Security of Reconciliation in Continuous Variable Quantum Key Distribution *

Size: px
Start display at page:

Download "Security of Reconciliation in Continuous Variable Quantum Key Distribution *"

Transcription

1 Scurty of Rconclaton n Contnuous Varabl Quantu Ky Dstrbuton * Y-bo Zhao, You-zhn Gu, Jn-an Chn, Zhng-fu Han **, Guang-can Guo Ky Lab of Quantu Inforaton of CS Unvrsty of Scnc & Tchnology of Chna, Chns cady of Scnc Hf, nhu 3006, P. R. Chna bstract W show that n contnuous varabl quantu y dstrbuton, whch s norally blvd to b scur undr any loss of th quantu channl, thr s stll a scurty loophol vn whn rvrs rconclaton s usd. Quanttatv analyss shows that th scurty of contnuous varabl quantu y dstrbuton s anly dtrnd by rconclaton, spcally by th sz of ach corrcton bloc. W also drv th rlatonshp aong th corrcton bloc sz, bt rror rat and scrt y rat. PCS nubr(s: Dd, p, c For hgh y rats, contnuous varabl quantu y dstrbuton (CVQKD has rcntly attractd or attnton copard to sngl photon countng schs. Th CVQKD schs typcally us th quadratur apltud of lght bas as nforaton carrr, and hoodyn dtcton rathr than photon countng. So of ths schs us non-classcal stats, such as squzd stats [, ] or ntangld stats [3,4], whl othrs us cohrnt stats [5-0]. ong th lattr, th sch n rfrnc [0] can provd hgh y rats whn ang sultanous quadratur asurnts. caus squzd stats and ntangld stats ar snstv to losss n th quantu channl, cohrnt stats ar uch or attractv for long dstanc transsson. For nstanc, an xprntal tabl-top stup at 780n that ncods nforaton n th phas and apltud of cohrnt stats has bn donstratd [], and a rcnt xprnt has also shown th fasblty of CVQKD n optcal fbrs up to a dstanc of 55 [,3]. lthough t has bn thortcally provd that only rvrs rconclaton can guarant th scurty undr any losss of th quantu channl [9], how to ralz propr rconclaton s a bg probl for practcal CVQKD. In th followng w wll dscuss th strct rqurnts of th rconclaton thod. Rconclaton convrts lc and ob s corrlatd raw y lnts nto * Supportd by:scnc Foundaton of Chna undr Grant No and No ; Th Knowldg Innovaton Proct of Chns cady of Scncs; ** To who corrspondnc should b addrssd, Eal: zfhan@ustc.du.cn

2 bnary bt strngs and prfors rror corrcton wth hgh probablty, to nsur that thy shar dntcal bt strngs. Unl th bnary bts gnratd fro th sngl photon schs, th bnary bts convrtd fro th contnuous varabls crtanly has hgh bt rror rats (ER. lso, th bnary bts Ev taps hav so rlatonshp wth lc s and ob s, unl that of th sngl photon schs, n whch Ev s bts ay hav no rlatonshp wth lc s and ob s. For th CVQKD, t s ust th cas that th ER of ach bt btwn Ev and ob s only slghtly hghr than that btwn lc and ob. Thn usng vry prcs rror corrctng thod, lc and ob can corrct th rrors btwn th, but Ev has so rrors that cannot b corrctd. ftr that, usng prvacy aplfcaton thy can gt th scrt ys. Th y pont s that lc and ob should guarant that thy can corrct thr rrors and Ev ust hav so rrors that cannot b corrctd. Othrws, thy cannot gnrat th scrt ys. Howvr, for th probablstc fluctuatons, t s vry dffcult to achv. Hr w wll gv a brf dscusson of rvrs rconclaton as dscrbd n rfrncs [4, 5] wth a quanttatv analyss of th dpndnc of th scurty on th rconclaton paratrs. ftr copltng th quantu councatons, lc and ob shar a st of contnuously E dstrbutd y lnts, dscrbd as {,, }, {,, }, {,, } ( N. To convrt C th nto bnary strngs, thy can ploy, for xapl, rvrs rconclaton. ob frst convrts C C ach C nto GF( l usng th slcd bnary functon S( x = ( S( x,, Sl ( x and consquntly obtans a st of l -bt strngs s = S ( C ( l. Gnrally, thr s a hgh probablty that lc can corrspondngly ap hr lnts nto GF( l bt by bt through usng prvously appd and corrctd bts. For hgh ffcncy, larg corrcton blocs should b usd for corrcton nforaton xchang. In th scond stp, ob sts bnary T strngs S = ( S ( C,, S ( C ( l as a corrcton bloc to corrct th rrors of th th bt, whr dnots th sz of th th corrcton bloc. Thrdly, ob snds th rror-corrcton nforaton T R of th bnary strng S = ( S ( C,, S ( C to lc va a publc authntc channl. Thn accordng to R, lc aps ach of hr lnts nto bts usng th optzd stators s ( C = S ( C, S ' ( C,, S ' ( C ( > and

