Rao Transforms: Application to the Restoration of Shift-Variant Blurred Images
|
|
- Griselda Barber
- 5 years ago
- Views:
Transcription
1 Rao rasors: Applcato to te Restorato o St-Varat Blurred Iages Dr. Muraldara SubbaRao ttp:// ural@ece.susb.edu ttp:// rao@tegralresearc.et Itroducto We descrbe cocrete oe-desoal D ad two-desoal D eaples o te practcal applcato o Rao rasors Rs []. e oe-desoal eaple s relevat to te restorato or deblurrg o st-varat oto blurred ages. We a potograp s captured b a ovg caera wt a te eposure perod sa. secod obects earer to te caera wll ave larger oto blur ta arter obects. s stuato ca arse we te caera s o a ovg plator suc as a car or a robot. A slar stuato.e. a st-varat deocus blur ca arse a laser barcode scaer or oe-desoal barcodes. We te plae o te barcode s slated stead o beg perpedcular to te drecto o vew te barcode age wll be blurred b a stvarat pot spread ucto SV-PSF. e two-desoal eaple s related to te restorato o ages or sgals degraded b a SV-PSF suc as blurred ages o a slated plae or a curved surace produced b a deocused caera sste. Oe Desoal case Let te orgal ocused age be ad te correspodg blurred age be g. e covetoal age blurrg odel ts case uses a st-varat pot spread ucto or kerel k. e blurred age g ad te kerel k are assued to be gve ad te proble s to solve or te ocused age. e covetoal blurrg odel s a tegral equato o te or: g k d It s a oe-desoal Fredol Itegral Equato o te Frst Kd. e above odel o blurrg as two probles. Frst t s dcult to d a closed-or verso orula tat s eplct ad uercall stable. For eaple te well-kow Sgular Value Decoposto SVD tecque s coputatoall epesve ad ustable. Secod te above odel does ot capture te pscal blurrg process a atural wa. It sees to pose ateatcal splct at te cost o drect atural odelg o te pscal blurrg process. We odel te blurred age g easured at as te su or tegral over all possble pot sources o te cotrbuto due to eac pot source located at -. e cotrbuto s gve b te product o te stregt o te sgal pot source - ad te value o a ew localzed st-varat pot spread ucto -. e ew odel o blurrg s: g d R M. SubbaRao 7 ural@ece.susb.edu
2 were k + ad RL 3 k IRL 4 e ew odel above s a tegral equato tat s eactl equvalet to te orgal tegral equato see [] or proo. Equato s reerred to as te Rao Itegral Equato RIE ad dees te Rao rasor R. Equato 3 dees te Rao Localzato rasor RL. Now te -t order partal dervatve o at wt respect to s deoted b. e -t dervatve o at wt respect to wll be deoted b d. 6 d e -t oet o te -t dervatve o s deed b d 7 Note tat te dervatve s wt respect to ad te oet s wt respect to. e orgal sgal wll be take to be soot or aaltc so tat t ca be epaded a alor seres. e alor seres epaso o - aroud te pot up to order N s N 5 a 8 were a. 9! e above equato s eact ad ree o a approato error we tsel s a poloal o degree less ta or equal to N. I ts case te dervatves o o order greater ta N are all zero. We as o-zero dervatves o order greater ta N te te above equato wll ave a approato error correspodg to te resdual ter o te alor seres epaso. s approato error usuall coverges rapdl to zero as N creases. I te lt as N teds to t te above seres epaso becoes eact ad coplete. Slarl te alor seres epaso o - aroud te pot up to order M s M a were a are as Eq. 9. Usg a trucated alor seres epaso as above gves ver accurate approatos a practcal applcatos suc as age deblurrg as te kerel ucto usuall cages sootl ad slowl wt respect to. M. SubbaRao 7 ural@ece.susb.edu
