Lecture 08 Multiple View Geometry 2. Prof. Dr. Davide Scaramuzza

Size: px
Start display at page:

Download "Lecture 08 Multiple View Geometry 2. Prof. Dr. Davide Scaramuzza"

Transcription

1 Lctr 8 Mltpl V Gomtry Prof. Dr. Dad Scaramzza sdad@f.zh.ch

2 Cors opcs Prncpls of mag formaton Imag fltrng Fatr dtcton Mlt- gomtry 3D Rconstrcton Rcognton

3 Mltpl V Gomtry San Marco sqar, Vnc 4,79 mags, 4,55,57 ponts

4 Mltpl V Gomtry 3D rconstrcton from mltpl s: Assmptons: K, and R ar knon. Goal: Rcor th 3D strctr from mags P =? Strctr From Moton: Assmptons: non (K,, and R ar nknon). K, R, K, R, K, R, Goal: Rcor smltanosly 3D scn strctr and camra poss (p to scal) from mltpl mags P =? K, R, =? K, R, =? K, R, =?

5 R: Prspct Projcton MP p Z Y X R K P c O p X c C Z c Y c [R ] Extrnsc Paramtrs W Z Y X = P K Normalzd mag coordnats Prspct Projcton Eqaton

6 oday s otln Strctr from Moton

7 Problm formlaton: Gn n ponts n corrspondnc across to mags, {(, ), (, )}, smltanosly compt th 3D locaton P, th camra rlat-moton paramtrs (R, t), and camra ntrnsc K, that satsfy R, =? P =? C C Strctr from Moton (SFM) Z Y X I K Z Y X R K

8 Strctr from Moton (SFM) o arants xst: Uncalbratd camra(s) -> K s nknon Calbratd camra(s) -> K s knon P =? C R, =? C

9 Lt s stdy th cas n hch th camra(s) s «calbratd» For connnc, lt s s normalzd mag coordnats hs, ant to fnd R,, P that satsfy R, =? P =? C C Strctr from Moton (SFM) K Z Y X I Z Y X R

10 Scal Ambgty Wth a sngl camra, only kno th rlat scal No nformaton abot th mtrc scal

11 Scal Ambgty Wth a sngl camra, only kno th rlat scal No nformaton abot th mtrc scal If scal th ntr scn by som factor s, th projctons of th scn ponts n th mag rman xactly th sam:

12 Scal Ambgty In monoclar son, t s mpossbl to rcor th absolt scal of th scn! Stro son? hs, only 5 dgrs of frdom ar masrabl: 3 paramtrs to dscrb th rotaton paramtrs for th translaton p to a scal ( can only compt th drcton of translaton bt not ts lngth)

13 Strctr from Moton (SfM) Ho many knons and nknons? 4n knons: n corrspondncs; ach on (, ) and (, ), = n 5 + 3n nknons 5 for th moton p to a scal (rotaton-> 3, translaton->) 3n = nmbr of coordnats of th n 3D ponts Dos a solton xst? If and only f nmbr of ndpndnt qatons nmbr of nknons 4n 5 + 3n n 5

14 Cross Prodct (or Vctor Prodct) Vctor cross prodct taks to ctors and rtrns a thrd ctor that s prpndclar to both npts So hr, c s prpndclar to both a and b, hch mans th dot prodct = Also, rcall that th cross prodct of to paralll ctors = h cross prodct btn a and b can also b xprssd n matrx form as th prodct btn th sk-symmtrc matrx of a and a ctor b c b a c b c a b a b a ] [ z y x x y x z y z b b b a a a a a a

15 Eppolar Gomtry P p p p ppolar plan p n p = Rp p, p, ar coplanar: p n p ( ') p p ( ( Rp )) p ] R p p E p [ ppolar constrant E [ ] R ssntal matrx

16 Eppolar Gomtry p p Normalzd mag coordnats p E p Eppolar constrant or Longt-Hggns qaton E [ ] R Essntal matrx h Essntal Matrx can b comptd from 5 mag corrspondncs [Krppa, 93]. h mor ponts, th hghr accracy n prsnc of nos h Essntal Matrx can b dcomposd nto R and rcallng that For dstnct soltons for R and ar possbl. E [ ] R H. Chrstophr Longt-Hggns (Sptmbr 98). "A comptr algorthm for rconstrctng a scn from to projctons". Natr 93 (588): PDF.