3 s ( C = S ( C ( = whr dnots th th bt, ( l and S ( 0 < < dnots ' lc s corrctd bts fro S = ( s,, s T va R by prvous stps. Thn lc corrcts S usng R. Howvr, Ev also attpts to us C E and R to gt s, and hr fnal stats ar E E S. For th purpos of scurty, w rqur S' = S S. For convnnc, lt M = ( M, and dnot th corrcton,, M T M = ( M,, M T M = ( M,, M T unts S, S and Ev s uncorrctd strng S, rspctvly. E E E E In th followng t s assud that th channl s bnary sytrc. W dfn Pr(, and a b= M M = b Pr( M E M Pr( a = M M as th bt rror rats. E Suppos R s th rror corrcton nforaton that ob snt to lc va th publc channl and nrc n + dnots th axu rrors that can b corrctd va R. Thn, only rrors that ar lss than or or than rc rc th lnar party-chc codng [6]. n can b corrctd usng R, whch s th gnral cas for ost of Lt n dnot th nubr of rrors n M rlatv to M. Provdd ach rror n M s ndpndnt, thn n obys th bnoal dstrbuton probablty:! n n n n pn ( = ( = C ( n!( n! n ( n n W ntroduc th paratr and st n=. Usng th Strlng forula n! n π n. W can gt:! pn ( = C ( = ( n!( n! n n n n n π n πn n π n n n n ( n [( ( ] ( ( [ ( ] ( ( π = ( π π = ( ( π ( ( ( ( Thn th dstrbuton of can b wrttn as:

4 p( ( ( π( ( ( Lws, th dstrbuton p ( btwn M and E M s p ( ( ( π( ( (3 Th llustratons of p( and p ' ( ar shown n fgur. Fg : llustraton of p( and p'( ( = 00, dot ln -- p( ; sold ln - p'( = 0. = 0. a b b Lt rc n rc =, and dfn β as: rc rc ( β = p( d+ p( d ( ( d 0 rc 0 (4 π( Norally, p( d s uch sallr than rc rc p( d and can b nglctd. It s asy to 0 s that β s ust th probablty that lc obtans ob s M corrctly aftr rror corrcton. Slarly, th probablty of Ev obtanng M s rc rc ( α p ( d = ( ( d 0 0 π( (5 For scurty, β and α 0 ar ncssary. For convnnc, w us a Gaussan approxaton. Th varanc of p( n s ( and t s an s a b. Thn,

5 through th cntral-lt thor, Eqs. ( and (3 bco, rspctvly: ( p( xp[ ] π ( ( ( p '( xp[ ] π ( ( (6 (7 Fro Eqs. (4 and (6 w can gt: ( β xp[ ] d rc π ( ( ( = xp[ ] d ( rc π ( ( ( = xp[ ] d( ( rc π ( ( y xp( = ( rc π ( dy ( Thus = Q ( β (8 ( rc whr Q(x s th soluton of th quaton y dy x Q x =. ( π Slarly, fro Eqs. (5 and (7 w obtan ( = Q ( α ( rc (9 For th scurty of practcal CVQKD, < rc< ust b satsfd. Fro Eqs. (8 and (9 w hav > Q Q ( ( ax[ ( α, ( β ] (0 Eqs. (8, (9 and (0 show us th basc rqurnts of rconclaton. Ths ans that should b largr than th largst of ts valus n th abov thr quatons. In fact, th prforanc of actual rror corrctng algorth s only dtrnd by th nubr of rrors rathr than th ERs. For short, t ay b possbl for Ev to obtan a raw y wth fwr rrors than lc bcaus of th statstcal fluctuatons, although th ERs btwn hr and ob ar hghr than that btwn lc and ob. Consquntly, th sz of ach corrcton

6 bloc for scur CVQKD should b suffcntly long, and ths ay b a crucal ltaton for practcal systs. If undr th condton of asytry or whn th bts ar corrlatd wth ach othr, w can us th quvalnt avrag ERs to calculat th nu ltatons. W dfn th bt rror rats of th th appd bt for lc and Ev as and, rspctvly. In gnral, th condton n[ h ( h ( ] =Δ I < Δ I= ( I I E s satsfd, whr ΔI s ntroducd to dnot th scrt y rat that can b dstlld fro th th bt, as Δ I s th scrt bt rat dfnd n Rf. [] and ( h s th Shannon ntropy dfnd H ( = log ( log ( ( It s asy to s fro Eq. ( that dh ( = log d ( Thrfor, whn Δ s vry sall, t follows that ( log =Δ I (3 Usng Eq. (0, w obtan > Q α Q β ( (log ( ax[ (, ( ] ΔI (4 whr dnots th nu sz of th corrcton bloc for th th bt. It s asy to undrstand fro rfrnc [5] that whn th ER of on bt s about 0., th contrbuton fro ths bt Δ I ay b largr than that of othrs. Furthror, Q ( α s a slowly varyng functon of α, so α and β do not affct th staton rsults sgnfcantly. Eq.(4shows that s anly dtrnd by Δ I, so w only gv so quanttatv stats of th varaton of wth Δ I n th followng Tabl Ⅰ. Lngth of th Thortcal axu Maxu scrt y Mnu sz of quantu channl ( y rat ΔI [] (bt rat Δ (bt I corrcton bloc ^0 3

