A General Method for SER Computation of M-PAM and M-PPM UWB Systems for Indoor Multiuser Communications
|
|
- Pamela Martin
- 6 years ago
- Views:
Transcription
1 A Genera Method for SER Computaton of M-PAM and M-PPM UWB Systems for Indoor Mutuser Communcatons G Durs Isttuto Superore Maro Boea Corso Trento 21 I Torno, Itay E-ma: durs@smt J Romme IMST GmH Car Fredrch Gauss Str 2 D Kamp-Lntfort, Germany E-ma: romme@mstde S Benedetto CERCOM-Potecnco d Torno Corso Duca deg Aruzz 24 I Torno, Itay E-ma: enedetto@potot Astract A genera method for the evauaton of the symo error proaty (SER) of oth M-PAM and M-PPM UWB systems, n presence of mutpath channe, mutuser and strong narrowand nterference s presented Ths method s shown to e ae to ncude a the prncpa mutaccess technques proposed so far for UWB, ke Tme Hoppng and Drect Sequence A comparson etween the performance of three of these technques s aso presented, for oth dea RAKE recever and MMSE equazer, n an ndoor communcatons scenaro I INTRODUCTION The successfu depoyment of Utra Wdeand (UWB) systems for hgh speed ndoor communcatons depends strongy on the deveopment of effcent mutaccess and moduaton technques and of ow compexty recevers, roust aganst narrowand and mutuser nterference As far as the frst ssue s concerned, severa proposas for UWB ar nterface are avaae n the terature, the prncpa ones eng Tme Hoppng (TH) [1], Drect Sequence (DS) [2] and Optca Orthogona Codes [3] A of them can e comned wth oth M-ary puse amptude and puse poston moduaton (M-PAM and M-PPM respectvey) In ths paper we present a genera method for the evauaton of the symo error proaty (SER) for M-PAM and M-PPM UWB systems The SER s evauated for oth dea RAKE recevers [4] and MMSE equazer [2], n the presence of dense mutpath [5] and strong narrowand nterference The paper s organzed as foows In Secton II and III we refy descre the genera system mode and ts tmedscrete verson In Secton IV the SER s anaytcay otaned for M-PAM systems, oth for MMSE and RAKE recevers, whe, n Secton V, the same approach eads to a smpe upper ound on the SER of M-PPM ones Fnay, n Secton VI, a performance comparson etween DS-2PAM, OOC-2PAM, TH-2PAM and TH-2PPM s presented, together wth some concudng remarks aout mutaccess codes desgn, gven the reference scenaro descred n Secton V Ths work has een partay sponsored y MIUR (Itaan Mnstry of Educaton and Research) under the projects CERCOM and PRIMO and y the European Unon under project numer IST whyesscom II SYSTEM MODEL In ths secton the prncpa characterstcs of the system w e refy descred The tme axs s dvded nto symo ntervas of T s seconds, each one further sudvded nto smaer ntervas, caed chps, of duraton T c seconds The sgnature sgna assgned to each user s a perodc sgna, wth perod equa to T For smpcty, we assume that T = N T s and T s = N c T c, wth N,N c N The perod T s onger than the symo tme, whenever the mutaccess code spans more than a snge symo The sgna transmtted y the user k can then e descred as foows: s (k) (t) = p (k) (t T) (1) p (k) (t) = N 1 j=0 N c 1 =0 ( ) g t T c jt s ;a (k) N +j and a (k) s the th symo, transmtted y user k Wth the notaton we denote the vaue assumed y the spreadng code assgned to the user k n the chp of the j symo nterva, n each perod T In ths anayss, we w assume {0, ±1}; n ths way a the prncpa mutuser technques proposed so far for UWB (TH, DS, OOC) are ncuded Note that the mutpe access code { } can e aso the comnaton of a spreadng sequence and a repetton code of ength N s The code assocated to the symo j transmtted y the user k can e wrtten n vector form as j = (2) [ j,0,c(k) j,1,,c(k) j,n c 1] T (3) The sgna g(t; a) represents the random process at the output of the moduator, whose expresson depends on the moduaton format Defnng wth N the numer of puses x(t), wth tme duraton T x T c, transmtted n one symo nterva, we w have, for M-PAM g (t;a) = E x ax(t) (4)
2 E x s the energy per puse and a A PAM = {2m 1 M} M m=1 The transmtted symos are assumed to e ndependent and equproae For M-PPM g (t;a) = E x x(t τ a ) (5) Ths tme a A PPM = {0,1,,M 1} and τ a s the assocated PPM deay We w further assume that T x + max a A P P M τ a T c (6) It s worth notng that the parameter N s a characterstc of the mutpe access and codng strategy, and determnes the reaton etween E x and E, the energy per t Assumng that N u users are actve, the receved sgna can e wrtten as N u r(t) = Ak s (k) (t) h (k) (t) + A n (t) + n(t), (7) k=1 n(t) s a whte Gaussan nose process wth two-sded power spectra densty N 0 /2 and n (t) s the narrowand nterference Furthermore, A k and A represent the attenuatons due to path oss, whch are a functon of the transmtter recever (TX-RX) dstance Fnay, h (k) (t) s the tme-nvarant, asynchronous mutpath channe mpuse response for user k Each asynchronous channe mpuse response h (k) (t) s assumed to have a maxmum deay of t (k) max seconds In the rest of the paper we w denote y q (k) (t) the convouton of the transmtted puse x(t) wth h (k) (t) III DISCRETE-TIME EQUIVALENT MODEL In order to anaytcay evauate the SER, we w construct a dscrete-tme