17 Ho to compt th Essntal Matrx? h Essntal Matrx can b comptd from 5 mag corrspondncs [Krppa, 93]. Hor, ths solton s not smpl. It took almost on cntry ntl an ffcnt solton as fond! [Nstr, CVPR 4] h frst poplar solton ss 8 ponts and s calld 8-pont algorthm Longt Hggns. A comptr algorthm for rconstrctng a scn from to projctons. Natr (98)

18 h ght-pont algorthm p,,), (,,) p E p ( p Mnmz: Q 3 E 33 ndr th constrant E = Q (ths matrx s knon) E (ths matrx s nknon) For n = 8 ponts, a nq solton xsts f th ponts ar not coplanar. For n > 8 noncoplanar ponts, a lnar last-sqar solton s gn by th gnctor of Q corrspondng to ts smallst gnal (hch s th nt ctor that mnmzs Q E ). It can b don sng Snglar Val Dcomposton.

19 8-pont algorthm: Matlab cod fncton F = calbratd_ghtpont( p, p) p = p'; % 3xN ctor; ach colmn = [;;] p = p'; % 3xN ctor; ach colmn = [;;] Q = [p(:,).*p(:,),... p(:,).*p(:,),... p(:,3).*p(:,),... p(:,).*p(:,),... p(:,).*p(:,),... p(:,3).*p(:,),... p(:,).*p(:,3),... p(:,).*p(:,3),... p(:,3).*p(:,3) ] ; [U,S,V] = sd(q); F = V(:,9); F = rshap(v(:,9),3,3)';

20 h ght-pont algorthm Manng of th lnar last-sqar rror Usng th dfnton of dot prodct, t can b obsrd that N ( p E p ) : p Ep = p Ep cos (θ) It can b obsrd that ths prodct s non zro hn, p, p, and ar not coplanar.

21 h ght-pont algorthm Nonlnar approach: mnmz sm of sqard ppolar dstancs N d ( p, l) d ( p, l) P =? l E p p p l Ep C C

22 Problm th ght-pont algorthm

23 Problm th ght-pont algorthm Poor nmrcal condtonng Can b fxd by rscalng th data: Normalzd 8-pont algorthm [Hartly, 995]

24 Comparson of stmaton algorthms 8-pont Normalzd 8-pont Nonlnar last sqars Rprojcton rror.33 pxls.9 pxl.86 pxl Rprojcton rror.8 pxls.85 pxl.8 pxl

25 Extract R and from E (ths sld ll not b askd at th xam) Snglar Val Dcomposton Enforcng rank- constrant V U E V U ˆ z y x x y x z y z t t t t t t t t t t ˆ ˆ ˆ ˆ RK K R t K t V U R ˆ

26 4 possbl soltons of R and Only on solton hr ponts ar n front of both camras hs to s ar rotatd of 8

27 Strctr from Moton (SFM) o arants xst: Calbratd camra(s) -> K s knon Uss th Essntal Matrx Uncalbratd camra(s) -> K s nknon Uss th Fndamntal Matrx P =? C R, =? C

28 h Fndamntal Matrx Bfor, assmd to kno th camra ntrnsc paramtrs and sd normalzd mag coordnats E p E p F K E K - - K Fndamntal Matrx K K ] [ K ] [ K E K R F R E F

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

Static/Dynamic Deformation with Finite Element Method. Graphics & Media Lab Seoul National University

Static/Dynamic Deformation with Finite Element Method. Graphics & Media Lab Seoul National University Statc/Dynamc Dormaton wth Fnt Elmnt Mthod Graphcs & Mda Lab Sol Natonal Unvrsty Statc/Dynamc Dormaton Statc dormaton Dynamc dormaton ndormd shap ntrnal + = nrta = trnal dormd shap statc qlbrm dynamc qlbrm

More information

Math 656 March 10, 2011 Midterm Examination Solutions

Math 656 March 10, 2011 Midterm Examination Solutions Math 656 March 0, 0 Mdtrm Eamnaton Soltons (4pts Dr th prsson for snh (arcsnh sng th dfnton of snh w n trms of ponntals, and s t to fnd all als of snh (. Plot ths als as ponts n th compl plan. Mak sr or

More information

Fourier Transform: Overview. The Fourier Transform. Why Fourier Transform? What is FT? FT of a pulse function. FT maps a function to its frequencies

Fourier Transform: Overview. The Fourier Transform. Why Fourier Transform? What is FT? FT of a pulse function. FT maps a function to its frequencies .5.3..9.7.5.3. -. -.3 -.5.8.6.4. -. -.4 -.6 -.8 -. 8. 6. 4. -. -. 4 -. 6 -. 8 -.8.6.4. -. -.4 -.6 -.8 - orr Transform: Ovrvw Th orr Transform Wh T s sfl D T, DT, D DT T proprts Lnar ltrs Wh orr Transform?