7 ^ ^0 5 Tabl Ⅰ: Mnu sz for dffrnt lngths of th quantu channl, whn 0 7 β =, Q ( β 7, th ER s 0., and th quantu channl attnuaton coffcnt s 0.d/. In concluson, w hav dscussd n dtal th crucal rqurnts of practcal rconclaton for scur CVQKD. W show that th sz of ach rror-corrcton bloc should b largr than th nu rqurnt to guarant th fasblty of CVQKD. It s also shown that th longr th lngth of th quantu channl, th largr ust b th sz of th rror-corrcton bloc. In addton, t should b notcd that n th rfrnc [] thy choos th bloc sz as for 3d loss, whch s uch largr than th gvn nus. cnowldgnt: Spcal thans ar gvn to Profssor Wu Lng-n for hr grat hlp n th syntax. W also gv sncr thans to P. Grangr who has pontd so probls n our prvous wors. Ths wor s supportd by Scnc Foundaton of Chna undr Grant No and No ; Th Knowldg Innovaton Proct of Chns cady of Scncs;. Rfrncs:. D. Gottsan and J. Prsll, Scur quantu y dstrbuton usng squzd stats. Phys. Rv. 63, 0309 (00. M. Hllry, Quantu cryptography wth squzd stats. Phys. Rv. 6, 0309 ( Ch. Slbrhorn, N. Korolova and G. Luchs, Quantu y dstrbuton wth brght ntangld bas. Phys. Rv. Ltt. 88, 6790 (00 4. M. D. Rd, Quantu cryptography wth prdtrnd y, usng contnuous-varabl Enstn-Podolsy-Rosn corrlatons, Phys. Rv., 6, ( F. Grosshans, and P. Grangr, Contnuous varabl quantu cryptography usng cohrnt stats. Phys. Rv. Ltt. 88, (00 6. S. Iblsdr, G. Van. ssch and N. J. Crf, Scurty of quantu y dstrbuton wth cohrnt stats and hoodyn dtcton, Phys. Rv. Ltt, 93, 7050 ( R. Na and T. Hrano, Practcal ltatons for contnuous-varabl quantu cryptography usng cohrnt stats, Phys. Rv. Ltt, 9, 790 (004

8 8. T. Hrano, H. Yaanaa, M. shaga, T. Konsh and R. Na, Quantu cryptography usng pulsd hoodyn dtcton, Phys. Rv., 68, 0433 ( Ch. Slbrhorn, T. C. Ralph, N. Lutnhaus and G. Luchs, Contnuous varabl quantu cryptography: batng th 3d loss lt, Phys. Rv. Ltt, 89, 6790 (00 0. C. Wdbroo,. M. Lanc, W. P. own, T. Syul, T. C. Ralph and P. K. La, Quantu cryptography wthout swtchng, Phys. Rv. Ltt, 93, (004. F. Grosshans, G. Van. ssch, J. Wngr,, R. rourl, N. J. Crf and P. Grangr, Quantu y dstrbuton usng Gaussan-odulatd cohrnt stats, Natur, 4, 38 (003. M. Lgr, H. Zbndn and N. Gsn, Iplntaton of contnuous varabl quantu cryptography n optcal fbrs usng a go-&-rturn confguraton, arxv:quant-ph/053, ( J. Lodwyc, T. Dbusschrt, R. T. rour and P. Grangr, Controllng xcss nos n fbr-optcs contnuous-varabl quantu y dstrbuton, Phys. Rv., 7, ( F. Grosshans and P. Grangr, Rvrs rconclaton protocols for quantu cryptography wth contnuous varabls, arxv: quant-ph/0047, ( G. Van. ssch, J. Cardnal and N. J. Crf, Rconclaton of a quantu-dstrbutd Gaussan y. IEEE Trans. Infor. Thory, 50, 394 ( S. S. Pradhan and K. Rachandran, Dstrbutd sourc codng usng syndros (DISCUS: dsgn and constructon, IEEE Transactons on Inforaton Thory, 49(3, 66 (003

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

More information

A Probabilistic Characterization of Simulation Model Uncertainties

A Probabilistic Characterization of Simulation Model Uncertainties A Proalstc Charactrzaton of Sulaton Modl Uncrtants Vctor Ontvros Mohaad Modarrs Cntr for Rsk and Rlalty Unvrsty of Maryland 1 Introducton Thr s uncrtanty n odl prdctons as wll as uncrtanty n xprnts Th

More information

Review - Probabilistic Classification

Review - Probabilistic Classification Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw

More information

Jones vector & matrices

Jones vector & matrices Jons vctor & matrcs PY3 Colást na hollscol Corcagh, Ér Unvrst Collg Cork, Irland Dpartmnt of Phscs Matr tratmnt of polarzaton Consdr a lght ra wth an nstantanous -vctor as shown k, t ˆ k, t ˆ k t, o o

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

UNIT 8 TWO-WAY ANOVA WITH m OBSERVATIONS PER CELL

UNIT 8 TWO-WAY ANOVA WITH m OBSERVATIONS PER CELL UNIT 8 TWO-WAY ANOVA WITH OBSERVATIONS PER CELL Two-Way Anova wth Obsrvatons Pr Cll Structur 81 Introducton Obctvs 8 ANOVA Modl for Two-way Classfd Data wth Obsrvatons r Cll 83 Basc Assutons 84 Estaton

More information

Basic Electrical Engineering for Welding [ ] --- Introduction ---

Basic Electrical Engineering for Welding [ ] --- Introduction --- Basc Elctrcal Engnrng for Wldng [] --- Introducton --- akayosh OHJI Profssor Ertus, Osaka Unrsty Dr. of Engnrng VIUAL WELD CO.,LD t-ohj@alc.co.jp OK 15 Ex. Basc A.C. crcut h fgurs n A-group show thr typcal

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

Outlier-tolerant parameter estimation

Outlier-tolerant parameter estimation Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln

More information

te Finance (4th Edition), July 2017.

te Finance (4th Edition), July 2017. Appndx Chaptr. Tchncal Background Gnral Mathmatcal and Statstcal Background Fndng a bas: 3 2 = 9 3 = 9 1 /2 x a = b x = b 1/a A powr of 1 / 2 s also quvalnt to th squar root opraton. Fndng an xponnt: 3