matrx equvaent mode, otaned y sampng r(t) every T r seconds, wth T r T c The recever conssts of a dgta fter, actve on an oservaton wndow T w T s The foowng parameters need aso to e defned: N w = T w /T r, the numer of sampes n the oservaton wndow T w, on whch the recever operates N r = T c /T r, the numer of sampes per chp N = (max k t (k) max)/t c + 1, the mnmum numer of chp ntervas n whch q (k) (t) s contaned, k L = N /N c, the mnmum numer of symo ntervas n whch q (k) (t) s contaned, k N r = T w /T c, the mnmum numer of symo ntervas n whch the oservaton wndow s contaned L r = N r /N c 1, the mnmum numer of symo ntervas n whch the porton of the oservaton wndow that exceeds a t nterva s contaned Note that the notaton x ndcates the nearest nteger arger than or equa to x For the SER computaton, the system under anayss s dentca to the superposton of N susystems, transmttng n a round ron fashon on successve sots of duraton T The presence of mutpath, however, causes the receved sgnas to ose ther mutua orthogonaty, wth the consequence of ntersymo nterference n the overa system The error proaty s otaned averagng on the performance of each susystem Ths task s performed f the error proaty s computed averagng on the SER of N successve symos Wthout oss of generaty, we assume that user 1 s the reference user and that the symo a (1) n s transmtted, wth n [0,,N 1] The vector contanng the dscrete sampes of the receved sgna w e r n = [r(nt s ),r(t r +nt s ),,r((n w 1)T r +nt s )] T (8) Wth the same notaton, we aso ntroduce a Gaussan nose and narrowand nterference vector n n = [n(nt s ),n(t r +nt s ),,n((n w 1)T r +nt s )] T, (9) n,n = [n (nt s ),n (T r +nt s ),,n ((N w 1)T r +nt s )] T (10) Moreover, t s aso necessary to defne the vectors of the transmtted symos a k,n = [a (k) L +n,,a(k) n,,a (k) L r+n ]T, (11) and the spreadng ock dagona matrces S k,n L +n S k,n = L +n L r+n (12) = ( mod N ) (13) and 0 s a N c szed zero vector Fnay, the channe matrces Q k R Nw,(L +L r+1)n c w e ntroduced as foows: Q k = q (k) (L N ct c) q (k) ((L N c 1)T c) q (k) (L N ct c+t r) q (k) ((L N c 1)T c+t r) q (k) (L N ct c+(n r 1)T r) q (k) ((L N c 1)T c+(n r 1)T r) 0 q (k) (L N ct c) 0 q (k) (L N ct c+t r) q (k) (0) q (k) (T r) q (k) ((N r 1)T r) q (k) (T c) q (k) (0) 0 0 q (k) (T c+t r) q (k) (T r) 0 0 IV M-PAM Usng the prevous defntons, k=1 (14) N u r n = A k E x (k) Q k S k,n a k,n + A n,n + n n (15)
3 The dgta recever can e fuy characterzed y ts coeffcents vector Rewrtng S k,n as w n = [w 0,n,w 1,n,,w Nw 1,n] T (16) S k,n = [ ] s (k) L +n,,s(k) n,,s (k) L r+n (17) and denotng y y n the decson varae for the symo a (1) n, we otan the foowng resut: y n = w T n r n = = A 1 E x (1) P n a (1) n + n MI,n + n,n + n G,n, (18) P n = w T n Q 1 s (1) n, (19) N u L r+n n MI,n = A k E x (k) w T n Q k s (k) a (k), (20) k=1 = L +n (k,) (1,n) n,n = A w n T n,n, (21) n G,n = w n T n n (22) In order to derve a smpe cosed-form for the SER, we w further mode the ntersymo-mutuser nterference term, n MI, and the narrowand ones, n, as ndependent, ergodc, zero mean, Gaussan random process The vadty of ths assumpton w e dscussed ater We aso assume that n (t) has a power spectra densty S n (f) wth the foowng characterstcs: S n (f) = N 2, f c B 2 f f c + B 2 0, otherwse (23) f c and B are the centra frequency and the andwdth of the narrowand nterference, respectvey The fter w n concdes wth an dea RAKE recever, f w n = Q 1 s (1) n (24) and T w = T s + t (1) max On the contrary, settng the oservaton wndow to a vaue T w T s (n our anayss we w assume T w = 2T s ), the dgta fter s an MMSE recever f { w n = argmn E z R Nw 2 x a (1) n z T r n 2 } (25) In oth cases, usng standard technques [6], the SER can e wrtten as foows: {( ) P(e) = M 1 N 1 3og erfc 2 M N M N(M 2 1) n=0 } P n ( σ 2 MI,n + σ2,n + σ2 G,n ), (26) the energy per t E (1) s gven y: E (1) = E (1) x N M2 1 3og 2 M (27) and the terms σ 2 MI,n, σ2,n and σ2 G,n are the varance of n MI, n and n G, respectvey V M-PPM As stated n [1], an M-PPM sgna can e vewed as the sum of M near moduators, fed y a non near transformaton of the nformaton symos a (k) Let us denote y x a (t) = x(t τ a ), for a A PPM the waveforms transmtted y each moduator, and wth q a (k) (t) the convouton etween these waveforms and the channe mpuse response assocated to user k Wth a notaton smar to equaton (15), the receved vector can e wrtten as r n = N u a=0 k=1 A k E x (k) Q a ks k,n β a (a k,n ) + A n,n + n n (28) Q a k s the channe matrx, contanng the sampes of q a (k) (t), ke n (14) The non near transformaton β a ( ) operates over each eement of the vector a k,n n the foowng way: { β a (a (k) 1 f a (k) = a ) = (29) 0 otherwse At the recever, the sgna s passed through a ank of M parae dgta fters w n,, = 0,,M 1, whose outputs yed the decson vector ˆβ n = [ˆβ 0 (a (1) n ), ˆβ 1 (a (1) n ),, ˆβ (a (1) n )] T (30) The output of the th dgta fter, ˆβ (a (1) n ) can e evauated as n MI,n, = ˆβ (a (1) n ) = x a=0 P a n,β a (a (1) n )+ + n MI,n, + n,n, + n G,n, (31) Pn, a = w T n, Q a 1s (1) n, (32) N u L r+n A k E x (k) w T n, Q a ks (k) β a (a (k) ), a=0 k=1 = L +n (k,) (1,n) (33) n,n, = A w n, T n,n, (34) Fnay, the estmated symo â (1) n to the foowng decson rue: â (1) n n G,n, = w n, T n n (35) w e seected accordng = max A P P M ˆβ (a (1) n ) (36) Note that each dgta fter s an dea RAKE recever f w n, = Q 1s (1) n, (37)