More information

Solutions to selected problems from homework 1.

Solutions to selected problems from homework 1. Jan Hagemejer 1 Soltons to selected problems from homeork 1. Qeston 1 Let be a tlty fncton hch generates demand fncton xp, ) and ndrect tlty fncton vp, ). Let F : R R be a strctly ncreasng fncton. If the

More information

Reminder: Affine Transformations. Viewing and Projection. Shear Transformations. Transformation Matrices in OpenGL. Specification via Ratios

Reminder: Affine Transformations. Viewing and Projection. Shear Transformations. Transformation Matrices in OpenGL. Specification via Ratios CSCI 420 Comptr Graphics Lctr 5 Viwing and Projction Jrnj Barbic Univrsity o Sothrn Caliornia Shar Transormation Camra Positioning Simpl Paralll Projctions Simpl Prspctiv Projctions [Angl, Ch. 5] Rmindr:

More information

Variational Approach in FEM Part II

Variational Approach in FEM Part II COIUUM & FIIE ELEME MEHOD aratonal Approach n FEM Part II Prof. Song Jn Par Mchancal Engnrng, POSECH Fnt Elmnt Mthod vs. Ralgh-Rtz Mthod On wants to obtan an appromat solton to mnmz a fnctonal. On of th

More information

Finite Element Method for Turbomachinery Flows

Finite Element Method for Turbomachinery Flows SOCRATES Tachng Staff Moblty Program 2000-200 DMA-URLS Fnt Elmnt Mthod for Trbomachnry Flos Alssandro Corsn Dpartmnto d Mccanca Aronatca, Unvrsty of Rom "La Sapnza" BUDAPEST Unvrsty of Tchnology and Economcs

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

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

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

Structure from Motion. Forsyth&Ponce: Chap. 12 and 13 Szeliski: Chap. 7

Structure from Motion. Forsyth&Ponce: Chap. 12 and 13 Szeliski: Chap. 7 Structure from Moton Forsyth&once: Chap. 2 and 3 Szelsk: Chap. 7 Introducton to Structure from Moton Forsyth&once: Chap. 2 Szelsk: Chap. 7 Structure from Moton Intro he Reconstructon roblem p 3?? p p 2

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

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Numerical solutions of fuzzy partial differential equations and its applications in computational mechanics. Andrzej Pownuk1

Numerical solutions of fuzzy partial differential equations and its applications in computational mechanics. Andrzej Pownuk1 mrcal soltons of fzzy partal dffrntal qatons and ts applcatons n comptatonal mcancs Abstract Andrz Pownk Car of Tortcal Mcancs Dpartmnt of Cvl Engnrng Slsan Unvrsty of Tcnology Calclaton of t solton of

More information

Robust Small Area Estimation Using Penalized Spline Mixed Models

Robust Small Area Estimation Using Penalized Spline Mixed Models Scton on Sry Rsarch thods JS 9 Robst Small Ara Estmaton Usng Pnalzd Spln xd odls J. N.. Rao, S.. Snha and. Ronossadat School of athmatcs and Statstcs, Carlton Unrsty, Ottawa, Canada S 5B6 Abstract Small

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

Lecture 4 BLUP Breeding Values

Lecture 4 BLUP Breeding Values Lctr 4 BLUP Brdng Vals Glhrm J. M. Rosa Unvrsty of Wsconsn-Madson Mxd Modls n Qanttatv Gntcs SISG, Sattl 8 Sptmbr 8 Lnar Mxd Effcts Modl y = Xβ + + rsponss ncdnc matrcs fxd ffcts random ffcts rsdals G

More information

Math 656 Midterm Examination March 27, 2015 Prof. Victor Matveev

Math 656 Midterm Examination March 27, 2015 Prof. Victor Matveev Math 656 Mdtrm Examnatn March 7, 05 Prf. Vctr Matvv ) (4pts) Fnd all vals f n plar r artsan frm, and plt thm as pnts n th cmplx plan: (a) Snc n-th rt has xactly n vals, thr wll b xactly =6 vals, lyng n

More information

Multi-linear Systems and Invariant Theory. in the Context of Computer Vision and Graphics. Class 5: Self Calibration. CS329 Stanford University

