Solving some Combinatorial Problems in grid n-ogons

Size: px
Start display at page:

Download "Solving some Combinatorial Problems in grid n-ogons"

Transcription

1 Solvng some Combnatoral Problems n grd n-ogons Antono L. Bajuelos, Santago Canales, Gregoro Hernández, Ana Mafalda Martns Abstract In ths paper we study some problems related to grd n-ogons. A grd n-ogon s a n-vertex orthogonal smple polygon, wth no collnear edges, that may be placed n a (n/)x(n/) square grd. We wll present some problems and results related to a subclass of grd n-ogons, the THIN grd n-ogons, n partcular a classfcaton for ths subclass of polygons. We follow by presentng the soluton of the MINIMUM VERTEX GUARD problem for the Mn-Area and for the Spral grd n-ogons. Fnally the soluton of the MAXIMUM HIDDEN VERTEX Set problem for THIN grd n-ogons s also presented. Keywords Art Gallery Problem, Grd n-ogon, Hdden Set, Orthogonal Polygon I I. INTRODUCTION N the feld of vsblty problems, guardng and hdng are among the most dstngushed and exhaustvely studed problems. In vsblty problems we are gven as nput a smple polygon (smple closed polygonal curve wth ts nteror). In guardng we need to fnd a mnmum number of guards postoned n the polygon, such that these guards collectvely see the whole polygon. Two ponts n the polygon see each other, f the lne segment connectng them les entrely n the polygon. In hdng, we need to fnd a maxmum number of postons n the polygon, such that no two of these postons see each other. The guardng problems started durng a conference, n 976, when Vctor Klee, posed the followng problem, whch today s known as the orgnal art gallery problem: How many statonary guards are needed to guard an art gallery room wth n walls? In the abstract verson of ths problem, the nput s a smple polygon P n the plane, representng the floor plan of the art gallery room and a guard s consdered a fxed Manuscrpt receved January 4, 007; Revsed receved May, 007 Ths work was supported n part by: the FCT (Portugal) fellowshp Grant SFRH/BD/865/006, the Grant MEC-HP and CAD-DPI (Span) and by CEOC (Portugal) trough Program POCTI, FCT, cofnanced by EC fund FEDER and by Acção Integrada Nº E-77/06. Antono L. Bajuelos, Assstant Professor of Computer Scence, Department of Mathematcs, Unversty of Avero & CEOC, Avero, Portugal (phone: (+35) , fax: ( , e-mal: lesle@ua.pt). Santago Canales, Assstant Professor of Computer Scence, Escuela Técnca Superor de Ingenera, Unvesdad Potfíca Comllas de Madrd, Span (e-mal: scanales@upcomllas.es). Gregoro Hernández, Assocate Professor of Computer Scence, Facultad de Informátca, Unversdad Poltécnca de Madrd, 8660 Madrd, Span (emal: gregoro@f.upm.es). Ana Mafalda Martns, Ph.D student of Computer Scence, Department of Mathematcs, Unversty of Avero & CEOC, Avero, Portugal (emal: mafalda.martns@ua.pt). Issue, Volume, pont n P wth π range vsblty. A set of guards covers P, f each pont of P s seen by at least one guard. Many varatons of the orgnal art gallery theorem have been studed over the years, such as: where the guards may be postoned (anywhere or n specfc postons, e.g., vertces), what knd of guards are to be used (e.g., statonary guards versus moble guards) and what assumptons are made for the nput polygon (such as beng orthogonal) (see [9]). The opposte problem of hdng a maxmum number of objects from each other n a gven smple polygon can have a practcal applcaton n computergames, where a player needs to fnd and collect or destroy as many objects as possble. Beng unable to see the next object whle collectng an object makes the game more nterestng. Such as the guardng problems, ths problem has many varatons []. In ths paper, of the guardng problems, we wll consder the MINIMUM VERTEX GUARD (MVG) problem that s the problem of fndng the mnmum number of guards placed on the vertces (vertex guards) needed to guard a gven smple polygon. And of the hdng problems, we wll consder the MAXIMUM HIDDEN VERTEX SET (MHVS) problem that s the problem of fndng the maxmum number of vertces of a gven smple polygon, such that no two vertces see each other. Both problems are NP-hard [3,7]. Important subclasses of polygons are the orthogonal smple polygons (smple polygons whose edges meet at rght angles). Indeed, they are useful as approxmatons to polygons; and they arse naturally n domans domnated by Cartesan coordnates, such as raster graphcs, VLSI desgn, or archtecture. The MVG and MHVS problems are stll NP-hard for orthogonal polygons. Ths paper has the ntenton of ntroducng a partcular type of orthogonal polygons - the grd n-ogons - that presents suffcently nterestng characterstcs that we are studyng and formalzng. Of the problems related to grd n-ogons, the vsblty problems are the ones that motvate us more, partcularly the guardng and hdng problems. The paper s structured as follows: n the next subsecton we wll ntroduce some prelmnary defntons and useful results. In secton 3 we wll present some problems and results related to THIN grd n-ogons, n partcular a classfcaton for these polygons. In secton 4 we wll expose some results related to the MVG problem on grd n-ogons and we wll study the MHVS problem on THIN grd n-ogons, a subclass of grd n-ogons. Fnally, n secton 5 we wll draw conclusons.

