Chapter 4. Non-Uniform Offsetting and Hollowing by Using Biarcs Fitting for Rapid Prototyping Processes

Size: px
Start display at page:

Download "Chapter 4. Non-Uniform Offsetting and Hollowing by Using Biarcs Fitting for Rapid Prototyping Processes"

Transcription

1 Chapter 4 Non-Unform Offsettng and Hollowng by Usng Barcs Fttng for Rapd Prototypng Processes Ths chapter presents a new method of Non-Unform offsettng and usng barc fttngs to hollow out sold objects or thck walls to speed up the part buldng processes on RP systems. By buldng a hollowed prototype nstead of a sold part, materal consumpton and buld tme can be reduced sgnfcantly. A rapd prototyped part wth constant wall thckness s mportant for many dfferent applcatons of rapd prototypng. To provde constant wall thckness, an Averaged Surface Normals method s developed to fnd the correct normals to offset the vertces of the STL models. Detaled algorthms are presented to elmnate self-ntersectons, loops and rregulartes of offsettng contours. 4.1 Introducton Rapd Prototypng (RP) bulds parts layer by layer. Unlke the tradtonal materal removal processes, most common rapd prototypng technques buld a part by gradually addng or soldfyng materals layer-by-layer. Depostng materal or tracng lqud polymer wth a laser over the cross-sectonal area of the part s the most tme consumng process. To reduce buld tme, the sold part can be hollowed out to speed up the rapd prototypng process [Yu 95]. Snce the hollowng operaton wll decrease the area that needs to be bult, depostng or soldfyng the materal on less area wll not only reduce the buld tme but also reduce the materal cost due to expensve RP buld materal. By buldng hollow rapd prototypes rather than completely solds, there are sgnfcant advantages wth the decrease n tme requred n buldng the prototypes on the RP systems [Ganesan 94]. Rapd prototyped parts can be used to create molds for dfferent castng operatons such as nvestment castng, de castng and sand castng. In castng operatons, the fabrcated part by a rapd prototypng process can be used as a core to make the 40

2 molds. A rapd prototyped part wth constant wall thckness s mportant for many dfferent applcatons. For nstance, a rapd prototyped part can be used as a core enclosed by a ceramc shell n nvestment castng. A core wth non-constant wall thckness can result n non-even shrnkage that may break the ceramc shell durng soldfcaton [L 98]. The molten materal may also not flow unformly nto the mold created wth a rapd prototypng part wth non-constant wall thckness. Therefore, constant wall thckness needs to be acheved when a hollowed part s used for a castng process. Before a part s fabrcated layer by layer n a rapd prototypng system, the STL model of the part needs to be slced to obtan cross-sectonal contours. One would offset the cross-sectonal contours by an offset dstance t to create the hollowed part. However, ths results n an naccurate hollowed part. Fgure 4.1(a) shows an example hollow part wth a constant wall thckness t. Several planes are used to ntersect wth the example part. Fgure 4.1(b) shows the ntersecton contours on the plane P 1, whch the offset dstance t 1 = t. Fgure 4.1(c) shows the ntersecton contours on the plane P 2. Due to the change of part surface normals, the offset dstance on plane P 2 vares (.e., t 2 > t 2 > t) as shown n Fgure 4.1(c). Fgure 4.1(d) shows the ntersecton contour on the plane P 3. Notce that there s only one ntersecton contour on plane P 3 due to the ntersecton locaton. Fgure 4.2(a) shows the cross-sectons of another hollowed part wth a constant thckness t. As shown n Fgure 4.2(b) the offset dstance t 1 and t 2 on the same cuttng plane are not equal (t 1 t 2 t). Fgure 4.2(c) shows the ncorrect nner offset boundary when the outer boundary s offset wth a constant dstance t. Therefore, the constant offsettng of the cross-sectonal contours cannot be used to create the hollowed parts wth varyng surface normals for RP processes. To create hollow objects for the rapd prototypng process, several methods have been proposed [Ganesan 94, Yu 95, Lam 97, Chu 98, L 98, Alexander 00]. These methods are classfed nto three categores: (1) spatal enumeraton methods, (2) CSG (Constructve Sold Geometry) offsettng methods, and (3) Curve offsettng methods. Some researchers [Chu 98, Alexander 00] used the spatal enumeraton technques to create hollow objects. Chu and Tan [Chu 98] performed a one-dmensonal Boolean 41

3 operaton between the ray representatons of the model and the voxel elements. Alexander and Dutta [Alexander 00] also used voxels to calculate the unform wall thckness of the part. The use of enumeraton methods causes the nternal starcase effect. Ther methods cannot be used f the accuracy of the nternal boundary of the part s mportant such as n castng operatons. Lam et al. [Lam 97, L 98] and Yu [Yu 95] used CSG technques to fnd the thn-shell sold by subtractng the orgnal sold from ts offset counterpart. However, ther methods can only be appled to CSG parts, whch are made from prmtves. Ther methods cannot be appled to parts n B-Rep (Boundary representaton) or other faceted approxmatons such as STL models. Thus, after a desgned part s converted to a STL fle for fabrcaton n rapd prototypng, ther methods cannot be used to generate a hollowed part from the STL model. Ganesan and Fadel [Ganesan 94] offset the slced CAD model to create the hollowed part. They offset cross-sectonal contours wth a constant offset dstance, whch wll cause a hollowed part wth non-constant wall as descrbed earler n Fgure 4.2. In ths research, we present a new method of usng Barcs fttng to hollow out the sold objects or thck walls to speed up the part buldng processes n the RP systems. Detals of the proposed technques are presented n the followng sectons. Secton 4.2 detals offsettng a part defned by a STL fle usng the Averaged Surface Normals method at each vertex to create an offset surface. Secton 4.3 presents the slcng contours and the technques of removng possble self-ntersectons, loops and rregulartes from the contours. Secton 4.4 concludes ths chapter. Computer mplementaton and llustratve examples of the developed technques wll be gven n Chapter Averaged Surface Normals Method for Vertex Offsettng The STL fles are generated by tessellatng the outsde skn of the CAD models. Tessellaton (STL) s done by approxmatng the boundary of the CAD object wth trangles. A STL fle contans coordnates of the vertces and normals for each facet. To offset the STL model of the part, one can offset each facet wth a gven offset dstance n ther correspondent normal drectons as shown n Fgure 4.3(a). However, ths could result n ntersectons or gaps among the offset segments, as shown n Fgure 4.3(a). 42

