Explicit Proactive Handoff with Motion Prediction for Mobile IP

Size: px
Start display at page:

Download "Explicit Proactive Handoff with Motion Prediction for Mobile IP"

Transcription

1 Explicit Practiv Handff with Mtin Prdictin fr Mbil IP Fang Fng, and Duglas S. Rvs Dpartmnts f Elctrical and Cmputr Enginring and Cmputr Scinc Nrth Carlina Stat Univrsity Raligh, NC Tl: (919) , {ffng, rvs}@s.ncsu.du Abstract Mbil IP has bn widly accptd, but lacks a fast handff mchanism. In this papr, w intrduc an xplicit practiv handff schm with mtin prdictin. Sinc ach usr has pattrns f mvmnt, a mbil nd prdicts its futur mtin and xplicitly ntifis its ld frign agnt which subnt it is likly t handff t. During a handff, th ld frign agnt duplicats and frwards packts t th prdictd subnts. With ur schm, ntwrk-layr handff latncy can b rducd t th lvl f link-layr handff latncy, and th numbr f packts lst during handffs is als minimizd. With a ral ntwrk activity trac, w dmnstrat that this schm is abl t prdict mtin accuratly, with nly a small vrhad in bandwidth cnsumptin and cmputatin. I. INTRODUCTION Wirlss lcal ara ntwrks (WLANs) hav bcm xtrmly ppular in ths yars. Link-layr mchanisms prvid supprt fr link-layr handff, which is usd t switch a mbil nd (MN) frm th radi link f n accss pint (AP) t that f anthr accss pint. Fr WLANs cnnctd by an IP backbn, Mbil IP [1] is th prtcl fr lcatin managmnt and ntwrk-layr handff. This updats th ruting infrmatin fr th MN, t rflct mvmnt frm n subnt t anthr subnt. Mbil IP, hwvr, still has prblms. First, it uss IP packts t transfr mbility managmnt infrmatin. Th latncy f ntwrk-layr handff is in th rdr f 0.1 t 1 scnd, 10 tims largr than link-layr handff latncy. This cannt mt th rquirmnt f dlay-snsitiv r ral-tim traffic. Th scnd prblm is handff disruptin. Mbil IP dsn t buffr packts snt t an MN during handffs. Thrfr, ths packts may b lst and nd t b rtransmittd. Th third prblm is that th tw typs f handffs ar cupld. Bcaus f th latncy gap btwn thm, packt lss will ccur vn aftr th cmpltin f link-layr handff. In mst circumstancs, an MN is carrid by an individual. As a rsult, it will b mvd accrding t hurly, daily r wkly pattrns crrspnding t a prsn s rgular activitis. Packt arrival pattrns als dpnd n th srvics and applicatins th prsn uss. Significant changs t ths pattrns ar likly t b infrqunt. Studis f wirlss ntwrks by Tang [2] [3] and Ktz [4] cnfirm this bsrvatin. An MN s pattrns can b utilizd t prdict its futur bhavir and assist handff. With prdictin, it is pssibl t prpar ntwrklayr handff bfr link-layr handff t rduc latncy and packt lss. Th MN s ptimal handff stratgy shuld als b adjustd dynamically accrding t th prdictin t minimiz handff cst. Handff dcisins basd n mvmnt prdictin liminats th nd t wait fr bacn signals frm thr subnts, and assists th discvry f handff targt in an nvirnmnt f vrlapping cvrag aras and changing wirlss channl cnditins. Basd n ths bsrvatins, w prps an xplicit practiv handff schm with mtin prdictin fr Mbil IPv4. It adpts a practiv apprach t prpar ntwrk-layr handff bfr link-layr handff. Each MN rcrds its mvmnt pattrns and prdicts its futur subnts. Bfr link-layr handff, th MN xplicitly ntifis its currnt frign agnt (FA) f th prdictd subnts. Th currnt FA thn duplicats packts snt t this MN, and frwards thm t ths prdictd subnts. Ntwrk-layr handff latncy is cls t that f link-layr handff, and packt lss is rducd by buffring frwardd packts at FAs. This schm is fully distributd bcaus th ntwrk-layr handff is cntrlld by th MN, and FAs ar ntifid nly if th MN rcmmnds packt frwarding. Th xtra bandwidth cnsumptin intrducd is much lss than that f thr practiv handff schms. Th rst f this papr is rganizd as fllws. W first rviw sm f th rlatd wrk in Sctin II. In Sctin III, th prpsd schm is dscribd. W valuat its prfrmanc with simulatin rsults in Sctin IV, and Sctin V is th cnclusin and futur wrk. II. RELATED WORK A numbr f schms hav bn prpsd t slv th prblms f Mbil IP mntind abv. Hr w fcus n ddicatd fast handff schms, which can b catgrizd as ractiv, practiv, r a cmbinatin f th tw accrding t whthr th packts snt t th MN start t arriv at th nw subnt aftr r bfr th link-layr handff. A. Ractiv Handff In simultanus and MIPv4 handff [5], whn an MN assciats with a nw AP, it snds MIPv4 rgistratin infrmatin t th nw FA. This infrmatin is cntaind within frams, and initiats MIPv4 lcatin rgistratin prcss. This schm rducs handff latncy, but dsn t

2 slv th prblms causd by th latncy gap btwn linklayr handff and ntwrk-layr handff. [6] utilizs a filtring databas and a MAC bridg cnncting WLANs. Whn an MN assciats with a nw WLAN, its MAC addrss is bradcast lcally by th nw AP. It is rcivd by th MAC bridg and strd in th filtring databas, alng with th crrspnding prt. Bfr th MN cmplts ntwrk-layr handff, th MAC bridg rlays MAC frams fr th MN frm th ld WLAN t th nw WLAN. Scalability and rliability may b prblms fr this schm, du t its dpndnc n a cntralizd bridg mchanism. B. Practiv Handff Th Dadalus prjct [7] uss IP multicast and buffring t rduc packt lss. Each bas statin and its nighbring bas statins frm a multicast grup. Whn an MN cnncts t a bas statin, it rgistrs a crrspnding multicast addrss at th hm agnt (HA). Using this addrss, packts ar multicast t and buffrd at th bas statins f this multicast grup. If an MN switchs t a nighbring bas statin, it can rciv packts bfr prfrming a ntwrk-layr handff t rgistr a nw multicast addrss. E. Shim t al. usd nighbrcasting [8] t achiv lw latncy handff. An MN can transfr its ld FA infrmatin t th nw FA, which thn cnstructs a nighbr tabl. Bfr link-layr handff, th MN ntifis th ld FA t frward duplicatd packts t all nighbring FAs. Ntwrk-layr handff latncy can b rducd significantly in this schm. R. Hsih t al. prpsd a samlss handff architctur fr Mbil IP [9]. A dcisin ngin is addd t th architctur f hirarchical Mbil IPv6. A handff is initiatd by an MN whn it rcivs bacn signals frm nighbring accss rutrs (ARs). And th dcisin ngin uss lcatin tracking infrmatin f th MN and lad infrmatin f ths ARs t dtrmin handff tim and targt. Packts snt t th MN ar initially frwardd t th nw AR by th ld AR, and simulcastd t bth ARs aftr th ld AR rqusts simulcast. This schm succssfully rducs ntwrk-layr handff latncy and packt lss, but it is cntralizd, rquirs xtra signaling and impss a bund n th spd f MNs. C. Cmbind Handff Lw latncy handff [10] [11] utilizs th L2 triggr mchanism (dscribd in sctin III-B). In pr-rgistratin handff, an MN prfrms ntwrk-layr handff bfr th link-layr handff, basd n infrmatin frm th L2 triggr. In pstrgistratin handff, an MN first cmplts link-layr handff. It cntinus using th ld FA and car-f addrss (CA) thrugh a bi-dirctinal tunnl btwn th ld FA and th nw FA. Th cmbind handff mthd first tris a pr-rgistratin handff. If it fails, pst-rgistratin handff is usd. Simultanus Binding [1] [13] aims at dcupling ntwrklayr handff frm link-layr handff, by nabling an MN t bind t multipl subnts simultanusly. An MN can rtriv th FA/CA infrmatin frm th bacn signals f APs, and rgistr multipl CAs with th HA bfr r aftr th linklayr handff. Th HA r crrspndnt nd (CN) frwards duplicatd packts t ths CAs simultanusly until th MN cmplts ntwrk-layr handff. Y. Gwn t al. intrducd Mbil Initiatd Tunnling Handff [14]. Bfr discnncting frm th ld AP, an MN can initiat a handff by snding th ld FA a handff rqust cntaining th nw FA s infrmatin gt frm th mbil prtriggr. Or it can initiat th handff prcss aftr cnncting t th nw AP and rciving a link-up triggr, and snd th nw FA a handff rqust cntaining th ld FA s infrmatin. A bi-dirctinal tunnl is st up btwn ths tw FAs fr th MN until it cmplts Mbil IP rgistratin. This schm achivd lw latncy and lw lss handffs with lss rquirmnts n L2 triggrs and accss ntwrks than thr fast handff schms. Gnrally th practiv apprach prvids bttr prfrmanc, sinc packts ar frwardd t th nw subnt in advanc. But ths practiv schms can caus unncssary handff prparatins and frward t many duplicatd packts. Nighbrcasting frwards duplicatd packts t all nighbr FAs withut cnsidring th MN s mving dirctin. With simultanus binding, an MN binds t any subnt frm which it can rciv a bacn signal, althugh in fact it may nt dirctly mv t all ths subnts. As argud in th shadw clustr cncpt [12], a MN has influnc nar its currnt lcatin and alng its anticipatd path, and rsurc rsrvatin shuld b mad fr th MN basd n th prdictd dmands. This ida can b applid t imprv th prfrmanc f practiv handff, and is n f th majr mtivatins f ur prpsal. III. PROACTIVE HANDOFF WITH MOTION PREDICTION A. Mtivatins W wish t minimiz th ffrt and ptimiz th prfrmanc f ntwrk-layr handff whn an MN is mving amng WLANs cnnctd by an IP backbn. Each MN prdicts its futur mvmnt basd n th rcrdd mvmnt pattrns, and us this t prpar ntwrk-layr handff t rduc handff latncy and packt lss. Handff dcisin basd n mvmnt pattrns liminats th nd fr th MN t wait fr bacn signals frm nighbring subnts, which typically taks hundrds f milliscnds. Handff targt discvry nly basd n rcivd signal strngth can b a prblm whn th MN is in th vrlapping cvrag aras f multipl subnts and wirlss channl cnditins hav grat fluctuatin. Mvmnt pattrns can prvid anthr critrin t find th mst pssibl handff targt in such a cas. Mvmnt pattrn and mtin prdictin ar nly cncrnd with ntwrk-layr mvmnt, i.., th lgical mvmnt btwn subnts. Physical r ggraphic lcatin is f n imprtanc t ur schm. W spculat that ntwrk-layr mvmnt is simplr and mr prdictabl than ggraphic mvmnt, and is mr maningful fr ur purpss. Fr xampl, sinc subnts ar f diffrnt shaps and sizs, an MN s ggraphic mvmnt paramtrs may nt dirctly

