GEORG UMLAUF. Abstract. Tools for the analysis of generalized triangular box spline subdivision schemes are

Size: px
Start display at page:

Download "GEORG UMLAUF. Abstract. Tools for the analysis of generalized triangular box spline subdivision schemes are"

Transcription

1 ANALYZING THE CHARACTERISTIC MAP OF TRIANGULAR SUBDIVISION SCHEMES GEORG UMLAUF Abstract. Tools for the aalysis of geeralized triagular box splie subdivisio schemes are developed. For the rst time the full aalysis of Loop's algorithm ca be carried out with these tools. Key words. Subdivisio, triagular schemes, Loop's algorithm, box splies. AMS subject classicatios. 65D17, 65D07, 68U07 Abbreviated title. Characteristic Map of Triagular Subdivisio Schemes 1. Itroductio. I the last two decades may subdivisio schemes for dieret purposes were devised, see e.g. [, 1, 6, 11, 3, 4, 7]. All these algorithms geerate from a iitial cotrol et a sequece of ets which coverges to a limitig surface. Although suciet coditios for the covergece to a smooth limitig surface were give i [1] ad [9], their rigorous applicatio has bee carried out oly for some of the above metioed schemes by Peters ad Reif [8, 7]. I this paper we will ivestigate the smoothess of the limitig surfaces obtaied by subdivisio algorithms for triagular ets. Accordig to the suciet coditios of [1] ad [9], we have to aalyze the spectral properties of the subdivisio matrix associated with the algorithm ad the so-called characteristic map. Symmetry properties of the algorithms help to simplify this aalysis sigicatly. Subsequetly the aalysis is carried out for Loop's algorithm [6]. The spectral properties of the subdivisio matrix imply some characteristics of the algorithm as already observed by Loop [6]. To prove regularity ad ijectivity of the characteristic map we use its Bezier represetatio as i [13] ad[8]. This leads to a rigorous proof of taget cotiuity of the limitig surface of Loop's algorithm.. Geeralized subdivisio. We presume that all subdivisio algorithms cosidered here are statioary, local, ad liear schemes for tri- or quadrilateral ets. Such a algorithm geerates startig from a iitial arbitrary tri- or quadrilateral et C 0 a sequece of ever er ets fc m g 1 m=0. Thereby oly ite, ae combiatios, represeted by so called masks, are used to compute the poits of the et C m from C m;1 m 1. This makes up for the locality ad liearity of these schemes. Sice we use the same ae combiatios i every step m of the iteratio, the subdivisio algorithm is said to be statioary. The sequece of ets fc m g 1 m=0 geerated by such a algorithm will evetually coverge to a limitig surface s cosistig of iitely may tri- or quadrilateral patches. A example for Loop's algorithm is show i Figure.1. The upper left et is the iitial et C 0. The other ets C 1 ::: C 4 are the result of the rst four iteratios of Loop's algorithm startig from C 0. Suppose that o the regular parts of a et, i.e. parts of the et that cotai oly ordiary vertices of valece 6 or 4 for tri- or quadrilateral ets respectively, stadard Fakultat fur Iformatik, Uiversitat Karlsruhe, D-7618 Karlsruhe, Germay, umlauf@ira.uka.de. Supported by DFG grat PR 565/1-1. 1

2 GEORG UMLAUF C 0 C 1 C C 3 C 4 Fig..1. The iitial triagular et C 0 (top left) ad the ets C 1 ::: C 4 of the rst four iteratio steps of Loop's algorithm. subdivisio rules for symmetric box splies apply. Examples for such stadard subdivisio rules are the subdivisio rules for tesorproduct splies [5] or for quartic box splies over the three-directioal grid. O this coditio the regular parts of a et determie C k -surfaces. Near vertices of valece 6= 6(6= 4) for tri- (quadri-)lateral ets, the so-called extraordiary vertices, special subdivisio rules are used, which dootchage the umber of extraordiary vertices i two cosecutive ets C m;1 ad C m, m. Sice the subdivisio masks of statioary, local schemes have xed ite size, we ca restrict the aalysis to ets C 0 with a sigle extraordiary vertex surrouded by r rigs of ordiary vertices. A example is illustrated i Figure. for r =3. Fig... A iitial et C 0 with a extraordiary vertex of valece 5 (marked by) surrouded by r =3rigs of ordiary vertices. The particular choice of r depeds o the subdivisio algorithm. It must be

3 CHARACTERISTIC MAP OF TRIANGULAR SUBDIVISION SCHEMES 3 such that the regular parts of C 0 dee at least oe complete surface rig. Loop's algorithm requires for example r = 3. If we deote by s m the surface that correspods to the regular parts of C m, the the limitig surface is give by s = [s m. Obviously s m;1 is part of s m for m 1. So takig s m;1 away froms m we obtai a surface S rig r m which is added to s m;1 i the mth iteratio step. This yields s = s 0 [ r m1 m. At a extraordiary vertex of valece the surface rigs r m ca be parametrized over a commo domai Z i terms of a subet D m C m ad certai fuctios N k.ifd 0 m ::: dk m deote the vertices of D m,wehave where is either (u v j) 7! r j m(u v) = i case of trilateral ets or r m : Z! IR 3 KX k=0 d k m N k (u v j) =:N(u v j) d m 4 = f(u v)ju v 0 ad 1 u + v g = f(u v)ju v 0 ad 1 maxfu vgg i case of quadrilateral ets, see Figure.3. p p e 4 e e 1 e 1 Fig..3. The domais 4 (left) ad (right). Note that all ets D m have equally may vertices. Hece the statioary, local, ad liear subdivisio algorithm ca be described by a square subdivisio matrix A, i.e. d m = Ad m;1 : 3. The subdivisio matrix ad the characteristic map. Let 0 ::: K be the eigevalues of A listed with all their algebraic multiplicities ad ordered by their modulus j 0 jj 1 j:::j K j ad deote by v 0 ::: v K the correspodig geeralized eigevectors. If j 0 j > j 1 j = j j > j 3 j, the two dimesioal surface that is deed by the et [v 1 v ] x(u v j) :=N(u v j)[v 1 v ]: Z! IR