4 or an MMSE equazer, f { w n, = argmn E z R Nw x β (a (1) n ) z T r n 2 } (38) In ths paper we present a ound on the SER of the system when M-PPM s empoyed, ased on the unon ound Ths approach, n fact, eads to a smpe cosed-form upper ound for the proaty of error aso when the transmtted sgna are not orthogona (overappng PPM technque) Gven two symos a and a, wth a a and a,a A PPM and defnng n n,a,a = n MI,n,a n MI,n,a + n,n,a n,n,a + then the SER s gven y P(e) = 1 2MN + n G,n,a n G,n,a, (39) N 1 a=0 a =0 n=0 a a erfc og2 M (Pn,a a Pn,a a ), (40) N 2σn,a,a 2 E (1) = NE (1) x /og 2 M s the energy per t A Transmtters postons VI REFERENCE SCENARIO We consder a propagaton envronment demted y a crcumference of 10 m radus, wth the recever n the center A the actve users are nsde ths area, wth a dstance of at east one meter from the recever The poston of a transmtters s randomy chosen, assumng a unform dstruton over the surface demted y the 1 and 10 m radus crcumferences Both LOS and NLOS cases are consdered B Channe mode In order to compare the performance of the prevousy descred mutpe access schemes, an adequate ndoor UWB channe shoud e ntroduced In ths paper, we w empoy the mode proposed y the IEEE a workng group [5], whch s ased on a modfcaton of the Saeh-Vaenzuea channe descrpton [7] Ths mode takes nto account the custerng phenomena oserved n severa UWB channe measurements [8] Accordng to [5], the channe mpuse response can e modeed as h (k) (t) = L H =0 h=0 α (k) (k),hδ(t T τ (k),h τ(k) a ), (41) α (k),h are the mutpath gan coeffcents, T (k) and represent the deay of the th custer and of the k th τ (k),h mutpath ray reatve to the th custer arrva tme The dstruton of custers and rays nterarrva tme s exponenta The average power deay profe shows a doue exponenta decay (for custer average power and for rays average power n each custer), and the fadng statstcs s ognorma Fnay, the sgn of each mutpath repca s ether postve or negatve, wth the same proaty In our anayss we ntroduce another random varae τ a (k), modeng the deay due to the asynchronsm etween users In partcuar, τ a (k) s assumed to e unformy dstruted over the nterva T In [5] four sets of parameters are gven, to characterze the statstca propertes of dfferent channes In partcuar, the foowng propagaton condtons are consdered: 1) LOS channe wth a TX-RX dstance etween 0 and 4 m 2) NLOS channe wth a TX-RX dstance etween 0 and 4 m 3) NLOS channe wth a TX-RX dstance etween 4 and 10 m 4) Extreme NLOS channe (RMS deay spread of 25 ns) In our anayss, a randomy generated channe w e assgned to each user accordng to the foowng rue: f the TX-RX dstance s ess than 4 m, then a channe mpuse response of type 1 or 2 (wth the same proaty) s consdered, otherwse one of type 3 or 4 Fnay, the path oss attenuatons {A k } and {A } are assumed proportona to d γ, d s the TX-RX dstance The parameter γ s set equa to 2 for LOS channe, 35 for NLOS ones C Narrowand nterference As narrowand nterferer we consder an IEEE 80211a system, a posse compettor for WPAN appcaton As shown n [2], ths sgna can e approxmated y a Gaussan narrowand process The centra frequency and the andwdth of the nterferer w then e set to 5 GHz and 200 MHz, respectvey Assumng that the UWB system, whch has a andwdth of approxmatey 3 GHz, operates at the mts set y the Federa Communcatons Commsson (FCC) of 41 dbm per MHz [9] and that the narrowand nterferer transmtted power s 100 mw, we otan a sgna to nterference rato of 26 db, gven that the two transmtters experence the same attenuaton [2] VII RESULTS AND CONCLUSIONS In ths secton we present some numerca resuts otaned empoyng the method descred n the prevous sectons The t error rate (BER) of three UWB mutaccess schemes, ased on TH, DS and OOC spreadng codes are compared For TH system oth 2PAM and 2PPM moduaton formats are consdered; t s worth pontng out that equaton (40) gves the exact BER f the moduaton scheme s nary In partcuar, we anayze two dfferent scenaros, n whch the t rate per user (PAM format) s around 200 Mt/s (T c = 07 ns, N c = 7) and 45 Mt/s (T c = 07 ns, N c = 31), respectvey; for PPM scheme the vaues are around 160 Mt/s (T c = 09 ns, N c = 7) and 35 Mt/s (T c = 09 ns, N c = 31) The decrease n t rate s caused y the ncrease n the chp ength, necessary for the overappng PPM moduaton format The mutaccess sequences are God codes for DS system and sequences ased on quadratc congruence [10] for TH The