3 crrspnd t its rgular activitis, which n th thr hand can b infrrd by its ntwrk-layr mvmnt. It is als dsirabl t imps as fw mdificatins t Mbil IPv4 as pssibl, and t rquir th minimum amunt f additinal capabilitis frm th link layr. Aftr dscribing th mthd, w will rturn t ths statd mtivatins. B. Systm Ovrviw Th systm is assumd t hav multipl WLANs cnnctd by a wird IP backbn. Mbil IPv4 is usd fr lcatin managmnt amng subnts. Th prpsd practiv handff schm is usd t rduc th latncy and packt lss f ntwrk-layr handff. Authnticatin and authrizatin f usrs ar issus that must b handld by sparat prtcls. Th prpsd schm utilizs th L2 (layr 2) triggr mchanism 1. Th Mbil Triggr ntifis th MN abut an impnding link-layr handff, and th Link-Up Triggr infrms th FA that an MN has cnnctd t th radi link f its subnt. Bacn signals (link-layr frams) pridically transmittd by ach AP cntain infrmatin fr bth link-layr and ntwrk-layr handffs, such as th ID f th AP, th AP s frquncy hpping r dirct squnc paramtrs, th subnt ID and th IP addrss f th FA in that subnt, tc. Thrfr an MN ds nt nd t wait fr an FA advrtismnt t initiat lcatin rgistratin whn it cnncts t th radi link f an AP in anthr subnt. Tw nw mssags ar ndd at th ntwrk layr. Th first, th Frwarding Rqust mssag, is snt frm an MN t th FA f its currnt subnt nc th mvmnt is prdictd and th MN dcids t handff. This mssag cntains th ID(s) and FA IP addrss(s) f th subnt(s) t which duplicatd packts shuld b frwardd. Th scnd, th Stp Frwarding mssag, is snt by th MN s HA t its prvius FA nc th MN cmplts lcatin rgistratin. This infrms th prvius FA t stp frwarding duplicatd packts. Th MN is rspnsibl fr rcrding ntwrk-layr mvmnts. Frm this mvmnt histry, th MN will prdict its futur mtin using th path prdictin algrithm. Each MN maintains a FIFO mvmnt histry cach. An ntry in th mvmnt histry cach rcrds th ID f a subnt t which th MN was prviusly attachd, th start tim f this attachmnt, and th IP addrss f th FA in that subnt. Aftr th MN cnncts t th radi link f a nw subnt, it crats a mvmnt histry cach ntry f this cnnctin, accrding t th bacn signal. It als has a pattrn databas t str mvmnt pattrns, which ar squncs f mvmnt histry cach ntris. C. Handff Prcss Figur 1 shws th mssag diagram f a handff. Th handff prcss is dscribd as th fllwing: 1) Th MN mvs t a subnt bundary. Th Mbil Triggr infrms th MN abut an impnding link-layr 1 As spcifid in th Intrnt Draft Supprting Optimizd Handvr fr IP Mbility - Rquirmnts fr Undrlying Systms d r i P g a rdin rw F l ta T MN r a l g rv t rig T In il b a n c Md v Old FA Frw arding R qust Packts Old AP A Disassciatin Rqust Disassciatin Rply a y r f Ḻ Assciatin k a n d n c y Rqust a t in H L Assciatin Rply L c y n IP a t L b il n ti M g istra R Fig. 1. Actual Actual Prdictd Nw AP Nw FA Nw FA(s) Mbil Triggr Fird Frward Duplicatd Packts Link-Up Triggr Fird Dlivr Frwardd Packts Lcatin Rgistratin Rqust Lcatin Rgistratin Rply Stp Frwarding Mssag Diagram during a Handff Lcatin R gistratin Rqust Lcatin Rgistratin Rply handff whn th rcivd signal strngth frm th currnt AP falls blw th thrshld lvl. 2) Th MN, with th infrmatin in its mvmnt histry cach and pattrn databas, uss th path prdictin algrithm t prdict th subnt r subnts t which it is likly t mv. (If th histry data is nt sufficint t mak a prdictin, th MN will randmly chs n f th nighbring subnts as th prdictd subnt. If it is first tim that th MN assciats with th currnt subnt, it will nt d any practiv handff, and th standard Mbil IPv4 mchanism will b usd.) Th numbr f prdictd subnts can b pr-dfind. 3) If thr is a prdictd subnt r subnts, th MN snds th ID(s) and FA IP addrss(s) f th prdictd subnt(s) t th currnt FA. Thy ar snt in a Frwarding Rqust mssag. 4) Data packts snt t th MN during th handff ar first tunnld t th currnt FA by th HA, r dirctly by th CN with rut ptimizatin. Th currnt FA first dcapsulats ths packts, and thn duplicats and frwards thm t th FA(s) f th prdictd subnt(s) using IP-within-IP ncapsulatin. Th prdictd FA(s) dcapsulats and buffrs ths data packts in anticipatin f th arrival f th MN. 5) If th MN cntinus mving, it vntually prfrms a link-layr handff t cnnct t th radi link f an AP in th nw subnt. A nw ntry fr this mvmnt is cratd in th mvmnt histry cach. Th MN thn cmpars th ID(s) f th prdictd subnt(s) with th HA