4 4 GEORG UMLAUF x 1 x 0 x ;1 Fig The characteristic map of Loop's algorithm for =7. is called the characteristic map of the subdivisio scheme [1]. Note that x ca be regarded as cosistig of segmets x j (u v) :=x(u v j). A example for the characteristic map of Loop's algorithm is show i Figure 3.1. The crucial theorem for the aalysis of subdivisio algorithms ca ow be stated i terms of the subdomiat eigevalue := = 3 ad x: Theorem 3.1. Let be areal eigevalue with geometric multiplicity. If the characteristic map x is regular ad ijective ad 0 =1> jj > j 3 j the the limitig surface isac 1 -maifold for almost all iitial cotrol ets C 0. Proofs of this theorem ca be foud i [1] or i a more geeral settig i [9]. I the sequel we apply Theorem 3.1 to subdivisio schemes with two additioal properties. 1. A subdivisio algorithm is said to be symmetric, if it is ivariat uder shifts ad reectios of the labellig of d m. This meas if permutatio matrices S ad R characterized by N(u v j +1)d m = N(u v j)sd m ad exist, the A commutes with S ad R: N(v u ;j)d m = N(u v j)rd m AS = SA ad AR = RA: Note that S ad R exist especially for subdivisio algorithms based o box splies with regular hexagoal or square support.. A subdivisio algorithm is said to have a ormalized characteristic map, if x 0 (p) =(p 0) with p >0adp = e 1 + e or p =e 1 +e i case of tri- or quadrilateral ets, respectively, see Figure.3. The rst property implies that the subdivisio matrix A has a block-circulat structure with square blocks A j j =0 ::: ; 1. Thus A is uitary similar to a

5 CHARACTERISTIC MAP OF TRIANGULAR SUBDIVISION SCHEMES 5 block-diagoal matrix A. b The diagoal blocks of A b result from Aj by the discrete Fourier trasform: ;1 X ba j = k=0! ;jk A k for j =0 ::: ; 1 where! =exp(i=) deotes a -th root of uity. This meas, if bv is a eigevector of some block b Aj correspodig to the eigevalue, the is also a eigevalue of A with eigevector (3.1) v =[! bv! 0 bv :::! j j(;1) bv]: If deotes the complex-cojugate, the blocks of b A satisfy b Aj = b A ;j for j = 1 ::: b=c. Hece there are always two liear idepedet, real eigevectors v 1 = <(v) ad v = =(v) correspodig to the real subdomiat eigevalue. From this a rst ecessary coditio for the subdomiat eigevalue ca easily be deduced [8]: Lemma 3.. The characteristic map of a symmetric subdivisio scheme i ot ijective, if the subdomiat eigevalue is from a block b Aj for j 6= 1 ; 1. Equatio (3.1) shows also that ormalizatio of a ijective characteristic map ca always be achieved by a appropriate scalig of bv. 4. Suciet coditios for regularity ad ijectivity. Throughout this chapter we will assume that the subdomiat eigevalue is a real eigevalue from the blocks b A1 ad b A;1. The two properties of the last chapter imply that the characteristic map x is symmetric uder rotatios ad reectios for subdivisio schemes for tri- or quadrilateral ets ([8]). They allow us to restrict the aalysis of the characteristic map to its sigle segmet x 0 : Theorem 4.1. Let x 0 =[x y] ad deote by x 0 v := [x v y v ] the partial derivatives of x 0 with respect to v. If the ormalized characteristic map x of a symmetric subdivisio scheme for quadrilateral ets satises x 0 v(u v) > 0 for all (u v) compoetwise, the the characteristic map is regular ad ijective. For a proof of this theorem see [8]. The proof also applies to ay map z : Z! IR that shares the above symmetry properties of the map x. Theorem 4.1 ca be trasfered to triagular ets by the observatio that ay triagular et as i Figure. ca be viewed as a quadrilateral et with diagoal edges. This is show i Figure 4.1 for the domais 4 ad. 4 4 Fig Covertig a triagular to a quadrilateral et.

6 6 GEORG UMLAUF I case of triagular ets the domai of the characteristic map y cosists of plae copies of 4.Further we have 4 [ 4, as illustrated i Figure 4.1. Dee the map z as z : Z! IR y (u v j) 7! j (u v) if (u v) 4 y j (u v) if (u v) 4 : Thus z is "covered" by y ad y ad adopts its symmetry properties. Hece Theorem 4.1 is valid for the map z ad ca be applied to subdivisio schemes for triagular ets, if x is replaced by y ad by 4 : Theorem 4.. If for the ormalized characteristic map y of a symmetric subdivisio scheme for triagular ets the segmet y 0 satises y 0 v(u v) > 0 for all (u v) 4 compoetwise, the the map z is regular ad ijective. I practice we will use the Bezier represetatio of y 0 to apply Theorem 4.. Hece the proof of the positivity ofy 0 v for all (u v) 4 reduces to the proof of the positivity of its Bezier poits. Corollary 4.3. If all Bezier poits of y 0 v are positive, the the ormalized characteristic map y of a symmetric subdivisio scheme for triagular ets is regular ad ijective. 5. Loop's algorithm. Loop's algorithm is a geeralizatio of the subdivisio scheme for quartic box-splies over a regular triagular grid. The masks are give i Figure 5.1, where the parameter ca be chose arbitrarily from the iterval ; ; cos(=)=4 (3 + cos(=))=4 : (5.1) 3=8 1=8 1=8 3=8 Fig The masks of Loop's algorithm. The limitig surface geerated by this algorithm is a piecewise quartic C -surface except at its extraordiary poits. Here the surface is cojectured to be taget cotiuous, see [6]. Obviously, Loop's algorithm is a symmetric scheme. The form of its subdivisio matrix A depeds o the labellig of the vertices i the cotrol et D m.ifwelabel them segmet after segmet couterclockwise as i Figure 5. the subdivisio matrix A has a block-circulat structure: A = 6 4 A 0 A 1 A ;1 A ;1 A 0 A ; IR77 : A 1 A ;1 A 0 3

7 CHARACTERISTIC MAP OF TRIANGULAR SUBDIVISION SCHEMES 7 d 1 d 7 i = i = ::: d i d 8 i d 4 i d 9 i d 7 i d 3 i d 6 i d 7;1 i d 7 i d 5 i Fig. 5.. The labellig of the vertices of the cotrol et D m for =5. Now we ca use the discrete Fourier trasform as i Chapter 3 to yield a uitary similar block-diagoal matrix b A with blocks ba 0 = ba j = ; 3=8 5=8 1=16 3=4 1=16 1=8 1=8 3=4 0 1=8 0 3=8 3=8 1= = 1=8 3= = 1=8 3= =8+c j =4 0 5=8+c j =8 1=16 1=16 +!j =16 ad 0 3=8+3! ;j =8 0 1=8 0 3=8 3=8 1=8+! j = =8+! ;j 0 1=8+3! ;j =8 1=8 3= =8 3= =8!;j for j =1 ::: ; 1ad c j + isj subdivisio matrix A as follows: 1, := ; 3=8, j := 3=8+c j =4 for j =1 ::: ; 1, =!j. From this we get the eigevalues of the 1=8 ad1=16 each -fold ad 0 which occurs (4 ; 1)-fold. Note that j = ;j for j =1 ::: b=c ad 1 > j for j = ::: b=c. Furthermore b A1 = b A ;1,sothat 1 has geometric multiplicity. Therefore 1 is the double subdomiat eigevalue of A, ifj j < 1. This last iequality yields the iterval (5.1) for give by [6]. This veries the coditios of Theorem 3.1 for the subdomiat eigevalue of the subdivisio matrix. What remais is the aalysis of the characteristic map. Remark 5.1. The spectral aalysis of A ca also be used to modify the subdivisio