2 II. CONVENTIONS, DEFINITIONS AND SOME RESULTS In ths paper, the nteror and the boundary of a smple polygon P wll be denoted by INT(P) and BND(P), respectvely. And, for convenence, we wll assume that the vertces of P are ordered n a counterclockwse (CCW) drecton around INT(P). A vertex of P s called convex f the nteror angle between ts two ncdent edges s at most π, otherwse t s called reflex. We use r to represent the number of reflex vertces of P. It has been shown by O'Rourke that n= r+ 4, for every orthogonal smple polygon of n vertces (n-ogon, for short). A rectlnear cut of a n-ogon P s a partton of P obtaned by extendng each edge ncdent to a reflex vertex of P towards INT(P) untl t hts BND(P). We denote ths partton by Π(P) and the number of ts peces by Π(P). Each pece s a rectangle and so we call t a r-pece. A n-ogon that may be placed n a ( n / ) ( n / ) square grd and that does not have collnear edges s called grd n-ogon. We assume that the grd s defned by the horzontal lnes y =,, y/ and the vertcal lnes x =,, x/ and that ts northwest corner s (, ). Each grd n-ogon has exactly one edge n every lne of the grd. Grd n-ogons that are symmetrcally equvalent are grouped []. A grd n-ogon Q s called FAT ff Π(Q) Π(P), for all grd n-ogons P. Smlarly, a grd n-ogon Q s called THIN ff Π(Q) Π(P), for all grd n-ogons P. Let P be a grd n-ogon and r the number of ts reflex vertces. In [] t has been proven that, f P s FAT then 3r + 6r+ 4 Π ( P) =, for r even and 4 3( r + ) Π ( P) =, for r odd; f P s THIN then 4 ( P) = r+. There s a sngle FAT grd n-ogon (see Fg. (a)); however, THIN grd n-ogons are not unque (see Fg. (b)). (a) INTERNATIONAL JOURNAL of MATHEMATICS AND COMPUTERS IN SIMULATION Fg. : (a) The unque FAT grd n-ogons, for r =, 3 and 4; (b) Two THIN 0-ogons (b) boundary can be dvded nto a reflex chan and a convex chan. A polygonal chan s called reflex f ts vertces are all reflex (all except the vertces at the end of the chan) wth respect to the nteror of the polygon. And, a polygonal chan s called convex f ts vertces are all convex wth respect to the nteror of the polygon. In [6] t has been proven that there are SPIRAL grd n-ogons, for all n 6 ; however, they are not unque, as we may see ths n Fg. (b). And t was also proven that every SPIRAL grd n-ogon, wth r reflex vertces, s a THIN grd n-ogon. Gven a n-ogon P, we can assocate to Π(P) a graph, denomnated the dual graph of Π(P) and denoted by G(P), whch captures the adjacency relaton between peces of the partton. Each node of the dual graph corresponds to a pece of the partton and ts non-orented edges connect adjacent peces,.e., peces wth a common edge. We prove that f P s a THIN grd n-ogon then G(P) s a path graph,.e., a tree wth two nodes of vertex of degree, called leaves, and the remanng nodes of vertex of degree. To prove ths result we ntroduce Lemma.. (a) Fg. : (a) The unque MIN-AREA grd n-ogon, for r =, and 3; (b) Two dfferent SPIRAL grd -ogons (reflex chan s bold). Lemma.: Let P be a THIN (n+)-ogon. Then every grd n- ogon that yelds P by INFLATE-PASTE (a correct and complete method to generate grd n-ogons, well descrbed n [8]) s also THIN. Proposton.: Let P be a THIN grd n-ogon wth r (r ) reflex vertces. Then G(P) s a path graph (see examples n Fg. 3). The proof of ths proposton s done by nducton on r and uses lemma.. (b) The area of a grd n-ogon s the number of grd cells n ts nteror. In [] t has been proven that for all grd n-ogon P, wth n 8, r+ A( P) r + 3. A grd n-ogon P s a MAX-AREA grd n-ogon ff AP ( ) = r + 3 and t s a MIN- AREA grd n-ogon ff A( P) = r+. There are MAX-AREA grd n-ogons for all n, but they are not unque. However, there s a sngle MIN-AREA grd n-ogon and ts form s llustrated n Fg. (a). Regardng MIN-AREA grd n-ogons, t s obvous that they are THIN grd n-ogons, because Π ( P) = r + holds only for THIN grd n-ogons. However, ths condton s not suffcent for a THIN grd n-ogon to be a MIN-AREA grd n-ogon. A grd n-ogon s called a SPIRAL grd n-ogon f ts Fg. 3: Three THIN grd 0-ogons and respectve dual graphs. Proposton.3: Let P be a grd n-ogon, wth n > 6. If P s not THIN then G(P) s not a tree (see example n Fg. 4). Fg. 4: A grd 0-ogon and respectve dual graph. Issue, Volume,

