs a twork dustry Tsuyosh Toshmtsu (School of Ecoomcs, Kwas Gaku Uvrsty Sptmbr 017 SCHOOL OF ECOOICS KWASEI GAKUI UIVERSITY
More information1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black
Stf-Boltzm lw stts tht th powr mttd pr ut r of th surfc of blck body s proportol to th fourth powr of th bsolut tmprtur: 4 S T whr T s th bsolut tmprtur d th Stf-Boltzm costt= 5 4 k B 3 5c h ( Clcult 5
More informationReliability Evaluation of Slopes Using Particle Swarm Optimization
atoal Uvrsty of Malaysa From th lctdworks of Mohammad Khajhzadh 20 Rlablty Evaluato of lops Usg Partcl warm Optmzato Mohammad Khajhzadh Mohd Raha Taha hmd El-shaf valabl at: https://works.bprss.com/mohammad_khajhzadh/24/
More informationLinear Prediction Analysis of
Lr Prdcto Alyss of Sch Souds Brl Ch Drtt of Coutr Scc & Iforto grg Ntol Tw Norl Uvrsty frcs: X Hug t l So Lgug g Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus
More informationIAEA-CN-184/61 Y. GOTO, T. KATO, K.NIDAIRA. Nuclear Material Control Center, Tokai-mura Japan.
IAEA-CN-84/6 Establshmt of accurat calbrato curv for atoal vrfcato at a larg scal ut accoutablt tak RRP - For strgthg stat sstm for mtg safguards oblgato. GOO. KAO K.NIDAIRA Nuclar Matral Cotrol Ctr oka-mura
More informationA METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 519.6(045)
FACTA UNIVERSITATIS Srs: Mcacs Automatc Cotrol ad Rootcs Vol 4 N o 6 4 pp 33-39 A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 59645 Prdrag M Raovć Momr S Staovć Slađaa D Marovć 3 Dpartmt
More information1985 AP Calculus BC: Section I
985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationA Multi-granular Linguistic Promethee Model
A Mult-graular Lgustc Promth Modl Nsr Haloua, Lus Martíz, Habb Chabchoub, Ja-Marc Martl, Ju Lu 4 Uvrsty of Ecoomc Sccs ad Maagmt, Sfax, Tusa, Uvrsty of Jaé, Spa, Uvrsty of Laval, Caada, 4 Uvrsty of Ulstr,
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationOn Approximation Lower Bounds for TSP with Bounded Metrics
O Approxmato Lowr Bouds for TSP wth Boudd Mtrcs Mark Karpsk Rchard Schmd Abstract W dvlop a w mthod for provg xplct approxmato lowr bouds for TSP problms wth boudd mtrcs mprovg o th bst up to ow kow bouds.
More informationA Study of Fundamental Law of Thermal Radiation and Thermal Equilibrium Process
Itratoal Joural of Hgh Ergy Physcs 5; (3): 38-46 Publshd ol May 6, 5 (http://www.sccpublshggroup.com/j/jhp) do:.648/j.jhp.53. ISSN: 376-745 (Prt); ISSN: 376-7448 (Ol) A Study of Fudamtal Law of Thrmal
More informationT and V be the total kinetic energy and potential energy stored in the dynamic system. The Lagrangian L, can be defined by
From MEC '05 Itrgratg Prosthtcs ad Mdc, Procdgs of th 005 MyoElctrc Cotrols/Powrd Prosthtcs Symposum, hld Frdrcto, Nw Bruswc, Caada, ugust 7-9, 005. EECROMECHNIC NYSIS OF COMPEE RM PROSHESIS (EMS) Prmary
More informationAdaptive Control OF Nonlinear Multivariable Dynamical Systems Using MRAN-RBF Neural Networks
Itratoal Joural of Elctrc & Computr Sccs IJECS-IJENS Vol: No: Adaptv Cotrol OF Nolar Multvarabl Damcal Sstms Usg MRAN-RBF Nural Ntwors amr A. Al-zohar Computr Scc Dpt. Arradh Commut Collag, Malaz, P.O
More informationChapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS
Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)
More informationIranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT
Iraa Joral of Mathatcal Chstry Vol No Dcbr 0 09 7 IJMC Two Tys of Gotrc Arthtc dx of V hylc Naotb S MORADI S BABARAHIM AND M GHORBANI Dartt of Mathatcs Faclty of Scc Arak Ursty Arak 856-8-89 I R Ira Dartt
More information7THE DIFFUSION OF PRODUCT INNOVATIONS AND MARKET STRUCTURE
7THE DIFFUSION OF PRODUCT INNOVATIONS AND MARKET STRUCTURE Isttut of Ecoomc Forcastg Roxaa IDU Abstract I ths papr I aalyz th dffuso of a product ovato that was rctly mad avalabl for lcsd purchas wth a
More informationOn the Beta Mekaham Distribution and Its Applications. Chukwu A. U., Ogunde A. A. *
Amrca Joural of Mathmatcs ad Statstcs 25, 5(3: 37-43 DOI:.5923/j.ajms.2553.5 O th Bta Mkaham Dstruto ad Its Applcatos Chukwu A. U., Ogud A. A. * Dpartmt of Statstcs, Uvrsty Of Iada, Dpartmt of Mathmatcs
More informationComplex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)
th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc
More informationInternational Journal of Mathematical Archive-6(5), 2015, Available online through ISSN
Itratoal Joural of Mathmatal Arhv-6), 0, 07- Avalabl ol through wwwjmafo ISSN 9 06 ON THE LINE-CUT TRANSFORMATION RAPHS B BASAVANAOUD*, VEENA R DESAI Dartmt of Mathmats, Karatak Uvrsty, Dharwad - 80 003,
More informationBayesian Test for Lifetime Performance Index of Ailamujia Distribution Under Squared Error Loss Function
Pur ad Appld Mathmatcs Joural 6; 5(6): 8-85 http://www.sccpublshggroup.com/j/pamj do:.648/j.pamj.656. ISSN: 36-979 (Prt); ISSN: 36-98 (Ol) Baysa Tst for ftm Prformac Idx of Alamuja Dstrbuto Udr Squard
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More information