4 Fndng all the ntersectons or fllng the gaps s not an easy job [Cohen 96]. In ths report, we nstead offset each vertex n ther correspondent normal drectons as shown n Fgure 4.3(b). Snce a STL fle does not contan vertex normals, normals at each vertex need to be calculated. In ths report, we use an averaged normal vector method to offset each vertex wth the corrected normal drecton, as shown n Fgure 4.3(b). There are several normal approxmaton methods. In ths report, an offset normal vector at a vertex s calculated by averagng the normals of all the adjacent facets that are connected to the vertex. As shown n Fgure 4.4(a), a vertex normal Nv at vertex V, where there are n facets connected to, can be calculated as follows: n, j = j= 1 NV n j= 1 N N, j (4.1) where N, are the normals of the facets that are connected to the vertex V. j Although Equaton (4.1) can work for smooth surfaces, t may stll cause problems (for some specal cases) f t s used for vertces at sharp corners or flat surfaces. Dependng on the trangulatons generated n the STL fles, the same vertex may have dfferent sets of adjacent trangle facets connected to the vertex. A vertex on a flat surface or on an edge of the flat surface mght be connected to several faces wth the normals parallel to each other, as shown n Fgure 4.4(b). In Fgure 4.4(b), the two facet normals N and N are parallel (.e.,,1, 2 N,1 N,2 ). In Fgure 4.4(b), the normal vector Nv at vertex V s calculated as follows: + N + N N,1 + N,2 + N,3 + N,4 N = (4.2) V N,1,2,3 + N,4 43

5 Drectly averagng these normals (Fgure 4.4(b)) to calculate the vertex normal may result n a normal vector shfted towards the faces wth parallel facet normals (.e., N,1 N,2 ). As shown n Fgure 4.4(b), the averaged surface normal Nv at the vertex V could result n a vector that s closer to the faces F,1 and F,2 due to the fact that these two adjacent faces have the same parallel normals ( N,1 N,2 ). Fgure 4.4(c) shows the corrected normal vector Nv found by elmnatng the duplcated parallel normals n the calculaton of averaged normal. In Fgure 4.4(c), the corrected surface normal vector Nv at the vertex V s calculated by averagng all the adjacent facet normals wthout the duplcated parallel normal as follows: + N N,1 + N,3 + N,4 N = (4.3) V N,1,3 + N,4 After the corrected normal vectors Nv at each vertex V are found, the offset vertces V can be calculated by offsettng the vertces n ther normal drectons wth a gven offset dstance t as follows: V = V ± t Nv (4.4) In Equaton (4.4), the +- sgn depends on whether t s offset outward or nward from the orgnal part surface. The algorthm for calculatng the averaged normal vectors and the offset vertces are shown as follows: Algorthm 4.1: Calculatng Averaged Normals and the Offset Vertces INPUT: STV={V }: a set of the vertces from a STL model, 1 num_vertex, num_vertex: total number of vertces. STF={ F,j }: a set of faces that are connected to vertex V, 1 j num_face, num_face : total number of faces that connect to vertex V, 44

6 STFN={ N,j }: normal vector of facet F j, t : vertex offset dstance. OUTPUT: STV ={V }: Offset vertces, 1 num_vertex Intalze 1, j 1, k 1, normal_check 0, WHILE ( num_vertex) { *** man loop of procedure *** Nv N 1 ; FOR (j = 2 to num_face, j++): { FOR (k = j-1 to 1; j--): { } END IF (Ν j.ν k ==1) *** Ν j and Ν k are parallel to each other*** THEN { normal_check 1; k 0; } ELSE {normal_check 0; } *** Ν j and Ν k are not parallel *** } *** End of for loop *** IF (normal_check == 0) THEN {Nv Nv + N j ; } } *** End of for loop *** Normalze Nv ; Calculate the offset vertex V usng Equaton (4.4); STV STV {V }; + 1; Algorthm 4.1 can be used to construct the slcng contours and offset contours for RP processes. Detals of the procedures are dscussed n the next secton. 4.3 Slce Contour Generaton and Self-ntersectonLoop Removal To generate cross-sectonal curves, a tessellated part s ntersected wth a set of planes perpendcular to the buld drecton of the STL fle. The dstance between the ntersectng planes can be a constant n unform slcng or can be varyng n adaptve slcng [Dolenc 94, Kulkarn 96, Tyberg98]. Gven a plane and ts heght (n z drecton), all the facets are searched to fnd a startng ntersecton pont. After an ntal ntersecton pont s found, the followng ntersecton ponts are traced usng the nformaton of neghborng facets. The ntersecton ponts are then connected to form the cross-sectonal contours [Lee 92]. Ths procedure s appled to both the orgnal surface and the offset surface untl all the facets are slced. 45