4 ID frm th bacn signal f th nw AP. 6) If th prdictin was nt crrct, th duplicatd packts snt t th prdictd FA will nt rach th MN. Ths packts will vntually hav t b rtransmittd, as is nrmally th cas withut practiv handff. Thrfr th schm falls back t th standard Mbil IPv4. 7) If th prdictin was crrct, th Link-up Triggr infrms th FA f th nw subnt t dlivr its buffrd data packts t th MN. 8) Th MN rgistrs its nw car-f-addrss with th HA using Mbil IPv4 mchanisms. 9) Th HA snds a Stp Frwarding mssag t th ld FA t finish th handff prcss. Our schm can als wrk in cnjunctin with thr schms t dtrmin th handff targt. A sparat mchanism can initially chs a crtain subnt as th handff targt accrding t a varity f critria such as th functinality f this subnt. Thn ur schm will captur such a rgular bhavir and us it t prdict th futur ntwrk-layr mvmnt f this MN. Th xtra vrhad f ur schm includs th cst t rcrd mvmnt histry and d prdictin, th transmissin and prcssing cst f Frwarding Rqust and Stp Frwarding mssags, and th cst t duplicat and frward data packts. D. Path Prdictin Algrithm Thr hav bn a numbr f mbility mdls, such as th fluid flw mdl and th randm walk mdl. Ths mdls dscrib th aggrgat bhavir f th MNs, and ach MN s mvmnt is indpndnt and randm frm th systm s viw. Ths mdls als dscrib mvmnt in trms f th chang in th physical lcatin f th trminals, rathr than in trms f th chang in th subnts thy attach t. Thrfr, w adptd an imprvd vrsin f th mtin prdictin algrithm prpsd by Liu and Maguir [15]. This mthd is dsignd fr tracking individual MNs using histrical mvmnt pattrns, and is basd n lgical mvmnts rathr than ggraphical mvmnts. In this mdl, an MN ntrs a nw stat whn it cnncts t th radi link f a subnt. If it stays cnnctd t this subnt fr mr than a spcifid prid f tim, this stat is a statinary stat. Othrwis it is a transitinal stat. A mvmnt track (MT) mdls th mvmnt f a MN n a rgular rut, starting frm and nding at diffrnt statinary stats. A mvmnt circl (MC) mdls th MN s lngtrm rgular bhavir, and starts frm and nds at th sam statinary stat. Fr prdictin purpss, th algrithm attmpts t crrlat currnt mvmnt (i.., a squnc f subnts that has just bn visitd) with past mvmnt (i.., a squnc f subnts that was visitd at sm prvius tim and strd in th pattrn databas). Thr ar thr typs f matching fr crrlatin analysis. Th first typ is stat-matching, which indicats th fractin f stats in cmmn btwn th currnt squnc and a past squnc. In Equatin 1, m s is th numbr f idntical stats and N s is th ttal numbr f stats. µ = m s (1) N s Th scnd typ f matching is vlcity-matching. This indicats th similarity btwn th mvmnt vlcitis f th currnt squnc and a past squnc. In Equatin 2, (t i+1 t i ) j is th intrval btwn stat i and stat i +1 f squnc j. Ns 1 i=1 (t i+1 t i ) currnt (t i+1 t i ) past η = (2) N s 1 W prps a third typ f matching, calld ccurrncmatching. Lt T currnt b th starting tim f th currnt squnc, T past,l b th tim f th last ccurrnc f sm past squnc, and τ past,k b th kth intrval btwn tw cnscutiv ccurrncs f this past squnc. Equatin 3 cmputs hw cls th intrval btwn th currnt squnc and th last ccurrnc f a past squnc matchs th intrval btwn any tw cnscutiv ccurrncs f th past squnc. (T currnt T past,l ) τ past,k Φ=min (3) k τ past,k Whn an MN cnncts t th radi link f a nw subnt, it ntrs a nw transitinal stat. If it stays attachd lng nugh, this stat bcms statinary, and th MN starts pattrn dtctin. It uss stat-matching t crrlat th currnt squnc in th mvmnt histry cach with ach MT and MC strd in th pattrn databas. If thr is n match, th currnt squnc is addd t th pattrn databas, thrwis th infrmatin f this MC/MT in th pattrn databas is updatd. Thn th currnt squnc is rmvd frm th mvmnt histry cach if ncssary. Whn a Mbil Triggr firs, th MN initiats th handff prcss and starts mtin prdictin. It crrlats th currnt squnc in th mvmnt histry cach t th strd MCs and MTs, using (in rdr) stat-matching, thn vlcity-matching, and aftr that ccurrnc-matching, until n f th MC/MT matchs th currnt squnc. Th subnt at th crrspnding psitin f th final matchd MC/MT is th prdictd subnt. Th numbr f prdictd subnts can b adjustd as rquird by th MN. Th MN can als ptinally maintain a transitin prbability matrix amng th subnts, fr us in cas th currnt mvmnt ds nt match any prviusly-strd pattrn. IV. PERFORMANCE EVALUATION Thr ar svral ways t valuat th prfrmanc f th prpsd practiv handff schm. T valuat its ffctivnss, w us handff latncy and prdictin miss rat. T valuat its fficincy, w us th fractin f duplicatd packts in all packts, which indicats hw many xtra data packts ar gnratd by ur schm. A. Simulatin Scnari Fr purpss f valuatin, w simulatd handff bhavir f a st f mbil nds. This simulatin is basd n an actual trac takn frm th campus-wid wirlss ntwrk f

5 Dartmuth Cllg [4]. It rcrdd th activitis f almst 2000 MNs fr an acadmic trm in an IEEE b ntwrk which cvrs 161 buildings and cntains 81 subnts. Th trac includs charactristics f bth rsidntial and wrk-rlatd mvmnts, sinc it rcrdd th ntwrk activity in bth drms and acadmic buildings. Th trac cntains tim-stamps and infrmatin n MNs assciatin and rassciatin activitis with accss pints, but lacks dtails abut th ntwrk tplgy, r rliabl disassciatin signaling. It als ds nt distinguish a dirct handff with th cas that an MN was turnd ff, mvd t anthr subnt and turn back n. Du t th limitatin f th trac, w mad th fllwing assumptins in ur simulatin: 1) Each subnt has n and nly n AP. 2) Each transitin frm a subnt t anthr subnt is a dirct handff. 3) Each MN stays at a subnt until it assciats with anthr subnt. 4) Tw subnts ar nighbrs if and nly if thr is at last n transitin btwn thm. 5) Rassciatin t th sam subnt is nt a handff. Thr is n training prid in ur simulatin. Fr purpss f cmputing packt lss and duplicatin rats, w usd a simulatd CBR traffic, with a packt intrarrival tim f 3.75ms fr asir cmparisn with thr prpsd schms. Sinc ur schm is fr highly mbil usrs, w nly shw th simulatin rsult fr th mbil nds with at last an avrag f 6 handffs daily; thr wr 585 such MNs. Fr prfrmanc cmparisn purpss, w assum link-layr handff latncy is 50ms, and Mbil IP rgistratin latncy is unifrmly distributd btwn 200ms and 700ms. W simulatd th prfrmanc f ur schm fr th cas f n prdictd subnt, and tw prdictd subnts. B. Handff Latncy Figur 2 shws th distributin f th avrag ntwrk-layr handff latncy f ach MN, using ur schm with prdictin f n r tw subnts. It can b sn that th latncy f ur schm is much lss than that f Mbil IP, and quit cls t link-layr handff latncy. With prdictin f tw subnts, th latncy f ur schm is lss than 160ms fr abut 80% f th MNs. This is 3X fastr than nrmal Mbil IP handff latncy. Th avrag tim t cmput a prdictin was lss than 0.15 ms pr handff n a Pntium IV 1.7GHz PC with 256MB mmry. Thrfr w spculat that th algrithm is mr than fast nugh t supprt practiv handff, vn n a PDAtyp dvic. C. Prdictin Miss Rat In Figur 3, w shw th distributin f prdictin miss rat f ach MN, using ur schm with prdictin f n r tw subnts. With prdictin f tw subnts, abut 80% f th MNs hav a prdictin miss rat lss than 0.3. With prdictin f n subnt, abut 80% f th MNs hav a prdictin miss rat f lss than Cmpard with th miss Fractin f MNs Fractin f MNs Empirical CDF L2 handff 2 prdictin 1 prdictin Mbil IP Avrag Latncy (sc) Fig. 2. CDF Plt f Avrag Handff Latncy Empirical CDF nighbrcast 2 prdictin 1 prdictin 2 randm 1 randm Miss Rat Fig. 3. CDF Plt f Prdictin Miss Rat rat f randmly slcting n r tw nighbring subnts, ur prpsd schm has a much lwr miss rat. Only abut 8% f th MNs hav a miss rat lss than 0.3 whn randmly slcting 2 nighbr subnts. Nighbrcasting snds packts t all nighbring subnts, and s shuld rprsnt th lwr limit f th prdictin miss rat. Its miss rat is shwn by th lftmst lin in th figur, and is gratr than zr bcaus a miss is incurrd whn a mvmnt frm n spcific subnt t anthr ccurs fr th first tim. Our rsult using prdictin f tw subnts is quit cls t this limit. This cnfirms th valu f prdicting MN mvmnt basd n past histry. W bliv that prdictin using mr cmplt tracs, with infrmatin abut ntwrk tplgy and MN pwr-dwn vnts, shuld d vn bttr. Using ur schm with prdictin f tw subnts, th avrag numbr f packt lsss pr handff du t prdictin misss is abut 19, cmpard with 133 fr Mbil IP and 5 fr nighbrcasting.