3 Eaple: We coclude our oe-desoal dscusso wt a specc eaple were we let N M ad let te orgal kerel k be a Gaussa tat s k ep πσ σ were ep e. e kerel above s a global kerel. It s localzed usg te Rao Localzato rasor RL to dee a ew local kerel as Eq. 3.e. k + wc becoes ep πσ σ For otatoal coveece we deote ρ. 3 σ ereore ρ ρ ep 4 π e alor seres epaso o aroud te pot up to order M s +. 5 It ca be sow tat we s as deed Eq. 4 ρ a ρ ρ were ρ s te dervatve o ρ wt respect to. Note tat te above ucto s a eve ucto o as t volves ol. s ucto s setrc wt respect to.e. -. ereore all odd oets o wt respect to wll be zero ad wt M ad N te R becoes g + d. 7 Splg we get g Sce all odd oets o ad are zero we set s 6 3. sples te proble. Furter we ave or ts case ad bot rst ad all ger dervatves o secod dervatves wt respect to o dervatve o ad are alwas zero. Also te rst ad ger ad are all zero. Ol te rst a ot be zero. It wll be deoted b 3. Sgcat splcato o a ateatcal proble suc as ere s lkel a practcal applcatos. Wt te above splcatos we get M. SubbaRao 7 3 ural@ece.susb.edu
4 g akg dervatves o te above equato oce ad twce we get g + + ad g. We treat te above equatos 9 to as tree algebrac equatos te tree ukows ad. e ca be easl solved troug successve elato ad back substtuto. I ts partcular eaple wc correspods to a tpcal practcal applcato te process o solvg becoes trval. We solve or to obta g g g 3 e soluto above ca be urter spled b otg tat 4 σ ad ρ ρ σ ρ 3 σ 3 ρ ρ 3 us we ave obtaed Eq. te Iverse Rao rasor IR or a case tat s useul practcal applcatos. It s a closed-or soluto up to secod order ters. Soluto up to a order N ca be obtaed slarl. A soluto or s gve ters o te dervatves o g at ad oets o dervatves o te localzed kerel. I all our searc o relevat researc lterature we ave ever see suc a closed-ro soluto or te Fredol Itegral Equato o te Frst Kd. s s a local soluto ad coverges rapdl or ts partcular eaple. A ew oter ore coplcated eaples are preseted te book [] but te sple eaple ere llustrates te potetal power o Rs. I atr otato te orward ad verse R or ts case ca be wrtte as g / g 3 / g / 3 g 3 / g g I te put ucto s a poloal ad te value o N s cose to be te sae as te degree o te poloal te ro te teor oe sees tat te recostructo sould be perect. s as bee vered sulato eperets. M. SubbaRao 7 4 ural@ece.susb.edu
5 wo-desoal case A st-varat deocused age wll be deoted b g. e st-varat pot spread ucto SV-PSF wll be deoted b were ad are st-varace varables ad ad are spread ucto varables. e orgal ucorrupted ocused put age wll be deoted b. e Rao rasor ts case s 48 g dd. e ollowg otato wll be used to represet partal dervatves o g ad te oets o : g g k k k d d 5 or. Usg te above otato te alor seres epaso o aroud up to order N ad aroud te pot up to order M are gve b were C k p a C N 53 M 54 a C C ad k! p! k p! C deotes te boal coecets deed b ad a ad a are costats as deed Eq. 9. Substtutg te above epressos to te Rao rasor o Eq. 48 ad splg we get N M + + g a C a C 55 e above equato ca be rewrtte as M. SubbaRao 7 5 ural@ece.susb.edu