7 After the cross-sectonal contours are generated, the self-ntersectons and loops need to be removed to ensure that offset contours are smple and closed as mentoned earler. As shown n Fgure 4.5(a), f the offset dstance t s greater than the mnmum radus n the concave regons, the offset surfaces may cause self-ntersectons [Maekawa 99]. The offset surface mght also ntersect tself, whch causes loops as shown n Fgure 4.5(a). Self-ntersectons and loops can be removed from the offset surfaces by frst detectng the ntersectons and loops. The self-ntersected surfaces are then trmmed from the offset surfaces. However, ths requres tme-consumng surface-to-surface ntersecton calculatons, complex self-ntersecton detectons, and trmmng operatons to form the closed offset surfaces. Even after the closed offset surface s generated, the orgnal part and ts offset surface have to be slced to buld the hollowed part layer by layer. Therefore, we can remove the self-ntersectons and loops from the cross-sectonal curves after the slcng operaton s carred out. Complex 3D ntersectons and trmmng operatons can be reduced to 2D curve-to-curve ntersectons and trmmng operatons. Fgure 4.5(b) shows the cross-sectonal contours from the orgnal part surface and ts offset surface shown n Fgure 4.5(a). As shown n Fgure 4.5(b), the self-ntersecton and loop stll exst but now on the 2D cross-sectonal plane. Fgure 4.5(c) shows the same cross-sectonal contours after the self-ntersecton and loop are removed from the offset contour. To elmnate the loops, the self-ntersecton ponts need to be detected frst. An ntersecton between two segments can be detected by checkng whether there s an ntersecton pont between the current segment and other segments along the offset crosssectonal contours. Fgure 4.6 shows an offset contour wth a self-ntersecton. The ntersecton pont P * between two segments P P + and P 1 k P k+ 1 on Fgure 4.6 can be calculated as follows [Lee 92]: P * = P + t V1 = Pk + s V2 (4.5) where = P P V and V P P k 2 = k + 1 be found as follows [Lee 92]:. By solvng the Equaton (4.5), parameter t and s can 46

8 t = s = P Pk V1 V2 V1 V2 V2 V1 V2 P Pk V1 V1 V2 V1 V2 V1 V2 (4.6) (4.7) If P P + and P 1 k P k+ 1 ntersect each other, both the parameters t and s have to be between [0,1]. The ntersecton pont P * can be calculated by substtutng s or t n Equaton (4.5). After the ntersecton pont P* s calculated, the ntersecton pont P* can be used to separate the curve nto two loops. As shown n Fgure 4.6, ntersecton pont P * dvdes the curve nto two loops, [P *, P +1 -P k, P * ] and [P *, P k+1 -P n, P 1 -P, P * ] (for < k). By usng the procedure, the self-ntersectons can be deleted from the offset contours. Snce we do not know whch loop s the correct offset contour and whch one s the self-ntersecton, a procedure s needed to determne the self-ntersecton. There are several self-ntersecton detecton methods n the lterature [Tller 84]: (1) Consder the number of segments n each loop and remove the loop wth the least segments (2) Consder the total length of the each loop and remove the shortest loop, (3) Consder loop nestng and remove the loop whch s nsde another loop. These methods mght not work f the orgnal loop s relatvely smaller than the loop that needs to be removed. For nstance, the loops LP 1 and LP 2 n Fgure 4.7(b) would be removed f one of the above methods were used. But then the orgnal loops would be removed ncorrectly. As shown n Fgure 4.7(c), the loop LP 2 should be removed and the loops LP 1 and LP 3 should be kept. To fnd the loops that need to be removed, the surface normals on the offset contours are used. The normals of each facet on the orgnal surface can be obtaned from the STL fle of the part. However, these normals cannot be used for the offset facets because the offset facets change the shape due to offsettng the vertces along ther 47

9 normal drecton. Therefore, normals on the offset facets are not the same as the normals on the orgnal facets. Gven a facet F and ts vertces V,1, V,2 and V,3 ordered n the counter-clockwse sequence, the offset facet F and ts vertces V,1, V,2 and V,3 can be calculated by usng Algorthm 4.1. Note that the offset vertces are also n the sequence along the counter-clockwse drecton. Then the normal vector N of the offset facet F can be calculated by takng the cross product of the two vectors N v, 1N v, 2 and N v, 2N v,3 as: N = N v N v,1,1 N v N v,2,2 N v N v,2,2 N v N v,3,3 (4.8) To calculate the projected normals on the cross-sectonal contours, the surface normals N need to be projected onto the cuttng plane. Gven the heght of cuttng plane z cut, the projected normals [ yp, zp,1] found as follows: where xp n the homogeneous coordnate system can be N, N N xp z N N N N N N cut (4.9) z N [, yp, zp,1] = [ x, y, z,1], are the x-y-z elements of the offset facet normals N. Fgure 4.7(b) x N yn, zn shows the surface normals projected on a cuttng plane. The normals pont outward on the outsde boundary whereas the normals pont nward n the nsde offset boundary of the STL model. Therefore, the loops whose normals pont outward along the ntersecton loops are the self-ntersectons, as the loop LP 2 shown n Fgure 4.7(b). In Fgure 4.7(b), the offset loops LP 1 and LP 3 have ther normals pontng nward whch ndcates they are the correct offset loops. However, the normals on the loop LP 2 pont nward whch ndcates LP 2 s an self-ntersecton loop and needs to be removed. Fgure 4.7(c) shows 48

