Geometric Modeling
|
|
- Adam Williamson
- 5 years ago
- Views:
Transcription
1 Geomerc Modelg Crves coed Cc Bezer ad B-Sle Crves Far Chaers Moreso Chaers 4 5
2 4 Tycal Tyes of Paramerc Crves Corol os flece crve shae. Ierolag Crve asses hrogh all corol os. Herme Defed y s edos ad age vecors a edos. Ierolaes all s corol os. o vara der affe rasformaos. Secal case of Bezer ad B-Sle. Bezer* Ierolaes frs ad las corol os. Ivara der affe rasformaos. Crve s age o frs ad las segmes of corol olygo. Easy o sdvde. Crve segme les wh covex hll of corol olygo. Varao-dmshg. Secal case of B-sle. B-Sle* o garaeed o erolae corol os. Ivara der affe rasformaos. Crve segme les wh covex hll of corol olygo. Varao-dmshg. Greaer local corol ha Bezer. *focs of hs lecre sorce: Moreso Agel
3 oao Moreso Corol os ad arameer: ad R Far Corol os ad arameer: ad R sorce: Moreso Far
4 Bezer Crves Far Chaer 4 The de Caselja Algorhm
5 Paraola Cosrco Chage of oao from Chaer revosly: [ [ ow: Add: By sso: sorce: Far
6 The de Caselja Algorhm Gve: se: Examle: cc r r sorce: Far r r r R ad crve o Coceally elega o ecessarly he fases algorhm sorce: Far
7 Some Bezer Crve Proeres Affe varace: Lear erolao s affely vara. Ivarace der affe arameer rasformaos eyod [ o [a: r a Covex hll: Crve says wh covex hll of corol olygo. Each ermedae o s a covex arycerc comao of revosly geeraed os. Plaar corol olygo geeraes laar crve. Helfl for erseco ess. Edo erolao: Crve asses hrogh ad. Verfy sg scheme for = ad =. r a a r sorce: Far
8 Bezer Crves Far Chaer 5- & Moreso Chaer 4 The Berse Form of a Bezer Crve
9 Bezer Geomerc form cc case: B B Berse olyomals. 4 = mer of corol os = degree Evalae a = ad = o show ages relaed o frs ad las corol olygo le segme. sorce: Moreso
10 Bezer Geomerc form geeral case: B B Berse olyomals. + = mer of corol os = degree + Raoal form s vara der ersecve rasformao: where h are rojecve sace coordaes weghs See Chaer of Far for raoal Bezer maeral. h B h B sorce: Moreso
11 More Bezer Crve Proeres Symmery: Berse olyomals are symmerc wh resec o ad -. Covex Hll aga: B B Covex comao so Bezer crve os all le wh covex hll of corol olygo. Bezer crve wh 4 corol os sorce: Far Moreso
12 More Bezer Crve Proeres Degree elevao leavg crve chaged Addg a corol o elevaes degree y. for B ew verces oaed from old olygo y ecewse lear erolao a arameer vales /+. ew corol olygo s covex hll of old oe. sorce: Far
13 More Bezer Crve Proeres Reeaed degree elevao: he lm rodces corol olygo seqece ha coverges o he acal crve. Varao Dmshg: Pecewse lear erolao s varao dmshg. Degree elevao ses ecewse lear erolao. Each sccessve corol olygo cao ersec a gve lae more ofe ha he corol olygo o whch s ased. Ths he crve cao ersec he lae more ofe ha ay of he corol olygos. Corollary: covex corol olygo rodces covex crve segme. sorce: Far
14 Comose Bezer Crves Jog adjace crve segmes s a alerave o degree elevao. Colleary of cc Bezer corol os rodces G coy a jo o: Evalae a = ad = o show ages relaed o frs ad las corol olygo le segme. For G coy a jo o 5 verces ms e colaar. sorce: Moreso
15 B-Sle Crves Moreso Chaer 5 & Far Chaer 8