6 Fractin f MNs Empirical CDF 1 prdictin 2 prdictin nighbrcast Fractin f Duplicatd Packts Fig. 4. CDF Plt f Fractin f Duplicatd Packts D. Extra Handff Ovrhad Sctin III discussd th xtra vrhad f th prpsd schm. Sinc ur schm is fully distributd, mvmnt rcrding and prdictin dn t incur any cst t th ntwrk. Th numbr f Frwarding Rqust and Stp Frwarding mssags is trivial cmpard with th ptntial numbr f duplicatd data packts, s w fcus n masuring ths duplicatd data packts. Figur 4 shws th distributin f th fractin f duplicatd packts in th 585 MNs, basd n CBR traffic surcs. With prdictin f tw subnts, th fractin f duplicatd packt is lss than fr abut 90% f th MNs, and lss than fr 80% f th MNs. This is much bttr than nighbrcasting. Fr instanc, nly abut 45% f th MNs hav a fractin f duplicatd packts lss than with nighbrcasting. Our schm is vn mr fficint whn thr ar multipl MNs laving th sam subnt at th sam tim and a burst f duplicatd data packt ccurs. V. CONCLUSION AND FUTURE WORK In this papr w prpsd an xplicit practiv handff schm basd n th mvmnt pattrns f mbil nds. An MN can anticipat a handff frm th L2 triggr, and us lcally strd mvmnt pattrns t dynamically prdict th nxt subnt. As a rsult, handff latncy and packt lss rat is dramatically rducd, with a cst f a small numbr f duplicatd packts. This schm wrks in a fully distributd fashin and intrducs much lss duplicatd packts than anthr practiv handff schm, nighbrcasting. It als liminats th nd t wait fr bacn signals, and slvs th prblm f handff targt discvry whn th cvrag aras f multipl subnts vrlap with ach thr. It ds nt rquir spcific radi tchnlgy, r spcial ruting tchniqus such as multicast. It kps th currnt Mbil IP infrastructur and augmnts it t imprv prfrmanc. Our futur wrk includs using a bttr trac, if availabl, t furthr dmnstrat th bnfits f th prpsd schm. W plan t xtnd this apprach t th cas f multi-tir wirlss ntwrks as th MN can practivly switch t subnts n diffrnt tirs accrding t its wn pattrn. W als plan t us it t nfrc QS in handffs sinc an MN can ngtiat handff paramtrs with FAs accrding t its QS rquirmnts. Adptin f this schm in Mbil IPv6 and implmntatin ar als undr cnsidratin. REFERENCES [1] C. Prkins, Ed., IP Mbility Supprt fr IPv4, IETF RFC3344, August [2] D. Tang and M. Bakr, Analysis f a Lcal-Ara Wirlss Ntwrk, in Prc. MOBICOM 2000, August [3] D. Tang and M. Bakr, Analysis f a Mtrplitan-Ara Wirlss Ntwrks, Kluwr Wirlss Ntwrks, 8(2-3), pp , March-May [4] D. Ktz and K. Essin, Analysis f a Campus-Wid Wirlss Ntwrk, in Prc. MOBICOM 2002, Sptmbr [5] S. Gswami, Simultanus Handff f Mbil-IPv4 and , Intrnt Draft, IETF, draft-gswami-mbilip-simultanus-handff-v4-02.txt, Fbruary [6] H. Ykta t al., Link Layr Assistd Mbil IP Fast Handff Mthd vr Wirlss LAN Ntwrks, in Prc. MOBICOM 2002, Sptmbr [7] S. Sshan t al., Handffs in Cllular Wirlss Ntwrks: Th Dadalus Implmntatin and Exprinc, Kluwr J. Wirlss Prsnal Cmmun., vl. 4, n. 2, pp , March [8] E. Shim t al., Lw Latncy Handff fr Wirlss IP QS with Nighbrcasting, in Prc. ICC 2002, April [9] R. Hsih t al.. S-MIP: A Samlss Handff Architctur fr Mbil IP, in Prc. INFOCOM 2003, March [10] K. Malki t al., Lw Latncy Handffs in Mbil IPv4, Intrnt Draft, IETF, draft-itf-mbilip-lwlatncy-handffs-v4-04.txt, Jun [11] R. Kdli, Ed., Fast Handvrs fr Mbil IPv6, Intrnt Draft, IETF, draft-itf-mbilip-fast-mipv6-7.txt, Sptmbr [12] D. Lvin, I. F. Akyildiz and M. Naghshinh, A Rsurc Estimatin and Call Admissin Algrithm fr Wirlss Multimdia Ntwrks Using th Shadw Clustr Cncpt, IEEE/ACM Trans. Ntwrking, vl. 5, n. 1, pp. 1-12, Fbruary [13] K. Malki and H. Sliman, Simultanus Binding fr Mbil IPv6 Fast Handffs, Intrnt Draft, IETF, draft-lmalki-mbilip-bicastingv6-02.txt, Jun [14] Y. Gwn t al., Fast Handffs in Wirlss LAN Ntwrks Using Mbil Initiatd Tunnling Handff Prtcl fr IPv4 (MITHv4), in Prc. WCNC 2003, March [15] G. Liu and G. Maguir, A Class f Mbil Mtin Prdictin Algrithms fr Wirlss Mbil Cmputing and Cmmunicatin, Mbil Ntwrks and Applicatins, vl. 1, n. 2, pp , Octbr 1996.

Lecture 26: Quadrature (90º) Hybrid.

Lecture 26: Quadrature (90º) Hybrid. Whits, EE 48/58 Lctur 26 Pag f Lctur 26: Quadratur (9º) Hybrid. Back in Lctur 23, w bgan ur discussin f dividrs and cuplrs by cnsidring imprtant gnral prprtis f thrand fur-prt ntwrks. This was fllwd by

More information

Lecture 27: The 180º Hybrid.

Lecture 27: The 180º Hybrid. Whits, EE 48/58 Lctur 7 Pag f 0 Lctur 7: Th 80º Hybrid. Th scnd rciprcal dirctinal cuplr w will discuss is th 80º hybrid. As th nam implis, th utputs frm such a dvic can b 80º ut f phas. Thr ar tw primary

More information

section 1 Influencing Change Toolkit How to: Influence People

section 1 Influencing Change Toolkit How to: Influence People Influncing Chang Tlkit Hw t: Influnc Ppl Influncing ppl mans having an ffct n thm, changing r mdifying thir viw. In rdr t influnc chang, w nd t influnc th ppl wh ar in a psitin t mak that chang happn.

More information

Topic 5: Discrete-Time Fourier Transform (DTFT)

Topic 5: Discrete-Time Fourier Transform (DTFT) ELEC36: Signals And Systms Tpic 5: Discrt-Tim Furir Transfrm (DTFT) Dr. Aishy Amr Cncrdia Univrsity Elctrical and Cmputr Enginring DT Furir Transfrm Ovrviw f Furir mthds DT Furir Transfrm f Pridic Signals

More information

LECTURE 5 Guassian Wave Packet

LECTURE 5 Guassian Wave Packet LECTURE 5 Guassian Wav Pact 1.5 Eampl f a guassian shap fr dscribing a wav pact Elctrn Pact ψ Guassian Assumptin Apprimatin ψ As w hav sn in QM th wav functin is ftn rprsntd as a Furir transfrm r sris.

More information

Random Access Techniques: ALOHA (cont.)

Random Access Techniques: ALOHA (cont.) Random Accss Tchniqus: ALOHA (cont.) 1 Exampl [ Aloha avoiding collision ] A pur ALOHA ntwork transmits a 200-bit fram on a shard channl Of 200 kbps at tim. What is th rquirmnt to mak this fram collision

More information

. This is made to keep the kinetic energy at outlet a minimum.

. This is made to keep the kinetic energy at outlet a minimum. Runnr Francis Turbin Th shap th blads a Francis runnr is cmplx. Th xact shap dpnds n its spciic spd. It is bvius rm th quatin spciic spd (Eq.5.8) that highr spciic spd mans lwr had. This rquirs that th

More information

The Language of SOCIAL MEDIA. Christine Dugan

The Language of SOCIAL MEDIA. Christine Dugan Th Languag f SOCIAL MEDIA Christin Dugan Tabl f Cntnts Gt th Wrd Out...4 A Nw Kind f Languag...6 Scial Mdia Talk...12 Cnncting with Othrs...28 Changing th Dictinary...36 Glssary...42 Indx...44 Chck It

More information

Voice and Data transmission over an Wireless network

Voice and Data transmission over an Wireless network vmbr 1995 dc: IEEE P802.11-9S/249 Vic and Data transmissin vr an 802.11 Wirlss ntwrk Matthijs A. Vissr1 vissr@duttvd.t.tudlft.nl Magda E1 Zarki 1,2 magda@.upnn.du I Dpartmnt f Elctrical Enginring, Dlft

More information

Lecture 2a. Crystal Growth (cont d) ECE723

Lecture 2a. Crystal Growth (cont d) ECE723 Lctur 2a rystal Grwth (cnt d) 1 Distributin f Dpants As a crystal is pulld frm th mlt, th dping cncntratin incrpratd int th crystal (slid) is usually diffrnt frm th dping cncntratin f th mlt (liquid) at

More information

N J of oscillators in the three lowest quantum

N J of oscillators in the three lowest quantum . a) Calculat th fractinal numbr f scillatrs in th thr lwst quantum stats (j,,,) fr fr and Sl: ( ) ( ) ( ) ( ) ( ).6.98. fr usth sam apprach fr fr j fr frm q. b) .) a) Fr a systm f lcalizd distinguishabl

More information

Another Explanation of the Cosmological Redshift. April 6, 2010.