10 the self-ntersecton loop LP 2 beng removed from the offset contour. Gven a segment P orented n the counter-clockwse drecton and ts normal vector N, the P + 1 locaton of the normal vector N can be determned as follows: = P P + N NC 1 (4.10) If the z-value of the vector NC s negatve, then the normal s at the rght sde of the segment P P + 1 otherwse t s at the left sde of the segment. For each loop on the offset contour, locaton of ther normals can be determned usng Equaton (4.10). If the normals are on the left sde of the segments then the offset loop s vald, otherwse t s nvald and needs to be removed. There mght be some nested ntersectons on the offset contours. Therefore, the self-ntersecton removal process needs to contnue untl all the ntersectons are removed. Offsettng surfaces can cause other rregularty problems on the offset contours besdes self-ntersectons and loops problems. These rregulartes nclude jagged lnes, collnear ponts and nflecton ponts, as shown n Fgures 4.8(a), (b) and (c) respectvely. In order to generate the correct cross-sectonal contours, these sngulartes need to be detected and corrected. As shown n Fgure 4.8(a), the jagged lnes can be detected by checkng the normal vector of each segment. If the change between the normal vectors of two consecutve segments s equal or close to π then these two segments are consdered to be jagged lne segments as shown n Fgure 4.8(a). In Fgure 4.8(b), the collnear ponts occur when the normal vectors of three or more ponts are parallel or close to parallel to each other. In Fgure 4.8(c), the nflecton ponts result from the dscontnuous tangents (.e. T, 1 and T, 2 ) at a pont P. Jagged lnes and nflecton ponts can be smoothed by the 2 nd -dfference farng technque [Cho 98]. As shown n Fgures 4.8(a) and (c), the corrected pont P can be calculated as follows [Cho 98]: d 1 d + 1 P P = d d P (4.11) 2 49

11 where d -1, d 0, and d +1 are 2 nd -dfferences and they are defned as follows: d = P 1 P, d + 1 = P + 1 P, d 0 = ( d 1 d 1) 2 (4.12) Collnear ponts can be easly removed except for the begnnng and the end ponts of the collnear segments. As shown n Fgure 4.8(b), only ponts P -1 and P +2 are saved whle the ponts P and P +1 are removed from the contour. After the ntersectons and the rregulartes are removed, the next step s to smooth the contours wth barc curves. Barc curve fttng s appled to both the orgnal part boundary and the offset contour. Therefore, smooth cross sectonal segments can be generated. Detals of the barc curve fttng are gven n Chapter 3. The Max-ft barcs fttng algorthm progresses through the STL slcng data ponts and offsettng contour ponts to fnd the most effcent barc curve fttng, whle mantanng the requred tolerance [Koc 00b]. After the smooth cross-sectonal contours are generated, the data can be sent to the RP systems for preprocessng and prototypng. Detals of the procedures of generatng offset contours for RP processes are shown as follows: Algorthm 4.2: Generatng Cross-sectonal Contours INPUT: STV={V }: a set of vertces 1 num_vertex, num_vertex: total number of vertces, STF={ F,j }: a set of faces (trangles) that connect to vertex V, 1 j num_face, num_face : total number of faces that connect to offset vertex V, STV ={V }: a set of offset vertces from Algorthm num_vertex, STF ={ F,j }: a set of faces (trangles) that connect to offset vertex V, 1 j num_face, layer_thcknes: layer thckness, OUTPUT: STI={ I l,k }: a set of ntersecton ponts of the orgnal contour at the cuttng plane k, 1 k num_layer, 1 l num_pont k, STI ={ I l,k }: a set of ntersecton ponts of the offset contour 50

12 at the cuttng plane k, 1 k num_layer, 1 l num_pont k, num_layer: number of layers, num_pont k : number of ntersecton ponts at layer k, NP, k : projected normal vectors of each segment at the cuttng plane k, 1 num_pont k -1. Intalze k 0, z mn mn {z V }, z max max {z V }; *** z V s the z-value of the vertex V *** num_layer ( z max - z mn )layer_thckness; WHILE (k num_layer) { *** man loop of procedure *** k k +1; z cut z mn + k*layer_thckness ; fnd ntal ntersecton pont at z cut by searchng all the facets calculate the ntersecton ponts I l,k, I l,k on the orgnal boundary surface and the offset surface by ntersectng cuttng plane at z cut and F j and F j ; calculate the projected normals NP usng Equatons (4.8) and (4.9); STI STI {I l,k }; STI STI {I l,k }; END Algorthm 4.3: Generatng Unform Offsettng for RP processes INPUT: STI={ I l,k }: a set of ntersecton ponts of the orgnal contour at the cuttng plane k, 1 k num_layer, 1 l num_pont k, STI ={ I l,k }: a set of ntersecton ponts of the offset contour at the cuttng plane k, 1 k num_layer, 1 l num_pont k, num_layer: number of layers, num_pont k : number of ntersecton ponts at layer k, 51