Multi-linear Systems and Invariant Theory. in the Context of Computer Vision and Graphics. Class 5: Self Calibration. CS329 Stanford University Mlti-linar Systms and Invariant hory in th ontt of omtr Vision and Grahics lass 5: Slf alibration S39 Stanford Univrsity Amnon Shasha lass 5 Matrial W Will ovr oday h basic qations and conting argmnts

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

ANALYTICITY THEOREM FOR FRACTIONAL LAPLACE TRANSFORM

ANALYTICITY THEOREM FOR FRACTIONAL LAPLACE TRANSFORM Sc. Rs. hm. ommn.: (3, 0, 77-8 ISSN 77-669 ANALYTIITY THEOREM FOR FRATIONAL LAPLAE TRANSFORM P. R. DESHMUH * and A. S. GUDADHE a Prof. Ram Mgh Insttt of Tchnology & Rsarch, Badnra, AMRAVATI (M.S. INDIA

More information

Distortional Analysis of Thin-Walled Box Girder Structure: A Comparative Study

Distortional Analysis of Thin-Walled Box Girder Structure: A Comparative Study Jornal of Emrgng Trnds n Engnrng and Appld Scncs (JETEAS) (5): 8788 Scholarln Rsarch Insttt Jornals, (ISSN: 76) jtas.scholarlnrsarch.org Jornal of Emrgng Trnds n Engnrng and Appld Scncs (JETEAS) (5) 8788

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

Dealing with quantitative data and problem solving life is a story problem! Attacking Quantitative Problems

Dealing with quantitative data and problem solving life is a story problem! Attacking Quantitative Problems Daling with quantitati data and problm soling lif is a story problm! A larg portion of scinc inols quantitati data that has both alu and units. Units can sa your butt! Nd handl on mtric prfixs Dimnsional

More information

HOMOGENEOUS LEAST SQUARES PROBLEM

HOMOGENEOUS LEAST SQUARES PROBLEM the Photogrammetrc Jornal of nl, Vol. 9, No., 005 HOMOGENEOU LE QURE PROLEM Kejo Inklä Helsnk Unversty of echnology Laboratory of Photogrammetry Remote ensng P.O.ox 00, IN-005 KK kejo.nkla@tkk.f RC he

More information

1.9 Cartesian Tensors

1.9 Cartesian Tensors Scton.9.9 Crtsn nsors s th th ctor, hghr ordr) tnsor s mthmtc obct hch rprsnts mny physc phnomn nd hch xsts ndpndnty of ny coordnt systm. In ht foos, Crtsn coordnt systm s sd to dscrb tnsors..9. Crtsn

More information

REVIEW Lecture 14: Elliptic PDEs, Continued. Parabolic PDEs and Stability

REVIEW Lecture 14: Elliptic PDEs, Continued. Parabolic PDEs and Stability 9 Nmrcal Fld Mchancs prng 015 Lctr 15 REIEW Lctr 14: Ellptc PDEs, Contnd Eampls, Hghr ordr fnt dffrncs Irrglar bondars: Drchlt and on Nmann BCs Intrnal bondars Parabolc PDEs and tablty Eplct schms (1D-spac

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

Definition of vector and tensor. 1. Vector Calculus and Index Notation. Vector. Special symbols: Kronecker delta. Symbolic and Index notation

Definition of vector and tensor. 1. Vector Calculus and Index Notation. Vector. Special symbols: Kronecker delta. Symbolic and Index notation Dfnton of vctor nd tnsor. Vctor Clculus nd Indx Notton Crtsn nsor only Pnton s Chp. Vctor nd ordr tnsor v = c v, v = c v = c c, = c c k l kl kl k l m physcl vrbl, sm symbolc form! Cn b gnrlzd to hghr ordr

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

Bruce A. Draper & J. Ross Beveridge, January 25, Geometric Image Manipulation. Lecture #1 January 25, 2013

Bruce A. Draper & J. Ross Beveridge, January 25, Geometric Image Manipulation. Lecture #1 January 25, 2013 Brce A. Draper & J. Ross Beerdge, Janar 5, Geometrc Image Manplaton Lectre # Janar 5, Brce A. Draper & J. Ross Beerdge, Janar 5, Image Manplaton: Contet To start wth the obos, an mage s a D arra of pels

More information

Load Equations. So let s look at a single machine connected to an infinite bus, as illustrated in Fig. 1 below.