3 Proposton.. establshes that, beng P a THIN grd n- ogon, G(P) s a path graph. So, each r-pece of Π(P) s adjacent to at most two r-peces. In ths way, each r-pece has at most nteror edges. Consequently, we conclude that n Π(P) there are 3 types of r-peces: Type : wth one nteror edge and three boundary edges; Type : wth two nteror edges not adjacent and two boundary edges not adjacent; Type 3: wth two adjacent nteror edges and two adjacent boundary edges. The r-peces of the Type correspond to leaves of G(P) and those of the Type and Type 3 correspond to nodes of degree. We showed that of the 4 vertces of the r-peces of Type three are vertces of P, beng two reflex and the other convex, and the other vertex s an nteror pont of an edge of P. Of the 4 vertces of the r-peces of Type two are convex vertces of P and the other two are nteror ponts of edges of P. And fnally, of the 4 vertces of the r-peces of Type two are vertces of P, beng one reflex and the other convex, and the other two are nteror ponts of edges of P (see Fg. 5). Fg. 6: THIN wth r = 4; on the left s represented ts dual graph and on the rght ts skeleton. Lemma.4: The skeleton of a THIN grd n-ogon s an orthogonal polygonal curve wth r+ vertces. III. SOME PROBLEMS RELATED TO THIN GRID N-OGONS As we have seen n Secton, on the contrary of the FATs, the THIN n-ogons are not unque. In fact, there are THIN 8- ogon, 30 THIN 0-ogons, 49 THIN -ogons, etc. Thus, t s nterestng to evdence that the number of THIN grd n-ogons ( THIN(n) ) grows exponentally. Does there exst some expresson that relates n to THIN(n)? As a step for the resoluton of ths problem we wll frst group the THINs nto classes. In Secton we defned the skeleton of a THIN grd n- ogon. Now, usng ths concept, we wll group THIN grd n- ogons nto classes. From the skeleton of the THIN grd n-ogon, we can always represent t by a chan of 0's and 's, wth length r. For that we proceed n the followng way: we transverse ts skeleton, startng at vertex u, and then we represent each turn left by and each turn rght by 0. Now, we wll defne two operatons on these chans: the complementary operaton and the nverson operaton. Type Type Type 3 Fg. 5: Types of r-peces of a THIN grd n-ogon. Now, we are gong to defne the skeleton of a THIN grd n- ogon. Let P be a THIN grd n-ogon. Snce G(P) s a path graph, we can say that P has two extremes : the r-peces assocated wth the leaves of the dual graph. We wll denote by kernel the extreme that has the horzontal edge wth hghest y-coordnate. From ths graph we can obtan an orthogonal polygonal curve (.e., a polygonal curve wth horzontal or vertcal edges) n the followng way: we take the centrod of each r-pece, then we connect each one wth the centrods of the adjacent r-peces and, fnally, we remove the central vertex of each three algned vertces, as we can see n Fg. 6. We choose, for the frst vertex of ths orthogonal curve the kernel's centrod. Therefore t s easy to prove the next result. Defnton 3.: Let c be a chan of 0's and 's, wth length r,.e., c = b b b r, where b = 0 or b =, for =,,, r. The complementary operaton s an operaton whch takes c as the argument and returns ts complementary c* = b *b * b r *, where b *= f b = 0 and b * = 0 f b =, =,,, r. The nverson operaton s an operaton whch takes c as the argument and returns ts nverse c - =b r b r- b. For example, the complementary of the chan c = 000 s the chan c* = 000 and ts nverse s c - = 000. Easly we can verfy that, (c*) - = (c - )*, (c*)* = c and (c - ) - = c. Proposton 3.: Let C r be the set of all chans, of 0's and 's, wth length r. The relaton ~ defned on C r by c ~ c c = c c = c - c = c * c = (c * ) -, s an equvalence relaton. Consder, now, the quotent set of C r by ~, C r / ~ = {[c ] ~ : c C r }. Note that, each equvalence class has more than one representatve. We assume that the representatve of each equvalence class always starts by. Proposton 3.3: Let P r be the set of all THIN grd n-ogons, wth r reflex vertces. The relaton defned on P r by P P c ~ c, where c and c are the chans that represent P and P, respectvely, s an equvalence relaton. The proof of ths proposton s trval. Let P r / = {[P ] : P C r }. Let P, P P r and c, c C r the chans that represent them, respectvely. Note that, P and P belong to the same class (.e., P and P are equvalents) f one of the followng condtons s true: () c = c ; () c = c - ; () c = c * or (v) c = (c *) -. Observe that, geometrcally, () can correspond to a horzontal reflecton and () to a vertcal reflecton. In Fg. Issue, Volume,