13 NP, k : projected normal vectors of each segment at the cuttng plane k, 1 num_pont k -1. OUTPUT: Barc(m k ): A set of barc curves of the orgnal contour at the layer k, 1 k num_layer, m k : number of barcs ftted to the orgnal contour at layer k. Barc (n k ): A set of barc curves of the offset contour at the layer k, 1 k num_layer, n k : number of barcs ftted to the offset contour at layer k. Intalze 1, j 1, k 0, current_segment { }, check_segment { }, vald_loop { }, loop 1 { }, loop 2 { }, FOR (each layer k and STI and STI ) { *** man loop of procedure *** WHILE ( ntersect_check == 1){ *** self ntersecton elmnaton *** FOR (each I l,k vald_loop) { current_segment I l, k I l+ 1, k ; FOR (each I sk where l < s < num_pont k ) { *search for the ntersecton * check_segment I s, k I s+ 1, k ; calculate the s and t between current_ segment and check_segment usng Equatons (4.6) and (4.7); IF ( 0 s $1' t THEN { *** curent_ segment and segment ntersects*** ntersect_check 1; calculate ntersecton pont P * usng Equaton (4.5); save P * as a nflecton pont; loop 1 {P *, I l+1k -I sk, P * }; loop 2 {P *, I s+1k -I num_pontk,k,i 1k -I lk, P * }; FOR (each loop, = 1,2) { Check f loop s vald usng Equaton (4.10); IF (loop s vald) THEN{ vald_loop loop }; 52

14 ELSE { ntersect_check 0; } } *** End of for loop *** } *** End of for loop *** } *** End of whle loop *** FOR (each I l,k vald_loop) { *** removng rregulartes ** current_segment check_segment I l, k I l+ 1, k ; I l+ 1, k I l+ 2, k ; N current N check l k NP, ; *** projected normal vector of current_segment *** NP l+ 1, k ; *** projected normal vector of check_segment *** IF ( the angle between N current and N check s close to π or I l,k s nflecton pont ) THEN{ calculate new pont I l,k usng Equatons (4.11) and (4.12) }; IF ( the angle between N current and N check s close to zero) THEN{ *** collnear ponts *** remove I l+1,k from vald_loop}; } *** End of for loop *** Barc(m k ) barc ft the vald_loop usng Algorthm 3.1; Barc (n k ) barc ft the vald_loop usng Algorthm 3.1; END These algorthms are used to construct and fnd the smoothed slce contours of the hollowed object for RP process. Computer mplementatons and llustratve examples wll be gven n Chapter Summary A new method of usng barc fttngs to hollow out thck walls or sold objects has been proposed. The developed algorthms provde hollowng wth constant wall thckness whch can avod non-constant shrnkage and warpage after the RP part s bult [Koc 00b]. To create the hollowed object, the vertces are frst offset wth a gven offset dstance along ther correspondent calculated normals. The resulted surfaces (both the orgnal and 53

15 the offset surfaces) are then slced to generate cross-sectonal curves. The selfntersecton and rregulartes are removed from the offset contours to form smple and closed contours. The fnal contours are then smoothed out by barc fttng. The resulted cross sectonal contours can be sent to a RP machne to generate support structure and fnally, to be fabrcated. Computer mplementaton and examples are presented n Chapter 6. 54

16 P 3 t P 2 P 1 (a) Hollowed part wth a constant thckness t P 1 P 2 t 1 t 1 t 2 t 2 (b) Cross-sectonal contours on plane P (t=t) 1 t=t 1 1 t>t >t 2 2 (c) Cross-sectonal contours on plane P (t >t >t) P 3 (d) Cross-sectonal contour on plane P 3 Fgure 4.1 Cross-sectonal contours of a hollowed object on dfferent planes 55

17 Cuttng plane t 1 t 2 t t t=t=t 1 2 (a) Hollowed part wth a constant thckness t t 1 t 1 t 2 t 2 t=t=t 1 2 (b) Actual cross-sectonal contour of the hollowed part t=t=t 1 2 (c ) Incorrect offset contour f a constant offset dstance s used on the cuttng plane Fgure 4.2 Cross-sectonal contours of a hollowed object 56

18 Intersecton t.n 2 F 2 F 3 F 1 F 2 t.n 1 t.n 2 t.n 3 t.n 4 t.n 3 F 3 F 4 F 1 F 4 t.n 1 Gap t.n 4 (a) Offsettng surfaces along the segments normal drecton v 1 v 3 F 2 F 3 F 4 F 1 t.nv1 v 1 F 1 F 2 v 2 t.nv2 F 3 v 2 v 3 t.nv3 F 4 (b) Offsettng vertces n the corrected surface normal drectons Fgure 4.3 Offsettng surfaces and vertces 57

19 Nv N, 4 N, 3 N, 5 F, 4 F, 3 F V, 5 F, 2 N, 2 n j 1 NV n j 1 N, j N, j 1 j 5 F, 1 N, 1 (a) Calculatng the vertex normal Nv by averagng the facet normals N, 3 N, 3 Nv Nv F, 2 F, 3 V F, 4 N, 4 F, 2 F, 3 V F, 4 N, 4 N, 2 N, 1 F, 1 N, 2 N, 1 N N N, 2 N, 1 F, 1, 2, 1 (b) Incorrect normal vector Nv shfted towards faces F and F, 1, 2 (c) Corrected normal vector Nv by deletng the parallel facet normals Fgure 4.4 Calculatng the averaged normal vectors Nv at a vertex 58

20 Self-ntersecton Part Surface Offset surface Loop Cuttng plane (a) Part surface and ts offset surface wth a self-ntersecton and a loop Self-ntersecton Orgnal contour Offset contour Loop (b) Cross-sectonal contours of the part surface and ts offset surface Orgnal contour Offset contour (c) The self-ntersecton and the loop are removed from the same offset contour Fgure 4.5 Self-ntersectons and loops of the offset surfaces 59