Load Equations. So let s look at a single machine connected to an infinite bus, as illustrated in Fig. 1 below. oa Euatons Thoughout all of chapt 4, ou focus s on th machn tslf, thfo w wll only pfom a y smpl tatmnt of th ntwok n o to s a complt mol. W o that h, but alz that w wll tun to ths ssu n Chapt 9. So lt

More information

SE Story Shear Frame. Final Project. 2 Story Bending Beam. m 2. u 2. m 1. u 1. m 3. u 3 L 3. Given: L 1 L 2. EI ω 1 ω 2 Solve for m 2.

SE Story Shear Frame. Final Project. 2 Story Bending Beam. m 2. u 2. m 1. u 1. m 3. u 3 L 3. Given: L 1 L 2. EI ω 1 ω 2 Solve for m 2. SE 8 Fnal Project Story Sear Frame Gven: EI ω ω Solve for Story Bendng Beam Gven: EI ω ω 3 Story Sear Frame Gven: L 3 EI ω ω ω 3 3 m 3 L 3 Solve for Solve for m 3 3 4 3 Story Bendng Beam Part : Determnng

More information

Geometric Registration for Deformable Shapes. 2.1 ICP + Tangent Space optimization for Rigid Motions

Geometric Registration for Deformable Shapes. 2.1 ICP + Tangent Space optimization for Rigid Motions Geometrc Regstraton for Deformable Shapes 2.1 ICP + Tangent Space optmzaton for Rgd Motons Regstraton Problem Gven Two pont cloud data sets P (model) and Q (data) sampled from surfaces Φ P and Φ Q respectvely.

More information

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule Lcur : Numrical ngraion Th Trapzoidal and Simpson s Rul A problm Th probabiliy of a normally disribud (man µ and sandard dviaion σ ) vn occurring bwn h valus a and b is B A P( a x b) d () π whr a µ b -

More information

First looking at the scalar potential term, suppose that the displacement is given by u = φ. If one can find a scalar φ such that u = φ. u x.

First looking at the scalar potential term, suppose that the displacement is given by u = φ. If one can find a scalar φ such that u = φ. u x. 7.4 Eastodynams 7.4. Propagaton of Wavs n East Sods Whn a strss wav travs throgh a matra, t ass matra parts to dspa by. It an b shown that any vtor an b wrttn n th form φ + ra (7.4. whr φ s a saar potnta

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

11/13/17. directed graphs. CS 220: Discrete Structures and their Applications. relations and directed graphs; transitive closure zybooks

11/13/17. directed graphs. CS 220: Discrete Structures and their Applications. relations and directed graphs; transitive closure zybooks dirctd graphs CS 220: Discrt Strctrs and thir Applications rlations and dirctd graphs; transiti closr zybooks 9.3-9.6 G=(V, E) rtics dgs dgs rtics/ nods Edg (, ) gos from rtx to rtx. in-dgr of a rtx: th

More information

The Penalty Cost Functional for the Two-Dimensional Energized Wave Equation

The Penalty Cost Functional for the Two-Dimensional Energized Wave Equation Lonardo Jornal of Scncs ISSN 583-033 Iss 9, Jly-Dcmbr 006 p. 45-5 Th Pnalty Cost Fnctonal for th Two-Dmnsonal Enrgd Wav Eqaton Vctor Onoma WAZIRI, Snday Agsts REJU Mathmatcs/Comptr Scnc dpartmnt, Fdral

More information

MEMBRANE ELEMENT WITH NORMAL ROTATIONS

MEMBRANE ELEMENT WITH NORMAL ROTATIONS 9. MEMBRANE ELEMENT WITH NORMAL ROTATIONS Rotatons Mst Be Compatble Between Beam, Membrane and Shell Elements 9. INTRODUCTION { XE "Membrane Element" }The comple natre of most bldngs and other cvl engneerng

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

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

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

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

BAR & TRUSS FINITE ELEMENT. Direct Stiffness Method

BAR & TRUSS FINITE ELEMENT. Direct Stiffness Method BAR & TRUSS FINITE ELEMENT Drect Stness Method FINITE ELEMENT ANALYSIS AND APPLICATIONS INTRODUCTION TO FINITE ELEMENT METHOD What s the nte element method (FEM)? A technqe or obtanng approxmate soltons

More information

From Structural Analysis to FEM. Dhiman Basu

From Structural Analysis to FEM. Dhiman Basu From Structural Analyss to FEM Dhman Basu Acknowldgmnt Followng txt books wr consultd whl prparng ths lctur nots: Znkwcz, OC O.C. andtaylor Taylor, R.L. (000). Th FntElmnt Mthod, Vol. : Th Bass, Ffth dton,