5 OOC codes are desgned such that two puses are transmtted over each symo tme P(e) DS PAM RAKE OOC PAM RAKE TH PAM RAKE TH PPM RAKE DS PAM MMSE OOC PAM MMSE TH PAM MMSE TH PPM MMSE E /N 0 (db) Fg 1 Proaty of error of the anayzed systems Hgher t rate (N c = 7) P(e) DS PAM RAKE OOC PAM RAKE TH PAM RAKE TH PPM RAKE DS PAM MMSE OOC PAM MMSE TH PAM MMSE TH PPM MMSE E /N (db) 0 Fg 2 Proaty of error of the anayzed systems Lower t rate (N c = 31) In Fg 1 and 2 the BER pots are depcted for a the anaysed systems, for the hgher and ower t rate condton, respectvey The curves are otaned averagng over 1000 dfferent scenaros; n a of them, the mutpath channe assocated to the reference user s assumed to e a LOS one The RAKE recever shows hgh error foor for a the mutuser and moduaton schemes, due to ts ack of roustness aganst strong narrowand nterference and near-far effects On the contrary, the MMSE recever offers, as expected, much etter performance, at the cost of hgher computatona compexty The Gaussan approxmaton was successfuy adopted to derve these curves; n fact the domnant nose n ths stuaton s the narrowand nterference, that was modeed as a coored Gaussan random process Some nterestng consderatons can e derved y the comparson of Fg 1 and 2 Increasng the numer of chp per frame (wth a reducton of the t rate) eads to an mprovement n the crosscorreaton and autocorreaton propertes of the mutaccess codes and therefore to a reducton of the mutuser nterference Ths fact justfes the average 1 db gan n performance of the MMSE recevers, n the ower t rate case For the RAKE recever, however, the hgh foor s unquey determned y the effect of the strong narrowand nterference, so that a mtgaton of the mutuser nterference does not ead to an mprovement n performance The dfference etween the poston of the BER foors for the dfferent schemes at oth rates are strongy nfuenced y the shapng effect of the mutuser code on the power spectrum of the transmtted sgna For exampe, the etter performance of OOC scheme wth RAKE recever n Fg 1 can e justfed y the spectra anayss, notng that the code assgned to the reference user ntroduces a spectra attenuaton n the vcnty of the centra frequency of the narrowand nterference Furthermore, when the code does not ntroduce any shapng (ke n TH-PAM wthout repetton code, as t can e easy verfed) the foor poston s ndependent from the vaue of N c Ths consderaton suggests that an effectve strategy to optmze the performance of UWB systems wth RAKE recepton, whenever the narrowand nterference s the mtng factor, coud e ased on the desgn of spreadng codes wth desred spectra characterstcs, rather than on the optmzaton of ther auto and crosscorreaton propertes REFERENCES [1] C J L Martret and G B Gannaks, A dgta mpuse rado wth mutuser detecton for wreess ceuar systems, IEEE Trans Commun, vo 50, pp , Sept 2002 [2] Q L and L A Rusch, Mutuser detecton for ds-cdma uw n the home envronment, IEEE J Seect Areas Commun, vo 20, pp , Dec 2002 [3] G Durs and SBenedetto, Performance evauaton and comparson of dfferen moduaton schemes for uw mutaccess systems, n Proc Int Conf Comm ICC, vo 3, Anchorage, USA, 2003, pp [4] D Casso, M Wn, F Vaataro, and A F Mosh, Performance of ow-compexty rake recepton n a reastc uw channe, n Proc Int Conf Comm ICC, vo 2, New York, USA, 2002, pp [5] J Foerster, Channe modeng su-commttee report fna, IEEE P /490r1 SG3a, Fe 2002 [6] S Benedetto and E Bger, Prncpe of Dgta Transmsson wth Wreess Appcatons New York, USA: Kuwer Academc/ Penum Pushers, 1999 [7] A Saeh and R Vaenzea, A statstca mode for ndoor mutpath propagaton, IEEE J Seect Areas Commun, vo 5, pp , Fe 1987 [8] J Kunsch and J Pamp, Measurement resuts and modeng aspects for the uw rado channe, n IEEE Conference on Utra Wdeand Systems and Technooges 2002, Dgest of Papers, Batmore, USA, 2002, pp [9] Revson of Part 15 of the Commsson s Rues Regardng Utra- Wdeand Transmsson, Federa Communcatons Commsson, 1st Rep and Order, 2002 [10] T Erseghe, Utra wde and puse communcatons, PhD dssertaton, Unversty of Padova, 2002
Optimal and Suboptimal Linear Receivers for Time-Hopping Impulse Radio Systems
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.mer.com Optma and Suboptma Lnear Recevers for Tme-Hoppng Impuse Rado Systems Snan Gezc Hsash Kbayash H. Vncent Poor Andreas F. Mosch TR2004-052 May
More informationOptimum Selection Combining for M-QAM on Fading Channels
Optmum Seecton Combnng for M-QAM on Fadng Channes M. Surendra Raju, Ramesh Annavajjaa and A. Chockangam Insca Semconductors Inda Pvt. Ltd, Bangaore-56000, Inda Department of ECE, Unversty of Caforna, San
More informationScientia Iranica, Vol. 13, No. 4, pp 337{347 c Sharif University of Technology, October 2006 Performance Evaluations and Comparisons of Several LDPC C
Scenta Iranca, Vo. 13, No. 4, pp 337{347 c Sharf Unversty of Technoogy, Octoer 006 Performance Evauatons and Comparsons of Severa LDPC Coded MC-FH-CDMA Systems H. Behrooz, J. Haghghat 1, M. Nasr-Kenar
More informationCOXREG. Estimation (1)
COXREG Cox (972) frst suggested the modes n whch factors reated to fetme have a mutpcatve effect on the hazard functon. These modes are caed proportona hazards (PH) modes. Under the proportona hazards
More informationInterference Alignment and Degrees of Freedom Region of Cellular Sigma Channel
2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Interference Agnment and Degrees of Freedom Regon of Ceuar Sgma Channe Huaru Yn 1 Le Ke 2 Zhengdao Wang 2 1 WINLAB Dept of EEIS Unv. of Sc.