Another Explanation of the Cosmological Redshift. April 6, 2010. Anthr Explanatin f th Csmlgical Rdshift April 6, 010. Jsé Francisc García Juliá C/ Dr. Marc Mrncian, 65, 5. 4605 Valncia (Spain) E-mail: js.garcia@dival.s h lss f nrgy f th phtn with th tim by missin f

More information

Port aggregation using a Cisco Catalyst 6513 switch and the IBM AIX operating system

Port aggregation using a Cisco Catalyst 6513 switch and the IBM AIX operating system Prt aggrgatin using a Cisc Catalyst 6513 switch and th IBM AIX prating systm hn Lumby (lumby@ca.ibm.cm) yc Clman (clmanj@ca.ibm.cm) 1. Intrductin... 2 2. Trminlgy... 2 3. Hardwar prrquisits... 4 4. Gnral

More information

Computing and Communications -- Network Coding

Computing and Communications -- Network Coding 89 90 98 00 Computing and Communications -- Ntwork Coding Dr. Zhiyong Chn Institut of Wirlss Communications Tchnology Shanghai Jiao Tong Univrsity China Lctur 5- Nov. 05 0 Classical Information Thory Sourc

More information

A Unified Theory of rf Plasma Heating. J.e. Sprott. July 1968

A Unified Theory of rf Plasma Heating. J.e. Sprott. July 1968 A Unifid Thry f rf Plasma Hating by J.. Sprtt July 968 PLP 3 Plasma Studis Univrsity f iscnsin INTRODUCfION In this papr, th majr rsults f PLP's 86 and 07 will b drivd in a mr cncis and rigrus way, and

More information

Chapter 2 Linear Waveshaping: High-pass Circuits

Chapter 2 Linear Waveshaping: High-pass Circuits Puls and Digital Circuits nkata Ra K., Rama Sudha K. and Manmadha Ra G. Chaptr 2 Linar Wavshaping: High-pass Circuits. A ramp shwn in Fig.2p. is applid t a high-pass circuit. Draw t scal th utput wavfrm

More information

Modern Physics. Unit 5: Schrödinger s Equation and the Hydrogen Atom Lecture 5.6: Energy Eigenvalues of Schrödinger s Equation for the Hydrogen Atom

Modern Physics. Unit 5: Schrödinger s Equation and the Hydrogen Atom Lecture 5.6: Energy Eigenvalues of Schrödinger s Equation for the Hydrogen Atom Mdrn Physics Unit 5: Schrödingr s Equatin and th Hydrgn Atm Lctur 5.6: Enrgy Eignvalus f Schrödingr s Equatin fr th Hydrgn Atm Rn Rifnbrgr Prfssr f Physics Purdu Univrsity 1 Th allwd nrgis E cm frm th

More information

Even/Odd Mode Analysis of the Wilkinson Divider

Even/Odd Mode Analysis of the Wilkinson Divider //9 Wilkinn Dividr Evn and Odd Md Analyi.dc / Evn/Odd Md Analyi f th Wilkinn Dividr Cnidr a matchd Wilkinn pwr dividr, with a urc at prt : Prt Prt Prt T implify thi chmatic, w rmv th grund plan, which

More information

Advanced Use of Pointers. CS2023 Winter 2004

Advanced Use of Pointers. CS2023 Winter 2004 Advancd Us f Pintrs CS2023 Wintr 2004 Outcms: Advancd us f Pintrs C fr Java Prgrammrs, Chaptr 8, sctin 8.11, 8.15 Othr txtbks n C n rsrv Aftr th cnclusin f this sctin yu shuld b abl t Allcat and dallcat

More information

6. Negative Feedback in Single- Transistor Circuits

6. Negative Feedback in Single- Transistor Circuits Lctur 8: Intrductin t lctrnic analg circuit 36--366 6. Ngativ Fdback in Singl- Tranitr ircuit ugn Paprn, 2008 Our aim i t tudy t ffct f ngativ fdback n t mall-ignal gain and t mall-ignal input and utput

More information

The Gemini Interconnect: Data Path Measurements and Performance Analysis. Ch'ng Shi Baw Roger D. Chamberlain Mark A. Franklin Michael G.

The Gemini Interconnect: Data Path Measurements and Performance Analysis. Ch'ng Shi Baw Roger D. Chamberlain Mark A. Franklin Michael G. Th Gmini Intrcnnct: Data Path Masurmnts and Prfrmanc Analysis Ch'ng Shi Baw Rgr D. Chambrlain Mark A. Franklin Michal G. Wrightn Ch'ng Shi Baw, Rgr D. Chambrlain, Mark A. Franklin, and Michal G. Wrightn,

More information

Hospital Readmission Reduction Strategies Using a Penalty-Incentive Model

Hospital Readmission Reduction Strategies Using a Penalty-Incentive Model Procdings of th 2016 Industrial and Systms Enginring Rsarch Confrnc H. Yang, Z. Kong, and MD Sardr, ds. Hospital Radmission Rduction Stratgis Using a Pnalty-Incntiv Modl Michll M. Alvarado Txas A&M Univrsity

More information

A Brief and Elementary Note on Redshift. May 26, 2010.

A Brief and Elementary Note on Redshift. May 26, 2010. A Brif and Elmntary Nt n Rdshift May 26, 2010. Jsé Francisc García Juliá C/ Dr. Marc Mrncian, 65, 5. 46025 Valncia (Spain) E-mail: js.garcia@dival.s Abstract A rasnabl xplanatin f bth rdshifts: csmlgical

More information

120~~60 o D 12~0 1500~30O, 15~30 150~30. ..,u 270,,,, ~"~"-4-~qno 240 2~o 300 v 240 ~70O 300

120~~60 o D 12~0 1500~30O, 15~30 150~30. ..,u 270,,,, ~~-4-~qno 240 2~o 300 v 240 ~70O 300 1 Find th plar crdinats that d nt dscrib th pint in th givn graph. (-2, 30 ) C (2,30 ) B (-2,210 ) D (-2,-150 ) Find th quatin rprsntd in th givn graph. F 0=3 H 0=2~ G r=3 J r=2 0 :.1 2 3 ~ 300 2"~ 2,

More information

ECE602 Exam 1 April 5, You must show ALL of your work for full credit.

ECE602 Exam 1 April 5, You must show ALL of your work for full credit. ECE62 Exam April 5, 27 Nam: Solution Scor: / This xam is closd-book. You must show ALL of your work for full crdit. Plas rad th qustions carfully. Plas chck your answrs carfully. Calculators may NOT b

More information

Signals and Systems View Point

Signals and Systems View Point Signals and Sstms Viw Pint Inpt signal Ozt Mdical Imaging Sstm LOzt Otpt signal Izt r Iz r I A signalssstms apprach twards imaging allws s as Enginrs t Gain a bttr ndrstanding f hw th imags frm and what

More information

EE 119 Homework 6 Solution

EE 119 Homework 6 Solution EE 9 Hmwrk 6 Slutin Prr: J Bkr TA: Xi Lu Slutin: (a) Th angular magniicatin a tlcp i m / th cal lngth th bjctiv ln i m 4 45 80cm (b) Th clar aprtur th xit pupil i 35 mm Th ditanc btwn th bjctiv ln and

More information

Search sequence databases 3 10/25/2016

Search sequence databases 3 10/25/2016 Sarch squnc databass 3 10/25/2016 Etrm valu distribution Ø Suppos X is a random variabl with probability dnsity function p(, w sampl a larg numbr S of indpndnt valus of X from this distribution for an

More information

ph People Grade Level: basic Duration: minutes Setting: classroom or field site

ph People Grade Level: basic Duration: minutes Setting: classroom or field site ph Popl Adaptd from: Whr Ar th Frogs? in Projct WET: Curriculum & Activity Guid. Bozman: Th Watrcours and th Council for Environmntal Education, 1995. ph Grad Lvl: basic Duration: 10 15 minuts Stting:

More information

Functional Verification for SystemC Descriptions Using Constraint Solving

Functional Verification for SystemC Descriptions Using Constraint Solving Functinal Vrificatin fr SystmC Dscriptins Using Cnstraint Slving Fabrizi Frrandi Plitcnic di Milan Dipartimnt di Elttrnica Infrmazin frrandi@lt.plimi.it Michl Rndin Plitcnic di Milan Dipartimnt di Elttrnica

More information

Answer Homework 5 PHA5127 Fall 1999 Jeff Stark

Answer Homework 5 PHA5127 Fall 1999 Jeff Stark Answr omwork 5 PA527 Fall 999 Jff Stark A patint is bing tratd with Drug X in a clinical stting. Upon admiion, an IV bolus dos of 000mg was givn which yildd an initial concntration of 5.56 µg/ml. A fw

More information

Design of an Online GIS Viewer by Wavelet Technology

Design of an Online GIS Viewer by Wavelet Technology sign f an Onlin GIS iwr by Wavlt Tchnlgy bstract Jingsng Wu, Kvin maratunga and Tuc Mng Lui 3 lng with th high-spd dvlpmnt f th Intrnt, usrs hav bgun t xpct highly intractiv nlin GIS. Wavlt tchnlgy prvids

More information

Cosmology. Outline. Relativity and Astrophysics Lecture 17 Terry Herter. Redshift (again) The Expanding Universe Applying Hubble s Law

Cosmology. Outline. Relativity and Astrophysics Lecture 17 Terry Herter. Redshift (again) The Expanding Universe Applying Hubble s Law Csmlgy Csmlgy Rlativity and Astrphysics ctur 17 Trry Hrtr Outlin Rdshit (again) Th Expanding Univrs Applying Hubbl s aw Distanc rm Rdshit Csmlgical Principl Olbrs Paradx A90-17 Csmlgy A90-17 1 Csmlgy Rdshit