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

Fundamentals of Continuum Mechanics. Seoul National University Graphics & Media Lab

Fundamentals of Continuum Mechanics. Seoul National University Graphics & Media Lab Fndmntls of Contnm Mchncs Sol Ntonl Unvrsty Grphcs & Md Lb Th Rodmp of Contnm Mchncs Strss Trnsformton Strn Trnsformton Strss Tnsor Strn T + T ++ T Strss-Strn Rltonshp Strn Enrgy FEM Formlton Lt s Stdy

More information

CS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras

CS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras CS4495/6495 Introducton to Computer Vson 3C-L3 Calbratng cameras Fnally (last tme): Camera parameters Projecton equaton the cumulatve effect of all parameters: M (3x4) f s x ' 1 0 0 0 c R 0 I T 3 3 3 x1

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

Shortest Paths in Graphs. Paths in graphs. Shortest paths CS 445. Alon Efrat Slides courtesy of Erik Demaine and Carola Wenk

Shortest Paths in Graphs. Paths in graphs. Shortest paths CS 445. Alon Efrat Slides courtesy of Erik Demaine and Carola Wenk S 445 Shortst Paths n Graphs lon frat Sls courtsy of rk man an arola Wnk Paths n raphs onsr a raph G = (V, ) wth -wht functon w : R. Th wht of path p = v v v k s fn to xampl: k = w ( p) = w( v, v + ).

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

Lesson 7. Chapter 8. Frequency estimation. Bengt Mandersson LTH. October Nonparametric methods: lesson 6. Parametric methods:

Lesson 7. Chapter 8. Frequency estimation. Bengt Mandersson LTH. October Nonparametric methods: lesson 6. Parametric methods: Otmal Sgnal Procssng Lsson 7 Otmal Sgnal Procssng Chatr 8, Sctrum stmaton onaramtrc mthods: lsson 6 Chatr 8. Frquncy stmaton Th rodogram Th modfd Prodogram (ndong Aragng rodogram Bartltt Wlch Th nmum aranc

More information

AE/ME 339. K. M. Isaac. 8/31/2004 topic4: Implicit method, Stability, ADI method. Computational Fluid Dynamics (AE/ME 339) MAEEM Dept.

AE/ME 339. K. M. Isaac. 8/31/2004 topic4: Implicit method, Stability, ADI method. Computational Fluid Dynamics (AE/ME 339) MAEEM Dept. AE/ME 339 Comptatonal Fld Dynamcs (CFD) Comptatonal Fld Dynamcs (AE/ME 339) Implct form of dfference eqaton In the prevos explct method, the solton at tme level n,,n, depended only on the known vales of,

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

ECE 650 1/8. Homework Set 4 - Solutions

ECE 650 1/8. Homework Set 4 - Solutions ECE 65 /8 Homwork St - Solutions. (Stark & Woods #.) X: zro-man, C X Find G such that Y = GX will b lt. whit. (Will us: G = -/ E T ) Finding -valus for CX: dt = (-) (-) = Finding corrsponding -vctors for

More information

A general N-dimensional vector consists of N values. They can be arranged as a column or a row and can be real or complex.

A general N-dimensional vector consists of N values. They can be arranged as a column or a row and can be real or complex. Lnr lgr Vctors gnrl -dmnsonl ctor conssts of lus h cn rrngd s column or row nd cn rl or compl Rcll -dmnsonl ctor cn rprsnt poston, loct, or cclrton Lt & k,, unt ctors long,, & rspctl nd lt k h th componnts

More information

Tensor Analysis. For orthogonal curvilinear coordinates, ˆ ˆ (98) Expanding the derivative, we have, ˆ. h q. . h q h q

Tensor Analysis. For orthogonal curvilinear coordinates, ˆ ˆ (98) Expanding the derivative, we have, ˆ. h q. . h q h q For orthogonal curvlnear coordnates, eˆ grad a a= ( aˆ ˆ e). h q (98) Expandng the dervatve, we have, eˆ aˆ ˆ e a= ˆ ˆ a h e + q q 1 aˆ ˆ ˆ a e = ee ˆˆ ˆ + e. h q h q Now expandng eˆ / q (some of the detals

More information

Linear Algebra Provides a Basis for Elasticity without Stress or Strain

Linear Algebra Provides a Basis for Elasticity without Stress or Strain Soft, 05, 4, 5-4 Publshd Onln Sptmbr 05 n ScRs. http://www.scrp.org/ournal/soft http://dx.do.org/0.46/soft.05.400 Lnar Algbra Provds a Bass for Elastcty wthout Strss or Stran H. H. Hardy Math/Physcs Dpartmnt,

More information

Moments of Inertia. and reminds us of the analogous equation for linear momentum p= mv, which is of the form. The kinetic energy of the body is.