More informationMARKOV CHAIN AND HIDDEN MARKOV MODEL
MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not
More informationNeural network-based athletics performance prediction optimization model applied research
Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped
More informationDemodulation of PPM signal based on sequential Monte Carlo model
Internatona Journa of Computer Scence and Eectroncs Engneerng (IJCSEE) Voume 1, Issue 1 (213) ISSN 232 428 (Onne) Demoduaton of M sgna based on seuenta Monte Caro mode Lun Huang and G. E. Atkn Abstract
More informationMultispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory
Proceedngs of the 2009 IEEE Internatona Conference on Systems Man and Cybernetcs San Antono TX USA - October 2009 Mutspectra Remote Sensng Image Cassfcaton Agorthm Based on Rough Set Theory Yng Wang Xaoyun
More informationSupplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks
Shengyang Sun, Changyou Chen, Lawrence Carn Suppementary Matera: Learnng Structured Weght Uncertanty n Bayesan Neura Networks Shengyang Sun Changyou Chen Lawrence Carn Tsnghua Unversty Duke Unversty Duke
More informationRetrodirective Distributed Transmit Beamforming with Two-Way Source Synchronization
Retrodrectve Dstrbuted Transmt Beamformng wth Two-Way Source Synchronzaton Robert D. Preuss and D. Rchard Brown III Abstract Dstrbuted transmt beamformng has recenty been proposed as a technque n whch
More informationON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION
European Journa of Mathematcs and Computer Scence Vo. No. 1, 2017 ON AUTOMATC CONTNUTY OF DERVATONS FOR BANACH ALGEBRAS WTH NVOLUTON Mohamed BELAM & Youssef T DL MATC Laboratory Hassan Unversty MORO CCO
More informationResearch on Complex Networks Control Based on Fuzzy Integral Sliding Theory
Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He
More informationChapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
More informationWeek3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle
More information3. Stress-strain relationships of a composite layer
OM PO I O U P U N I V I Y O F W N ompostes ourse 8-9 Unversty of wente ng. &ech... tress-stran reatonshps of a composte ayer - Laurent Warnet & emo Aerman.. tress-stran reatonshps of a composte ayer Introducton
More informationConsider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder.
PASSBAND DIGITAL MODULATION TECHNIQUES Consder the followng passband dgtal communcaton system model. cos( ω + φ ) c t message source m sgnal encoder s modulator s () t communcaton xt () channel t r a n
More informationAssociative Memories
Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete
More informationECE559VV Project Report
ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate
More informationREAL-TIME IMPACT FORCE IDENTIFICATION OF CFRP LAMINATED PLATES USING SOUND WAVES
8 TH INTERNATIONAL CONERENCE ON COMPOSITE MATERIALS REAL-TIME IMPACT ORCE IDENTIICATION O CRP LAMINATED PLATES USING SOUND WAVES S. Atobe *, H. Kobayash, N. Hu 3 and H. ukunaga Department of Aerospace
More informationLecture 3: Shannon s Theorem
CSE 533: Error-Correctng Codes (Autumn 006 Lecture 3: Shannon s Theorem October 9, 006 Lecturer: Venkatesan Guruswam Scrbe: Wdad Machmouch 1 Communcaton Model The communcaton model we are usng conssts
More information[WAVES] 1. Waves and wave forces. Definition of waves
1. Waves and forces Defnton of s In the smuatons on ong-crested s are consdered. The drecton of these s (μ) s defned as sketched beow n the goba co-ordnate sstem: North West East South The eevaton can
More informationNested case-control and case-cohort studies
Outne: Nested case-contro and case-cohort studes Ørnuf Borgan Department of Mathematcs Unversty of Oso NORBIS course Unversty of Oso 4-8 December 217 1 Radaton and breast cancer data Nested case contro
More informationDownlink Power Allocation for CoMP-NOMA in Multi-Cell Networks
Downn Power Aocaton for CoMP-NOMA n Mut-Ce Networs Md Shpon A, Eram Hossan, Arafat A-Dwe, and Dong In Km arxv:80.0498v [eess.sp] 6 Dec 207 Abstract Ths wor consders the probem of dynamc power aocaton n
More informationPolite Water-filling for Weighted Sum-rate Maximization in MIMO B-MAC Networks under. Multiple Linear Constraints
2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Pote Water-fng for Weghted Sum-rate Maxmzaton n MIMO B-MAC Networks under Mutpe near Constrants An u 1, Youjan u 2, Vncent K. N. au 3, Hage
More informationGENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS
GENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS F. Rodríguez Henríquez (1), Member, IEEE, N. Cruz Cortés (1), Member, IEEE, J.M. Rocha-Pérez (2) Member, IEEE. F. Amaro Sánchez
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationNumerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes
Numerca Investgaton of Power Tunabty n Two-Secton QD Superumnescent Dodes Matta Rossett Paoo Bardea Ivo Montrosset POLITECNICO DI TORINO DELEN Summary 1. A smpfed mode for QD Super Lumnescent Dodes (SLD)
More informationPerformance Analysis of Iterative Multistage Detection Scheme for Overloaded DS-CDMA System
Performance Analyss of Iteratve ultstage Detecton Scheme for Overloaded DS-CDA System Preetam Kumar,. Ramesh and Saswat Charaart G. S. Sanyal School of elecommuncatos, II Kharagpur Department of Electroncs
More informationCommunication with AWGN Interference
Communcaton wth AWG Interference m {m } {p(m } Modulator s {s } r=s+n Recever ˆm AWG n m s a dscrete random varable(rv whch takes m wth probablty p(m. Modulator maps each m nto a waveform sgnal s m=m
More informationChapter 6. Rotations and Tensors
Vector Spaces n Physcs 8/6/5 Chapter 6. Rotatons and ensors here s a speca knd of near transformaton whch s used to transforms coordnates from one set of axes to another set of axes (wth the same orgn).