6 were N 56 g S M + + S a C a C 57 We ca ow wrte epressos or te varous partal dervatves o g as or ad g S. N p q pq p q 58 p+ q N. Note tat p q M p+ q S S a C a C p q p q + p + q p q + p+ q. p q 6 p q e above equato or g or pq N ad p+ q N p q costtute N + N + / equatos as a ukows equatos or p q g. e sste o ca be epressed atr or wt a sutable R coecet atr o sze N + N + / rows ad colus. ese equatos ca be solved p q eter uercall or algebracall to obta ad partcular. e soluto or were S ca be epressed as N S g 6 are te verse R coecets or te -desoal case. Eaple: We preset a soluto or te case o N ad M or te case o a -D Gaussa SV-PSF gve b + ep We wll dee a ew paraeter ρ as ρ σ. πσ σ 6 63 M. SubbaRao 7 6 ural@ece.susb.edu
7 ereore te SV-PSF ca be wrtte as ρ ρ + ep π For ts case as te -D case a oet paraeters ad ter dervatves becoe zero. Speccall Slarl ρ ρ +. ρ ρ ρ +. ρ We see tat ad are bot rotatoall setrc wt respect to ad. ereore all odd oets are zero.e. Also s odd or s odd. 67 dd 68 or all ad tereore all dervatves o wt respect to ad are zero. Also sce M all dervatves o o order ore ta wt respect to ad are zero. I suar 3 3. ereore we get R to be g e above equato gves a etod o coputg te output sgal g gve te put sgal. It ca be wrtte a or slar to Eq. 56 to obta te R coecets S. We ca derve te verse R or ts case usg Equato 69. As te -desoal case we cosder te varous dervatves o g Eq. 69 ad solve or te dervatves o as ukows. I ts partcular eaple we rst solve or ters o oter ters usg Eq. 69. e we take te dervatve o te epresso or wt respect to M. SubbaRao 7 7 ural@ece.susb.edu
8 ad solve or. Net we take te dervatve o. e we take te dervatve wt respect to o wt respect to ad solve or ad ad solve or ad respectvel. Slarl we take dervatves wt respect to o ad ad solve or ad respectvel. Fall we back substtute tese results ad elate ad to get te ollowg eplct soluto or ters o te dervatves o g ad oets o te dervatves o as below : 3 g g g g g Furter splcato o te above equato s possble due to rotatoal setr e.g. ad. e above equato gves a eplct closed-or orula or restorg a age blurred b a st-varat Gaussa pot spread ucto. e above equato ca be wrtte a or slar to Equato 6 or verse R to obta te verse R coecets. It s clear ro te above dscusso tat te etod or pleetg te orward ad verse R or te two-desoal case s slar to te oe-desoal case eplaed earler. Eperets: Several sulato eperets were doe to ver te teor above. e eperets cossted o bot D ad D cases. Frst te ukow ucto was cose to be a poloal o a certa order e.g or a se ucto o a certa perod e.g. S te te kerel was cose to be oe o Gaussa or rect ad a Cldrcal te D case wt a alor seres epaso up to order M or. e order N o te poloal was vared 3 to 8 ad te perod o te se ucto was vared s to s deret eperets. e spread paraeter sga o te SV- PSF te deret cases was vared learl e.g. s.5+.. e aaltc epressos or te blurred age ad te restored age were plotted a terval e.g. _- to _a wt sapled pots. As epected we te ukow ucto was a poloal te soluto or was eact. However te case o se uctos due to trucato o te seres epaso as epected te soluto ad sall errors. s error creased we te rato o te paraeter sga to te perod o te se wave creased. e error was sall up to a rato o.. wo eaples o D put uctos are as below N 3 M. s.5 + cos.5 N 4 M 5 ad 5. a a M. SubbaRao 7 8 ural@ece.susb.edu