16 B-Sle Geomerc form o-form o-raoal case where K corols degree K - of ass fcos: k k k f oherwse k k k K are ++K ko vales ha relae o he corol os. Uform case: sace kos a eqal ervals of. Reeaed kos move crve closer o corol os. Cc B-sles ca rovde C coy a crve segme jo os. Raoal form URBS s vara der ersecve rasformao where h are rojecve sace coordaes weghs. See Chaer of Far for raoal B-sle maeral. K Covex comao so B-sle crve os all le wh covex hll of corol olygo. h h K K sorce: Moreso
17 Some B-Sle Crve Proeres Affe varace: Lear erolao s affely vara. Ivarace der affe arameer rasformaos eyod [ o [a: Covex hll: Crve says wh covex hll of corol olygo see laer slde. Each ermedae o s a covex arycerc comao of revosly geeraed os. Plaar corol olygo geeraes laar crve. Helfl for erseco ess. o edo erolao: lke Bezer Varao dmshg Trasformao o some oher aramerc forms: B-Sle crve ca e rasformed o Bezer form. See Moreso. 7. B-Sle crve ca e rasformed o Herme form. See Moreso sorce: Far Moreso
18 Cc B-Sle Geomerc form cc case: 4 sorce: Moreso Evalae a = ad = o show crve edos ad age drecos relaed o corol olygo. 4 B-sle cc ass fcos 4 = mer of corol os = degree + K oherwse f k k k k k k Covex comao so B-sle crve os all le wh covex hll of corol olygo. K 4 Uform cc B-sle:
19 More B-Sle Crve Proeres Symmery: Bass fcos ossess aramerc symmery: Examle: Uform cc B-sle: K K K sorce: Far Moreso
20 Shae Characerzao of Uform Cc B-Sle Crve Segme Shae deeds o corol olygo o-collear corol os deerme ragle Tye of crve segme ad s mooocy deeds o locao of 4 h o relave o ragle : SP: sral corol olygo dces covex moooe crve segme. U: U-shaed covex corol olygo dces covex moooe crve segme. V:V-shaed covex corol olygo dces fleco o a crve segme edo. Crve segme s covex moooe. : -shaed corol olygo dces fleco o wh covex moooe eces o oh sdes of fleco o. SI: self-ersecg corol olygo dces or moooe crve ssegmes. D: self-ersecg corol olygo may dce loo cs or fleco os. I all cases crve segme ca e aroed o a mos moooe eces. SP SP SP SI D SP SP SP U V V sorce: Daels e al. ldg o Wag e al.
21 The deboor Algorhm Gve: kos & corol os & arameer geeralze de Caselja algorhm. Examle: qadrac case wh 4 kos ad corol os sorce: Far [ [ [ [ [ [ crve o Coceally elega o ecessarly he fases algorhm sorce: Far [ [ [ [ [ [ [ [ [ ko seqece = 4 ad =.
22 Geomerc Modelg OeGL Demo o accomay HW#
Geometric Modeling
Geometrc Modelg 9.580.0 Crves Morteso Chater -5 ad Agel Chater 9 Crve Bascs Crve: Locs of a ot movg wth degree of freedom. Some tyes of eqatos to descrbe crves: Itrsc o relace o exteral frame of referece
More informationMechanical Design Technology (Free-form Surface) April 28, /12
Mechacal Desg echolog Free-form Srface Prof. amos Mrakam Assgme #: Free-form Srface Geerao Make a program ha geeraes a bcbc eer srface from 4 4 defg polgo e pos ad dsplas he srface graphcall a a ha allos
More informationInteractive Shape Preserving Interpolation by T-conic Spiral Spline
hp://www.spubs.org/cgacc/ Compuer Graphcs ad CAD/CAM (8 5 4 A Ieraoal Joural Ieracve Shape Preservg Ierpolao b T-coc Spral Sple Zulfqar Habb * Deparme of Compuer Scece Naoal Uvers of Compuer & Emergg Sceces
More informationNUMERICAL EVALUATION of DYNAMIC RESPONSE
NUMERICAL EVALUATION of YNAMIC RESPONSE Aalycal solo of he eqao of oo for a sgle degree of freedo syse s sally o ossble f he excao aled force or grod accelerao ü g -vares arbrarly h e or f he syse s olear.