More informationAdaptive Beamforming in Multi path fading Channels for Voice Enhancements
Adaptve Beamformng n ut path fadng Channes for Voce Enhancements usnan Abbas Internatona Isamc Unversty Isamabad Waqas Ahmed Internatona Isamc Unversty Isamabad Shehzad Ashraf Internatona Isamc Unversty
More informationError Probability for M Signals
Chapter 3 rror Probablty for M Sgnals In ths chapter we dscuss the error probablty n decdng whch of M sgnals was transmtted over an arbtrary channel. We assume the sgnals are represented by a set of orthonormal
More informationComposite Hypotheses testing
Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter
More informationExample: Suppose we want to build a classifier that recognizes WebPages of graduate students.
Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.
More informationA Lower Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control
A ower Bound on SIR Threshold for Call Admsson Control n Multple-Class CDMA Systems w Imperfect ower-control Mohamed H. Ahmed Faculty of Engneerng and Appled Scence Memoral Unversty of ewfoundland St.
More informationwhere I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X).
11.4.1 Estmaton of Multple Regresson Coeffcents In multple lnear regresson, we essentally solve n equatons for the p unnown parameters. hus n must e equal to or greater than p and n practce n should e
More informationA finite difference method for heat equation in the unbounded domain
Internatona Conerence on Advanced ectronc Scence and Technoogy (AST 6) A nte derence method or heat equaton n the unbounded doman a Quan Zheng and Xn Zhao Coege o Scence North Chna nversty o Technoogy
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationPulse Coded Modulation
Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More information2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification
E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton
More informationOn a direct solver for linear least squares problems
ISSN 2066-6594 Ann. Acad. Rom. Sc. Ser. Math. Appl. Vol. 8, No. 2/2016 On a drect solver for lnear least squares problems Constantn Popa Abstract The Null Space (NS) algorthm s a drect solver for lnear
More informationCyclic Codes BCH Codes
Cycc Codes BCH Codes Gaos Feds GF m A Gaos fed of m eements can be obtaned usng the symbos 0,, á, and the eements beng 0,, á, á, á 3 m,... so that fed F* s cosed under mutpcaton wth m eements. The operator
More informationOn the Power Function of the Likelihood Ratio Test for MANOVA
Journa of Mutvarate Anayss 8, 416 41 (00) do:10.1006/jmva.001.036 On the Power Functon of the Lkehood Rato Test for MANOVA Dua Kumar Bhaumk Unversty of South Aabama and Unversty of Inos at Chcago and Sanat
More informationIterative Multiuser Receiver Utilizing Soft Decoding Information
teratve Multuser Recever Utlzng Soft Decodng nformaton Kmmo Kettunen and Tmo Laaso Helsn Unversty of Technology Laboratory of Telecommuncatons Technology emal: Kmmo.Kettunen@hut.f, Tmo.Laaso@hut.f Abstract
More informationRichard Socher, Henning Peters Elements of Statistical Learning I E[X] = arg min. E[(X b) 2 ]
1 Prolem (10P) Show that f X s a random varale, then E[X] = arg mn E[(X ) 2 ] Thus a good predcton for X s E[X] f the squared dfference s used as the metrc. The followng rules are used n the proof: 1.
More informationNote 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2
Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................