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

CHAPTER 33: PARTICLE PHYSICS

CHAPTER 33: PARTICLE PHYSICS Collg Physcs Studnt s Manual Chaptr 33 CHAPTER 33: PARTICLE PHYSICS 33. THE FOUR BASIC FORCES 4. (a) Fnd th rato of th strngths of th wak and lctromagntc forcs undr ordnary crcumstancs. (b) What dos that

More information

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University xtrnal quvalnt 5 Analyss of Powr Systms Chn-Chng Lu, ong Dstngushd Profssor Washngton Stat Unvrsty XTRNAL UALNT ach powr systm (ara) s part of an ntrconnctd systm. Montorng dvcs ar nstalld and data ar

More information

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm . Grundlgnd Vrfahrn zur Übrtragung dgtalr Sgnal (Zusammnfassung) wt Dc. 5 Transmsson of Dgtal Sourc Sgnals Sourc COD SC COD MOD MOD CC dg RF s rado transmsson mdum Snk DC SC DC CC DM dg DM RF g physcal

More information

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline Introucton to Ornar Dffrntal Equatons Sptmbr 7, 7 Introucton to Ornar Dffrntal Equatons Larr artto Mchancal Engnrng AB Smnar n Engnrng Analss Sptmbr 7, 7 Outln Rvw numrcal solutons Bascs of ffrntal quatons

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

NATURAL VIBRATION ANALYSIS OF CONTINUOUS HORIZONTALLY CURVED GIRDER BRIDGES USING DQEM *

NATURAL VIBRATION ANALYSIS OF CONTINUOUS HORIZONTALLY CURVED GIRDER BRIDGES USING DQEM * Th 4 th World Confrnc on Earthquak Engnrng Octobr -7, 008, Bng, Chna TURL VIBRTIO LYSIS OF COTIUOUS HORIZOTLLY CURVED GIRDER BRIDGES USIG DQEM * LI Hongng, WG Tong and WEI Shuangk 3 Profssor, Collg of

More information

Epistemic Foundations of Game Theory. Lecture 1

Epistemic Foundations of Game Theory. Lecture 1 Royal Nthrlands cadmy of rts and Scncs (KNW) Mastr Class mstrdam, Fbruary 8th, 2007 Epstmc Foundatons of Gam Thory Lctur Gacomo onanno (http://www.con.ucdavs.du/faculty/bonanno/) QUESTION: What stratgs

More information

Analyzing Frequencies

Analyzing Frequencies Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5

More information

FREE VIBRATION ANALYSIS OF FUNCTIONALLY GRADED BEAMS

FREE VIBRATION ANALYSIS OF FUNCTIONALLY GRADED BEAMS Journal of Appl Mathatcs an Coputatonal Mchancs, (), 9- FREE VIBRATION ANAYSIS OF FNCTIONAY GRADED BEAMS Stansław Kukla, Jowta Rychlwska Insttut of Mathatcs, Czstochowa nvrsty of Tchnology Czstochowa,

More information

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation Lctur Rlc nutrnos mpratur at nutrno dcoupln and today Effctv dnracy factor Nutrno mass lmts Saha quaton Physcal Cosmoloy Lnt 005 Rlc Nutrnos Nutrnos ar wakly ntractn partcls (lptons),,,,,,, typcal ractons

More information

4D SIMPLICIAL QUANTUM GRAVITY

4D SIMPLICIAL QUANTUM GRAVITY T.YUKAWA and S.HORATA Soknda/KEK D SIMPLICIAL QUATUM GRAITY Plan of th talk Rvw of th D slcal quantu gravty Rvw of nurcal thods urcal rsult and dscusson Whr dos th slcal quantu gravty stand? In short dstanc

More information

Guo, James C.Y. (1998). "Overland Flow on a Pervious Surface," IWRA International J. of Water, Vol 23, No 2, June.

Guo, James C.Y. (1998). Overland Flow on a Pervious Surface, IWRA International J. of Water, Vol 23, No 2, June. Guo, Jams C.Y. (006). Knmatc Wav Unt Hyrograph for Storm Watr Prctons, Vol 3, No. 4, ASCE J. of Irrgaton an Dranag Engnrng, July/August. Guo, Jams C.Y. (998). "Ovrlan Flow on a Prvous Surfac," IWRA Intrnatonal

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

HORIZONTAL IMPEDANCE FUNCTION OF SINGLE PILE IN SOIL LAYER WITH VARIABLE PROPERTIES

HORIZONTAL IMPEDANCE FUNCTION OF SINGLE PILE IN SOIL LAYER WITH VARIABLE PROPERTIES 13 th World Confrnc on Earthquak Engnrng Vancouvr, B.C., Canada August 1-6, 4 Papr No. 485 ORIZONTAL IMPEDANCE FUNCTION OF SINGLE PILE IN SOIL LAYER WIT VARIABLE PROPERTIES Mngln Lou 1 and Wnan Wang Abstract:

More information

Excess Error, Approximation Error, and Estimation Error

Excess Error, Approximation Error, and Estimation Error E0 370 Statstcal Learnng Theory Lecture 10 Sep 15, 011 Excess Error, Approxaton Error, and Estaton Error Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton So far, we have consdered the fnte saple

More information

Physics 256: Lecture 2. Physics

Physics 256: Lecture 2. Physics Physcs 56: Lctur Intro to Quantum Physcs Agnda for Today Complx Numbrs Intrfrnc of lght Intrfrnc Two slt ntrfrnc Dffracton Sngl slt dffracton Physcs 01: Lctur 1, Pg 1 Constructv Intrfrnc Ths wll occur

More information

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d)