More informationAML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending
CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral
More informationCS5620 Intro to Computer Graphics
CS56 Itro to Computer Graphcs Geometrc Modelg art II Geometrc Modelg II hyscal Sples Curve desg pre-computers Cubc Sples Stadard sple put set of pots { } =, No dervatves specfed as put Iterpolate by cubc
More informationMoving Least Squares Coordinates. Josiah Manson and Scott Schaefer Texas A&M University
Movg Leas Squares Coordaes Josah Maso ad Sco Schaefer Teas A&M Uversy Barycerc Coordaes olygo Doma p 4 p 3 p 2 p 0 p Barycerc Coordaes olygo Doma 4 3 2 0 Barycerc Coordaes olygo Doma f 4 f 3 f 2 f 0 f
More informationFORCED VIBRATION of MDOF SYSTEMS
FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me
More informationContinuous Time Markov Chains
Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,
More informationQR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA
QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.
More informationDetermination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction
refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
More informationThe Linear Regression Of Weighted Segments
The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed
More informationReal-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF
EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae
More informationLearning of Graphical Models Parameter Estimation and Structure Learning
Learg of Grahal Models Parameer Esmao ad Sruure Learg e Fukumzu he Isue of Sasal Mahemas Comuaoal Mehodology Sasal Iferee II Work wh Grahal Models Deermg sruure Sruure gve by modelg d e.g. Mxure model
More informationLeast squares and motion. Nuno Vasconcelos ECE Department, UCSD
Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem
More informationLeast Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters
Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo
More informationB-spline curves. 1. Properties of the B-spline curve. control of the curve shape as opposed to global control by using a special set of blending
B-sple crve Copyrght@, YZU Optmal Desg Laboratory. All rghts reserved. Last pdated: Yeh-Lag Hs (--9). ote: Ths s the corse materal for ME Geometrc modelg ad compter graphcs, Ya Ze Uversty. art of ths materal
More informationChapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)
Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were
More informationLinear Regression Linear Regression with Shrinkage
Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso
More informationComparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution
Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm
More informationSolution set Stat 471/Spring 06. Homework 2
oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o
More information14. Poisson Processes
4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur
More informationAxiomatic Definition of Probability. Problems: Relative Frequency. Event. Sample Space Examples
Rado Sgals robabl & Rado Varables: Revew M. Sa Fadal roessor o lecrcal geerg Uvers o evada Reo Soe phscal sgals ose cao be epressed as a eplc aheacal orla. These sgals s be descrbed probablsc ers. ose
More informationChapter 8. Simple Linear Regression
Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple
More informationEE 6885 Statistical Pattern Recognition
EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,
More informationSolution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.
ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh
More informationTHE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 10, Number 2/2009, pp
THE PUBLIHING HOUE PROCEEDING OF THE ROMANIAN ACADEMY, eres A, OF THE ROMANIAN ACADEMY Volume 0, Number /009,. 000-000 ON ZALMAI EMIPARAMETRIC DUALITY MODEL FOR MULTIOBJECTIVE FRACTIONAL PROGRAMMING WITH
More informationFALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.
Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50
More informationThe Bernstein Operational Matrix of Integration
Appled Mahemacal Sceces, Vol. 3, 29, o. 49, 2427-2436 he Berse Operaoal Marx of Iegrao Am K. Sgh, Vee K. Sgh, Om P. Sgh Deparme of Appled Mahemacs Isue of echology, Baaras Hdu Uversy Varaas -225, Ida Asrac
More informationCurves. Curves. Many objects we want to model are not straight. How can we represent a curve? Ex. Text, sketches, etc.