9 Cocluso Equato 7 gves a closed-or soluto or te restorato o a st-varat deocused age. It as bee vered eperetall troug sulatos. s localzed soluto to st-varat age restorato ca be easl eteded to ger order local poloal approatos ad a deret odels o SV-PSF Gaussa cldrcal rect etc.. e resultg coputatoal approac s to 3 orders o agtude aster ta te classcal SVD approac [34]. e etod ere as bee pleeted o actual ages wt sulated blur ad vered. s etod olds uc prose a applcatos. Apped I ts secto we preset closed-or eplct epressos or te oets o te dervatves o te SV-PSF or deret cases. D Gaussa e PSF as te ollowg or : ep πσ σ t ereore te oet s epressed as te ollowg tegral A ep d πσ σ A Sce te lts o tegrato do ot deped o te varable we ca tercage te tegrato wt respect to ad deretato wt respect to. ereore to get te dervatves o te oets tat s we copute te tegral A ad te deretate te. e ollowg steps lead us to a geeral orula or. ep d πσ σ + ep d πσ σ ep s a oddeve ucto s oddeve. We ake te ollowg σ σdt substtuto : t tereore σ t ad d. Wt ts substtuto te σ t above equato becoes: + + σ t ep t dt πσ M. SubbaRao 7 9 ural@ece.susb.edu
10 + σ + Γ A3 π I te eperets we ave cose σ to var learl as σ.5 +. ad N. ereore ad σ tereore +.5. σ σ D Rectagular PSF e PSF or te rectagular case as te ollowg or or A4 Oterwse t ereore te oet ca be epressed as: d A5 As see ro te orula A5 te lts o tegrato ow do deped o te varable So we caot take dervatves o A5 to get s. Below we derve a geeral orula or uder te assupto tat s learl varg tat s secod ad ger order dervatves o are all zero. d d A6 d Now uder te assupto tat s lear we ave or te dervatve: d! + d Substtutg A7 to A6 we get! + d A7 +! + + A8 D Gaussa e PSF as te ollowg or : M. SubbaRao 7 ural@ece.susb.edu
11 M. SubbaRao 7 ural@ece.susb.edu + ep σ πσ A9 ereore te t oet s epressed as te ollowg tegral σ πσ d d + ep A We splt te double tegral as a product o two sgle tegrals σ σ πσ d d ep ep We tegrate eac as we dd or te oe desoal case. e result s as ollows: [ ] π σ Γ + Γ + A We deretate A to get te dervatves. D Rectagular PSF e PSF or te rectagular case as te ollowg or or A Oterwse ereore te t oet ca be epressed as: d d A3 As te Oe-Desoal case te lts o tegrato deped o. ereore to get te dervatves o te oets we eed to deretate uder te tegral. e orula or te dervatves o te oets s: + d d A4 Uder te assupto tat vares learl wt ad +! A5 Here s te rst partal dervatve o wt respect to ad s te rst partal dervatve o wt respect to. ereore puttg A5 to A4 ad tegratg we get
12 ! [ + + ] A6 D Cldrcal PSF: e dervato s ver slar to te Rectagular case. So we wll skp ost o te steps ere. e PSF or te cldrcal case as te ollowg or B R A7 πr Oterwse e oets ad ter dervatves ca be obtaed b evaluatg te ollowg tegral + da A8 B R πr We ake te assupto tat R vares learl wt ad. ereore te partal dervatves o are as ollows: + + +! R + R A9 + + πr πr o copute A8 we swtc to polar co-ordates tat s we replace b r cosθ ad b r sθ ad te area eleet da b rdrdθ. ereore te tegral A8 becoes! R R π R r cos θ s πr θdrdθ were + +! R π π + + R R R + rg A + rg cos θ s θdθ ca be coputed separatel. Reereces. "Rao rasors: eor ad Applcatos" b M. Subbarao Rao U.S. Coprgt Regstrato No. X Jue 5. Purcase at ttp:// M. Subbarao Passve ragg ad rapd autoocusg U.S. patet No Sept W. K. Pratt Dgtal Iage Processg Secod Edto Secto.3 pages 376 to 38 Jo Wle ad Sos 99 ISBN A. K. Ja Fudaetals o Dgtal Iage Processg Secto 8.9 pages 99 to 3 Pretce-Hall Ic. 989 ISBN M. SubbaRao 7 ural@ece.susb.edu