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d) Ερωτήσεις και ασκησεις Κεφ 0 (για μόρια ΠΑΡΑΔΟΣΗ 9//06 Th coffcnt A of th van r Waals ntracton s: (a A r r / ( r r ( (c a a a a A r r / ( r r ( a a a a A r r / ( r r a a a a A r r / ( r r 4 a a a a 0 Th

More information

Lecture 3: Phasor notation, Transfer Functions. Context

Lecture 3: Phasor notation, Transfer Functions. Context EECS 5 Fall 4, ctur 3 ctur 3: Phasor notaton, Transfr Functons EECS 5 Fall 3, ctur 3 Contxt In th last lctur, w dscussd: how to convrt a lnar crcut nto a st of dffrntal quatons, How to convrt th st of

More information

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr

More information

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1)

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1) Conrgnc Thors for Two Itrt Mthods A sttonry trt thod for solng th lnr syst: Ax = b (.) ploys n trton trx B nd constnt ctor c so tht for gn strtng stt x of x for = 2... x Bx c + = +. (.2) For such n trton

More information

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION*

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* Dr. G.S. Davd Sam Jayakumar, Assstant Profssor, Jamal Insttut of Managmnt, Jamal Mohamd Collg, Truchraall 620 020, South Inda,

More information

Group Codes Define Over Dihedral Groups of Small Order

Group Codes Define Over Dihedral Groups of Small Order Malaysan Journal of Mathmatcal Scncs 7(S): 0- (0) Spcal Issu: Th rd Intrnatonal Confrnc on Cryptology & Computr Scurty 0 (CRYPTOLOGY0) MALAYSIA JOURAL OF MATHEMATICAL SCIECES Journal hompag: http://nspm.upm.du.my/ournal

More information

Lecture 3: Shannon s Theorem

Lecture 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

Fakultät III Wirtschaftswissenschaften Univ.-Prof. Dr. Jan Franke-Viebach

Fakultät III Wirtschaftswissenschaften Univ.-Prof. Dr. Jan Franke-Viebach Unvrstät Sgn Fakultät III Wrtschaftswssnschaftn Unv.-rof. Dr. Jan Frank-Vbach Exam Intrnatonal Fnancal Markts Summr Smstr 206 (2 nd Exam rod) Avalabl tm: 45 mnuts Soluton For your attnton:. las do not

More information

perm4 A cnt 0 for for if A i 1 A i cnt cnt 1 cnt i j. j k. k l. i k. j l. i l

perm4 A cnt 0 for for if A i 1 A i cnt cnt 1 cnt i j. j k. k l. i k. j l. i l h 4D, 4th Rank, Antisytric nsor and th 4D Equivalnt to th Cross Product or Mor Fun with nsors!!! Richard R Shiffan Digital Graphics Assoc 8 Dunkirk Av LA, Ca 95 rrs@isidu his docunt dscribs th four dinsional

More information

A NON-LINEAR MODEL FOR STUDYING THE MOTION OF A HUMAN BODY. Piteşti, , Romania 2 Department of Automotive, University of Piteşti

A NON-LINEAR MODEL FOR STUDYING THE MOTION OF A HUMAN BODY. Piteşti, , Romania 2 Department of Automotive, University of Piteşti ICSV Carns ustrala 9- July 7 NON-LINER MOEL FOR STUYING THE MOTION OF HUMN OY Ncola-oru Stănscu Marna Pandra nl Popa Sorn Il Ştfan-Lucan Tabacu partnt of ppld Mchancs Unvrsty of Ptşt Ptşt 7 Roana partnt

More information

Hostel Occupancy Survey (YHOS) Methodology

Hostel Occupancy Survey (YHOS) Methodology Hostl Occupancy Survy (HOS) Mthodology March 205 Indx rsntaton 3 2 Obctvs 4 3 Statstcal unt 5 4 Survy scop 6 5 fnton of varabls 7 6 Survy frawork and sapl dsgn 9 7 Estators 0 8 Inforaton collcton 3 9 Coffcnts

More information

Journal of Theoretical and Applied Information Technology 10 th January Vol. 47 No JATIT & LLS. All rights reserved.

Journal of Theoretical and Applied Information Technology 10 th January Vol. 47 No JATIT & LLS. All rights reserved. Journal o Thortcal and Appld Inormaton Tchnology th January 3. Vol. 47 No. 5-3 JATIT & LLS. All rghts rsrvd. ISSN: 99-8645 www.att.org E-ISSN: 87-395 RESEARCH ON PROPERTIES OF E-PARTIAL DERIVATIVE OF LOGIC

More information

Phys 2310 Fri. Nov. 7, 2014 Today s Topics. Begin Chapter 15: The Superposition of Waves Reading for Next Time

Phys 2310 Fri. Nov. 7, 2014 Today s Topics. Begin Chapter 15: The Superposition of Waves Reading for Next Time Phys 3 Fr. Nov. 7, 4 Today s Topcs Bgn Chaptr 5: Th Suprposton of Wavs Radng for Nxt T Radng ths Wk By Frday: Bgn Ch. 5 (5. 5.3 Addton of Wavs of th Sa Frquncy, Addton of Wavs of Dffrnt Frquncy, Rad Supplntary

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

More information

ME 200 Thermodynamics I Spring 2014 Examination 3 Thu 4/10/14 6:30 7:30 PM WTHR 200, CL50 224, PHY 112 LAST NAME FIRST NAME