Curves Ton Sellarès Unversa e Grona Curves Many objecs we wan o moel are no sragh. Ex. Tex skeches ec. How can we reresen a curve? A large number of ons on he curve. Aroxmae wh connece lne segmens. ecewse
More informationEE 6885 Statistical Pattern Recognition
EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://www.ee.columba.edu/~sfchag Lecure 5 (9//05 4- Readg Model Parameer Esmao ML Esmao, Chap. 3. Mure of Gaussa ad EM Referece Boo, HTF Chap. 8.5 Teboo,
More informationan I -indexed set of σ-algebras that is increasing and becoming more complete in the sense that:
The Ba of Thalad Facal Io Polcy Gro Qaave odel & Facal Egeerg Team Facal ahemac Fodao oe 7 STOCHASTIC PROCESS COCEPTS & DEFIITIOS. ก FITRATIO defed o a mearable ace ( Ω a I -deed e of σ-algebra { } I ha
More informationK3 p K2 p Kp 0 p 2 p 3 p
Mah 80-00 Mo Ar 0 Chaer 9 Fourier Series ad alicaios o differeial equaios (ad arial differeial equaios) 9.-9. Fourier series defiiio ad covergece. The idea of Fourier series is relaed o he liear algebra
More informationDensity estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square
Lecure 5 esy esmao Mlos Hauskrec mlos@cs..edu 539 Seo Square esy esmaos ocs: esy esmao: Mamum lkelood ML Bayesa arameer esmaes M Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Noaramerc
More informationTime-Domain Finite Element Method in Electromagnetics A Brief Review
Tme-Doma Fe leme ehod lecromagecs A ref Revew y D. Xe Ph.D. Tme doma compao of awell eqaos s reqred elecromagec radao scaerg ad propagao problems. I addo s ofe more ecoomcal o ge freqecy doma resls va
More informationBrownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus
Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales
More informationUnit 10. The Lie Algebra of Vector Fields
U 10. The Le Algebra of Vecor Felds ================================================================================================================================================================ -----------------------------------
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationChapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I
CEE49b Chaper - Free Vbrao of M-Degree-of-Freedo Syses - I Free Udaped Vbrao The basc ype of respose of -degree-of-freedo syses s free daped vbrao Aaogos o sge degree of freedo syses he aayss of free vbrao
More informationCurves and Curved Surfaces. Where there is matter, there is geometry. Johannes Kepler
Cures and Cured Surfaces Where here s maer, here s geomery. Johannes Keler Paramerc cure Bezer cure Herme cure Kochanek-Barels Slnes Paramerc surface Bezer ach Bezer rangle Oulne Subdson Surfaces Chaken
More informationComputer Graphics. Geometric Modeling. Geometric Modeling. Page. Copyright Gotsman, Elber, Barequet, Karni, Sheffer Computer Science - Technion
Computer Graphcs Geometrc Modelg Geometrc Modelg A Example 4 Outle Objectve: Develop methods ad algorthms to mathematcally model shape of real world objects Categores: Wre-Frame Represetato Object s represeted
More informationfor each of its columns. A quick calculation will verify that: thus m < dim(v). Then a basis of V with respect to which T has the form: A
Desty of dagoalzable square atrces Studet: Dael Cervoe; Metor: Saravaa Thyagaraa Uversty of Chcago VIGRE REU, Suer 7. For ths etre aer, we wll refer to V as a vector sace over ad L(V) as the set of lear
More informationLecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination
Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of
More informationOther Topics in Kernel Method Statistical Inference with Reproducing Kernel Hilbert Space
Oher Topcs Kerel Mehod Sascal Iferece wh Reproducg Kerel Hlber Space Kej Fukumzu Isue of Sascal Mahemacs, ROIS Deparme of Sascal Scece, Graduae Uversy for Advaced Sudes Sepember 6, 008 / Sascal Learg Theory
More informationFeature Space. 4. Feature Space and Feature Extraction. Example: DNA. Example: Faces (appearance-based)
Feaure Sace 4. Feaure Sace ad Feaure Exraco Alex M. Marez alex@ece.osu.edu Hadous Hadousfor forece ECE874 874S S2007 May roblems scece ad egeerg ca be formulaed as a PR oe. For hs, we eed o defe a feaure
More informationFor the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.
