Image Processing on Tensor-Product Basis

Size: px
Start display at page:

Download "Image Processing on Tensor-Product Basis"

Transcription

1 Óbuda Uversty e Bullet Vol. 2 o. 20 mage Processg o Tesor-Product Bass Adrás Rövd László Szedl 2 mre J. Rudas Péter Várla 2 Óbuda Uversty Joh vo euma Faculty of formatcs Bécs út 96/b 04 Budapest Hugary rovd.adras@.u-obuda.hu rudas@u-obuda.hu 2 Széchey stvá Uversty System Theory Laboratory Egyetem tér 9026 Győr Hugary szedl@sze.hu varla@sze.hu Abstract: The paper troduces a tesor-product based represetato of dgtal mages ad shows how ther processg ca be performed. The mage fucto ths case s expressed by oe-varable smooth fuctos formg a orthoormal bass whch are specfc to the expressed fucto ad therefore less umber of compoets s eeded to acheve the same approxmato accuracy tha case of trgoometrc fuctos or orthoormal polyomals. t wll also be show how these oe-varable fuctos ca be determed usg the hgher order sgular value decomposto (HOSVD). The proposed techques wor well eve cases whe besde the color further attrbutes are assged to the pxels e.g. temperature varous type of labels etc. Fally the results are compared to the techques worg the frequecy doma. t ca be observed that may cases the usage of the proposed doma s more advatageous. Keywords: scalg; smoothg; compresso; tesor-product; approxmato; HOSVD troducto There are umerous dgtal mage processg tass e.g. mage smoothg edge detecto etc. whch effcecy strogly depeds o the doma they are worg o e.g. mage resoluto ehacemet flterg [] mage compresso [2] etc. The two-dmesoal array of pxels s the most atural way to represet dscrete mages applcable for example for hstogram modfcato pxel ad eghbor operatos etc. [0]. O the other had some applcatos the data are actually collected the frequecy doma specfcally the form of Fourer coeffcets e.g. MR or CT magg ad spectral methods for PDEs []. Aother type of represetato s based o Wavelet trasforms whch are mult-resoluto represetatos of sgals ad mages decomposg them to mult-scale detals [2]. eural etwor based represetato techques stad for a further group of mage represetatos e.g o-lear mage represetatos based o pyramdal 247

2 A. Rövd et al. mage Processg o Tesor Product Bass decomposto wth eural etwor [] etc. The ma am of ths paper s to propose a tesor-product based represetato doma whch the mage ca be expressed by less umber of compoets tha for example the frequecy based represetato requres by supportg the effcet multdmesoal flterg compresso ad rescalg. There are umerous mage processg tass whch ca be performed more effcetly whe swtchg to aother doma e.g. the well ow frequecy doma the mage flterg or compresso. O the other had to represet the mage frequecy doma wthout meagful qualty decle relatvely large umber of compoets s eeded cotrast to the proposed tesor-product based represetato where the umber of compoets to acheve the same approxmato accuracy tha case of trgoometrc oes s much more less. As show the upcomg sectos ay -varable smooth fucto ca be expressed wth the help of a system of orthoormal oe-varable smooth fuctos o hgher order sgular value decomposto (HOSVD) bass. The ma am of the paper s to umercally recostruct these specally determed oe-varable fuctos usg the HOSVD ad to show how ths approach ca gve support for certa mage processg tass ad problems. The paper s orgazed as follows: Secto 2 deals wth the recostructo of the oe-varable fuctos detal Secto shows how ths represetato ca be appled mage processg for resoluto ehacemet ad flterg whle Secto 4 the propertes of the proposed method are compared to the well ow Fourer trasformato. Fally Secto 5 expermetal results ad coclusos are reported. 2 The HOSVD-based Represetato of Fuctos The approxmato methods of mathematcs are wdely used theory ad practce for several problems. f we cosder a -varable smooth fucto [ ] T f ( x) x= ( x... x ) x a b the we ca approxmate the fucto f ( x) wth a seres f ( x ) =... α p ( x )... p ( x ). ()... = = where the system of orthoormal fuctos p ( x ) ca be chose classcal way by orthoormal polyomals or trgoometrc fuctos separate varables ad the umbers of fuctos playg role () are large eough. Wth the help of Hgher Order Sgular Value Decomposto (HOSVD) a ew approxmato method was developed [7] [5] whch a specally determed system of orthoormal fuctos ca be used depedg o fucto f ( x ) stead of some other systems of orthoormal polyomals or trgoometrc fuctos. 248