ME 200 Thermodynamics I Spring 2014 Examination 3 Thu 4/10/14 6:30 7:30 PM WTHR 200, CL50 224, PHY 112 LAST NAME FIRST NAME M 00 hrodynac Sprng 014 xanaton 3 hu 4/10/14 6:30 7:30 PM WHR 00, CL50 4, PHY 11 Crcl your dvon: PHY 11 WHR 00 WHR 00 CL50 4 CL50 4 PHY 11 7:30 Joglkar 9:30 Wagrn 10:30 Gor 1:30 Chn :30 Woodland 4:30 Srcar

More information

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors Dfnton Th nvr of an n by n atrx A an n by n atrx B whr, Not: nar Algbra Matrx Invron atrc on t hav an nvr. If a atrx ha an nvr, thn t call. Proprt of Matrx Invr. If A an nvrtbl atrx thn t nvr unqu.. (A

More information

Fakultät III Univ.-Prof. Dr. Jan Franke-Viebach

Fakultät III Univ.-Prof. Dr. Jan Franke-Viebach Unv.Prof. r. J. FrankVbach WS 067: Intrnatonal Economcs ( st xam prod) Unvrstät Sgn Fakultät III Unv.Prof. r. Jan FrankVbach Exam Intrnatonal Economcs Wntr Smstr 067 ( st Exam Prod) Avalabl tm: 60 mnuts

More information

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12 EEC 686/785 Modlng & Prformanc Evaluaton of Computr Systms Lctur Dpartmnt of Elctrcal and Computr Engnrng Clvland Stat Unvrsty wnbng@.org (basd on Dr. Ra Jan s lctur nots) Outln Rvw of lctur k r Factoral

More information

On Properties of the difference between two modified C p statistics in the nested multivariate linear regression models

On Properties of the difference between two modified C p statistics in the nested multivariate linear regression models Global Journal o Pur Ald Mathatcs. ISSN 0973-1768 Volu 1, Nubr 1 (016),. 481-491 Rsarch Inda Publcatons htt://www.rublcaton.co On Prorts o th drnc btwn two odd C statstcs n th nstd ultvarat lnar rgrsson

More information

CS 491 G Combinatorial Optimization

CS 491 G Combinatorial Optimization CS 49 G Cobinatorial Optiization Lctur Nots Junhui Jia. Maiu Flow Probls Now lt us iscuss or tails on aiu low probls. Thor. A asibl low is aiu i an only i thr is no -augnting path. Proo: Lt P = A asibl

More information

Fermi-Dirac statistics

Fermi-Dirac statistics UCC/Physcs/MK/EM/October 8, 205 Fer-Drac statstcs Fer-Drac dstrbuton Matter partcles that are eleentary ostly have a type of angular oentu called spn. hese partcles are known to have a agnetc oent whch

More information

Problem Set 4 Solutions Distributed: February 26, 2016 Due: March 4, 2016

Problem Set 4 Solutions Distributed: February 26, 2016 Due: March 4, 2016 Probl St 4 Solutions Distributd: Fbruary 6, 06 Du: March 4, 06 McQuarri Probls 5-9 Ovrlay th two plots using Excl or Mathatica. S additional conts blow. Th final rsult of Exapl 5-3 dfins th forc constant

More information

Folding of Regular CW-Complexes

Folding of Regular CW-Complexes Ald Mathmatcal Scncs, Vol. 6,, no. 83, 437-446 Foldng of Rgular CW-Comlxs E. M. El-Kholy and S N. Daoud,3. Dartmnt of Mathmatcs, Faculty of Scnc Tanta Unvrsty,Tanta,Egyt. Dartmnt of Mathmatcs, Faculty

More information

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13)

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13) Pag- Econ7 Appld Economtrcs Topc : Dummy Dpndnt Varabl (Studnmund, Chaptr 3) I. Th Lnar Probablty Modl Suppos w hav a cross scton of 8-24 yar-olds. W spcfy a smpl 2-varabl rgrsson modl. Th probablty of

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More 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

Radial Cataphoresis in Hg-Ar Fluorescent Lamp Discharges at High Power Density

Radial Cataphoresis in Hg-Ar Fluorescent Lamp Discharges at High Power Density [NWP.19] Radal Cataphorss n Hg-Ar Fluorscnt Lamp schargs at Hgh Powr nsty Y. Aura, G. A. Bonvallt, J. E. Lawlr Unv. of Wsconsn-Madson, Physcs pt. ABSTRACT Radal cataphorss s a procss n whch th lowr onzaton

More information

Calculus Revision A2 Level

Calculus Revision A2 Level alculus Rvision A Lvl Tabl of drivativs a n sin cos tan d an sc n cos sin Fro AS * NB sc cos sc cos hain rul othrwis known as th function of a function or coposit rul. d d Eapl (i) (ii) Obtain th drivativ

More information

GPC From PeakSimple Data Acquisition

GPC From PeakSimple Data Acquisition GPC From PakSmpl Data Acquston Introducton Th follong s an outln of ho PakSmpl data acquston softar/hardar can b usd to acqur and analyz (n conjuncton th an approprat spradsht) gl prmaton chromatography

More information

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION CHAPTER 7d. DIFFERENTIATION AND INTEGRATION A. J. Clark School o Engnrng Dpartmnt o Cvl and Envronmntal Engnrng by Dr. Ibrahm A. Assakka Sprng ENCE - Computaton Mthods n Cvl Engnrng II Dpartmnt o Cvl and

More information

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS MATEMATICA MONTISNIRI Vol XL (2017) MATEMATICS ON TE COMPLEXITY OF K-STEP AN K-OP OMINATIN SETS IN RAPS M FARAI JALALVAN AN N JAFARI RA partmnt of Mathmatcs Shahrood Unrsty of Tchnology Shahrood Iran Emals:

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

The Fourier Transform

The Fourier Transform /9/ Th ourr Transform Jan Baptst Josph ourr 768-83 Effcnt Data Rprsntaton Data can b rprsntd n many ways. Advantag usng an approprat rprsntaton. Eampls: osy ponts along a ln Color spac rd/grn/blu v.s.