The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe
More information( ) :. : - 1. : - 2. : - 3. : - 4., - :. - :1 -.,M /M/1. :2 -. :* - 3.,M /M/ M, /M/c/, K M/M/c :4 - :. : - 1. : - 2.
75 35 75 8 ' / ' ' * / 3 4 * 5 I 6 II 3 ' 4 5 * 6 7 / 8 * Hazard 3 4 5 6 * 7 *8 *9 75 3 8 3 4 M/M/ *3 4 M/M/c/K M/M/c M/M/ 3 75 ' 8 4 75 / [] D ' D ' D T ' T T T [ T T T S { } D D T S T 8 5 75 / = = =
More informationTheory and application of the generalized integral representation method (GIRM) in advection diffusion problem
Appled ad ompaoal Mahemacs 4; 4: 7-49 blshed ole Ags 4 hp://www.scecepblshggrop.com//acm do:.648/.acm.44.5 IN: 8-565 r; IN: 8-56 Ole Theory ad applcao of he geeralzed egral represeao mehod IRM adveco dffso
More informationLecture 15: Three-tank Mixing and Lead Poisoning
Lecure 15: Three-ak Miig ad Lead Poisoig Eigevalues ad eigevecors will be used o fid he soluio of a sysem for ukow fucios ha saisfy differeial equaios The ukow fucios will be wrie as a 1 colum vecor [
More informationMultiphase Flow Simulation Based on Unstructured Grid
200 Tuoral School o Flud Dyamcs: Topcs Turbulece Uversy of Marylad, May 24-28, 200 Oule Bacgroud Mulphase Flow Smulao Based o Usrucured Grd Bubble Pacg Mehod mehod Based o he Usrucured Grd Remar B CHEN,
More information= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n
.. Soluto of Problem. M s obvously cotuous o ], [ ad ], [. Observe that M x,..., x ) M x,..., x ) )..) We ext show that M s odecreasg o ], [. Of course.) mles that M s odecreasg o ], [ as well. To show
More informationSOLUTION OF PARABOLA EQUATION BY USING REGULAR,BOUNDARY AND CORNER FUNCTIONS
SOLUTION OF PAABOLA EQUATION BY USING EGULA,BOUNDAY AND CONE FUNCTIONS Dr. Hayder Jabbar Abood, Dr. Ifchar Mdhar Talb Deparme of Mahemacs, College of Edcao, Babylo Uversy. Absrac:- we solve coverge seqece
More informationGeometric Modeling
Geometrc Modelng 9.580.0 Notes on Crve and Srface Contnty Parts of Mortenson, Farn, Angel, Hll and others From Prevos Lectres Contnty at Jon Ponts (from Lectre ) Dscontnos: hyscal searaton Parametrc Contnty
More informationQuantitative Portfolio Theory & Performance Analysis
550.447 Quaave Porfolo heory & Performace Aalyss Week February 4 203 Coceps. Assgme For February 4 (hs Week) ead: A&L Chaper Iroduco & Chaper (PF Maageme Evrome) Chaper 2 ( Coceps) Seco (Basc eur Calculaos)
More informationConvexity Preserving C 2 Rational Quadratic Trigonometric Spline
Ieraoal Joural of Scefc a Researc Publcaos, Volume 3, Issue 3, Marc 3 ISSN 5-353 Covexy Preservg C Raoal Quarac Trgoomerc Sple Mrula Dube, Pree Twar Deparme of Maemacs a Compuer Scece, R. D. Uversy, Jabalpur,
More informationOP = OO' + Ut + Vn + Wb. Material We Will Cover Today. Computer Vision Lecture 3. Multi-view Geometry I. Amnon Shashua
Comuer Vson 27 Lecure 3 Mul-vew Geomer I Amnon Shashua Maeral We Wll Cover oa he srucure of 3D->2D rojecon mar omograh Marces A rmer on rojecve geomer of he lane Eolar Geomer an Funamenal Mar ebrew Unvers
More informationSuppose we have observed values t 1, t 2, t n of a random variable T.
Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).
More informationDensity estimation III.
Lecure 4 esy esmao III. Mlos Hauskrec mlos@cs..edu 539 Seo Square Oule Oule: esy esmao: Mamum lkelood ML Bayesa arameer esmaes MP Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Eoeal
More informationGauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year
Gau Thors Elmary Parcl Physcs Sro Iraco Fomoloy o Bo cadmc yar - Gau Ivarac Gau Ivarac Whr do Laraas or Hamloas com from? How do w kow ha a cra raco should dscrb a acual hyscal sysm? Why s h lcromac raco
More information4. THE DENSITY MATRIX
4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o
More informationPEGN 513 Reservoir Simulation I Fall 2009
Hmer #3 l The smples rm r aerld a lear cre ally saraed h l ad a resdal aer sara h gravy r capllary eecs s represeed by he -dmesal Bcley-Levere maeral balace eqa () Eplc sl Csderg he space dscreza sh Fgre
More information(1) Cov(, ) E[( E( ))( E( ))]
Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )
More informationSome Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables
Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3
More informationModeling of the linear time-variant channel. Sven-Gustav Häggman
Moelg of he lear me-vara chael Sve-Gusav Häggma 2 1. Characerzao of he lear me-vara chael 3 The rasmsso chael (rao pah) of a rao commucao sysem s mos cases a mulpah chael. Whe chages ae place he propagao
More informationCompetitive Facility Location Problem with Demands Depending on the Facilities
Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg
More informationCyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles
Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of
More informationIntegral Form of Popoviciu Inequality for Convex Function
Procees of e Paksa Acaey of Sceces: A. Pyscal a ozaoal Sceces 53 3: 339 348 206 oyr Paksa Acaey of Sceces ISSN: 258-4245 r 258-4253 ole Paksa Acaey of Sceces Researc Arcle Ieral For of Pooc Ieqaly for
More informationComputer Graphics. Shi-Min Hu. Tsinghua University
Computer Graphcs Sh-M Hu Tsghua Uversty Bézer Curves ad Surfaces arametrc curves ad surface: Some Cocepts Bézer Cuvres: Cocept ad propertes Bézer surfaces: Rectagular ad Tragular Coverso of Rectagular
More informationThe Poisson Process Properties of the Poisson Process
Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad
More information11/8/2002 CS 258 HW 2
/8/ CS 58 HW. G o a a qc of aa h < fo a I o goa o co a C cc c F ch ha F fo a I A If cc - c a co h aoa coo o ho o choo h o qc? I o g o -coa o o-coa? W ca choo h o qc o h a a h aa a. Tha f o o a h o h a:.