More information

Chapter 6 Folding. Folding

Chapter 6 Folding. Folding Chaptr 6 Folding Wintr 1 Mokhtar Abolaz Folding Th folding transformation is usd to systmatically dtrmin th control circuits in DSP architctur whr multipl algorithm oprations ar tim-multiplxd to a singl

More information

Multiple Priority, Per Flow, Dual GCRA Rate Controller for ATM Switches

Multiple Priority, Per Flow, Dual GCRA Rate Controller for ATM Switches 1 Multipl Pririty, Pr Flw, Dual GCRA Rat Cntrllr r ATM Switchs Avi Hagai avihwintgracil Baz Patt-Shamir bazngtauacil Dpt Elctrical Enginring Tl-Aviv Univrsity Tl Aviv 658 Isral Abstract W prps a rat cntrllr

More information

Accepted Manuscript. DMMS: A Flexible Architecture for Multicast Listener Support in a Distributed Mobility Management Environment

Accepted Manuscript. DMMS: A Flexible Architecture for Multicast Listener Support in a Distributed Mobility Management Environment Accptd Manuscript DMMS: A Flxibl Architctur for Multicast Listnr Support in a Distributd Mobility Managmnt Environmnt Tin-Thinh Nguyn, Christian Bonnt PII: S1389-1286(15)00448-X DOI: 10.1016/j.comnt.2015.11.015

More information

ME 354, MECHANICS OF MATERIALS LABORATORY COMPRESSION AND BUCKLING

ME 354, MECHANICS OF MATERIALS LABORATORY COMPRESSION AND BUCKLING ME 354, MECHANICS OF MATERIALS LABATY COMPRESSION AND BUCKLING 01 January 000 / mgj PURPOSE Th purps f this xrcis is t study th ffcts f nd cnditins, clumn lngth, and matrial prprtis n cmprssiv bhaviur

More information

Ch. 24 Molecular Reaction Dynamics 1. Collision Theory

Ch. 24 Molecular Reaction Dynamics 1. Collision Theory Ch. 4 Molcular Raction Dynamics 1. Collision Thory Lctur 16. Diffusion-Controlld Raction 3. Th Matrial Balanc Equation 4. Transition Stat Thory: Th Eyring Equation 5. Transition Stat Thory: Thrmodynamic

More information

EEO 401 Digital Signal Processing Prof. Mark Fowler

EEO 401 Digital Signal Processing Prof. Mark Fowler EEO 401 Digital Signal Procssing Prof. Mark Fowlr Dtails of th ot St #19 Rading Assignmnt: Sct. 7.1.2, 7.1.3, & 7.2 of Proakis & Manolakis Dfinition of th So Givn signal data points x[n] for n = 0,, -1

More information

Inheritance Gains in Notional Defined Contributions Accounts (NDCs)

Inheritance Gains in Notional Defined Contributions Accounts (NDCs) Company LOGO Actuarial Tachrs and Rsarchrs Confrnc Oxford 14-15 th July 211 Inhritanc Gains in Notional Dfind Contributions Accounts (NDCs) by Motivation of this papr In Financial Dfind Contribution (FDC)

More information

EXST Regression Techniques Page 1

EXST Regression Techniques Page 1 EXST704 - Rgrssion Tchniqus Pag 1 Masurmnt rrors in X W hav assumd that all variation is in Y. Masurmnt rror in this variabl will not ffct th rsults, as long as thy ar uncorrlatd and unbiasd, sinc thy

More information

Multipacket Reception Enabled Aggregation for Very High-Speed WLANs

Multipacket Reception Enabled Aggregation for Very High-Speed WLANs Multipackt Rcption Enabld Aggrgation for Vry High-Spd WLANs Tianji Li, David Malon, Douglas Lith Hamilton Institut, National Univ. of Irland at Maynooth, Irland Email: {tianji.li, david.malon, doug.lith}@nuim.i

More information

Continuous probability distributions

Continuous probability distributions Continuous probability distributions Many continuous probability distributions, including: Uniform Normal Gamma Eponntial Chi-Squard Lognormal Wibull EGR 5 Ch. 6 Uniform distribution Simplst charactrizd

More information

VHDL Implementation of Fast and Efficient Viterbi decoder

VHDL Implementation of Fast and Efficient Viterbi decoder VHDL Implmntation of Fast and Efficint Vitrbi dcodr Rajsh. C 1, A Srnivasa Murthy 2 Abstract Vitrbi dcodrs ar usd in wid varity of communication applicatio. In this papr, w focus on diffrnt typs of VHDL

More information

Differentiation of Exponential Functions

Differentiation of Exponential Functions Calculus Modul C Diffrntiation of Eponntial Functions Copyright This publication Th Northrn Albrta Institut of Tchnology 007. All Rights Rsrvd. LAST REVISED March, 009 Introduction to Diffrntiation of

More information

Frequency Response. Lecture #12 Chapter 10. BME 310 Biomedical Computing - J.Schesser

Frequency Response. Lecture #12 Chapter 10. BME 310 Biomedical Computing - J.Schesser Frquncy Rspns Lcur # Chapr BME 3 Bimdical Cmpuing - J.Schssr 99 Idal Filrs W wan sudy Hω funcins which prvid frquncy slciviy such as: Lw Pass High Pass Band Pass Hwvr, w will lk a idal filring, ha is,

More information

Types of Communication

Types of Communication Tps f Cmmunicatin Analg: cntinuus ariabls with nis {rrr 0} 0 (imprfct) Digital: dcisins, discrt chics, quantizd, nis {rrr} 0 (usuall prfct) Mssag S, S, r S M Mdulatr (t) channl; adds nis and distrtin M-ar

More information

Physical Organization

Physical Organization Lctur usbasd symmtric multiprocssors (SM s): combin both aspcts Compilr support? rchitctural support? Static and dynamic locality of rfrnc ar critical for high prformanc M I M ccss to local mmory is usually

More information

Sensors and Actuators Introduction to sensors

Sensors and Actuators Introduction to sensors Snsrs and Actuatrs Intrductin t snsrs Sandr Stuijk (s.stuijk@tu.nl) Dpartmnt f Elctrical Enginring Elctrnic Systms APAITIVE IUITS (haptr., 7., 9., 0.6,.,.) apaciti snsr capacitanc dpnds n physical prprtis

More information

SER/BER in a Fading Channel

SER/BER in a Fading Channel SER/BER in a Fading Channl Major points for a fading channl: * SNR is a R.V. or R.P. * SER(BER) dpnds on th SNR conditional SER(BER). * Two prformanc masurs: outag probability and avrag SER(BER). * Ovrall,

More information

Acid Base Reactions. Acid Base Reactions. Acid Base Reactions. Chemical Reactions and Equations. Chemical Reactions and Equations

Acid Base Reactions. Acid Base Reactions. Acid Base Reactions. Chemical Reactions and Equations. Chemical Reactions and Equations Chmial Ratins and Equatins Hwitt/Lyns/Suhki/Yh Cnptual Intgratd Sin During a hmial ratin, n r mr nw mpunds ar frmd as a rsult f th rarrangmnt f atms. Chaptr 13 CHEMICAL REACTIONS Ratants Prduts Chmial

More information

All staff should All work as team to communication achieve highest should be clinical quality smooth and no in healthcare barriers should exist 4

All staff should All work as team to communication achieve highest should be clinical quality smooth and no in healthcare barriers should exist 4 rd a h u Hav y ls? f Si 1 Sils 2 av h t n Sils d in with ct cnn thr ach but k s i ris it ships t c a f In latin tin r n i nt niza a g r an within althcar sp h 3 uld h s f f All sta am t st a k r w st h

More information

5 Curl-free fields and electrostatic potential

5 Curl-free fields and electrostatic potential 5 Curl-fr filds and lctrstatic tntial Mathmaticall, w can gnrat a curl-fr vctr fild E(,, ) as E = ( V, V, V ), b taking th gradint f an scalar functin V (r) =V (,, ). Th gradint f V (,, ) is dfind t b

More information

That is, we start with a general matrix: And end with a simpler matrix:

That is, we start with a general matrix: And end with a simpler matrix: DIAGON ALIZATION OF THE STR ESS TEN SOR INTRO DUCTIO N By th us of Cauchy s thorm w ar abl to rduc th numbr of strss componnts in th strss tnsor to only nin valus. An additional simplification of th strss

More information

Wavelet-Based Method for Fog Signal Denoising

Wavelet-Based Method for Fog Signal Denoising Jurnal f Autmatin and Cntrl Enginring, Vl., N. 2, Jun 23 Wavlt-Basd Mthd fr Fg Signal Dnising Yu Zu Cllg f Autmatin, Harbin Enginring Univrsity, Harbin, China Email: andrw22.zu@gmail.cm Jinchu Ca Dpartmnt

More information

Need to understand interaction of macroscopic measures

Need to understand interaction of macroscopic measures CE 322 Transportation Enginring Dr. Ahmd Abdl-Rahim, h. D.,.E. Nd to undrstand intraction o macroscopic masurs Spd vs Dnsity Flow vs Dnsity Spd vs Flow Equation 5.14 hlps gnraliz Thr ar svral dirnt orms