4 7 sx THINs wth 4 reflex vertces that belong to the same class are llustrated. the begnnng of the polygon, t s ndfferent the one that s modfed. Nevertheless, when the polygon has two collnear edges, the choce of the edge s not always ndfferent. Step Fg. 7: THINs wth 4 reflex vertces and respectve chans. We can place the followng queston: Let c be chan of 0's and 's wth length r, started by. Is t always possble to construct a THIN, wth r reflex vertces, whose chan that represents t s c? To answer ths queston we present the next algorthm: Step Step 3 Algorthm 3.4: Construct a THIN form a chan of 0's and 's, of length r Let c be a chan of 0's and 's, of length r, started by.. From the chan draw a skeleton gnorng collneartes.. Move e vertcal sweep lne from left to rght to elmnate vertcal collneartes. Repeat the prevous step untl there are no more collnear vertcal edges. 3. Move a horzontal sweep lne from bottom to top to elmnate horzontal collneartes. Repeat the prevous step untl there are no more collnear horzontal edges. Fgure 8 llustrate ths algorthm from the chan 0. Fg. 9: Constructng the THIN from the chan 0 Anyhow, ths algorthm always generates a THIN grd n- ogon whose chan that represents t s equvalent to c. Thus, f the chan that represents the Thn, generated by ths algorthm, s c*; c - or (c*) -, t s enough to make a vertcal reflecton, a horzontal reflecton or a vertcal reflecton followed by a horzontal reflecton, respectvely, so that the chan that represents t s exactly c, see Fg.0 for llustraton. Thus, ths algorthm proves that for each chan of 0's and 's, wth length r, started by, there s a Thn grd n-ogon wth r reflex vertces represented by t. Step Step Step 3 Fg. 8: Constructng the THIN for the chan 0 Important remarks:. In the Step, f we construct a skeleton, gnorng the collneartes, we can obtan a skeleton that not correspond to the gven chan, for example n the Fg. 9, the chan that represents the constructed Thn s (complementary followed by nverson of the gven chan).. To elmnate collneartes, n step and 3, t s necessary to modfy the edge correspondng to the begnnng of the polygon. If two edges correspond to the begnnng of the polygon, or no edge correspond to Fg. 0: (a) The chan that represents the THIN after the reflectons s c = 0; (b) The chan that represents the THIN after the reflectons s c = 00. Based on the prevous reasonng and defntons t s not dffcult to prove the followng result. Proposton 3.5: The correspondence f: P r / C r / ~ defned by f([p ]) = [c ], where c C r s the chan that represents P P r, whch s a representatve of the class [P ], s a bjectve functon. Issue, Volume,

5 Now, we may show the next result that allows us to count the number of classes of THIN grd n-ogons wth r reflex vertces. Proposton 3.6: The number of classes of THIN grd n-ogons wth r reflex vertces (r ) s equal to: ( r 3) r +, f r s odd ( r ) r +, f r s even Proof. By proposton 3.5 we can conclude that P r / = C r / ~, so we just have to calculate C r / ~. The cardnal of C r s r and the number of symmetrcal chans (c = c - ), wth length r, s / r. If a chan c s symmetrcal, then ts equvalence class s consttuted by two chans, c and c*. If a chan c s not symmetrcal, to fnd the cardnal of ts class, we have to dstngush two cases: r odd and r even. If r s odd, all the chans have 4 equvalent chans: c, c -, c* and (c*) - (for example: c = 00, 00, 000 and 000). If r s even, there are chans that have 4 equvalent chans (e.g., c = 0) and chans that only have equvalent chans; ths case happens when c* = c - (e.g., for the chan c = 00, c* = c - = 00. Let us now count the number of equvalence classes. If r s odd, the number of equvalence classes of the symmetrcal r+ r chans (SC) s SC = = and the number of equvalence classes of the non symmetrcal chans (NSC) s r+ r 3 r r NSC = =. Thus, f r s odd, the total 4 4 number of equvalence classes s ( 3 r ) r r 3 r + = + r. If r s even, the number of equvalence classes of r ( ) symmetrcal chans s equal to r SC = =. The number of equvalence classes of non symmetrcal chans consttuted by two chans (for example, the classes of the r ( ) chans 000, 00, 000, ) s r =. In fact, to obtan c* = c -, the second half of the chan s completely determned by the frst half. Therefore, the cardnal of these classes s half of the number of chans of ths type. And, the number of equvalence classes of non symmetrcal chans consttuted by four chans s r r ( * All Symmetrc Chans wth c = c ) = r = 4 4 ( r ) r. Thus, f r s even, n the total, the number of ( ) r equvalence classes s equal to + r INTERNATIONAL JOURNAL of MATHEMATICS AND COMPUTERS IN SIMULATION However, there are stll some open problems to solve, such as: How many elements THIN grd n-ogons does each class have? Wll t be possble to fnd an algorthm that generates all THIN grd n-ogons of the same class? Note that, solvng the frst problem we also solve the ntal problem, that s: Does there exst some expresson that relates n to THIN(n)? IV. SOME VISIBILITY PROBLEMS ON GRID n-ogons Of the problems related to grd n-ogons, the guardng and hdng problems are the ones that motvate us more, partcularly the MVG and MHVS problems. Snce THIN and FAT n-ogons are the classes for whch the number of r-peces s mnmum and maxmum, we thnk that they can be representatve of extremal behavor. Besdes that they are used expermentally to evaluate approxmate methods of resoluton of the MVG problem, so we started wth them. We have already proven that to guard any FAT grd n-ogon t s always suffcent two π/ vertex guards (vertex guards wth π/ range vsblty) and establshed where they must be placed [4]. However, THIN grd n-ogons are much more dffcult to guard, n spte of havng much fewer r-peces than FATs. Besdes, they are not unque, so we tred to characterze structural propertes of classes of THINs that allow for smplfyng the problem's study. Up to now the only qute characterzed subclasses are the MIN-AREA and the SPIRAL grd n-ogons. We proved that to guard any MIN-AREA and SPIRAL grd n- ogon n/6 and n/4 vertex guards are necessary, respectvely. Moreover, we showed where those guards could be placed [5, 6]. MAXIMUM HIDDEN VERTEX Set Problem on THIn grd n-ogons Gven a smple polygon, P, and a subset of vertces of P, HV, we say that HV s a hdden vertex set f no two vertces n HV see each other. The MAXIMUM HIDDEN VERTEX SET problem on a smple polygon asks for an hdden vertex set, HV, of maxmum cardnalty. We wll call the elements of HV hdden vertces. Shermer [7] proved that the sze of the MAXIMUM HIDDEN VERTEX SET of a n-ogon s at most (n-)/. Ths tght bound s acheved n starcase polygons. We wll show that, gven a THIN grd n-ogon the maxmum cardnalty of a hdden vertex set s n/4. Let P be a THIN grd n-ogon and S = u u u n/ ts skeleton. Let us assume, wthout loss of generalty that the frst edge of S, [u u ], s horzontal and that u s to the rght of u. Note that, the boundary of P conssts of two joned polygonal chans, c and c, parallel to S, where the frst edge of c s a bottom edge and the frst edge of c s a top edge. Note that, c and c can be expressed as ordered sequences of vertces c v v... v n = and / c v v... v n =, where / v denotes the th vertex of c and v denotes the th vertex of c (see Fg. ). Ths way, BND(P) = c vn/ v n/ c vv. Observe, also, that, f we transverse S, startng at vertex u, c s always on the rght of S and c on the left. Issue, Volume, 007 8