8 8 GEORG UMLAUF algorithm so as to geerate smoother limitig surfaces [10]. 6. The characteristic map of Loop's algorithm. Accordig to Corollary 4.3 we oly eed to show positivity of the Bezier poits of y 0 v to proof regularity ad ijectivity of the characteristic map y of Loop's algorithm. Some calculatios usig a computer algebra system yield the Bezier poits of y 0 v as give i Figure 6.1. Except for positive costats the deomiators are give by D 1 =5+4c 1 D =54+36c 1 D 3 =19+c1 +4c : Obviously D 1 D D3 are positive for arbitrary 3 sice c 1 ;1=. The umerators i Figure 6.1 are of the form E c = a 0 + a 1 c 1 + a c + a 3 c 3 a 0 a 1 a a 3 Z or E s = b 1 s 1 + b s + b 3 s 3 b 1 b b 3 Z: Sice a 0 a 1 > 0, the umerators E c are positive if a 0 >a 1 =+ja j + ja 3 j: This last coditio is fullled by all E c. The positivity of the other umerators Es for 3 ca be show i a similar fashio. This completes the proof for Lemma 6.1. Loop's algorithm geerates C 1 -maifolds for almost all iitial triagular ets C 0. Remark 6.. This proof applies also to the subdivisio schemes proposed i [10]. Thus these schemes geerate curvature cotiuous surfaces with at spots at the extraordiary poits for almost all iitial triagular ets C 0. REFERENCES [1] E. Catmull ad J. Clark, Recursive geerated b-splie surfaces o arbitrary topological meshes, Computer Aided-Desig, 10 (1978), pp. 350{355. [] D. Doo ad M. Sabi, Behaviour of recursive divisio surfaces ear extraordiary poits, Computer Aided-Desig, 10 (1978), pp. 356{360. [3] N. Dy, J. Gregory, ad D. Levi, A buttery subdivisio scheme for surface iterpolatio with tesio cotrol, ACM Trasactios o Graphics, 9 (1990), pp. 160{169. [4] L. Kobbelt, Iterative Erzeugug glatter Iterpolate, PhD thesis, IBDS, Uiversitat Karlsruhe, Verlag Shaker, Aache. [5] J. Lae ad R. Riesefeld, A Theoretical Developmet for the Computer Geeratio ad Display of Piecewise Polyomial Surfaces, IEEE Trasactios o Patter Aalysis ad Machie Itelligece, (1980), pp. 35{46. [6] C. Loop, Smooth Subdivisio Surfaces Based o Triagles, master's thesis, Departmet of Mathematics, Uiversity of Utah, Aug [7] J. Peters ad U. Reif, The simplest subdivisio scheme for smoothig polyhedra, ACM Trasactios o Graphics, 16 (1997), pp. 40{431. [8], Aalysis of algorithms geeralizig b-splie subdivisio, SIAM Joural o Numerical Aalysis, 35 (1998), pp. 78{748. [9] H. Prautzsch, Aalysis of C k -subdivisio surfaces at extraordiary poits, preprit 98/4, Fakultat fur Iformatik, Uiversitat Karlsruhe, [10] H. Prautzsch ad G. Umlauf, Improved triagular subdivisio schemes, i Computer Graphics Iteratioal 1998, F.-E. Wolter ad N. Patrikalakis, eds., Haover,.-6. Jui 1998, IEEE Computer Society, pp. 66{63. [11] R. Qu, Recursive subdivisio algorithms for curve ad surface desig, PhD thesis, Departmet of Mathematics ad Statistics, Burel Uiversity, Uxbridge, Middlesex, Eglad, Aug

9 CHARACTERISTIC MAP OF TRIANGULAR SUBDIVISION SCHEMES 9 " 1 ;c ;4c c 3 9D " 1 +4c ;4c c 3 9D " 1 ;7c ;c c 1 18D " 1 ;c ;c c 1 18D " 1 ;c 84+80c 3 9D " 1 +c ;c c 3 6D " 1 ;6c ;4c c 3 18D " 1 ;5c 44+33c 1 18D " 1 ;6c ;8c c 3 18D " 1 ;6c ;c c 3 6D " 1 +c 44+39c 1 18D " 1 ;7c 46+7c 1 18D " 1 ;18c ;10c c 3 18D " 1 ;30c ;8c c 3 18D " 1 +c 44+39c 1 18D " 1 ;11c 47+1c 1 18D " 1 ;18c ;7c c 3 9D " 1 ;17c ;4c c 1 18D " 1 ;1c ;c c 1 18D " 1 ;8c ;c c 3 3D " 1 ;30c ;10c c 3 9D " 1 ;34c ;8c c 3 9D 1 +6s ;4s 3 78s 3 9D 1 +1s ;4s 3 7s 3 9D 1 ;3s ;s 3 45s 1 18D 1 +3s ;s 3 39s 1 18D 1 +1s ;s 3 7s 3 9D 1 +18s ;s 3 66s 3 9D 1 +s 13s 1 6D 1 +3s 11s 1 6D 1 +6s s 3 3D 1 +4s +s3 60s 3 9D 1 +3s 11s 1 6D 1 +s 5s s +s3 60s 3 9D 1 +4s 0s D 1 +5s 9s 1 6D 1 +1s +4s3 1s 1 18D 1 +4s 0s D 1 +36s +7s3 48s 3 9D 1 +1s +s3 1s 1 18D 1 +1s +s3 16s 3 3D 1 +4s +10s3 4s 3 9D 1 +4s +8s3 4s 3 9D Fig The Bezier poits of y 0 v.