More informationFAST CONVERGENCE ADAPTIVE MMSE RECEIVER FOR ASYNCHRONOUS DS-CDMA SYSTEMS
Électronque et transmsson de l nformaton FAST CONVERGENCE ADAPTIVE MMSE RECEIVER FOR ASYNCHRONOUS DS-CDMA SYSTEMS CĂLIN VLĂDEANU, CONSTANTIN PALEOLOGU 1 Key words: DS-CDMA, MMSE adaptve recever, Least
More informationσ τ τ τ σ τ τ τ σ Review Chapter Four States of Stress Part Three Review Review
Chapter Four States of Stress Part Three When makng your choce n lfe, do not neglect to lve. Samuel Johnson Revew When we use matrx notaton to show the stresses on an element The rows represent the axs
More informationCorrespondence. Performance Evaluation for MAP State Estimate Fusion I. INTRODUCTION
Correspondence Performance Evauaton for MAP State Estmate Fuson Ths paper presents a quanttatve performance evauaton method for the maxmum a posteror (MAP) state estmate fuson agorthm. Under dea condtons
More informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationA Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach
A Bayes Algorthm for the Multtask Pattern Recognton Problem Drect Approach Edward Puchala Wroclaw Unversty of Technology, Char of Systems and Computer etworks, Wybrzeze Wyspanskego 7, 50-370 Wroclaw, Poland
More informationLECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem
V. DEMENKO MECHANICS OF MATERIALS 05 LECTURE Mohr s Method for Cacuaton of Genera Dspacements The Recproca Theorem The recproca theorem s one of the genera theorems of strength of materas. It foows drect
More informationG : Statistical Mechanics
G25.2651: Statstca Mechancs Notes for Lecture 11 I. PRINCIPLES OF QUANTUM STATISTICAL MECHANICS The probem of quantum statstca mechancs s the quantum mechanca treatment of an N-partce system. Suppose the
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationDigital Modems. Lecture 2
Dgtal Modems Lecture Revew We have shown that both Bayes and eyman/pearson crtera are based on the Lkelhood Rato Test (LRT) Λ ( r ) < > η Λ r s called observaton transformaton or suffcent statstc The crtera
More informationLab 2e Thermal System Response and Effective Heat Transfer Coefficient
58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),
More informationANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)
Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of
More information2.3 Nilpotent endomorphisms
s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms
More informationCHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE
CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng
More informationNP-Completeness : Proofs
NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem
More informationAchieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach
Achevng Optma Throughput Utty and Low Deay wth SMA-ke Agorthms: A Vrtua Mut-hanne Approach Po-Ka Huang, Student Member, IEEE, and Xaojun Ln, Senor Member, IEEE Abstract SMA agorthms have recenty receved
More informationJournal of Multivariate Analysis
Journa of Mutvarate Anayss 3 (04) 74 96 Contents sts avaabe at ScenceDrect Journa of Mutvarate Anayss journa homepage: www.esever.com/ocate/jmva Hgh-dmensona sparse MANOVA T. Tony Ca a, Yn Xa b, a Department
More informationPerformance of SDMA Systems Using Transmitter Preprocessing Based on Noisy Feedback of Vector-Quantized Channel Impulse Responses
Performance of SDMA Systems Usng Transmtter Preprocessng Based on Nosy Feedback of Vector-Quantzed Channe Impuse Responses Du Yang, Le-Lang Yang and Lajos Hanzo Schoo of ECS, Unversty of Southampton, SO7
More informationOn Uplink-Downlink Sum-MSE Duality of Multi-hop MIMO Relay Channel
On Upn-Downn Sum-MSE Duat of Mut-hop MIMO Rea Channe A Cagata Cr, Muhammad R. A. handaer, Yue Rong and Yngbo ua Department of Eectrca Engneerng, Unverst of Caforna Rversde, Rversde, CA, 95 Centre for Wreess
More informationTLCOM 612 Advanced Telecommunications Engineering II
TLCOM 62 Advanced Telecommuncatons Engneerng II Wnter 2 Outlne Presentatons The moble rado sgnal envronment Combned fadng effects and nose Delay spread and Coherence bandwdth Doppler Shft Fast vs. Slow
More informationFUZZY GOAL PROGRAMMING VS ORDINARY FUZZY PROGRAMMING APPROACH FOR MULTI OBJECTIVE PROGRAMMING PROBLEM
Internatonal Conference on Ceramcs, Bkaner, Inda Internatonal Journal of Modern Physcs: Conference Seres Vol. 22 (2013) 757 761 World Scentfc Publshng Company DOI: 10.1142/S2010194513010982 FUZZY GOAL
More informationChapter 6. Wideband channels. Slides for Wireless Communications Edfors, Molisch, Tufvesson
Chapter 6 Wdeband channels 128 Delay (tme) dsperson A smple case Transmtted mpulse h h a a a 1 1 2 2 3 3 Receved sgnal (channel mpulse response) 1 a 1 2 a 2 a 3 3 129 Delay (tme) dsperson One reflecton/path,
More informationTransfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system
Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng
More informationWeek 9 Chapter 10 Section 1-5
Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,
More informationL-Edge Chromatic Number Of A Graph
IJISET - Internatona Journa of Innovatve Scence Engneerng & Technoogy Vo. 3 Issue 3 March 06. ISSN 348 7968 L-Edge Chromatc Number Of A Graph Dr.R.B.Gnana Joth Assocate Professor of Mathematcs V.V.Vannaperuma
More information2010 Black Engineering Building, Department of Mechanical Engineering. Iowa State University, Ames, IA, 50011
Interface Energy Couplng between -tungsten Nanoflm and Few-layered Graphene Meng Han a, Pengyu Yuan a, Jng Lu a, Shuyao S b, Xaolong Zhao b, Yanan Yue c, Xnwe Wang a,*, Xangheng Xao b,* a 2010 Black Engneerng
More informationSTAT 511 FINAL EXAM NAME Spring 2001
STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte
More informationAn Upper Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control
An Upper Bound on SINR Threshold for Call Admsson Control n Multple-Class CDMA Systems wth Imperfect ower-control Mahmoud El-Sayes MacDonald, Dettwler and Assocates td. (MDA) Toronto, Canada melsayes@hotmal.com
More informationA DIMENSION-REDUCTION METHOD FOR STOCHASTIC ANALYSIS SECOND-MOMENT ANALYSIS
A DIMESIO-REDUCTIO METHOD FOR STOCHASTIC AALYSIS SECOD-MOMET AALYSIS S. Rahman Department of Mechanca Engneerng and Center for Computer-Aded Desgn The Unversty of Iowa Iowa Cty, IA 52245 June 2003 OUTLIE
More informationn α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0
MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector
More informationResource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud
Resource Allocaton wth a Budget Constrant for Computng Independent Tasks n the Cloud Wemng Sh and Bo Hong School of Electrcal and Computer Engneerng Georga Insttute of Technology, USA 2nd IEEE Internatonal
More informationAnalysis of CMPP Approach in Modeling Broadband Traffic
Anayss of Approach n Modeng Broadband Traffc R.G. Garroppo, S. Gordano, S. Lucett, and M. Pagano Department of Informaton Engneerng, Unversty of Psa Va Dotsav - 566 Psa - Itay {r.garroppo, s.gordano, s.ucett,
More informationNegative Binomial Regression
STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...