6 Fg. : Two THINs grd n-ogons, ts skeletons and the chans c and c (c n bold). In Case the vertex that s marked as hdden s the vertex vk and n Case s the vertex v k. In both cases the marked vertex does not see none of the already marked as hdden, snce of the already vsted vertces ths one only sees v and v (see Fg.4). k k To each vertex of the skeleton we correspond two vertces of the polygon, one n c and another one n c. That s, to u S, we correspond the vertces v c and v c. And to each edge of the skeleton we correspond two parallel edges of the polygon, one n c and another one n c. That s, to uu + S, we correspond the edges c and c. Note that, by constructon of the skeleton, we can easly see that any pont of sees any pont of. Now, for each u k- S wth k =,, n/4, we mark an hdden vertex n P, n the followng way: for k = we mark v ; for k we mark v or v, dependng f v s k k k reflex or convex, respectvely (see Fg., for llustraton). Fg. 4: The shaded zones are not vsble by the marked vertces. Observe that f we mark as hdden the vertces v k (n Case ) and v k (n Case ) we can't guarantee that they don't see any of the vertces already marked as hdden, snce they see more backwards (see Fg. 5). Fg. 5: The shaded zones are not vsble by the marked vertces. Fg. : Two THINs n-ogons and marked hdden vertces (c n bold). Note that, the n/4 marked vertces form an hdden vertex set, snce each tme that we mark a new vertex as hdden we can guarantee that t does not see none of the vertces that prevously had been marked as hdden. In fact, for k = t s trval. For k, we have two cases, dependng f v k s reflex (Case ) or convex (Case ), as we can se n Fg.3. Fg. 3: In Case the vertex v v k k s reflex and n Case the vertex s convex. Therefore t s easy to prove the next result. Lemma 4.: For any THIN grd n-ogon there s an hdden vertex HV and HV = n/4. And then usng ths Lemma we prove our man result. Theorem 4.: Let P be a THIN grd n-ogon. The maxmum cardnalty of an hdden vertex set n P s n/4. V. CONCLUSION We presented some results related to grd n-ogons. Of the hdng problems related to the grd n-ogons, t s the MHVS problem that motvates us more. We proved that the maxmum cardnalty of an hdden vertex set n a THIN n-ogon s n/4. Moreover, we establshed a possble postonng for those hdden vertces. We also establshed a possble classfcaton for THIN n-ogon, as a step to launch an expresson that relates n to THIN(n). Issue, Volume, 007 8

7 ACKNOWLEDGMENT We wsh to thank Inês Perera Matos from the Unversty of Avero for helpful dscussons about the problem of classfcaton of THIN grd n-ogons. REFERENCES [] A. L. Bajuelos, A. P. Tomás: Parttonng Orthogonal Polygons by Extenson of All Edges Incdent to Reflex Vertces: lower and upper bounds on the number of peces. Proc. of ICCSA 004, LNCS 3045, Sprnger-Verlag (004) [] S. Edenbenz: In-approxmablty of Fndng Maxmum Hdden Sets on Polygons and Terrans. Computatonal Geometry (00) [3] D. Lee, A. Ln: Computatonal Complexty of Art Gallery Problems. IEEE Transactons on Informaton Theory IT-3 (986) [4] A. M. Martns, A. Bajuelos: Some Propertes of Fat and Thn grd n- ogons. n Wley-VCH Verlag (Eds): Proc. of ICNAAM 005 (005) [5] A. M. Martns, A. Bajuelos: Characterzng and Coverng some Classes of Orthogonal polygons. In Proc. ICCS 006, LNCS 399, Sprnger- Verlag (006) [6] A. M. Martns, A. Bajuelos: Vertex Guards n a Subclass of Orthogonal Polygons. Internatonal Journal of Computer Scence and Network Securty (IJCSNS), Vol. 6 No. 9, September 006. [7] T. Shermer: Hdng people n polygons, Computng 4 (989) [8] A. P. Tomás, A. Bajuelos: Quadratc-Tme Lnear-Space Algorthms for Generatng Orthogonal Polygons wth a Gven Number of Vertces. In Proc. of ICCSA 004, LNCS 3045, Sprnger-Verlag (004) 7-6. [9] J. Urruta: Art Gallery and Illumnaton Problems. In J.-R. Sack and J. Urruta (edtors), Handbook of Computatonal Geometry, Elsever (000). Issue, Volume,