10 10 GEORG UMLAUF [1] U. Reif, A uied approach to subdivisio algorithms ear extraordiary vertices, Computer Aided Geometric Desig, 1 (1995), pp. 153{174. [13] J. Schweitzer, Aalysis ad Applicatio of Subdivisio Surfaces, PhD thesis, Departmet of Computer Sciece ad Egieerig, Uiversity of Washigto, Seattle, Washigto 98195, Aug Techical Report UW-CSE

Improved triangular subdivision schemes 1

Improved triangular subdivision schemes 1 Improved triagular subdivisio schemes Hartmut Prautzsch 2 Georg Umlauf 3 Faultät für Iformati, Uiversität Karlsruhe, D-762 Karlsruhe, Germay E-mail: 2 prau@ira.ua.de 3 umlauf@ira.ua.de Abstract I this

More information

HARTMUT PRAUTZSCH and GEORG UMLAUF. Fakultat fur Informatik, Universitat Karlsruhe. [prau/umlauf

HARTMUT PRAUTZSCH and GEORG UMLAUF. Fakultat fur Informatik, Universitat Karlsruhe.   [prau/umlauf A G AND A G SUBDIVISION SCHEME FOR TRIANGULAR NETS HARTMUT PRAUTZSCH ad GEORG UMLAUF Fakultat fur Iformatik, Uiversitat Karlsruhe D-768 Karlsruhe, Germay E-mail: [prau/umlauf ]@ira.uka.de I this article

More information

Convex hull ( 凸殻 ) property

Convex hull ( 凸殻 ) property Covex hull ( 凸殻 ) property The covex hull of a set of poits S i dimesios is the itersectio of all covex sets cotaiig S. For N poits P,..., P N, the covex hull C is the give by the expressio The covex hull

More information

Reading. Subdivision curves and surfaces. Subdivision curves. Chaikin s algorithm. Recommended:

Reading. Subdivision curves and surfaces. Subdivision curves. Chaikin s algorithm. Recommended: Readig Recommeded: Stollitz, DeRose, ad Salesi. Wavelets for Computer Graphics: Theory ad Applicatios, 996, sectio 6.-6.3, 0., A.5. Subdivisio curves ad surfaces Note: there is a error i Stollitz, et al.,

More information

Cubic Polynomial Curves with a Shape Parameter

Cubic Polynomial Curves with a Shape Parameter roceedigs of the th WSEAS Iteratioal Coferece o Robotics Cotrol ad Maufacturig Techology Hagzhou Chia April -8 00 (pp5-70) Cubic olyomial Curves with a Shape arameter MO GUOLIANG ZHAO YANAN Iformatio ad

More information

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro

More information

Tuned Ternary Quad Subdivision

Tuned Ternary Quad Subdivision Tued Terary Quad Subdivisio Tiayu Ni ad Ahmad H. Nasri 2 Dept. CISE, Uiversity of Florida 2 Dept. of Computer Sciece America Uiversity of Beirut Abstract. A well-documeted problem of Catmull ad Clark subdivisio

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

Loop Subdivision Surface Based Progressive Interpolation

Loop Subdivision Surface Based Progressive Interpolation Cheg FH (F), Fa FT, Lai SH et al Loop subdivisio surface based progressive iterpolatio JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 24(1): 39 46 Ja 2009 Loop Subdivisio Surface Based Progressive Iterpolatio

More information

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by

More information

On Spectral Theory Of K-n- Arithmetic Mean Idempotent Matrices On Posets

On Spectral Theory Of K-n- Arithmetic Mean Idempotent Matrices On Posets Iteratioal Joural of Sciece, Egieerig ad echology Research (IJSER), Volume 5, Issue, February 016 O Spectral heory Of -- Arithmetic Mea Idempotet Matrices O Posets 1 Dr N Elumalai, ProfRMaikada, 3 Sythiya

More information

Protected points in ordered trees

Protected points in ordered trees Applied Mathematics Letters 008 56 50 www.elsevier.com/locate/aml Protected poits i ordered trees Gi-Sag Cheo a, Louis W. Shapiro b, a Departmet of Mathematics, Sugkyukwa Uiversity, Suwo 440-746, Republic

More information

A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH

A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH J. Appl. Math. & Computig Vol. 21(2006), No. 1-2, pp. 233-238 Website: http://jamc.et A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH YEON SOO YOON AND JU KYUNG KIM Abstract.

More information

Lecture 2: Spectra of Graphs

Lecture 2: Spectra of Graphs Spectral Graph Theory ad Applicatios WS 20/202 Lecture 2: Spectra of Graphs Lecturer: Thomas Sauerwald & He Su Our goal is to use the properties of the adjacecy/laplacia matrix of graphs to first uderstad

More information

Lecture 18. Optimization in n dimensions

Lecture 18. Optimization in n dimensions Lecture 8 Optimizatio i dimesios Itroductio We ow cosider the problem of miimizig a sigle scalar fuctio of variables, f x, where x=[ x, x,, x ]T. The D case ca be visualized as fidig the lowest poit of

More information

New Results on Energy of Graphs of Small Order

New Results on Energy of Graphs of Small Order Global Joural of Pure ad Applied Mathematics. ISSN 0973-1768 Volume 13, Number 7 (2017), pp. 2837-2848 Research Idia Publicatios http://www.ripublicatio.com New Results o Eergy of Graphs of Small Order

More information

Convergence results for conditional expectations

Convergence results for conditional expectations Beroulli 11(4), 2005, 737 745 Covergece results for coditioal expectatios IRENE CRIMALDI 1 ad LUCA PRATELLI 2 1 Departmet of Mathematics, Uiversity of Bologa, Piazza di Porta Sa Doato 5, 40126 Bologa,

More information

Some cycle and path related strongly -graphs

Some cycle and path related strongly -graphs Some cycle ad path related strogly -graphs I. I. Jadav, G. V. Ghodasara Research Scholar, R. K. Uiversity, Rajkot, Idia. H. & H. B. Kotak Istitute of Sciece,Rajkot, Idia. jadaviram@gmail.com gaurag ejoy@yahoo.co.i

More information

Pattern Recognition Systems Lab 1 Least Mean Squares

Pattern Recognition Systems Lab 1 Least Mean Squares Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig

More information

BASED ON ITERATIVE ERROR-CORRECTION

BASED ON ITERATIVE ERROR-CORRECTION A COHPARISO OF CRYPTAALYTIC PRICIPLES BASED O ITERATIVE ERROR-CORRECTIO Miodrag J. MihaljeviC ad Jova Dj. GoliC Istitute of Applied Mathematics ad Electroics. Belgrade School of Electrical Egieerig. Uiversity

More information

Parametric curves. Reading. Parametric polynomial curves. Mathematical curve representation. Brian Curless CSE 457 Spring 2015

Parametric curves. Reading. Parametric polynomial curves. Mathematical curve representation. Brian Curless CSE 457 Spring 2015 Readig Required: Agel 0.-0.3, 0.5., 0.6-0.7, 0.9 Parametric curves Bria Curless CSE 457 Sprig 05 Optioal Bartels, Beatty, ad Barsy. A Itroductio to Splies for use i Computer Graphics ad Geometric Modelig,

More information

Smooth Spline Surfaces over Irregular Meshes

Smooth Spline Surfaces over Irregular Meshes Smooth Splie Surfaces over Irregular Meshes Charles Loop Apple Computer, Ic. Abstract A algorithm for creatig smooth splie surfaces over irregular meshes is preseted. The algorithm is a geeralizatio of

More information

Bicubic Polar Subdivision

Bicubic Polar Subdivision Bicubic Polar Subdivisio K. Karčiauskas Vilius Uiversity ad J. Peters Uiversity of Florida We describe ad aalyze a subdivisio scheme that geeralizes bicubic splie subdivisio to cotrol ets with polar structure.