Moments of Inertia. and reminds us of the analogous equation for linear momentum p= mv, which is of the form. The kinetic energy of the body is. Moments of Inerta Suppose a body s movng on a crcular path wth constant speed Let s consder two quanttes: the body s angular momentum L about the center of the crcle, and ts knetc energy T How are these

More information

Two-level parameter estimates GSTARX-GLS model

Two-level parameter estimates GSTARX-GLS model Procdngs of th IConSSE FS SWCU (), pp SC ISB: 7-6-7--7 SC Two-lvl paramtr stmats GSTARX-GS modl Andra Prma Dtago and Shartono Statstcs Dpartmnt, Splh ovmbr of Insttt Tchnology, Srabaya 6, Indonsa Abstract

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

Army Ants Tunneling for Classical Simulations

Army Ants Tunneling for Classical Simulations Electronc Supplementary Materal (ESI) for Chemcal Scence. Ths journal s The Royal Socety of Chemstry 2014 electronc supplementary nformaton (ESI) for Chemcal Scence Army Ants Tunnelng for Classcal Smulatons

More information

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem Mahmacal ascs 8 Chapr VIII amplg Dsrbos ad h Cral Lm Thorm Fcos of radom arabls ar sall of rs sascal applcao Cosdr a s of obsrabl radom arabls L For ampl sppos h arabls ar a radom sampl of s from a poplao

More information

Rectification and Depth Computation

Rectification and Depth Computation Dpatmnt of Comput Engnng Unvst of Cafona at Santa Cuz Rctfcaton an Dpth Computaton CMPE 64: mag Anass an Comput Vson Spng 0 Ha ao 4/6/0 mag cosponncs Dpatmnt of Comput Engnng Unvst of Cafona at Santa Cuz

More information

So far: simple (planar) geometries

So far: simple (planar) geometries Physcs 06 ecture 5 Torque and Angular Momentum as Vectors SJ 7thEd.: Chap. to 3 Rotatonal quanttes as vectors Cross product Torque epressed as a vector Angular momentum defned Angular momentum as a vector

More information

Math 217 Fall 2013 Homework 2 Solutions

Math 217 Fall 2013 Homework 2 Solutions Math 17 Fall 013 Homework Solutons Due Thursday Sept. 6, 013 5pm Ths homework conssts of 6 problems of 5 ponts each. The total s 30. You need to fully justfy your answer prove that your functon ndeed has

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 07 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns Summary: Solvng a Homognous Sysm of Two Lnar Frs Ordr Equaons n Two Unknowns Gvn: A Frs fnd h wo gnvalus, r, and hr rspcv corrspondng gnvcors, k, of h coffcn mar A Dpndng on h gnvalus and gnvcors, h gnral

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

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 0 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat th

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

Conservation of Angular Momentum = "Spin"

Conservation of Angular Momentum = Spin Page 1 of 6 Conservaton of Angular Momentum = "Spn" We can assgn a drecton to the angular velocty: drecton of = drecton of axs + rght hand rule (wth rght hand, curl fngers n drecton of rotaton, thumb ponts

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 301 Signals & Systms Prof. Mark Fowlr ot St #21 D-T Signals: Rlation btwn DFT, DTFT, & CTFT 1/16 W can us th DFT to implmnt numrical FT procssing This nabls us to numrically analyz a signal to find

More information

Calculation of GDOP Coefficient

Calculation of GDOP Coefficient Calclaton of DOP Coffcnt Ing. Martna Mronovova, Ing. Hynk Havlš Sprvsor: Prof. Ing. Frantšk Vjražka, CSc., Prof. Ing. Jří Bíla DrSc. Abstrakt Účlm této prác j odvodt kofcnt DOP (omtrcal Dlton of Prcson,

More information

1 Matrix representations of canonical matrices

1 Matrix representations of canonical matrices 1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:

More information

Why switching? Modulation. Switching amp. Losses. Converter topology. i d. Continuous amplifiers have low efficiency. Antag : u i

Why switching? Modulation. Switching amp. Losses. Converter topology. i d. Continuous amplifiers have low efficiency. Antag : u i Modlaton Indtral Elctrcal Engnrng and Atomaton Lnd nvrty, Swdn Why wtchng? Contno amplfr hav low ffcncy a b Contno wtch pt ( t ) = pn( t) = ( a b) Antag : ( a b) = Pn = Pt η = = = Pn Swtchng amp. Lo Convrtr