More information

Observer Bias and Reliability By Xunchi Pu

Observer Bias and Reliability By Xunchi Pu Obsrvr Bias and Rliability By Xunchi Pu Introduction Clarly all masurmnts or obsrvations nd to b mad as accuratly as possibl and invstigators nd to pay carful attntion to chcking th rliability of thir

More information

Testing for Machine Consciousness Using Insight Learning

Testing for Machine Consciousness Using Insight Learning Tsting fr Machin Cnsciusnss Using Insight Larning Cathrin Marcarlli and Jffry L. McKinstry Pint Lma Nazarn Univrsity 3900 Lmaland Driv San Dig, CA 92106 CMarcar@pintlma.du JffMcKinstry@pintlma.du Abstract

More information

CS 6353 Compiler Construction, Homework #1. 1. Write regular expressions for the following informally described languages:

CS 6353 Compiler Construction, Homework #1. 1. Write regular expressions for the following informally described languages: CS 6353 Compilr Construction, Homwork #1 1. Writ rgular xprssions for th following informally dscribd languags: a. All strings of 0 s and 1 s with th substring 01*1. Answr: (0 1)*01*1(0 1)* b. All strings

More information

Chapter 33 Gauss s Law

Chapter 33 Gauss s Law Chaptr 33 Gauss s Law 33 Gauss s Law Whn askd t find th lctric flux thrugh a clsd surfac du t a spcifid nn-trivial charg distributin, flks all t ftn try th immnsly cmplicatd apprach f finding th lctric

More information

What are those βs anyway? Understanding Design Matrix & Odds ratios

What are those βs anyway? Understanding Design Matrix & Odds ratios Ral paramtr stimat WILD 750 - Wildlif Population Analysis of 6 What ar thos βs anyway? Undrsting Dsign Matrix & Odds ratios Rfrncs Hosmr D.W.. Lmshow. 000. Applid logistic rgrssion. John Wily & ons Inc.

More information

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing-

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing- Outlin Advancd Multimdia Signal Procssing #5:Spch Signal Procssing -Procssing- Intllignt Elctronic Systms Group Dpt. of Elctronic Enginring, UEC Basis of Spch Procssing Nois Rmoval Spctral Subtraction

More information

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0 unction Spacs Prrquisit: Sction 4.7, Coordinatization n this sction, w apply th tchniqus of Chaptr 4 to vctor spacs whos lmnts ar functions. Th vctor spacs P n and P ar familiar xampls of such spacs. Othr

More information

A Prey-Predator Model with an Alternative Food for the Predator, Harvesting of Both the Species and with A Gestation Period for Interaction

A Prey-Predator Model with an Alternative Food for the Predator, Harvesting of Both the Species and with A Gestation Period for Interaction Int. J. Opn Problms Compt. Math., Vol., o., Jun 008 A Pry-Prdator Modl with an Altrnativ Food for th Prdator, Harvsting of Both th Spcis and with A Gstation Priod for Intraction K. L. arayan and. CH. P.

More information

Higher order derivatives

Higher order derivatives Robrto s Nots on Diffrntial Calculus Chaptr 4: Basic diffrntiation ruls Sction 7 Highr ordr drivativs What you nd to know alrady: Basic diffrntiation ruls. What you can larn hr: How to rpat th procss of

More information

Multiple Source Multiple. using Network Coding

Multiple Source Multiple. using Network Coding Multiple Surce Multiple Destinatin Tplgy Inference using Netwrk Cding Pegah Sattari EECS, UC Irvine Jint wrk with Athina Markpulu, at UCI, Christina Fraguli, at EPFL, Lausanne Outline Netwrk Tmgraphy Gal,

More information

MODEL-INVERSE BASED REPETITIVE CONTROL

MODEL-INVERSE BASED REPETITIVE CONTROL MODEL-INVERSE BASED REPETITIVE CONTROL T. Hart*, J. Hätönn*, D.H. Owns* Dparmnt f Autmatic Cntrl and Systms Enginring Th Univrsity f Shffild Mappin Strt S1 3JD Shffild Unitd Kingdm Tl: +44(01114 550 Fax:

More information

Chapter 3: Cluster Analysis

Chapter 3: Cluster Analysis Chapter 3: Cluster Analysis } 3.1 Basic Cncepts f Clustering 3.1.1 Cluster Analysis 3.1. Clustering Categries } 3. Partitining Methds 3..1 The principle 3.. K-Means Methd 3..3 K-Medids Methd 3..4 CLARA

More information

Ph.D. students Department of Electronics and Telecommunications, Politecnico di Torino

Ph.D. students Department of Electronics and Telecommunications, Politecnico di Torino 01OPIIU Il softwar libro Dvic-to-dvic communications: Wi-Fi Dirct Laura Cocona s189195 Carlo Borgiattino s189149 Ph.D. studnts Dpartmnt of Elctronics and Tlcommunications, Politcnico di Torino Rport for

More information

Supplementary Materials

Supplementary Materials 6 Supplmntary Matrials APPENDIX A PHYSICAL INTERPRETATION OF FUEL-RATE-SPEED FUNCTION A truck running on a road with grad/slop θ positiv if moving up and ngativ if moving down facs thr rsistancs: arodynamic

More information

THE P-PERSISTENT CSMA WITH THE FUNCTION OF MONITORING BASED ON TIME DIVISION MECHA- NISM

THE P-PERSISTENT CSMA WITH THE FUNCTION OF MONITORING BASED ON TIME DIVISION MECHA- NISM ISSN:3-56 Intrnational Journal of Innovativ Rsarch in Tchnology & Scinc(IJIRTS) THE P-PERSISTENT CSMA WITH THE FUNCTION OF MONITORING BASED ON TIME DIVISION MECHA- NISM Yifan Zhao, Yunnan Univrsity, Kunming,

More information

EXERGY ANALYSIS OF A DUAL-MODE REFRIGERATION SYSTEM FOR ICE STORAGE AIR CONDITIONING

EXERGY ANALYSIS OF A DUAL-MODE REFRIGERATION SYSTEM FOR ICE STORAGE AIR CONDITIONING Intrnatinal Jurnal n Architctural Scinc, Vlum 6, Numbr 1, p.1-6, 005 EXERGY ANALYSIS OF A DUAL-MODE REFRIGERATION SYSTEM FOR ICE STORAGE AIR CONDITIONING Guiyin Fang*, Lin Xing*, Fan Yang* and Hui Li #

More information

Problem Set 6 Solutions

Problem Set 6 Solutions 6.04/18.06J Mathmatics for Computr Scinc March 15, 005 Srini Dvadas and Eric Lhman Problm St 6 Solutions Du: Monday, March 8 at 9 PM in Room 3-044 Problm 1. Sammy th Shark is a financial srvic providr

More information

Lesson 8 Case Studies

Lesson 8 Case Studies 0.43 Lssn 8 Cas Studis Yu Will Larn Instrumntatin fr:. Lasr phtchmistry and phtlctrchmistry B. Scanning Prb Micrscpy C. PI cntrl in chmical nginring s wll as data tratmnt and prsntatin 1. Lasr phtchmistry

More information

7' The growth of yeast, a microscopic fungus used to make bread, in a test tube can be

7' The growth of yeast, a microscopic fungus used to make bread, in a test tube can be N Sction A: Pur Mathmatics 55 marks] / Th rgion R is boundd by th curv y, th -ais, and th lins = V - +7 and = m, whr m >. Find th volum gnratd whn R is rotatd through right angls about th -ais, laving

More information

MCB137: Physical Biology of the Cell Spring 2017 Homework 6: Ligand binding and the MWC model of allostery (Due 3/23/17)

MCB137: Physical Biology of the Cell Spring 2017 Homework 6: Ligand binding and the MWC model of allostery (Due 3/23/17) MCB37: Physical Biology of th Cll Spring 207 Homwork 6: Ligand binding and th MWC modl of allostry (Du 3/23/7) Hrnan G. Garcia March 2, 207 Simpl rprssion In class, w drivd a mathmatical modl of how simpl

More information

The pn junction: 2 Current vs Voltage (IV) characteristics

The pn junction: 2 Current vs Voltage (IV) characteristics Th pn junction: Currnt vs Voltag (V) charactristics Considr a pn junction in quilibrium with no applid xtrnal voltag: o th V E F E F V p-typ Dpltion rgion n-typ Elctron movmnt across th junction: 1. n

More information

Note If the candidate believes that e x = 0 solves to x = 0 or gives an extra solution of x = 0, then withhold the final accuracy mark.