More information

BOOLEAN MATHEMATICS: GENERAL THEORY

BOOLEAN MATHEMATICS: GENERAL THEORY CHAPTER 3 BOOLEAN MATHEMATICS: GENERAL THEORY 3.1 ISOMORPHIC PROPERTIES The ame Boolea Arithmetic was chose because it was discovered that literal Boolea Algebra could have a isomorphic umerical aspect.

More information

5.3 Recursive definitions and structural induction

5.3 Recursive definitions and structural induction /8/05 5.3 Recursive defiitios ad structural iductio CSE03 Discrete Computatioal Structures Lecture 6 A recursively defied picture Recursive defiitios e sequece of powers of is give by a = for =0,,, Ca

More information

Eigenimages. Digital Image Processing: Bernd Girod, Stanford University -- Eigenimages 1

Eigenimages. Digital Image Processing: Bernd Girod, Stanford University -- Eigenimages 1 Eigeimages Uitary trasforms Karhue-Loève trasform ad eigeimages Sirovich ad Kirby method Eigefaces for geder recogitio Fisher liear discrimat aalysis Fisherimages ad varyig illumiatio Fisherfaces vs. eigefaces

More information

Combination Labelings Of Graphs

Combination Labelings Of Graphs Applied Mathematics E-Notes, (0), - c ISSN 0-0 Available free at mirror sites of http://wwwmaththuedutw/ame/ Combiatio Labeligs Of Graphs Pak Chig Li y Received February 0 Abstract Suppose G = (V; E) is

More information

On (K t e)-saturated Graphs

On (K t e)-saturated Graphs Noame mauscript No. (will be iserted by the editor O (K t e-saturated Graphs Jessica Fuller Roald J. Gould the date of receipt ad acceptace should be iserted later Abstract Give a graph H, we say a graph

More information

Perhaps the method will give that for every e > U f() > p - 3/+e There is o o-trivial upper boud for f() ad ot eve f() < Z - e. seems to be kow, where

Perhaps the method will give that for every e > U f() > p - 3/+e There is o o-trivial upper boud for f() ad ot eve f() < Z - e. seems to be kow, where ON MAXIMUM CHORDAL SUBGRAPH * Paul Erdos Mathematical Istitute of the Hugaria Academy of Scieces ad Reu Laskar Clemso Uiversity 1. Let G() deote a udirected graph, with vertices ad V(G) deote the vertex

More information

Improved Random Graph Isomorphism

Improved Random Graph Isomorphism Improved Radom Graph Isomorphism Tomek Czajka Gopal Paduraga Abstract Caoical labelig of a graph cosists of assigig a uique label to each vertex such that the labels are ivariat uder isomorphism. Such

More information

Approximating Catmull-Clark Subdivision Surfaces with Bicubic Patches

Approximating Catmull-Clark Subdivision Surfaces with Bicubic Patches Approximatig Catmull-Clark Subdivisio Surfaces with Bicubic Patches CHARLES LOOP Microsoft Research ad SCOTT SCHAEFER Texas A&M Uiversity We preset a simple ad computatioally efficiet algorithm for approximatig

More information

Mean cordiality of some snake graphs

Mean cordiality of some snake graphs Palestie Joural of Mathematics Vol. 4() (015), 49 445 Palestie Polytechic Uiversity-PPU 015 Mea cordiality of some sake graphs R. Poraj ad S. Sathish Narayaa Commuicated by Ayma Badawi MSC 010 Classificatios:

More information

INSCRIBED CIRCLE OF GENERAL SEMI-REGULAR POLYGON AND SOME OF ITS FEATURES

INSCRIBED CIRCLE OF GENERAL SEMI-REGULAR POLYGON AND SOME OF ITS FEATURES INTERNATIONAL JOURNAL OF GEOMETRY Vol. 2 (2013), No. 1, 5-22 INSCRIBED CIRCLE OF GENERAL SEMI-REGULAR POLYGON AND SOME OF ITS FEATURES NENAD U. STOJANOVIĆ Abstract. If above each side of a regular polygo

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

Eigenimages. Digital Image Processing: Bernd Girod, 2013 Stanford University -- Eigenimages 1

Eigenimages. Digital Image Processing: Bernd Girod, 2013 Stanford University -- Eigenimages 1 Eigeimages Uitary trasforms Karhue-Loève trasform ad eigeimages Sirovich ad Kirby method Eigefaces for geder recogitio Fisher liear discrimat aalysis Fisherimages ad varyig illumiatio Fisherfaces vs. eigefaces

More information

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0 Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity

More information

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract O Ifiite Groups that are Isomorphic to its Proper Ifiite Subgroup Jaymar Talledo Baliho Abstract Two groups are isomorphic if there exists a isomorphism betwee them Lagrage Theorem states that the order

More information

The Platonic solids The five regular polyhedra

The Platonic solids The five regular polyhedra The Platoic solids The five regular polyhedra Ole Witt-Hase jauary 7 www.olewitthase.dk Cotets. Polygos.... Topologically cosideratios.... Euler s polyhedro theorem.... Regular ets o a sphere.... The dihedral

More information

On Characteristic Polynomial of Directed Divisor Graphs

On Characteristic Polynomial of Directed Divisor Graphs Iter. J. Fuzzy Mathematical Archive Vol. 4, No., 04, 47-5 ISSN: 30 34 (P), 30 350 (olie) Published o April 04 www.researchmathsci.org Iteratioal Joural of V. Maimozhi a ad V. Kaladevi b a Departmet of

More information

Random Graphs and Complex Networks T

Random Graphs and Complex Networks T Radom Graphs ad Complex Networks T-79.7003 Charalampos E. Tsourakakis Aalto Uiversity Lecture 3 7 September 013 Aoucemet Homework 1 is out, due i two weeks from ow. Exercises: Probabilistic iequalities

More information

The Adjacency Matrix and The nth Eigenvalue

The Adjacency Matrix and The nth Eigenvalue Spectral Graph Theory Lecture 3 The Adjacecy Matrix ad The th Eigevalue Daiel A. Spielma September 5, 2012 3.1 About these otes These otes are ot ecessarily a accurate represetatio of what happeed i class.