3 Óbuda Uversty e Bullet Vol. 2 o. 20 Assume that the fucto f ( x) [ ] w ( x) x a b the form ca be gve wth some fuctos f ( x)=... α w ( x )... w ( x ). (2)... = =... Deote by A R the -dmesoal tesor determed by the elemets α ad let us use the followg otatos (see : [4]).... A U = : the -mode tesor-matrx product A U : the multple product as A U... 2 U2 U. The -mode tesor-matrx product s defed by the followg way. Let U be a K M -matrx the A U s a M... M K M+... M -tesor for whch the relato ( A U) def = m... m m +... m m... m... m m m M a holds. Detaled dscusso of tesor otatos ad operatos s gve [4]. We also ote that we use the sg stead the sg gve [4]. Usg ths defto the fucto (2) ca be rewrtte as a tesor product form f ( x)= A w ( x ) () = where w ( ) = ( ( )... ( )) T x w x w x. Based o HOSVD t was proved [6] that uder mld codtos the () ca be represeted the form f ( x)= D w ( x ) (4) where = r... r D R s a specal (so called core) tesor wth the propertes: (a) r = ra( A ) s the -mode ra of the tesor A.e. ra of the lear space spaed by the -mode vectors of A : {( a... a ) : j } T j (b) all-orthogoalty of tesor D : two subtesors D = α ad D = β (the th dces = α ad = β of the elemets of the tesor D eepg fx) orthogoal for all possble values of α ad β : D = α D = β =0 U 249

4 A. Rövd et al. mage Processg o Tesor Product Bass whe α β. Here the scalar product D = α D = β deotes the sum of products of the approprate elemets of subtesors D = α ad D = β (c) orderg: D = D =2 D = >0 for all possble values of r ( D = α = D = α D = α deotes the Kroecer-orm of the tesor D ). = α Compoets w ( x ) of the vector valued fuctos r T w ( x )=( w ( x )... w ( x )) are orthoormal L2 -sese o the terval [ a b ].e. b δ a j j : w ( x ) w ( x ) dx = j r where δ j s a Kroecer-fucto ( δ j= f = j ad δ j=0 f j) The form (4) was called [6] HOSVD caocal form of the fucto (2). Let us decompose the tervals [ a b ] =.. to M umber of dsjuct subtervals Δ m m M as follows: ξ = a < ξ < < ξ = b Δ =[ ξ ξ ). 0 M m m m w ( x) x a b the equato (2) are pece-wse cotuously dfferetable ad assume also that we ca observe the values of the fucto f ( x ) the pots Assume that the fuctos [ ] y = ( x... x ) M (5)... where x m Δm m M Based o the HOSVD a ew method was developed [6] for umercal recostructo of the caocal form of the fucto f ( x ) usg the values f ( y ).... M We dscretze fucto f ( x ) for all grd pots as: b = f( y ). m.. m m.. m 250

5 Óbuda Uversty e Bullet Vol. 2 o. 20 The we costruct dmesoal tesor B =( b m m ) from the values b m.. m. Obvously the sze of ths tesor s M... M. Further we dscretze vector valued fuctos w ( x) over the dscretzato pots x m ad costruct matrces W from the dscretzed values as: w ( x ) w2 ( x ) w r ( x) w ( x2 ) w2 ( x2 ) w r ( x2) W = w ( x M ) w2 ( x M ) w r ( x M ) The tesor B ca smply be gve by (4) ad (6) as B = D W. (7) Matrces see [5]. = W ad tesor D ca be obtaed by HOSVD of B. For further detals (6) mage Scalg the HOSVD-Based Doma Let f ( x) x =( x x2 x) T represet the mage fucto where x ad x 2 correspod to the vertcal ad horzotal coordates of the pxel respectvely. x s related to the color compoets of the pxel.e. the red gree ad blue color compoets case of RGB mage. Fucto f ( x ) ca be approxmated (based o otes dscussed the prevous secto) the followg way: 2 f ( x )= w ( x ) w ( x ) w ( x ). (8) α = 2 = = The red gree ad blue color compoets of pxels ca be stored a m tesor where ad m correspod to the wdth ad heght of the mage respectvely. Let B deote ths tesor. The frst step s to recostruct the fuctos w based o the HOSVD of tesor B as follows: B = D W (9) ( ) = where D s the so called core tesor. Vectors correspodg to the colums of ( matrces W ) as descrbed the prevous secto are represetg the dscretzed form of fuctos w ( x ) correspodg to the approprate dmeso. 25