More information

1 Input-Output Stability

1 Input-Output Stability Inut-Outut Stability Inut-outut stability analysis allows us to analyz th stability of a givn syst without knowing th intrnal stat x of th syst. Bfor going forward, w hav to introduc so inut-outut athatical

More information

Elements of Statistical Thermodynamics

Elements of Statistical Thermodynamics 24 Elmnts of Statistical Thrmodynamics Statistical thrmodynamics is a branch of knowldg that has its own postulats and tchniqus. W do not attmpt to giv hr vn an introduction to th fild. In this chaptr,

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

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

More information

Comparative Analysis of Error Correcting Codes for Noisy Channel

Comparative Analysis of Error Correcting Codes for Noisy Channel Intrnatonal Journal of Appld Inforaton ysts (IJAI) IN : 49-0868 Volu 3 No.5, July 0 www.jas.org Coparat Analyss of Error Corrctng Cods for Nosy Channl ukhjt Kaur Loly Profssonal Unrsty G.. Road, Phagwara,

More information

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time. Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss

More information

Langmuir 1994,lO)

Langmuir 1994,lO) Langur 994,lO) 99339 993 lctrcal Doubl Layr ntracton btwn Dsslar Sphrcal ollodal Partcls and btwn a Sphr and a Plat: Th Lnarzd PossonBoltzann Thory Stvn L. arn,* Drk Y.. han, and Jas S. Gunnng Dpartnt

More information

Phys 774: Nonlinear Spectroscopy: SHG and Raman Scattering

Phys 774: Nonlinear Spectroscopy: SHG and Raman Scattering Last Lcturs: Polaraton of Elctromagntc Wavs Phys 774: Nonlnar Spctroscopy: SHG and Scattrng Gnral consdraton of polaraton Jons Formalsm How Polarrs work Mullr matrcs Stoks paramtrs Poncar sphr Fall 7 Polaraton

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

Naresuan University Journal: Science and Technology 2018; (26)1

Naresuan University Journal: Science and Technology 2018; (26)1 Narsuan Unvrsty Journal: Scnc and Tchnology 018; (6)1 Th Dvlopmnt o a Corrcton Mthod or Ensurng a Contnuty Valu o Th Ch-squar Tst wth a Small Expctd Cll Frquncy Kajta Matchma 1 *, Jumlong Vongprasrt and

More information

BIO Lab 2: TWO-LEVEL NORMAL MODELS with school children popularity data

BIO Lab 2: TWO-LEVEL NORMAL MODELS with school children popularity data Lab : TWO-LEVEL NORMAL MODELS wth school chldren popularty data Purpose: Introduce basc two-level models for normally dstrbuted responses usng STATA. In partcular, we dscuss Random ntercept models wthout

More information

Α complete processing methodology for 3D monitoring using GNSS receivers

Α complete processing methodology for 3D monitoring using GNSS receivers 7-5-5 NATIONA TECHNICA UNIVERSITY OF ATHENS SCHOO OF RURA AND SURVEYING ENGINEERING DEPARTMENT OF TOPOGRAPHY AORATORY OF GENERA GEODESY Α complt procssng mthodology for D montorng usng GNSS rcvrs Gorg

More information

Discrete Shells Simulation

Discrete Shells Simulation Dscrt Shlls Smulaton Xaofng M hs proct s an mplmntaton of Grnspun s dscrt shlls, th modl of whch s govrnd by nonlnar mmbran and flxural nrgs. hs nrgs masur dffrncs btwns th undformd confguraton and th

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

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

At the end of this lesson, the students should be able to understand:

At the end of this lesson, the students should be able to understand: Instructional Objctivs: At th nd of this lsson, th studnts should b abl to undrstand: Dsign thod for variabl load Equivalnt strss on shaft Dsign basd on stiffnss and torsional rigidit Critical spd of shaft

More information

XII.3 The EM (Expectation-Maximization) Algorithm

XII.3 The EM (Expectation-Maximization) Algorithm XII.3 The EM (Expectaton-Maxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles

More information

ECE507 - Plasma Physics and Applications

ECE507 - Plasma Physics and Applications ECE57 - Plasa Physcs and Applcatons Lctur Prof. Jorg Rocca and Dr. Frnando Toasl Dpartnt of Elctrcal and Coputr Engnrng Introducton: What s a plasa? A quas-nutral collcton of chargd (and nutral) partcls

More information

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS Darusz Bskup 1. Introducton The paper presents a nonparaetrc procedure for estaton of an unknown functon f n the regresson odel y = f x + ε = N. (1) (

More information

Hardy-Littlewood Conjecture and Exceptional real Zero. JinHua Fei. ChangLing Company of Electronic Technology Baoji Shannxi P.R.