More informationMaximum Likelihood Estimation
Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~
More informationSolving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision
Frs Jo Cogress o Fuzzy ad Iellge Sysems Ferdows Uversy of Mashhad Ira 9-3 Aug 7 Iellge Sysems Scefc Socey of Ira Solvg fuzzy lear programmg problems wh pecewse lear membershp fucos by he deermao of a crsp
More informationA New Iterative Method for Solving Initial Value Problems
A New Ierave Meod or Solvg Ial Value Problems Mgse Wu ad Weu Hog Dearme o Ma., Sascs, ad CS, Uvers o Wscos-Sou, Meomoe, WI 5475, USA Dearme o Maemacs, Clao Sae Uvers, Morrow, GA 36, USA Absrac - I s aer,
More informationChapter 2. Review of Hydrodynamics and Vector Analysis
her. Ree o Hdrodmcs d Vecor Alss. Tlor seres L L L L ' ' L L " " " M L L! " ' L " ' I s o he c e romed he Tlor seres. O he oher hd ' " L . osero o mss -dreco: L L IN ] OUT [mss l [mss l] mss ccmled h me
More informationComputational Fluid Dynamics CFD. Solving system of equations, Grid generation
Compaoal ld Dyamcs CD Solvg sysem of eqaos, Grd geerao Basc seps of CD Problem Dscrezao Resl Gov. Eq. BC I. Cod. Solo OK??,,... Solvg sysem of eqaos he ype of eqaos decdes solo sraegy Marchg problems Eqlbrm
More informationA Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition
SSN 76-7659 Eglad K Joural of forao ad Copug Scece Vol 7 No 3 pp 63-7 A Secod Kd Chebyshev olyoal Approach for he Wave Equao Subec o a egral Coservao Codo Soayeh Nea ad Yadollah rdokha Depare of aheacs
More informationTraining Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ
Stat 74 Estmato for Geeral Lear Model Prof. Goel Broad Outle Geeral Lear Model (GLM): Trag Samle Model: Gve observatos, [[( Y, x ), x = ( x,, xr )], =,,, the samle model ca be exressed as Y = µ ( x, x,,
More informationContinuous Random Variables: Conditioning, Expectation and Independence
Cotuous Radom Varables: Codtog, xectato ad Ideedece Berl Che Deartmet o Comuter cece & Iormato geerg atoal Tawa ormal Uverst Reerece: - D.. Bertsekas, J.. Tstskls, Itroducto to robablt, ectos 3.4-3.5 Codtog
More informationarxiv: v1 [stat.ml] 21 Mar 2017
Sochasc Prmal Dual Coordae Mehod wh No-Uform Samplg Based o Opmaly Volaos Asush Shbagak shbagak.a.mllab.@gmal.com Deparme of Scefc ad Egeerg Smulao, Nagoya Isue of Techology arxv:703.0706v [sa.ml] Mar
More informationArtificial Intelligence Learning of decision trees
Artfcal Itellgece Learg of decso trees Peter Atal atal@mt.bme.hu A.I. November 21, 2016 1 Problem: decde whether to wat for a table at a restaurat, based o the followg attrbutes: 1. Alterate: s there a
More informationON THE LOGARITHMIC INTEGRAL
Hacettepe Joural of Mathematcs ad Statstcs Volume 39(3) (21), 393 41 ON THE LOGARITHMIC INTEGRAL Bra Fsher ad Bljaa Jolevska-Tueska Receved 29:9 :29 : Accepted 2 :3 :21 Abstract The logarthmc tegral l(x)
More information18.413: Error Correcting Codes Lab March 2, Lecture 8
18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse
More informationAs evident from the full-sample-model, we continue to assume that individual errors are identically and
Maxmum Lkelhood smao Greee Ch.4; App. R scrp modsa, modsb If we feel safe makg assumpos o he sascal dsrbuo of he error erm, Maxmum Lkelhood smao (ML) s a aracve alerave o Leas Squares for lear regresso
More informationOptimal Eye Movement Strategies in Visual Search (Supplement)
Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case
More informationSingular Value Decomposition. Linear Algebra (3) Singular Value Decomposition. SVD and Eigenvectors. Solving LEs with SVD
Sgular Value Decomosto Lear Algera (3) m Cootes Ay m x matrx wth m ca e decomosed as follows Dagoal matrx A UWV m x x Orthogoal colums U U I w1 0 0 w W M M 0 0 x Orthoormal (Pure rotato) VV V V L 0 L 0
More informationLecture 2: The Simple Regression Model
Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.