More informationP R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering /
Theory and Applcatons of Pattern Recognton 003, Rob Polkar, Rowan Unversty, Glassboro, NJ Lecture 4 Bayes Classfcaton Rule Dept. of Electrcal and Computer Engneerng 0909.40.0 / 0909.504.04 Theory & Applcatons
More informationLectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix
Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could
More informationGreyworld White Balancing with Low Computation Cost for On- Board Video Capturing
reyword Whte aancng wth Low Computaton Cost for On- oard Vdeo Capturng Peng Wu Yuxn Zoe) Lu Hewett-Packard Laboratores Hewett-Packard Co. Pao Ato CA 94304 USA Abstract Whte baancng s a process commony
More informationModule 2. Random Processes. Version 2 ECE IIT, Kharagpur
Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be
More informationSINGLE OUTPUT DEPENDENT QUADRATIC OBSERVABILITY NORMAL FORM
SINGLE OUTPUT DEPENDENT QUADRATIC OBSERVABILITY NORMAL FORM G Zheng D Boutat JP Barbot INRIA Rhône-Alpes, Inovallée, 655 avenue de l Europe, Montbonnot Sant Martn, 38334 St Ismer Cedex, France LVR/ENSI,
More informationDelay tomography for large scale networks
Deay tomography for arge scae networks MENG-FU SHIH ALFRED O. HERO III Communcatons and Sgna Processng Laboratory Eectrca Engneerng and Computer Scence Department Unversty of Mchgan, 30 Bea. Ave., Ann
More informationWAVELET-BASED IMAGE COMPRESSION USING SUPPORT VECTOR MACHINE LEARNING AND ENCODING TECHNIQUES
WAVELE-BASED IMAGE COMPRESSION USING SUPPOR VECOR MACHINE LEARNING AND ENCODING ECHNIQUES Rakb Ahmed Gppsand Schoo of Computng and Informaton echnoogy Monash Unversty, Gppsand Campus Austraa. Rakb.Ahmed@nfotech.monash.edu.au
More informationInner Product. Euclidean Space. Orthonormal Basis. Orthogonal
Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,
More informationComparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method
Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method
More informationDevelopment of whole CORe Thermal Hydraulic analysis code CORTH Pan JunJie, Tang QiFen, Chai XiaoMing, Lu Wei, Liu Dong
Deveopment of whoe CORe Therma Hydrauc anayss code CORTH Pan JunJe, Tang QFen, Cha XaoMng, Lu We, Lu Dong cence and technoogy on reactor system desgn technoogy, Nucear Power Insttute of Chna, Chengdu,
More informationCOMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN
Transactons, SMRT- COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN Mchae O Leary, PhD, PE and Kevn Huberty, PE, SE Nucear Power Technooges Dvson, Sargent & Lundy, Chcago, IL 6060 ABSTRACT Accordng to Reguatory
More informationLOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin
Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence
More informationExpected Value and Variance
MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or
More informationTHE SMOOTH INDENTATION OF A CYLINDRICAL INDENTOR AND ANGLE-PLY LAMINATES
THE SMOOTH INDENTATION OF A CYLINDRICAL INDENTOR AND ANGLE-PLY LAMINATES W. C. Lao Department of Cvl Engneerng, Feng Cha Unverst 00 Wen Hwa Rd, Tachung, Tawan SUMMARY: The ndentaton etween clndrcal ndentor
More informationAnalysis of Bipartite Graph Codes on the Binary Erasure Channel
Anayss of Bpartte Graph Codes on the Bnary Erasure Channe Arya Mazumdar Department of ECE Unversty of Maryand, Coege Par ema: arya@umdedu Abstract We derve densty evouton equatons for codes on bpartte
More informationExponential Type Product Estimator for Finite Population Mean with Information on Auxiliary Attribute
Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 193-9466 Vol. 10, Issue 1 (June 015), pp. 106-113 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) Exponental Tpe Product Estmator
More information3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X
Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number
More informationLecture Note 3. Eshelby s Inclusion II
ME340B Elastcty of Mcroscopc Structures Stanford Unversty Wnter 004 Lecture Note 3. Eshelby s Incluson II Chrs Wenberger and We Ca c All rghts reserved January 6, 004 Contents 1 Incluson energy n an nfnte
More informationLow Complexity Soft-Input Soft-Output Hamming Decoder
Low Complexty Soft-Input Soft-Output Hammng Der Benjamn Müller, Martn Holters, Udo Zölzer Helmut Schmdt Unversty Unversty of the Federal Armed Forces Department of Sgnal Processng and Communcatons Holstenhofweg
More informationA particle in a state of uniform motion remain in that state of motion unless acted upon by external force.
The fundamental prncples of classcal mechancs were lad down by Galleo and Newton n the 16th and 17th centures. In 1686, Newton wrote the Prncpa where he gave us three laws of moton, one law of gravty,
More information