6 A. Rövd et al. mage Processg o Tesor Product Bass 2 W 2 B W r 2 r r D r r 2 r W Fgure llustrato of the hgher order sgular value decomposto for a -dmesoal array. Here D s the core tesor the W -s are the -mode sgular matrces. Our goal s to demostrate the effectveess of mage scalg the proposed doma. Let s {2...} deote the scalg factor of the mage. Frst let us () () cosder the frst colum W of matrx W. Based o the prevous sectos t ca be see that the value w () correspods to the st elemet of () W w (2) to the 2d elemet... w ( M ) to the M th elemet of () W. To elarge the mage by a factor s the () W =..2 matrces should be updated based o the scalg factor s as follows: The umber of colums remas the same the umber of les wll be exteded accordg to the factor s. For example let us cosder the () () () colum W of W. W () does ot chage () () () () () () W ( s) := W (2) W (2 s) := W ()... W (( M ) s) := W ( M ). () () () () () The mssg elemets W (2) W ()... W ( s ) W ( s+ )... W (2s ) () () W (2s+ )... W (( M ) s ) ca be determed by terpolato. the paper the cubc sple terpolato was appled. The remag colums should be processed smlarly. After every matrx elemet has bee determed the elarged mage ca be obtaed usg the equato (9). 4 HOSVD vs. Frequecy Doma Comparg the proposed represetato to the frequecy doma smlartes ca be observed ther behavour. As t s well ow the Fourer Trasformato s coected to trgoometrc fuctos whle case of HOSVD approach the fuctos w ( x ) are cosdered whch are specfc to the approxmated - varable fucto. both cases the fuctos are formg a orthoormal bass. Let us meto some commo wdely used applcatos of both approaches. case of the Fourer based smoothg some of the hgh frequecy compoets are dsmssed resultg a smoothed mage (low pass flter). case of the HOSVD 252

7 Óbuda Uversty e Bullet Vol. 2 o. 20 smlar effect ca be observed whe dsmssg fuctos correspodg to smaller sgular values. the opposte case.e. dsmssg small frequeces yelds a edge detector (hgh pass flter) whch HOSVD case s equvalet to dsmssg compoets correspodg to hgher sgular values []. 5 Examples 5. Part- (Approxmato) ths secto some approxmatos ca be observed performed by the proposed ad by the Fourer-based approach. As the umber of the used compoets decreases the observable dffereces qualty become more sgfcat. the examples below both the HOSVD-based ad Fourer-based cases the same umber of compoets have bee used order to show how the form of determed fuctos flueces the qualty. Fgure 2 Orgal mage (24bt RGB) 25

8 A. Rövd et al. mage Processg o Tesor Product Bass Fgure HOSVD-based approxmato usg 2700 compoets composed from polylear fuctos o HOSVD bass Fgure 4 Fourer-based approxmato usg 2700 compoets composed from trgoometrc fuctos 254

9 Óbuda Uversty e Bullet Vol. 2 o Part-2 (Scalg) The pctures are llustratg the effectveess of the mage scalg by applyg the proposed approach. The result s compared to the output obtaed by the blear ad bcubc mage terpolato methods. Fgure 5 The orgal mage Fgure 6 Elarged segmet usg blear terpolato 255

10 A. Rövd et al. mage Processg o Tesor Product Bass Fgure 7 Elarged segmet usg bcubc terpolato Fgure 8 Elarged segmet usg the proposed HOSVD-based method. Smoother edges ca be observed 256