More information

EE 570: Location and Navigation: Theory & Practice

EE 570: Location and Navigation: Theory & Practice EE 570: Locaton and Navgaton: Thor & Practc Navgaton Snsors and INS Mchanaton Thursda 8 F 013 NMT EE 570: Locaton and Navgaton: Thor & Practc Sld 1 of 10 Navgaton Snsors and INS Mchanaton Navgaton Equatons

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

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint Optmal Ordrng Polcy n a Two-Lvl Supply Chan wth Budgt Constrant Rasoul aj Alrza aj Babak aj ABSTRACT Ths papr consdrs a two- lvl supply chan whch consst of a vndor and svral rtalrs. Unsatsfd dmands n rtalrs

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

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY Stud of Dnamc Aprtur for PETRA III Rng K. Balws, W. Brfld, W. Dcng, Y. L DESY FLS6 Hamburg PETRA III Yong-Jun L t al. Ovrvw Introducton Dnamcs of dampng wgglrs hoc of machn tuns, and optmzaton of stupol

More information

The Finite Element Method. Jerzy Podgórski

The Finite Element Method. Jerzy Podgórski Th Fnt Elmnt Mthod Jr Podgórs Novmbr 8 Introdcton Ths boo dals wth th s of th fnt lmnt mthod (FEM s an abbrvaton for th Fnt Elmnts Mthod or FEA for Fnt Elmnts Analss) to solv lnar problms of sold mchancs.

More information

4.8 Huffman Codes. Wordle. Encoding Text. Encoding Text. Prefix Codes. Encoding Text

4.8 Huffman Codes. Wordle. Encoding Text. Encoding Text. Prefix Codes. Encoding Text 2/26/2 Word A word a word coag. A word contrctd ot of on of th ntrctor ar: 4.8 Hffan Cod word contrctd ng th java at at word.nt word a randozd grdy agorth to ov th ackng rob Encodng Txt Q. Gvn a txt that

More information

Modeling of the Through-the-Thickness Electric Potentials of a Piezoelectric Bimorph Using the Spectral Element Method

Modeling of the Through-the-Thickness Electric Potentials of a Piezoelectric Bimorph Using the Spectral Element Method Snsors 214, 14, 3477-3492; do:1.339/s1423477 Artcl OPEN ACCESS snsors ISSN 1424-822 www.mdp.com/jornal/snsors Modlng of th hrogh-th-hcknss Elctrc Potntals of a Pzolctrc Bmorph Usng th Spctral Elmnt Mthod

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

Combinatorial Networks Week 1, March 11-12

Combinatorial Networks Week 1, March 11-12 1 Nots on March 11 Combinatorial Ntwors W 1, March 11-1 11 Th Pigonhol Principl Th Pigonhol Principl If n objcts ar placd in hols, whr n >, thr xists a box with mor than on objcts 11 Thorm Givn a simpl

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More 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

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

Rigid body simulation

Rigid body simulation Rgd bod smulaton Rgd bod smulaton Once we consder an object wth spacal etent, partcle sstem smulaton s no longer suffcent Problems Problems Unconstraned sstem rotatonal moton torques and angular momentum

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

An Overview of Markov Random Field and Application to Texture Segmentation

An Overview of Markov Random Field and Application to Texture Segmentation An Ovrvw o Markov Random Fld and Applcaton to Txtur Sgmntaton Song-Wook Joo Octobr 003. What s MRF? MRF s an xtnson o Markov Procss MP (D squnc o r.v. s unlatral (causal: p(x t x,

More information

Economics 201b Spring 2010 Solutions to Problem Set 3 John Zhu

Economics 201b Spring 2010 Solutions to Problem Set 3 John Zhu Economics 20b Spring 200 Solutions to Problm St 3 John Zhu. Not in th 200 vrsion of Profssor Andrson s ctur 4 Nots, th charactrization of th firm in a Robinson Cruso conomy is that it maximizs profit ovr

More information

Invariant deformation parameters from GPS permanent networks using stochastic interpolation

Invariant deformation parameters from GPS permanent networks using stochastic interpolation Invarant deformaton parameters from GPS permanent networks usng stochastc nterpolaton Ludovco Bag, Poltecnco d Mlano, DIIAR Athanasos Dermans, Arstotle Unversty of Thessalonk Outlne Startng hypotheses

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