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

Partitioning Orthogonal Polygons by Extension of All Edges Incident to Reflex Vertices: lower and upper bounds on the number of pieces

Partitioning Orthogonal Polygons by Extension of All Edges Incident to Reflex Vertices: lower and upper bounds on the number of pieces Partitioning Orthogonal Polygons by Extension of All Edges Incident to Reflex Vertices: lower and upper bounds on the number of pieces António Leslie Bajuelos 1, Ana Paula Tomás and Fábio Marques 3 1 Dept.

More information

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane An Approach n Colorng Sem-Regular Tlngs on the Hyperbolc Plane Ma Louse Antonette N De Las Peñas, mlp@mathscmathadmueduph Glenn R Lago, glago@yahoocom Math Department, Ateneo de Manla Unversty, Loyola

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

Related-Mode Attacks on CTR Encryption Mode

Related-Mode Attacks on CTR Encryption Mode Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan

More information

The Shortest Path of Touring Lines given in the Plane

The Shortest Path of Touring Lines given in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He

More information

Fast Computation of Shortest Path for Visiting Segments in the Plane

Fast Computation of Shortest Path for Visiting Segments in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm Internatonal Journal of Advancements n Research & Technology, Volume, Issue, July- ISS - on-splt Restraned Domnatng Set of an Interval Graph Usng an Algorthm ABSTRACT Dr.A.Sudhakaraah *, E. Gnana Deepka,

More information

F Geometric Mean Graphs

F Geometric Mean Graphs Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 2 (December 2015), pp. 937-952 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) F Geometrc Mean Graphs A.

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Constructing Minimum Connected Dominating Set: Algorithmic approach

Constructing Minimum Connected Dominating Set: Algorithmic approach Constructng Mnmum Connected Domnatng Set: Algorthmc approach G.N. Puroht and Usha Sharma Centre for Mathematcal Scences, Banasthal Unversty, Rajasthan 304022 usha.sharma94@yahoo.com Abstract: Connected

More information

The Erdős Pósa property for vertex- and edge-disjoint odd cycles in graphs on orientable surfaces

The Erdős Pósa property for vertex- and edge-disjoint odd cycles in graphs on orientable surfaces Dscrete Mathematcs 307 (2007) 764 768 www.elsever.com/locate/dsc Note The Erdős Pósa property for vertex- and edge-dsjont odd cycles n graphs on orentable surfaces Ken-Ich Kawarabayash a, Atsuhro Nakamoto

More information

Ramsey numbers of cubes versus cliques

Ramsey numbers of cubes versus cliques Ramsey numbers of cubes versus clques Davd Conlon Jacob Fox Choongbum Lee Benny Sudakov Abstract The cube graph Q n s the skeleton of the n-dmensonal cube. It s an n-regular graph on 2 n vertces. The Ramsey

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

Electrical analysis of light-weight, triangular weave reflector antennas

Electrical analysis of light-weight, triangular weave reflector antennas Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna

More information

A Five-Point Subdivision Scheme with Two Parameters and a Four-Point Shape-Preserving Scheme

A Five-Point Subdivision Scheme with Two Parameters and a Four-Point Shape-Preserving Scheme Mathematcal and Computatonal Applcatons Artcle A Fve-Pont Subdvson Scheme wth Two Parameters and a Four-Pont Shape-Preservng Scheme Jeqng Tan,2, Bo Wang, * and Jun Sh School of Mathematcs, Hefe Unversty

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Bran Curless Sprng 2008 Announcements (5/14/08) Homework due at begnnng of class on Frday. Secton tomorrow: Graded homeworks returned More dscusson

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Bridges and cut-vertices of Intuitionistic Fuzzy Graph Structure

Bridges and cut-vertices of Intuitionistic Fuzzy Graph Structure Internatonal Journal of Engneerng, Scence and Mathematcs (UGC Approved) Journal Homepage: http://www.jesm.co.n, Emal: jesmj@gmal.com Double-Blnd Peer Revewed Refereed Open Access Internatonal Journal -

More information

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES

More information

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples

More information

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

Cordial and 3-Equitable Labeling for Some Star Related Graphs

Cordial and 3-Equitable Labeling for Some Star Related Graphs Internatonal Mathematcal Forum, 4, 009, no. 31, 1543-1553 Cordal and 3-Equtable Labelng for Some Star Related Graphs S. K. Vadya Department of Mathematcs, Saurashtra Unversty Rajkot - 360005, Gujarat,

More information

VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES

VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES UbCC 2011, Volume 6, 5002981-x manuscrpts OPEN ACCES UbCC Journal ISSN 1992-8424 www.ubcc.org VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES

More information

Reading. 14. Subdivision curves. Recommended:

Reading. 14. Subdivision curves. Recommended: eadng ecommended: Stollntz, Deose, and Salesn. Wavelets for Computer Graphcs: heory and Applcatons, 996, secton 6.-6., A.5. 4. Subdvson curves Note: there s an error n Stollntz, et al., secton A.5. Equaton