21 P k+1 V 2 P +1 V 1 P * P k P Fgure 4.6 Detecton of a self-ntersecton on the offset cross-sectonal contour 60

22 Orgnal surface Offset surface (a) A secton of the part surface and ts offset surface wth an ntersecton Orgnal contour Offset contour Surface normals LP LP LP (b) Cross sectonal contours of the orgnal and offset surfaces wth projected surface normals LP LP 1 3 (c) Intersecton s removed from the offset contour Fgure 4.7 The orgnal and offset surfaces and ther cross-sectonal contours before and after the ntersecton s removed from the offset contour 61

23 P P +1 P +2 T, 1 T, 2 P -1 P P -1 P P +1 P +2 P -1 P +1 P P +1 P +2 P P -1 P +2 P P -1 P +1 P P -1 (a) Jagged lnes (b) Collnear ponts (c) Inflecton ponts Fgure 4.8 Irregulartes and ther correctons 62

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

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

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

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

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

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

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

2D Raster Graphics. Integer grid Sequential (left-right, top-down) scan. Computer Graphics

2D Raster Graphics. Integer grid Sequential (left-right, top-down) scan. Computer Graphics 2D Graphcs 2D Raster Graphcs Integer grd Sequental (left-rght, top-down scan j Lne drawng A ver mportant operaton used frequentl, block dagrams, bar charts, engneerng drawng, archtecture plans, etc. curves

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

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

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

Feature Based Fabrication in Layered Manufacturing

Feature Based Fabrication in Layered Manufacturing Xaopng Qan Debassh Dutta Department of Mechancal Engneerng, The Unversty of Mchgan, Ann Arbor, MI 48109 e-mal: xpqan/dutta@engn.umch.edu Feature Based Fabrcaton n Layered Manufacturng To address the conflctng

More information

Collision Detection. Overview. Efficient Collision Detection. Collision Detection with Rays: Example. C = nm + (n choose 2)

Collision Detection. Overview. Efficient Collision Detection. Collision Detection with Rays: Example. C = nm + (n choose 2) Overvew Collson detecton wth Rays Collson detecton usng BSP trees Herarchcal Collson Detecton OBB tree, k-dop tree algorthms Multple object CD system Collson Detecton Fundamental to graphcs, VR applcatons

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

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

AP PHYSICS B 2008 SCORING GUIDELINES

AP PHYSICS B 2008 SCORING GUIDELINES AP PHYSICS B 2008 SCORING GUIDELINES General Notes About 2008 AP Physcs Scorng Gudelnes 1. The solutons contan the most common method of solvng the free-response questons and the allocaton of ponts for

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

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

Model Clipping Triangle Strips and Quad Meshes.

Model Clipping Triangle Strips and Quad Meshes. Model Clppng Trangle Strps and Quad Meshes. Patrc-Glles Mallot Sun Mcrosystems, Inc. 2550 Garca Avenue, Mountan Vew, CA 94043 Abstract Ths paper descrbes an orgnal software mplementaton of 3D homogeneous

More information

Scan Conversion & Shading

Scan Conversion & Shading Scan Converson & Shadng Thomas Funkhouser Prnceton Unversty C0S 426, Fall 1999 3D Renderng Ppelne (for drect llumnaton) 3D Prmtves 3D Modelng Coordnates Modelng Transformaton 3D World Coordnates Lghtng

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

Scan Conversion & Shading

Scan Conversion & Shading 1 3D Renderng Ppelne (for drect llumnaton) 2 Scan Converson & Shadng Adam Fnkelsten Prnceton Unversty C0S 426, Fall 2001 3DPrmtves 3D Modelng Coordnates Modelng Transformaton 3D World Coordnates Lghtng

More information

Line Clipping by Convex and Nonconvex Polyhedra in E 3

Line Clipping by Convex and Nonconvex Polyhedra in E 3 Lne Clppng by Convex and Nonconvex Polyhedra n E 3 Václav Skala 1 Department of Informatcs and Computer Scence Unversty of West Bohema Unverztní 22, Box 314, 306 14 Plzeò Czech Republc e-mal: skala@kv.zcu.cz

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

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

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

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL)

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL) Crcut Analyss I (ENG 405) Chapter Method of Analyss Nodal(KCL) and Mesh(KVL) Nodal Analyss If nstead of focusng on the oltages of the crcut elements, one looks at the oltages at the nodes of the crcut,

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

Cell Count Method on a Network with SANET

Cell Count Method on a Network with SANET CSIS Dscusson Paper No.59 Cell Count Method on a Network wth SANET Atsuyuk Okabe* and Shno Shode** Center for Spatal Informaton Scence, Unversty of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

More information

A topological hierarchy-based approach to layered manufacturing of functionally graded multi-material objects

A topological hierarchy-based approach to layered manufacturing of functionally graded multi-material objects Ttle A topologcal herarchy-based approach to layered manufacturng of functonally graded mult-materal objects Author(s) Cho, SH; Cheung, HH Ctaton Computers In Industry, 2009, v. 60 n. 5, p. 349-363 Issued

More information

Radial Basis Functions

Radial Basis Functions Radal Bass Functons Mesh Reconstructon Input: pont cloud Output: water-tght manfold mesh Explct Connectvty estmaton Implct Sgned dstance functon estmaton Image from: Reconstructon and Representaton of

More information

TN348: Openlab Module - Colocalization

TN348: Openlab Module - Colocalization TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages

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

Form-factors Josef Pelikán CGG MFF UK Praha.