Basic Concepts in Numerical Analysis November 6, 2017
Basc Cocepts Nuercal Aalyss Noveber 6, 7 Basc Cocepts Nuercal Aalyss Larry Caretto Mecacal Egeerg 5AB Sear Egeerg Aalyss Noveber 6, 7 Outle Revew last class Mdter Exa Noveber 5 covers ateral o deretal
More informationSome Different Perspectives on Linear Least Squares
Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,
More informationNumerical Differentiation
College o Egeerg ad Computer Scece Mecacal Egeerg Departmet Numercal Aalyss Notes November 4, 7 Istructor: Larry Caretto Numercal Deretato Itroducto Tese otes provde a basc troducto to umercal deretato
More information2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission
/0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power
More informationOutline. Remaining Course Schedule. Review Systems of ODEs. Example. Example Continued. Other Algorithms for Ordinary Differential Equations
ter Nuercal DE Algorts Aprl 8 0 ter Algorts or rdar Deretal Equatos Larr aretto Mecacal Egeerg 09 Nuercal Aalss o Egeerg Sstes Aprl 8 0 utle Scedule Revew sstes o DEs Sprg-ass-daper proble wt two asses
More informationA Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions
Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple
More informationIntroduction to Numerical Differentiation and Interpolation March 10, !=1 1!=1 2!=2 3!=6 4!=24 5!= 120
Itroducto to Numercal Deretato ad Iterpolato Marc, Itroducto to Numercal Deretato ad Iterpolato Larr Caretto Mecacal Egeerg 9 Numercal Aalss o Egeerg stems Marc, Itroducto Iterpolato s te use o a dscrete
More informationDerivation of the Modified Bi-quintic B-spline Base Functions: An Application to Poisson Equation
Aerca Joural of Coputatoal ad Appled Mateatcs 3 3(): 6-3 DOI:.93/j.ajca.33. Dervato of te Modfed B-qutc B-sple Base Fuctos: A Applcato to Posso Equato S. Kutlua N. M. Yagurlu * Departet of Mateatcs İöü
More information3D Reconstruction from Image Pairs. Reconstruction from Multiple Views. Computing Scene Point from Two Matching Image Points
D Recostructo fro Iage ars Recostructo fro ultple Ves Dael Deetho Fd terest pots atch terest pots Copute fudaetal atr F Copute caera atrces ad fro F For each atchg age pots ad copute pot scee Coputg Scee
More informationA New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming
ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research
More informationNumerical Differentiation
College o Egeerg ad Computer Scece Mecacal Egeerg Departmet ME 9 Numercal Aalyss Marc 4, 4 Istructor: Larry Caretto Numercal Deretato Itroducto Tese otes provde a basc troducto to umercal deretato usg
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationSolutions to problem set ); (, ) (
Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated
More informationLecture Notes 2. The ability to manipulate matrices is critical in economics.
Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets
More informationChapter 5. Curve fitting
Chapter 5 Curve ttg Assgmet please use ecell Gve the data elow use least squares regresso to t a a straght le a power equato c a saturato-growthrate equato ad d a paraola. Fd the r value ad justy whch
More informationAlgorithms behind the Correlation Setting Window
Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree
More information( t) ( t) ( t) ρ ψ ψ. (9.1)
Adre Toaoff, MT Departet of Cestry, 3/19/29 p. 9-1 9. THE DENSTY MATRX Te desty atrx or desty operator s a alterate represetato of te state of a quatu syste for wc we ave prevously used te wavefucto. Altoug
More information4 Round-Off and Truncation Errors
HK Km Slgtly moded 3//9, /8/6 Frstly wrtte at Marc 5 4 Roud-O ad Trucato Errors Errors Roud-o Errors Trucato Errors Total Numercal Errors Bluders, Model Errors, ad Data Ucertaty Recallg, dv dt Δv v t Δt
More informationLecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES
FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato
More informationDATA DOMAIN DATA DOMAIN
3//6 Coprght otce: Most ages these sldes are Gozalez ad oods Pretce-Hall Note: ages are [spatall] ostatoar sgals. e eed tools to aalze the locall at dfferet resolutos e ca do ths the data doa or sutable
More informationCS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x
CS 75 Mache Learg Lecture 7 Lear regresso Mlos Hauskrecht los@cs.ptt.edu 59 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear cobato of put copoets f + + + K d d K k - paraeters eghts
More informationA New Mathematical Approach for Solving the Equations of Harmonic Elimination PWM
New atheatcal pproach for Solvg the Equatos of Haroc Elato PW Roozbeh Nader Electrcal Egeerg Departet, Ira Uversty of Scece ad Techology Tehra, Tehra, Ira ad bdolreza Rahat Electrcal Egeerg Departet, Ira
More informationGeneral Method for Calculating Chemical Equilibrium Composition
AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt
More informationBounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy
Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationAssignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix
Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,
More informationESS Line Fitting
ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationFourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION
Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar
More informationPRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION
PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of
More informationNonlinear Piecewise-Defined Difference Equations with Reciprocal Quadratic Terms
Joural of Matematcs ad Statstcs Orgal Researc Paper Nolear Pecewse-Defed Dfferece Equatos wt Recprocal Quadratc Terms Ramada Sabra ad Saleem Safq Al-Asab Departmet of Matematcs, Faculty of Scece, Jaza
More informationSome results and conjectures about recurrence relations for certain sequences of binomial sums.
Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have
More informationBabatola, P.O Mathematical Sciences Department Federal University of Technology, PMB 704 Akure, Ondo State, Nigeria.
Iteratoal Joural o Matematcs a Statstcs Stues ol., No., Marc 0, pp.45-6 Publse b Europea Cetre or esearc, rag a Developmet, UK www.ea-jourals.org NUMEICAL SOLUION OF ODINAY DIFFEENIAL EQUAIONUSING WO-
More informationFREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM
Joural of Appled Matematcs ad Computatoal Mecacs 04, 3(4), 7-34 FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM Ata Cekot, Stasław Kukla Isttute of Matematcs, Czestocowa Uversty of Tecology Częstocowa,
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More informationCS475 Parallel Programming
CS475 Parallel Programmg Deretato ad Itegrato Wm Bohm Colorado State Uversty Ecept as otherwse oted, the cotet o ths presetato s lcesed uder the Creatve Commos Attrbuto.5 lcese. Pheomea Physcs: heat, low,
More informationAsymptotic Formulas Composite Numbers II
Iteratoal Matematcal Forum, Vol. 8, 203, o. 34, 65-662 HIKARI Ltd, www.m-kar.com ttp://d.do.org/0.2988/mf.203.3854 Asymptotc Formulas Composte Numbers II Rafael Jakmczuk Dvsó Matemátca, Uversdad Nacoal
More informationIdea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx
Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet
More informationQR Factorization and Singular Value Decomposition COS 323
QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?
More informationDIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS
DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS Course Project: Classcal Mechacs (PHY 40) Suja Dabholkar (Y430) Sul Yeshwath (Y444). Itroducto Hamltoa mechacs s geometry phase space. It deals
More informationThe equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use.
INTRODUCTORY NOTE ON LINEAR REGREION We have data of the form (x y ) (x y ) (x y ) These wll most ofte be preseted to us as two colum of a spreadsheet As the topc develops we wll see both upper case ad
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
More informationA Class of Deformed Hyperbolic Secant Distributions Using Two Parametric Functions. S. A. El-Shehawy
A Class o Deormed Hyperbolc Secat Dstrbutos Usg Two Parametrc Fuctos S. A. El-Shehawy Departmet o Mathematcs Faculty o Scece Meoua Uversty Sheb El-om Egypt shshehawy6@yahoo.com Abstract: Ths paper presets
More informationNumerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract
Numercal Smulatos of the Complex Moded Korteweg-de Vres Equato Thab R. Taha Computer Scece Departmet The Uversty of Georga Athes, GA 002 USA Tel 0-542-2911 e-mal thab@cs.uga.edu Abstract I ths paper mplemetatos
More informationThe Modified Bi-quintic B-spline Base Functions: An Application to Diffusion Equation
Iteratoal Joural of Partal Dfferetal Equatos ad Applcatos 017 Vol. No. 1 6-3 Avalable ole at http://pubs.scepub.co/jpdea//1/4 Scece ad Educato Publshg DOI:10.1691/jpdea--1-4 The Modfed B-qutc B-sple Base
More informationMidterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More informationx y exp λ'. x exp λ 2. x exp 1.