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Steve Setz Wnter 2009 Qucksort Qucksort uses a dvde and conquer strategy, but does not requre the O(N) extra space that MergeSort does. Here s the

More information

Math Homotopy Theory Additional notes

Math Homotopy Theory Additional notes Math 527 - Homotopy Theory Addtonal notes Martn Frankland February 4, 2013 The category Top s not Cartesan closed. problem. In these notes, we explan how to remedy that 1 Compactly generated spaces Ths

More information

Approximations for Steiner Trees with Minimum Number of Steiner Points

Approximations for Steiner Trees with Minimum Number of Steiner Points Journal of Global Optmzaton 18: 17 33, 000. 17 000 Kluwer Academc ublshers. rnted n the Netherlands. Approxmatons for Stener Trees wth Mnmum Number of Stener onts 1, 1,,,,3, DONGHUI CHEN *, DING-ZHU DU

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

Harmonic Coordinates for Character Articulation PIXAR

Harmonic Coordinates for Character Articulation PIXAR Harmonc Coordnates for Character Artculaton PIXAR Pushkar Josh Mark Meyer Tony DeRose Bran Green Tom Sanock We have a complex source mesh nsde of a smpler cage mesh We want vertex deformatons appled to

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole Appled Mathematcs, 04, 5, 37-3 Publshed Onlne May 04 n ScRes. http://www.scrp.org/journal/am http://dx.do.org/0.436/am.04.584 The Research of Ellpse Parameter Fttng Algorthm of Ultrasonc Imagng Loggng

More information

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an On Central Spannng Trees of a Graph S. Bezrukov Unverstat-GH Paderborn FB Mathematk/Informatk Furstenallee 11 D{33102 Paderborn F. Kaderal, W. Poguntke FernUnverstat Hagen LG Kommunkatonssysteme Bergscher

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

UNIT 2 : INEQUALITIES AND CONVEX SETS

UNIT 2 : INEQUALITIES AND CONVEX SETS UNT 2 : NEQUALTES AND CONVEX SETS ' Structure 2. ntroducton Objectves, nequaltes and ther Graphs Convex Sets and ther Geometry Noton of Convex Sets Extreme Ponts of Convex Set Hyper Planes and Half Spaces

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

Classifier Selection Based on Data Complexity Measures *

Classifier Selection Based on Data Complexity Measures * Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.

More information

5 The Primal-Dual Method

5 The Primal-Dual Method 5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton

More information

1 Introducton Gven a graph G = (V; E), a non-negatve cost on each edge n E, and a set of vertces Z V, the mnmum Stener problem s to nd a mnmum cost su

1 Introducton Gven a graph G = (V; E), a non-negatve cost on each edge n E, and a set of vertces Z V, the mnmum Stener problem s to nd a mnmum cost su Stener Problems on Drected Acyclc Graphs Tsan-sheng Hsu y, Kuo-Hu Tsa yz, Da-We Wang yz and D. T. Lee? September 1, 1995 Abstract In ths paper, we consder two varatons of the mnmum-cost Stener problem

More information

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis Internatonal Mathematcal Forum, Vol. 6,, no. 7, 8 Soltary and Travelng Wave Solutons to a Model of Long Range ffuson Involvng Flux wth Stablty Analyss Manar A. Al-Qudah Math epartment, Rabgh Faculty of

More information

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to

More information

Reducing Frame Rate for Object Tracking

Reducing Frame Rate for Object Tracking Reducng Frame Rate for Object Trackng Pavel Korshunov 1 and We Tsang Oo 2 1 Natonal Unversty of Sngapore, Sngapore 11977, pavelkor@comp.nus.edu.sg 2 Natonal Unversty of Sngapore, Sngapore 11977, oowt@comp.nus.edu.sg

More information

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water.

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water. Purpose Theory REFRACTION a. To study the refracton of lght from plane surfaces. b. To determne the ndex of refracton for Acrylc and Water. When a ray of lght passes from one medum nto another one of dfferent

More information

Hierarchical clustering for gene expression data analysis

Hierarchical clustering for gene expression data analysis Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally

More information

Dynamic wetting property investigation of AFM tips in micro/nanoscale

Dynamic wetting property investigation of AFM tips in micro/nanoscale Dynamc wettng property nvestgaton of AFM tps n mcro/nanoscale The wettng propertes of AFM probe tps are of concern n AFM tp related force measurement, fabrcaton, and manpulaton technques, such as dp-pen

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

Introduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2

Introduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2 Introducton to Geometrcal Optcs - a D ra tracng Ecel model for sphercal mrrors - Part b George ungu - Ths s a tutoral eplanng the creaton of an eact D ra tracng model for both sphercal concave and sphercal

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

Discrete Applied Mathematics. Shortest paths in linear time on minor-closed graph classes, with an application to Steiner tree approximation

Discrete Applied Mathematics. Shortest paths in linear time on minor-closed graph classes, with an application to Steiner tree approximation Dscrete Appled Mathematcs 7 (9) 67 684 Contents lsts avalable at ScenceDrect Dscrete Appled Mathematcs journal homepage: www.elsever.com/locate/dam Shortest paths n lnear tme on mnor-closed graph classes,