Form-factors Josef Pelikán CGG MFF UK Praha. Form-factors 1996-2016 Josef Pelkán CGG MFF UK Praha pepca@cgg.mff.cun.cz http://cgg.mff.cun.cz/~pepca/ FormFactor 2016 Josef Pelkán, http://cgg.mff.cun.cz/~pepca 1 / 23 Form-factor F It ndcates the proporton

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered

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

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

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

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

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

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

A Comparison and Evaluation of Three Different Pose Estimation Algorithms In Detecting Low Texture Manufactured Objects

A Comparison and Evaluation of Three Different Pose Estimation Algorithms In Detecting Low Texture Manufactured Objects Clemson Unversty TgerPrnts All Theses Theses 12-2011 A Comparson and Evaluaton of Three Dfferent Pose Estmaton Algorthms In Detectng Low Texture Manufactured Objects Robert Krener Clemson Unversty, rkrene@clemson.edu

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

ELEC 377 Operating Systems. Week 6 Class 3

ELEC 377 Operating Systems. Week 6 Class 3 ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems

More information

The example below contains two doors and no floor level obstacles. Your panel calculator should now look something like this: 2,400

The example below contains two doors and no floor level obstacles. Your panel calculator should now look something like this: 2,400 Step 1: A r c h t e c t u r a l H e a t n g o begn wth you must prepare a smple drawng for each room n whch you wsh to nstall our Heat Profle Skrtng Heatng System. You certanly don't need to be Pcasso,

More information

Simplification of 3D Meshes

Simplification of 3D Meshes Smplfcaton of 3D Meshes Addy Ngan /4/00 Outlne Motvaton Taxonomy of smplfcaton methods Hoppe et al, Mesh optmzaton Hoppe, Progressve meshes Smplfcaton of 3D Meshes 1 Motvaton Hgh detaled meshes becomng

More information

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.

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

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

Accessibility Analysis for the Automatic Contact and Non-contact Inspection on Coordinate Measuring Machines

Accessibility Analysis for the Automatic Contact and Non-contact Inspection on Coordinate Measuring Machines Proceedngs of the World Congress on Engneerng 008 Vol I Accessblty Analyss for the Automatc Contact and Non-contact Inspecton on Coordnate Measurng Machnes B. J. Álvarez, P. Fernández, J. C. Rco and G.

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

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

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

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit LOOP ANALYSS The second systematic technique to determine all currents and voltages in a circuit T S DUAL TO NODE ANALYSS - T FRST DETERMNES ALL CURRENTS N A CRCUT AND THEN T USES OHM S LAW TO COMPUTE

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

Brave New World Pseudocode Reference

Brave New World Pseudocode Reference Brave New World Pseudocode Reference Pseudocode s a way to descrbe how to accomplsh tasks usng basc steps lke those a computer mght perform. In ths week s lab, you'll see how a form of pseudocode can be

More information

A high precision collaborative vision measurement of gear chamfering profile

A high precision collaborative vision measurement of gear chamfering profile Internatonal Conference on Advances n Mechancal Engneerng and Industral Informatcs (AMEII 05) A hgh precson collaboratve vson measurement of gear chamferng profle Conglng Zhou, a, Zengpu Xu, b, Chunmng

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

Optimal Workload-based Weighted Wavelet Synopses

Optimal Workload-based Weighted Wavelet Synopses Optmal Workload-based Weghted Wavelet Synopses Yoss Matas School of Computer Scence Tel Avv Unversty Tel Avv 69978, Israel matas@tau.ac.l Danel Urel School of Computer Scence Tel Avv Unversty Tel Avv 69978,

More information

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method NICOGRAPH Internatonal 2012, pp. 114-119 3D Vrtual Eyeglass Frames Modelng from Multple Camera Image Data Based on the GFFD Deformaton Method Norak Tamura, Somsangouane Sngthemphone and Katsuhro Ktama

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

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

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

Computation of a Minimum Average Distance Tree on Permutation Graphs*

Computation of a Minimum Average Distance Tree on Permutation Graphs* Annals of Pure and Appled Mathematcs Vol, No, 0, 74-85 ISSN: 79-087X (P), 79-0888(onlne) Publshed on 8 December 0 wwwresearchmathscorg Annals of Computaton of a Mnmum Average Dstance Tree on Permutaton

More information

Consistency constraints and 3D building reconstruction

Consistency constraints and 3D building reconstruction Consstency constrants and 3D buldng reconstructon Sébasten Horna, Danel Meneveaux, Gullaume Damand, Yves Bertrand To cte ths verson: Sébasten Horna, Danel Meneveaux, Gullaume Damand, Yves Bertrand. Consstency

More information

Intra-Parametric Analysis of a Fuzzy MOLP

Intra-Parametric Analysis of a Fuzzy MOLP Intra-Parametrc Analyss of a Fuzzy MOLP a MIAO-LING WANG a Department of Industral Engneerng and Management a Mnghsn Insttute of Technology and Hsnchu Tawan, ROC b HSIAO-FAN WANG b Insttute of Industral

More information

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for

More information

PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS

PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS Po-Lun La and Alper Ylmaz Photogrammetrc Computer Vson Lab Oho State Unversty, Columbus, Oho, USA -la.138@osu.edu,

More information

Evaluation of an Enhanced Scheme for High-level Nested Network Mobility

Evaluation of an Enhanced Scheme for High-level Nested Network Mobility IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.15 No.10, October 2015 1 Evaluaton of an Enhanced Scheme for Hgh-level Nested Network Moblty Mohammed Babker Al Mohammed, Asha Hassan.