More information

Characterizing graphs of maximum principal ratio

Characterizing graphs of maximum principal ratio Characterizig graphs of maximum pricipal ratio Michael Tait ad Josh Tobi November 9, 05 Abstract The pricipal ratio of a coected graph, deoted γg, is the ratio of the maximum ad miimum etries of its first

More information

Octahedral Graph Scaling

Octahedral Graph Scaling Octahedral Graph Scalig Peter Russell Jauary 1, 2015 Abstract There is presetly o strog iterpretatio for the otio of -vertex graph scalig. This paper presets a ew defiitio for the term i the cotext of

More information

Dynamic Programming and Curve Fitting Based Road Boundary Detection

Dynamic Programming and Curve Fitting Based Road Boundary Detection Dyamic Programmig ad Curve Fittig Based Road Boudary Detectio SHYAM PRASAD ADHIKARI, HYONGSUK KIM, Divisio of Electroics ad Iformatio Egieerig Chobuk Natioal Uiversity 664-4 Ga Deokji-Dog Jeoju-City Jeobuk

More information

Lecture 24: Bezier Curves and Surfaces. thou shalt be near unto me Genesis 45:10

Lecture 24: Bezier Curves and Surfaces. thou shalt be near unto me Genesis 45:10 Lecture 24: Bezier Curves ad Surfaces thou shalt be ear uto me Geesis 45:0. Iterpolatio ad Approximatio Freeform curves ad surfaces are smooth shapes ofte describig ma-made objects. The hood of a car,

More information

1 Graph Sparsfication

1 Graph Sparsfication CME 305: Discrete Mathematics ad Algorithms 1 Graph Sparsficatio I this sectio we discuss the approximatio of a graph G(V, E) by a sparse graph H(V, F ) o the same vertex set. I particular, we cosider

More information

Civil Engineering Computation

Civil Engineering Computation Civil Egieerig Computatio Fidig Roots of No-Liear Equatios March 14, 1945 World War II The R.A.F. first operatioal use of the Grad Slam bomb, Bielefeld, Germay. Cotets 2 Root basics Excel solver Newto-Raphso

More information

Assignment Problems with fuzzy costs using Ones Assignment Method

Assignment Problems with fuzzy costs using Ones Assignment Method IOSR Joural of Mathematics (IOSR-JM) e-issn: 8-8, p-issn: 9-6. Volume, Issue Ver. V (Sep. - Oct.06), PP 8-89 www.iosrjourals.org Assigmet Problems with fuzzy costs usig Oes Assigmet Method S.Vimala, S.Krisha

More information

Counting Regions in the Plane and More 1

Counting Regions in the Plane and More 1 Coutig Regios i the Plae ad More 1 by Zvezdelia Stakova Berkeley Math Circle Itermediate I Group September 016 1. Overarchig Problem Problem 1 Regios i a Circle. The vertices of a polygos are arraged o

More information

The Extended Weibull Geometric Family

The Extended Weibull Geometric Family The Exteded Weibull Geometric Family Giovaa Oliveira Silva 1 Gauss M. Cordeiro 2 Edwi M. M. Ortega 3 1 Itroductio The literature o Weibull models is vast, disjoited, ad scattered across may differet jourals.

More information

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure

More information

Reading. Parametric curves. Mathematical curve representation. Curves before computers. Required: Angel , , , 11.9.

Reading. Parametric curves. Mathematical curve representation. Curves before computers. Required: Angel , , , 11.9. Readig Required: Agel.-.3,.5.,.6-.7,.9. Optioal Parametric curves Bartels, Beatty, ad Barsky. A Itroductio to Splies for use i Computer Graphics ad Geometric Modelig, 987. Fari. Curves ad Surfaces for

More information

Fuzzy Minimal Solution of Dual Fully Fuzzy Matrix Equations

Fuzzy Minimal Solution of Dual Fully Fuzzy Matrix Equations Iteratioal Coferece o Applied Mathematics, Simulatio ad Modellig (AMSM 2016) Fuzzy Miimal Solutio of Dual Fully Fuzzy Matrix Equatios Dequa Shag1 ad Xiaobi Guo2,* 1 Sciece Courses eachig Departmet, Gasu

More information

Counting the Number of Minimum Roman Dominating Functions of a Graph

Counting the Number of Minimum Roman Dominating Functions of a Graph Coutig the Number of Miimum Roma Domiatig Fuctios of a Graph SHI ZHENG ad KOH KHEE MENG, Natioal Uiversity of Sigapore We provide two algorithms coutig the umber of miimum Roma domiatig fuctios of a graph

More information

Harris Corner Detection Algorithm at Sub-pixel Level and Its Application Yuanfeng Han a, Peijiang Chen b * and Tian Meng c

Harris Corner Detection Algorithm at Sub-pixel Level and Its Application Yuanfeng Han a, Peijiang Chen b * and Tian Meng c Iteratioal Coferece o Computatioal Sciece ad Egieerig (ICCSE 015) Harris Corer Detectio Algorithm at Sub-pixel Level ad Its Applicatio Yuafeg Ha a, Peijiag Che b * ad Tia Meg c School of Automobile, Liyi

More information

G 2 Tensor Product Splines over Extraordinary Vertices

G 2 Tensor Product Splines over Extraordinary Vertices Eurographics Symposium o Geometry rocessig 28 ierre Alliez ad Szymo Rusikiewicz Guest Editors Volume 27 28, Number 5 G 2 Tesor roduct Splies over Extraordiary Vertices Charles Loop 1 ad Scott Schaefer

More information

Recursive Estimation

Recursive Estimation Recursive Estimatio Raffaello D Adrea Sprig 2 Problem Set: Probability Review Last updated: February 28, 2 Notes: Notatio: Uless otherwise oted, x, y, ad z deote radom variables, f x (x) (or the short

More information

found that now considerable work has been done in this started with some example, which motivates the later results.

found that now considerable work has been done in this started with some example, which motivates the later results. 8 Iteratioal Joural of Comuter Sciece & Emergig Techologies (E-ISSN: 44-64) Volume, Issue 4, December A Study o Adjacecy Matrix for Zero-Divisor Grahs over Fiite Rig of Gaussia Iteger Prajali, Amit Sharma