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on

More information

DESIGN OF VERTICAL ALIGNMET

DESIGN OF VERTICAL ALIGNMET DESIN OF VERTICAL ALINMET Longtudnal gradent : max 0,5% (max see the assgnment paper) Markng of longtudnal gradent n drecton of chanage: + [%].. ascent n the drecton of chanage [%].. descent n the drecton

More information

Covering Pairs in Directed Acyclic Graphs

Covering Pairs in Directed Acyclic Graphs Advance Access publcaton on 5 November 2014 c The Brtsh Computer Socety 2014. All rghts reserved. For Permssons, please emal: ournals.permssons@oup.com do:10.1093/comnl/bxu116 Coverng Pars n Drected Acyclc

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Open Access A New Algorithm for the Shortest Path of Touring Disjoint Convex Polygons

Open Access A New Algorithm for the Shortest Path of Touring Disjoint Convex Polygons Send Orders for Reprnts to reprnts@benthamscence.ae 1364 The Open Automaton and Control Systems Journal, 2015, 7, 1364-1368 Open Access A New Algorthm for the Shortest Path of Tourng Dsjont Convex Polygons

More information

Any Pair of 2D Curves Is Consistent with a 3D Symmetric Interpretation

Any Pair of 2D Curves Is Consistent with a 3D Symmetric Interpretation Symmetry 2011, 3, 365-388; do:10.3390/sym3020365 OPEN ACCESS symmetry ISSN 2073-8994 www.mdp.com/journal/symmetry Artcle Any Par of 2D Curves Is Consstent wth a 3D Symmetrc Interpretaton Tadamasa Sawada

More information

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

A Clustering Algorithm for Chinese Adjectives and Nouns 1

A Clustering Algorithm for Chinese Adjectives and Nouns 1 Clusterng lgorthm for Chnese dectves and ouns Yang Wen, Chunfa Yuan, Changnng Huang 2 State Key aboratory of Intellgent Technology and System Deptartment of Computer Scence & Technology, Tsnghua Unversty,

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

Multiblock method for database generation in finite element programs

Multiblock method for database generation in finite element programs Proc. of the 9th WSEAS Int. Conf. on Mathematcal Methods and Computatonal Technques n Electrcal Engneerng, Arcachon, October 13-15, 2007 53 Multblock method for database generaton n fnte element programs

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

Positive Semi-definite Programming Localization in Wireless Sensor Networks

Positive Semi-definite Programming Localization in Wireless Sensor Networks Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer

More information

Visual Curvature. 1. Introduction. y C. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2007

Visual Curvature. 1. Introduction. y C. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2007 IEEE onf. on omputer Vson and Pattern Recognton (VPR June 7 Vsual urvature HaRong Lu, Longn Jan Lateck, WenYu Lu, Xang Ba HuaZhong Unversty of Scence and Technology, P.R. hna Temple Unversty, US lhrbss@gmal.com,

More information

Local and Global Accessibility Evaluation with Tool Geometry

Local and Global Accessibility Evaluation with Tool Geometry 19 Local and Global Accessblty Evaluaton wth Tool Geometry Jnnan Wang 1, Chell A. Roberts 2 and Scott Danelson 3 1 Arzona State Unversty, wangn@asu.edu 2 Arzona State Unversty, chell.roberts@asu.edu 2

More information

COPS AND ROBBER WITH CONSTRAINTS

COPS AND ROBBER WITH CONSTRAINTS COPS AND ROBBER WITH CONSTRAINTS FEDOR V. FOMIN, PETR A. GOLOVACH, AND PAWE L PRA LAT Abstract. Cops & Robber s a classcal pursut-evason game on undrected graphs, where the task s to dentfy the mnmum number

More information

Specifications in 2001

Specifications in 2001 Specfcatons n 200 MISTY (updated : May 3, 2002) September 27, 200 Mtsubsh Electrc Corporaton Block Cpher Algorthm MISTY Ths document shows a complete descrpton of encrypton algorthm MISTY, whch are secret-key

More information

Shape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram

Shape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram Shape Representaton Robust to the Sketchng Order Usng Dstance Map and Drecton Hstogram Department of Computer Scence Yonse Unversty Kwon Yun CONTENTS Revew Topc Proposed Method System Overvew Sketch Normalzaton

More information

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method Internatonal Journal of Computatonal and Appled Mathematcs. ISSN 89-4966 Volume, Number (07), pp. 33-4 Research Inda Publcatons http://www.rpublcaton.com An Accurate Evaluaton of Integrals n Convex and

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

Metric Characteristics. Matrix Representations of Graphs.

Metric Characteristics. Matrix Representations of Graphs. Graph Theory Metrc Characterstcs. Matrx Representatons of Graphs. Lecturer: PhD, Assocate Professor Zarpova Elvra Rnatovna, Department of Appled Probablty and Informatcs, RUDN Unversty ezarp@mal.ru Translated

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

Backpropagation: In Search of Performance Parameters

Backpropagation: In Search of Performance Parameters Bacpropagaton: In Search of Performance Parameters ANIL KUMAR ENUMULAPALLY, LINGGUO BU, and KHOSROW KAIKHAH, Ph.D. Computer Scence Department Texas State Unversty-San Marcos San Marcos, TX-78666 USA ae049@txstate.edu,

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information