Hardy-Littlewood Conjecture and Exceptional real Zero. JinHua Fei. ChangLing Company of Electronic Technology Baoji Shannxi P.R. Hardy-Littlwood Conjctur and Excptional ral Zro JinHua Fi ChangLing Company of Elctronic Tchnology Baoji Shannxi P.R.China E-mail: fijinhuayoujian@msn.com Abstract. In this papr, w assum that Hardy-Littlwood

More information

Electrochemical Equilibrium Electromotive Force. Relation between chemical and electric driving forces

Electrochemical Equilibrium Electromotive Force. Relation between chemical and electric driving forces C465/865, 26-3, Lctur 7, 2 th Sp., 26 lctrochmcal qulbrum lctromotv Forc Rlaton btwn chmcal and lctrc drvng forcs lctrochmcal systm at constant T and p: consdr G Consdr lctrochmcal racton (nvolvng transfr

More information

Maxwellian Collisions

Maxwellian Collisions Maxwllian Collisions Maxwll ralizd arly on that th particular typ of collision in which th cross-sction varis at Q rs 1/g offrs drastic siplifications. Intrstingly, this bhavior is physically corrct for

More information

Rayleigh-Schrödinger Perturbation Theory

Rayleigh-Schrödinger Perturbation Theory Raylgh-Schrödngr Prturbaton Thory Introducton Consdr so physcal syst for whch w had alrady solvd th Schrödngr quaton copltly but thn wshd to prfor anothr calculaton on th sa physcal syst whch has bn slghtly

More information

VII. Quantum Entanglement

VII. Quantum Entanglement VII. Quantum Entanglmnt Quantum ntanglmnt is a uniqu stat of quantum suprposition. It has bn studid mainly from a scintific intrst as an vidnc of quantum mchanics. Rcntly, it is also bing studid as a basic

More information

Phy213: General Physics III 4/10/2008 Chapter 22 Worksheet 1. d = 0.1 m

Phy213: General Physics III 4/10/2008 Chapter 22 Worksheet 1. d = 0.1 m hy3: Gnral hyscs III 4/0/008 haptr Worksht lctrc Flds: onsdr a fxd pont charg of 0 µ (q ) q = 0 µ d = 0 a What s th agntud and drcton of th lctrc fld at a pont, a dstanc of 0? q = = 8x0 ˆ o d ˆ 6 N ( )

More information

Laboratory work # 8 (14) EXPERIMENTAL ESTIMATION OF CRITICAL STRESSES IN STRINGER UNDER COMPRESSION

Laboratory work # 8 (14) EXPERIMENTAL ESTIMATION OF CRITICAL STRESSES IN STRINGER UNDER COMPRESSION Laboratory wor # 8 (14) XPRIMNTAL STIMATION OF CRITICAL STRSSS IN STRINGR UNDR COMPRSSION At action of comprssing ffort on a bar (column, rod, and stringr) two inds of loss of stability ar possibl: 1)

More information

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added 4.3, 4.4 Phas Equlbrum Dtrmn th slops of th f lns Rlat p and at qulbrum btwn two phass ts consdr th Gbbs functon dg η + V Appls to a homognous systm An opn systm whr a nw phas may form or a nw componnt

More information

First order differential equation Linear equation; Method of integrating factors

First order differential equation Linear equation; Method of integrating factors First orr iffrntial quation Linar quation; Mtho of intgrating factors Exampl 1: Rwrit th lft han si as th rivativ of th prouct of y an som function by prouct rul irctly. Solving th first orr iffrntial

More information

A Self-adaptive open loop architecture for weak GNSS signal tracking

A Self-adaptive open loop architecture for weak GNSS signal tracking NTERNATONAL JOURNAL OF CRCUTS, SYSTEMS AND SGNAL PROCESSNG Volum 8, 014 A Slf-adaptv opn loop archtctur for wa GNSS sgnal tracng Ao Png, Gang Ou, Janghong Sh Abstract An FFT-basd opn loop carrr tracng

More information

[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then

[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then SYSTEM PERFORMANCE Lctur 0: Stady-tat Error Stady-tat Error Lctur 0: Stady-tat Error Dr.alyana Vluvolu Stady-tat rror can b found by applying th final valu thorm and i givn by lim ( t) lim E ( ) t 0 providd

More information

Logistic Regression I. HRP 261 2/10/ am

Logistic Regression I. HRP 261 2/10/ am Logstc Rgrsson I HRP 26 2/0/03 0- am Outln Introducton/rvw Th smplst logstc rgrsson from a 2x2 tabl llustrats how th math works Stp-by-stp xampls to b contnud nxt tm Dummy varabls Confoundng and ntracton

More information

arxiv: v1 [math.pr] 28 Jan 2019

arxiv: v1 [math.pr] 28 Jan 2019 CRAMÉR-TYPE MODERATE DEVIATION OF NORMAL APPROXIMATION FOR EXCHANGEABLE PAIRS arxv:190109526v1 [mathpr] 28 Jan 2019 ZHUO-SONG ZHANG Abstract In Stn s mthod, an xchangabl par approach s commonly usd to

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

1) They represent a continuum of energies (there is no energy quantization). where all values of p are allowed so there is a continuum of energies.

1) They represent a continuum of energies (there is no energy quantization). where all values of p are allowed so there is a continuum of energies. Unbound Stats OK, u untl now, w a dalt solly wt stats tat ar bound nsd a otntal wll. [Wll, ct for our tratnt of t fr artcl and w want to tat n nd r.] W want to now consdr wat ans f t artcl s unbound. Rbr

More information

Exercises for lectures 7 Steady state, tracking and disturbance rejection

Exercises for lectures 7 Steady state, tracking and disturbance rejection Exrc for lctur 7 Stady tat, tracng and dturbanc rjcton Martn Hromčí Automatc control 06-3-7 Frquncy rpon drvaton Automatcé řízní - Kybrnta a robota W lad a nuodal nput gnal to th nput of th ytm, gvn by

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

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