More information

Stone Images Retrieval Based on Color Histogram

Stone Images Retrieval Based on Color Histogram Stoe Images Retrieval Based o Color Histogram Qiag Zhao, Jie Yag, Jigyi Yag, Hogxig Liu School of Iformatio Egieerig, Wuha Uiversity of Techology Wuha, Chia Abstract Stoe images color features are chose

More information

ON THE DEFINITION OF A CLOSE-TO-CONVEX FUNCTION

ON THE DEFINITION OF A CLOSE-TO-CONVEX FUNCTION I terat. J. Mh. & Math. Sci. Vol. (1978) 125-132 125 ON THE DEFINITION OF A CLOSE-TO-CONVEX FUNCTION A. W. GOODMAN ad E. B. SAFF* Mathematics Dept, Uiversity of South Florida Tampa, Florida 33620 Dedicated

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

Elementary Educational Computer

Elementary Educational Computer Chapter 5 Elemetary Educatioal Computer. Geeral structure of the Elemetary Educatioal Computer (EEC) The EEC coforms to the 5 uits structure defied by vo Neuma's model (.) All uits are preseted i a simplified

More information

Normals. In OpenGL the normal vector is part of the state Set by glnormal*()

Normals. In OpenGL the normal vector is part of the state Set by glnormal*() Ray Tracig 1 Normals OpeG the ormal vector is part of the state Set by glnormal*() -glnormal3f(x, y, z); -glnormal3fv(p); Usually we wat to set the ormal to have uit legth so cosie calculatios are correct

More information

ON A PROBLEM OF C. E. SHANNON IN GRAPH THEORY

ON A PROBLEM OF C. E. SHANNON IN GRAPH THEORY ON A PROBLEM OF C. E. SHANNON IN GRAPH THEORY m. rosefeld1 1. Itroductio. We cosider i this paper oly fiite odirected graphs without multiple edges ad we assume that o each vertex of the graph there is

More information

6.854J / J Advanced Algorithms Fall 2008

6.854J / J Advanced Algorithms Fall 2008 MIT OpeCourseWare http://ocw.mit.edu 6.854J / 18.415J Advaced Algorithms Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.415/6.854 Advaced Algorithms

More information

Compactness of Fuzzy Sets

Compactness of Fuzzy Sets Compactess of uzzy Sets Amai E. Kadhm Departmet of Egieerig Programs, Uiversity College of Madeat Al-Elem, Baghdad, Iraq. Abstract The objective of this paper is to study the compactess of fuzzy sets i

More information

Section 7.2: Direction Fields and Euler s Methods

Section 7.2: Direction Fields and Euler s Methods Sectio 7.: Directio ields ad Euler s Methods Practice HW from Stewart Tetbook ot to had i p. 5 # -3 9-3 odd or a give differetial equatio we wat to look at was to fid its solutio. I this chapter we will

More information

Homework 1 Solutions MA 522 Fall 2017

Homework 1 Solutions MA 522 Fall 2017 Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear

More information

Fundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le

Fundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le Fudametals of Media Processig Shi'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dih Le Today's topics Noparametric Methods Parze Widow k-nearest Neighbor Estimatio Clusterig Techiques k-meas Agglomerative Hierarchical

More information

2D Isogeometric Shape Optimization considering both control point positions and weights as design variables

2D Isogeometric Shape Optimization considering both control point positions and weights as design variables 1 th World Cogress o tructural ad Multidiscipliary Optimizatio May 19-24, 213, Orlado, Florida, UA 2D Isogeometric hape Optimizatio cosiderig both cotrol poit positios ad weights as desig variables Yeo-Ul

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

State-space feedback 6 challenges of pole placement

State-space feedback 6 challenges of pole placement State-space feedbac 6 challeges of pole placemet J Rossiter Itroductio The earlier videos itroduced the cocept of state feedbac ad demostrated that it moves the poles. x u x Kx Bu It was show that whe

More information

3D Model Retrieval Method Based on Sample Prediction

3D Model Retrieval Method Based on Sample Prediction 20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer

More information

EE123 Digital Signal Processing

EE123 Digital Signal Processing Last Time EE Digital Sigal Processig Lecture 7 Block Covolutio, Overlap ad Add, FFT Discrete Fourier Trasform Properties of the Liear covolutio through circular Today Liear covolutio with Overlap ad add

More information

Markov Chain Model of HomePlug CSMA MAC for Determining Optimal Fixed Contention Window Size

Markov Chain Model of HomePlug CSMA MAC for Determining Optimal Fixed Contention Window Size Markov Chai Model of HomePlug CSMA MAC for Determiig Optimal Fixed Cotetio Widow Size Eva Krimiger * ad Haiph Latchma Dept. of Electrical ad Computer Egieerig, Uiversity of Florida, Gaiesville, FL, USA

More information

Arithmetic Sequences

Arithmetic Sequences . Arithmetic Sequeces COMMON CORE Learig Stadards HSF-IF.A. HSF-BF.A.1a HSF-BF.A. HSF-LE.A. Essetial Questio How ca you use a arithmetic sequece to describe a patter? A arithmetic sequece is a ordered

More information

Alpha Individual Solutions MAΘ National Convention 2013

Alpha Individual Solutions MAΘ National Convention 2013 Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5

More information

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein 068.670 Subliear Time Algorithms November, 0 Lecture 6 Lecturer: Roitt Rubifeld Scribes: Che Ziv, Eliav Buchik, Ophir Arie, Joatha Gradstei Lesso overview. Usig the oracle reductio framework for approximatig

More information

Computational Geometry

Computational Geometry Computatioal Geometry Chapter 4 Liear programmig Duality Smallest eclosig disk O the Ageda Liear Programmig Slides courtesy of Craig Gotsma 4. 4. Liear Programmig - Example Defie: (amout amout cosumed

More information

Handwriting Stroke Extraction Using a New XYTC Transform

Handwriting Stroke Extraction Using a New XYTC Transform Hadwritig Stroke Etractio Usig a New XYTC Trasform Gilles F. Houle 1, Kateria Bliova 1 ad M. Shridhar 1 Computer Scieces Corporatio Uiversity Michiga-Dearbor Abstract: The fudametal represetatio of hadwritig

More information

A Note on Least-norm Solution of Global WireWarping

A Note on Least-norm Solution of Global WireWarping A Note o Least-orm Solutio of Global WireWarpig Charlie C. L. Wag Departmet of Mechaical ad Automatio Egieerig The Chiese Uiversity of Hog Kog Shati, N.T., Hog Kog E-mail: cwag@mae.cuhk.edu.hk Abstract