egecosmcd Egevalue-egevector of the secod dervatve operator d /d hs leads to Fourer seres (se, cose, Legedre, Bessel, Chebyshev, etc hs s a eample of a systematc way of geeratg a set of mutually orthogoal
More informationKURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.
KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces
More informationMidterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..
More informationInvestigation of Partially Conditional RP Model with Response Error. Ed Stanek
Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a
More informationThe theoretical background of
he theoretcal backgroud of -echologes he theoretcal backgroud of FactSage he followg sldes gve a abrdged overvew of the ajor uderlyg prcples of the calculatoal odules of FactSage. -echologes he bbs Eergy
More informationRelations to Other Statistical Methods Statistical Data Analysis with Positive Definite Kernels
Relatos to Other Statstcal Methods Statstcal Data Aalyss wth Postve Defte Kerels Kej Fukuzu Isttute of Statstcal Matheatcs, ROIS Departet of Statstcal Scece, Graduate Uversty for Advaced Studes October
More informationANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK
Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION
More informationOutline. Numerical Heat Transfer. Review All Black Surfaces. Review View Factor, F i j or F ij. Review Gray Diffuse Opaque II
umercao Heat raser ay 9 ad, 7 umercal Heat raser arry Caretto ecacal geerg 75 Heat raser ay 9 ad, 7 Outle Wat s umercal aalyss Cosderatos o coducto, covecto ad radato evew umercal aalyss bascs ervatve
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 information2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America
SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More informationGlobal Optimization for Solving Linear Non-Quadratic Optimal Control Problems
Joural of Appled Matheatcs ad Physcs 06 4 859-869 http://wwwscrporg/joural/jap ISSN Ole: 37-4379 ISSN Prt: 37-435 Global Optzato for Solvg Lear No-Quadratc Optal Cotrol Probles Jghao Zhu Departet of Appled
More information02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES
0/5/04 ITERESTIG FIITE AD IFIITE PRODUCTS FROM SIMPLE ALGEBRAIC IDETITIES Thomas J Osler Mathematcs Departmet Rowa Uversty Glassboro J 0808 Osler@rowaedu Itroducto The dfferece of two squares, y = + y
More informationObjectives of Multiple Regression
Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of
More informationUniform DFT Filter Banks 1/27
.. Ufor FT Flter Baks /27 Ufor FT Flter Baks We ll look at 5 versos of FT-based flter baks all but the last two have serous ltatos ad are t practcal. But they gve a ce trasto to the last two versos whch
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationPerformance of a Queuing System with Exceptional Service
Iteratoal Joural o Eeer ad Matheatcal Sceces Ja.- Jue 0, Volue, Issue, pp.66-79 ISSN Prt 39-4537, Ole 39-4545. All rhts reserved www.jes.or IJEMS Abstract Perorace o a Queu Syste wth Exceptoal Servce Dr.