More informationConstruction of resilient S-boxes with higherdimensional vectorial outputs and strictly almost optimal non-linearity
IET Iformao Secry Research Arcle Cosrco of resle S-boxes wh hgherdmesoal vecoral ops ad srcly almos opmal o-leary ISSN 1751-8709 Receved o 1s Aprl 2016 Acceped o 21s Je 2016 E-Frs o 12h Sepember 2016 do:
More informationHydraulic Model of Dam Break Using Navier Stokes Equation with Arbitrary Lagrangian-Eulerian Approach
IACIT Ieraoal Joral of Egeerg ad Tecolog ol. 8 No. 4 Ags 6 dralc odel of Dam Break Usg Naer okes Eqao Arbrar Lagraga-Elera Aroac Alreza Lorasb oarram Dolasa Prooz ad Alreza Laae Absrac Te lqd flo ad e
More informationUse of Non-Conventional Measures of Dispersion for Improved Estimation of Population Mean
Amerca Joural of Operaoal esearch 06 6(: 69-75 DOI: 0.59/.aor.06060.0 Use of o-coveoal Measures of Dsperso for Improve Esmao of Populao Mea ubhash Kumar aav.. Mshra * Alok Kumar hukla hak Kumar am agar
More informationAsymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse
P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc
More informationA note on Turán number Tk ( 1, kn, )
A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco
More informationSimple Linear Regression: 1. Finding the equation of the line of best fit
Cocerao Wegh kg mle Lear Regresso:. Fdg he equao of he le of es f Ojecves: To fd he equao of he leas squares regresso le of o. Backgroud ad geeral rcle The am of regresso s o fd he lear relaosh ewee wo
More information,m = 1,...,n; 2 ; p m (1 p) n m,m = 0,...,n; E[X] = np; n! e λ,n 0; E[X] = λ.
CS70: Lecture 21. Revew: Dstrbutos Revew: Idepedece Varace; Iequaltes; WLLN 1. Revew: Dstrbutos 2. Revew: Idepedece 3. Varace 4. Iequaltes Markov Chebyshev 5. Weak Law of Large Numbers U[1,...,] : Pr[X
More informationVertical Differentiation Models
Vercal Dffereao Models Vercal Dffereao - cosmers agree o deal rodc vary o wllgess o ay ecase of refereces or come Glle: "faser s eer dffere raes of rer for seed" Qaly - loo a scale wh m ad max feasle level
More informationComplete Identification of Isotropic Configurations of a Caster Wheeled Mobile Robot with Nonredundant/Redundant Actuation
486 Ieraoal Joural Sugbok of Corol Km Auomao ad Byugkwo ad Sysems Moo vol 4 o 4 pp 486-494 Augus 006 Complee Idefcao of Isoropc Cofguraos of a Caser Wheeled Moble Robo wh Noreduda/Reduda Acuao Sugbok Km
More information-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for
Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my
More informationMultivariate Transformation of Variables and Maximum Likelihood Estimation
Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty
More informationApproximation of Parametric Functions by Bicubic B-spline Functions. Majid Amirfakhrian a, Sahar Didab b.
Joral of Aerca Scece ;9() htt://wwwofaercasceceorg Aroxato of Paraetrc Fctos by Bcbc B-sle Fctos Mad Arfakhra a Sahar Ddab b a Deartet of Matheatcs Islac Azad Uversty Cetral Tehra Brach Tehra Ira arfakhra@actbacr
More information3/3/2014. CDS M Phil Econometrics. Heteroskedasticity is a problem where the error terms do not have a constant variance.
3/3/4 a Plla N OS Volao of Assmpos Assmpo of Sphercal Dsrbaces Var T T I Var O Cov, j, j,..., Therefore he reqreme for sphercal dsrbaces s ad j I O homoskedascy No aocorrelao Heeroskedascy: Defo Heeroscedascy
More information