More information

Slide 1 SPH3UW: OPTICS I. Slide 2. Slide 3. Introduction to Mirrors. Light incident on an object

Slide 1 SPH3UW: OPTICS I. Slide 2. Slide 3. Introduction to Mirrors. Light incident on an object Slde 1 SPH3UW: OPTICS I Introducton to Mrrors Slde 2 Lght ncdent on an object Absorpton Relecton (bounces)** See t Mrrors Reracton (bends) Lenses Oten some o each Everythng true or wavelengths

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

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

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

The Theory and Application of an Adaptive Moving Least. Squares for Non-uniform Samples

The Theory and Application of an Adaptive Moving Least. Squares for Non-uniform Samples Xanpng Huang, Qng Tan, Janfe Mao, L Jang, Ronghua Lang The Theory and Applcaton of an Adaptve Movng Least Squares for Non-unform Samples Xanpng Huang, Qng Tan, Janfe Mao*, L Jang, Ronghua Lang College

More information

MOTION BLUR ESTIMATION AT CORNERS

MOTION BLUR ESTIMATION AT CORNERS Gacomo Boracch and Vncenzo Caglot Dpartmento d Elettronca e Informazone, Poltecnco d Mlano, Va Ponzo, 34/5-20133 MILANO boracch@elet.polm.t, caglot@elet.polm.t Keywords: Abstract: Pont Spread Functon Parameter

More information

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints TPL-ware Dsplacement-drven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process

More information

Active Contour Models

Active Contour Models Actve Contour Models By Taen Lee A PROJECT submtted to Oregon State Unversty n partal fulfllment of The requrements for the Degree of Master of Scence n Computer Scence Presented September 9 005 Commencement

More information

Multi-view 3D Position Estimation of Sports Players

Multi-view 3D Position Estimation of Sports Players Mult-vew 3D Poston Estmaton of Sports Players Robbe Vos and Wlle Brnk Appled Mathematcs Department of Mathematcal Scences Unversty of Stellenbosch, South Afrca Emal: vosrobbe@gmal.com Abstract The problem

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

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

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

Accelerating X-Ray data collection using Pyramid Beam ray casting geometries

Accelerating X-Ray data collection using Pyramid Beam ray casting geometries Acceleratng X-Ray data collecton usng Pyramd Beam ray castng geometres Amr Averbuch Guy Lfchtz Y. Shkolnsky 3 School of Computer Scence Department of Appled Mathematcs, School of Mathematcal Scences Tel

More information

CS 534: Computer Vision Model Fitting

CS 534: Computer Vision Model Fitting CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust

More information

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005 Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed

More information

Edge Detection in Noisy Images Using the Support Vector Machines

Edge Detection in Noisy Images Using the Support Vector Machines Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona

More information

Some Tutorial about the Project. Computer Graphics

Some Tutorial about the Project. Computer Graphics Some Tutoral about the Project Lecture 6 Rastersaton, Antalasng, Texture Mappng, I have already covered all the topcs needed to fnsh the 1 st practcal Today, I wll brefly explan how to start workng on

More information

A Hybrid Geometric Modeling Method for Large Scale Conformal Cellular Structures

A Hybrid Geometric Modeling Method for Large Scale Conformal Cellular Structures A Hybrd Geometrc Modelng Method for Large Scale Conformal Cellular Structures Hongqng Wang a Senor Research Engneer Yong Chen b Assstant Professor Davd W. Rosen c* Professor a 3D Systems, 26081 Avenue

More information

CHAPTER 3 ENCODING VIDEO SEQUENCES IN FRACTAL BASED COMPRESSION. Day by day, the demands for higher and faster technologies are rapidly

CHAPTER 3 ENCODING VIDEO SEQUENCES IN FRACTAL BASED COMPRESSION. Day by day, the demands for higher and faster technologies are rapidly 65 CHAPTER 3 ENCODING VIDEO SEQUENCES IN FRACTAL BASED COMPRESSION 3.1 Introducton Day by day, the demands for hgher and faster technologes are rapdly ncreasng. Although the technologes avalable now are

More information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

A Volumetric Approach for Interactive 3D Modeling

A Volumetric Approach for Interactive 3D Modeling A Volumetrc Approach for Interactve 3D Modelng Dragan Tubć Patrck Hébert Computer Vson and Systems Laboratory Laval Unversty, Ste-Foy, Québec, Canada, G1K 7P4 Dens Laurendeau E-mal: (tdragan, hebert, laurendeau)@gel.ulaval.ca

More information

Barycentric Coordinates. From: Mean Value Coordinates for Closed Triangular Meshes by Ju et al.

Barycentric Coordinates. From: Mean Value Coordinates for Closed Triangular Meshes by Ju et al. Barycentrc Coordnates From: Mean Value Coordnates for Closed Trangular Meshes by Ju et al. Motvaton Data nterpolaton from the vertces of a boundary polygon to ts nteror Boundary value problems Shadng Space

More information

Loop Transformations, Dependences, and Parallelization

Loop Transformations, Dependences, and Parallelization Loop Transformatons, Dependences, and Parallelzaton Announcements Mdterm s Frday from 3-4:15 n ths room Today Semester long project Data dependence recap Parallelsm and storage tradeoff Scalar expanson

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

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

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

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell Module 6: FEM for Plates and Shells Lecture 6: Fnte Element Analyss of Shell 3 6.6. Introducton A shell s a curved surface, whch by vrtue of ther shape can wthstand both membrane and bendng forces. A shell

More information

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume

More information