More informationA Collocation Method for Solving Abel s Integral Equations of First and Second Kinds
A Collocato Method for Solvg Abel s Itegral Equatos of Frst ad Secod Kds Abbas Saadatmad a ad Mehd Dehgha b a Departmet of Mathematcs, Uversty of Kasha, Kasha, Ira b Departmet of Appled Mathematcs, Faculty
More information1 Lyapunov Stability Theory
Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may
More information3.1 Introduction to Multinomial Logit and Probit
ES3008 Ecooetrcs Lecture 3 robt ad Logt - Multoal 3. Itroducto to Multoal Logt ad robt 3. Estato of β 3. Itroducto to Multoal Logt ad robt The ultoal Logt odel s used whe there are several optos (ad therefore
More information5 Short Proofs of Simplified Stirling s Approximation
5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:
More informationNumerical Analysis Formulae Booklet
Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx
More informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationChapter 11 Systematic Sampling
Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of
More informationCS 1675 Introduction to Machine Learning Lecture 12 Support vector machines
CS 675 Itroducto to Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mdterm eam October 9, 7 I-class eam Closed book Stud materal: Lecture otes Correspodg chapters
More informationLecture Notes Forecasting the process of estimating or predicting unknown situations
Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg
More informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More informationClass 13,14 June 17, 19, 2015
Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationTheoretical Physics. Course codes: Phys2325 Course Homepage:
Theoretcal Phscs Course codes: Phs35 Course Homepage: http://bohr.phscs.hku.hk/~phs35/ Lecturer: Z.D.Wag, Oce: Rm58, Phscs Buldg Tel: 859 96 E-mal: wag@hkucc.hku.hk Studet Cosultato hours: :3-4:3pm Tuesda
More information. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)
Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationCHAPTER 3 POSTERIOR DISTRIBUTIONS
CHAPTER 3 POSTERIOR DISTRIBUTIONS If scece caot measure the degree of probablt volved, so much the worse for scece. The practcal ma wll stck to hs apprecatve methods utl t does, or wll accept the results
More informationLecture 1: Introduction to Regression
Lecture : Itroducto to Regresso A Eample: Eplag State Homcde Rates What kds of varables mght we use to epla/predct state homcde rates? Let s cosder just oe predctor for ow: povert Igore omtted varables,
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More informationChapter 3 Sampling For Proportions and Percentages
Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys
More informationMOLECULAR VIBRATIONS
MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal
More informationBeam Warming Second-Order Upwind Method
Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet
More informationNon-degenerate Perturbation Theory
No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationLaboratory I.10 It All Adds Up
Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.
More informationRational Laguerre Functions and Their Applications
Joural of ateatcs a coputer Scece 4 (5) 4-4 Ratoal Laguerre Fuctos a er Applcatos A. Aatae * S. Aa-Asl Z. KalateBo Departet of Apple Mateatcs Faculty of Mateatcs K.. oos Uversty of ecology P.O. Bo 635-68
More informationTHE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5
THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should
More informationd dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin
Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace
More informationThe Mathematics of Portfolio Theory
The Matheatcs of Portfolo Theory The rates of retur of stocks, ad are as follows Market odtos state / scearo) earsh Neutral ullsh Probablty 0. 0.5 0.3 % 5% 9% -3% 3% % 5% % -% Notato: R The retur of stock
More informationLecture 1: Introduction to Regression
Lecture : Itroducto to Regresso A Eample: Eplag State Homcde Rates What kds of varables mght we use to epla/predct state homcde rates? Let s cosder just oe predctor for ow: povert Igore omtted varables,
More informationOn Pfaff s solution of the Pfaff problem
Zur Pfaff scen Lösung des Pfaff scen Probles Mat. Ann. 7 (880) 53-530. On Pfaff s soluton of te Pfaff proble By A. MAYER n Lepzg Translated by D. H. Delpenc Te way tat Pfaff adopted for te ntegraton of
More informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More informationCamera calibration & radiometry
Caera calbrato & radoetr Readg: Chapter 2, ad secto 5.4, Forsth & oce Chapter, Hor Optoal readg: Chapter 4, Forsth & oce Sept. 2, 22 MI 6.8/6.866 rofs. Freea ad Darrell Req: F 2, 5.4, H Opt: F 4 Req: F
More informationStationary states of atoms and molecules
Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal
More information