More information

BAYESIAN WITH FULL CONDITIONAL POSTERIOR DISTRIBUTION APPROACH FOR SOLUTION OF COMPLEX MODELS. Pudji Ismartini

BAYESIAN WITH FULL CONDITIONAL POSTERIOR DISTRIBUTION APPROACH FOR SOLUTION OF COMPLEX MODELS. Pudji Ismartini Proceedig of Iteratioal Coferece O Research, Implemetatio Ad Educatio Of Mathematics Ad Scieces 014, Yogyakarta State Uiversity, 18-0 May 014 BAYESIAN WIH FULL CONDIIONAL POSERIOR DISRIBUION APPROACH FOR

More information

Chapter 3 Classification of FFT Processor Algorithms

Chapter 3 Classification of FFT Processor Algorithms Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As

More information

Kernel Smoothing Function and Choosing Bandwidth for Non-Parametric Regression Methods 1

Kernel Smoothing Function and Choosing Bandwidth for Non-Parametric Regression Methods 1 Ozea Joural of Applied Scieces (), 009 Ozea Joural of Applied Scieces (), 009 ISSN 943-49 009 Ozea Publicatio Kerel Smoothig Fuctio ad Choosig Badwidth for No-Parametric Regressio Methods Murat Kayri ad

More information

Force Network Analysis using Complementary Energy

Force Network Analysis using Complementary Energy orce Network Aalysis usig Complemetary Eergy Adrew BORGART Assistat Professor Delft Uiversity of Techology Delft, The Netherlads A.Borgart@tudelft.l Yaick LIEM Studet Delft Uiversity of Techology Delft,

More information

ME 261: Numerical Analysis Lecture-9&10: Numerical Interpolation

ME 261: Numerical Analysis Lecture-9&10: Numerical Interpolation ME 6: Numerical Aalysis Lecture-9&: Numerical Iterpolatio Md. Taver Hossai Departmet o Mechaical Egieerig BUET http://tatusher.uet.ac.d To estimate itermediate values etwee precise data poits most commoly

More information

CSCI 5090/7090- Machine Learning. Spring Mehdi Allahyari Georgia Southern University

CSCI 5090/7090- Machine Learning. Spring Mehdi Allahyari Georgia Southern University CSCI 5090/7090- Machie Learig Sprig 018 Mehdi Allahyari Georgia Souther Uiversity Clusterig (slides borrowed from Tom Mitchell, Maria Floria Balca, Ali Borji, Ke Che) 1 Clusterig, Iformal Goals Goal: Automatically

More information

Matrix Partitions of Split Graphs

Matrix Partitions of Split Graphs Matrix Partitios of Split Graphs Tomás Feder, Pavol Hell, Ore Shklarsky Abstract arxiv:1306.1967v2 [cs.dm] 20 Ju 2013 Matrix partitio problems geeralize a umber of atural graph partitio problems, ad have

More information

An Efficient Algorithm for Graph Bisection of Triangularizations

An Efficient Algorithm for Graph Bisection of Triangularizations A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045 Oe Brookigs Drive St. Louis, Missouri 63130-4899, USA jaegerg@cse.wustl.edu

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

Strong Complementary Acyclic Domination of a Graph

Strong Complementary Acyclic Domination of a Graph Aals of Pure ad Applied Mathematics Vol 8, No, 04, 83-89 ISSN: 79-087X (P), 79-0888(olie) Published o 7 December 04 wwwresearchmathsciorg Aals of Strog Complemetary Acyclic Domiatio of a Graph NSaradha

More information

Cluster Analysis. Andrew Kusiak Intelligent Systems Laboratory

Cluster Analysis. Andrew Kusiak Intelligent Systems Laboratory Cluster Aalysis Adrew Kusiak Itelliget Systems Laboratory 2139 Seamas Ceter The Uiversity of Iowa Iowa City, Iowa 52242-1527 adrew-kusiak@uiowa.edu http://www.icae.uiowa.edu/~akusiak Two geeric modes of

More information

EVALUATION OF TRIGONOMETRIC FUNCTIONS

EVALUATION OF TRIGONOMETRIC FUNCTIONS EVALUATION OF TRIGONOMETRIC FUNCTIONS Whe first exposed to trigoometric fuctios i high school studets are expected to memorize the values of the trigoometric fuctios of sie cosie taget for the special

More information

Neuro Fuzzy Model for Human Face Expression Recognition

Neuro Fuzzy Model for Human Face Expression Recognition IOSR Joural of Computer Egieerig (IOSRJCE) ISSN : 2278-0661 Volume 1, Issue 2 (May-Jue 2012), PP 01-06 Neuro Fuzzy Model for Huma Face Expressio Recogitio Mr. Mayur S. Burage 1, Prof. S. V. Dhopte 2 1

More information

Dimensionality Reduction PCA

Dimensionality Reduction PCA Dimesioality Reductio PCA Machie Learig CSE446 David Wadde (slides provided by Carlos Guestri) Uiversity of Washigto Feb 22, 2017 Carlos Guestri 2005-2017 1 Dimesioality reductio Iput data may have thousads

More information

IMP: Superposer Integrated Morphometrics Package Superposition Tool

IMP: Superposer Integrated Morphometrics Package Superposition Tool IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College

More information

FEATURE RECOGNITION OF ROTATIONAL PARTS USING BIQUADRATIC BEZIER PATCHES

FEATURE RECOGNITION OF ROTATIONAL PARTS USING BIQUADRATIC BEZIER PATCHES roceedigs of the Iteratioal Coferece o Mechaical Egieerig 7 (ICME7) 9-31 December 7, Dhaka, Bagladesh ICME7- FEATURE RECOGNITION OF ROTATIONAL ARTS USING BIQUADRATIC BEZIER ATCHES J. Kumar 1 ad N. Roy

More information

Super Vertex Magic and E-Super Vertex Magic. Total Labelling

Super Vertex Magic and E-Super Vertex Magic. Total Labelling Proceedigs of the Iteratioal Coferece o Applied Mathematics ad Theoretical Computer Sciece - 03 6 Super Vertex Magic ad E-Super Vertex Magic Total Labellig C.J. Deei ad D. Atoy Xavier Abstract--- For a

More information

Math Section 2.2 Polynomial Functions

Math Section 2.2 Polynomial Functions Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably

More information

arxiv: v2 [cs.ds] 24 Mar 2018

arxiv: v2 [cs.ds] 24 Mar 2018 Similar Elemets ad Metric Labelig o Complete Graphs arxiv:1803.08037v [cs.ds] 4 Mar 018 Pedro F. Felzeszwalb Brow Uiversity Providece, RI, USA pff@brow.edu March 8, 018 We cosider a problem that ivolves

More information