Note If the candidate believes that e x = 0 solves to x = 0 or gives an extra solution of x = 0, then withhold the final accuracy mark. . (a) Eithr y = or ( 0, ) (b) Whn =, y = ( 0 + ) = 0 = 0 ( + ) = 0 ( )( ) = 0 Eithr = (for possibly abov) or = A 3. Not If th candidat blivs that = 0 solvs to = 0 or givs an tra solution of = 0, thn withhold

More information

A Propagating Wave Packet Group Velocity Dispersion

A Propagating Wave Packet Group Velocity Dispersion Lctur 8 Phys 375 A Propagating Wav Packt Group Vlocity Disprsion Ovrviw and Motivation: In th last lctur w lookd at a localizd solution t) to th 1D fr-particl Schrödingr quation (SE) that corrsponds to

More information

First derivative analysis

First derivative analysis Robrto s Nots on Dirntial Calculus Chaptr 8: Graphical analysis Sction First drivativ analysis What you nd to know alrady: How to us drivativs to idntiy th critical valus o a unction and its trm points

More information

Performance of Microstrip Directional Coupler Using Synthesis Technique

Performance of Microstrip Directional Coupler Using Synthesis Technique ISSN: 78 8875 Vl., Issu, March 0 Prrmanc Micrstrip Dirctinal uplr Using Synthsis Tchniqu Vijayan T Asst. Prssr, Dpt E&I, Bharath Univrsity, hnnai-60007, India ABSTRAT: Th intrducd dsign mthd rquirs nly

More information

Multi-level Discrete Wavelet Transform Architecture Design

Multi-level Discrete Wavelet Transform Architecture Design Prcdings f th Wrld Cngrss n Enginring 009 Vl I WCE 009, July - 3, 009, ndn, U.K. Multi-lvl Discrt Wavlt Transfrm Architctur Dsign Dhaha Dia, Mdin Zghid, Taufi Saidani, Mhamd Atri, Blgacm Buallgu, Mhsn

More information

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches. Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M

More information

Aim To manage files and directories using Linux commands. 1. file Examines the type of the given file or directory

Aim To manage files and directories using Linux commands. 1. file Examines the type of the given file or directory m E x. N o. 3 F I L E M A N A G E M E N T Aim To manag ils and dirctoris using Linux commands. I. F i l M a n a g m n t 1. il Examins th typ o th givn il or dirctory i l i l n a m > ( o r ) < d i r c t

More information

ENSC Discrete Time Systems. Project Outline. Semester

ENSC Discrete Time Systems. Project Outline. Semester ENSC 49 - iscrete Time Systems Prject Outline Semester 006-1. Objectives The gal f the prject is t design a channel fading simulatr. Upn successful cmpletin f the prject, yu will reinfrce yur understanding

More information

Network Congestion Games

Network Congestion Games Ntwork Congstion Gams Assistant Profssor Tas A&M Univrsity Collg Station, TX TX Dallas Collg Station Austin Houston Bst rout dpnds on othrs Ntwork Congstion Gams Travl tim incrass with congstion Highway

More information

Decentralized Resource Allocation in Application Layer Networks

Decentralized Resource Allocation in Application Layer Networks Dcntraliz Rsurc Allcatin in Applicatin Layr Ntwrks T. Eymann, M. Rinick Institut fr Cmputr Scinc an Scial Stuis Albrt-Luwigs-Univrsity Friburg, Grmany ymann,rinick@iig.uni-friburg. Abstract Applicatin-layr

More information

Machine Detector Interface Workshop: ILC-SLAC, January 6-8, 2005.

Machine Detector Interface Workshop: ILC-SLAC, January 6-8, 2005. Intrnational Linar Collidr Machin Dtctor Intrfac Workshop: ILCSLAC, January 68, 2005. Prsntd by Brtt Parkr, BNLSMD Mssag: Tools ar now availabl to optimiz IR layout with compact suprconducting quadrupols

More information

Mathematics. Complex Number rectangular form. Quadratic equation. Quadratic equation. Complex number Functions: sinusoids. Differentiation Integration

Mathematics. Complex Number rectangular form. Quadratic equation. Quadratic equation. Complex number Functions: sinusoids. Differentiation Integration Mathmatics Compl numbr Functions: sinusoids Sin function, cosin function Diffrntiation Intgration Quadratic quation Quadratic quations: a b c 0 Solution: b b 4ac a Eampl: 1 0 a= b=- c=1 4 1 1or 1 1 Quadratic

More information

Difference -Analytical Method of The One-Dimensional Convection-Diffusion Equation

Difference -Analytical Method of The One-Dimensional Convection-Diffusion Equation Diffrnc -Analytical Mthod of Th On-Dimnsional Convction-Diffusion Equation Dalabav Umurdin Dpartmnt mathmatic modlling, Univrsity of orld Economy and Diplomacy, Uzbistan Abstract. An analytical diffrncing

More information

IMPACT OF MULTIPLE TRANSMIT ANTENNAS IN A QUEUED SDMA/TDMA DOWNLINK. Mari Kobayashi, Giuseppe Caire, and David Gesbert

IMPACT OF MULTIPLE TRANSMIT ANTENNAS IN A QUEUED SDMA/TDMA DOWNLINK. Mari Kobayashi, Giuseppe Caire, and David Gesbert IMPAC OF MULIPLE RANSMI ANENNAS IN A QUEUED SDMA/DMA DOWNLINK Mari Kbayashi, Giuspp Cair, and David Gsbrt Institut EURECOM, Sphia-Antiplis, Franc E-mail: firstnam.namurcm.fr ABSRAC W invstigat th impact

More information

:2;$-$(01*%<*=,-./-*=0;"%/;"-*

:2;$-$(01*%<*=,-./-*=0;%/;-* !"#$%'()%"*#%*+,-./-*+01.2(.*3+456789*!"#$%"'()'*+,-."/0.%+1'23"45'46'7.89:89'/' ;8-,"$4351415,8:+#9' Dr. Ptr T. Gallaghr Astrphyscs Rsarch Grup Trnty Cllg Dubln :2;$-$(01*%

More information

From Elimination to Belief Propagation

From Elimination to Belief Propagation School of omputr Scinc Th lif Propagation (Sum-Product lgorithm Probabilistic Graphical Modls (10-708 Lctur 5, Sp 31, 2007 Rcptor Kinas Rcptor Kinas Kinas X 5 ric Xing Gn G T X 6 X 7 Gn H X 8 Rading: J-hap

More information

4037 ADDITIONAL MATHEMATICS

4037 ADDITIONAL MATHEMATICS CAMBRIDGE INTERNATIONAL EXAMINATIONS GCE Ordinary Lvl MARK SCHEME for th Octobr/Novmbr 0 sris 40 ADDITIONAL MATHEMATICS 40/ Papr, maimum raw mark 80 This mark schm is publishd as an aid to tachrs and candidats,

More information

Scalable IPv6 Lookup/Update Design for High-Throughput Routers

Scalable IPv6 Lookup/Update Design for High-Throughput Routers Scalabl IPv6 Lookup/Updat sign for High-Throughput Routrs 26 Scalabl IPv6 Lookup/Updat sign for High-Throughput Routrs Chung-Ho Chn, Chao-Hsin Hsu, Chn-Chih Wang partmnt of lctrical nginring and Institut

More information

Physics 2010 Motion with Constant Acceleration Experiment 1

Physics 2010 Motion with Constant Acceleration Experiment 1 . Physics 00 Mtin with Cnstant Acceleratin Experiment In this lab, we will study the mtin f a glider as it accelerates dwnhill n a tilted air track. The glider is supprted ver the air track by a cushin

More information

Evaluating Reliability Systems by Using Weibull & New Weibull Extension Distributions Mushtak A.K. Shiker

Evaluating Reliability Systems by Using Weibull & New Weibull Extension Distributions Mushtak A.K. Shiker Evaluating Rliability Systms by Using Wibull & Nw Wibull Extnsion Distributions Mushtak A.K. Shikr مشتاق عبذ الغني شخير Univrsity of Babylon, Collg of Education (Ibn Hayan), Dpt. of Mathmatics Abstract

More information

Speeding Up Back-Propagation Neural Networks

Speeding Up Back-Propagation Neural Networks Prcdings f t 005 Infrming Scinc and IT Educatin Jint Cnfrnc Spding Up Back-Prpagatin Nural Ntwrks Mammd A. Otair Jrdan Univrsity f Scinc and Tcnlgy, Irbd, Jrdan tair@just.du.j Walid A. Salam Princss Summaya

More information

Roadmap. XML Indexing. DataGuide example. DataGuides. Strong DataGuides. Multiple DataGuides for same data. CPS Topics in Database Systems

Roadmap. XML Indexing. DataGuide example. DataGuides. Strong DataGuides. Multiple DataGuides for same data. CPS Topics in Database Systems Roadmap XML Indxing CPS 296.1 Topics in Databas Systms Indx fabric Coopr t al. A Fast Indx for Smistructurd Data. VLDB, 2001 DataGuid Goldman and Widom. DataGuids: Enabling Qury Formulation and Optimization

More information

GEOMETRICAL PHENOMENA IN THE PHYSICS OF SUBATOMIC PARTICLES. Eduard N. Klenov* Rostov-on-Don, Russia

GEOMETRICAL PHENOMENA IN THE PHYSICS OF SUBATOMIC PARTICLES. Eduard N. Klenov* Rostov-on-Don, Russia GEOMETRICAL PHENOMENA IN THE PHYSICS OF SUBATOMIC PARTICLES Eduard N. Klnov* Rostov-on-Don, Russia Th articl considrs phnomnal gomtry figurs bing th carrirs of valu spctra for th pairs of th rmaining additiv

More information