11 Óbuda Uversty e Bullet Vol. 2 o. 20 Coclusos the preset paper a ew mage represetato doma ad recostructo techque has bee troduced. The results show that how the effcecy of the certa tass depeds o the appled doma. mage rescalg has bee performed usg the proposed techque ad has bee compared to other well ow mage terpolato methods. Usg ths techque the resulted mage matas the edges more accurately the the other well-ow mage terpolato methods. Furthermore some propertes of the proposed represetato doma have bee compared to the correspodg propertes of the Fourer-based approxmato. The results show that the proposed doma some tass ca be performed more effcetly tha other domas. Acowledgemet The research was supported by the Jáos Bolya Research Scholarshp of the Hugara Academy of Sceces ad part by the Óbuda Uversty. Refereces [] P. Bojarcza ad S. Osows "Deosg of mages - A Comparso of Dfferet Flterg Approaches" WSEAS Trasactos o Computers ssue Vol. pp July 2004 [2] A. Khashma ad K. Dmller "mage Compresso usg eural etwors ad Haar Wavelet" WSEAS Trasactos o Sgal Processg Vol. 4 o pp. 0-9 [] Roume Koutchev Stuart Rub Marofaa Mlaova Vladmr Todorov Roumaa Koutcheva "o-lear mage Represetato Based o DP wth " WSEAS Trasactos o Sgal Processg SS: ssue 9 Volume 5 pp September 2009 [4] L. De Lathauwer B. De Moor ad J. Vadewalle "A multlear sgular value decomposto" SAM Joural o Matrx Aalyss ad Applcatos vol. 2 o. 4 pp [5] L. Szedl P. Várla "HOSVD Based Caocal Form for Polytopc Models of Dyamc Systems " Joural of Advaced Computatoal tellgece ad tellget formatcs SS : 4-00 Vol. o. pp [6] L. Szedl P. Baray Z. Petres ad P. Várla "umercal Recostructo of the HOSVD Based Caocal Form of Polytopc Dyamc Models" rd teratoal Symposum o Computatoal tellgece ad tellget formatcs Agadr Morocco 2007 pp. -6 [7] L. Szedl. Rudas A. Rövd P. Várla "HOSVD Based Method for Surface Data Approxmato ad Compresso " 2th teratoal Coferece o tellget Egeerg Systems Mam Florda February pp

12 A. Rövd et al. mage Processg o Tesor Product Bass [8] M. colaus L. Yue. Do Mh "mage terpolato usg multscale geometrc represetatos " Proceedgs of the SPE Volume 6498 pp [9] S. YUA M. ABE A. TAGUCH ad M. KAWAMATA "Hgh Accuracy Bcubc terpolato Usg mage Local Features " ECE Trasactos o Fudametals of Electrocs Commucatos ad Computer Sceces E90-A(8) pp [0] R. avarro A. Taberero ad G. Crstóbal "mage Represetato wth Gabor Wavelets ad ts Applcatos " Advaces magg ad Electro Physcs vol. 97 P. W. Hawes Academc Press Sa Dego CA pp [] Ae Gelb Taylor Hes "Detecto of Edges from ouform Fourer Data " Joural of Fourer Aalyss ad Applcatos DO 0.007/s pp [2] Yasu Xu Joh B. Weaver Des M. Healy Jr. ad Ja Lu "Wavelet Trasform Doma Flters: A Spatally Selectve ose Fltrato Techque " EEE Trasactos o mage Processg Vol. o. 6 pp [] A. Rövd. J. Rudas Sz. Sergyá L. Szedl "HOSVD Based mage Processg Techques" Proc. of the 0th WSEAS teratoal Coferece o Artfcal tellgece Kowledge Egeerg ad Data Bases Cambrdge UK SB: pp February

Analysis of Lagrange Interpolation Formula

Analysis 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 information

Transforms that are commonly used are separable

Transforms that are commonly used are separable Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic 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 information

Beam Warming Second-Order Upwind Method

Beam 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 information

L5 Polynomial / Spline Curves

L5 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 information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Decomposition of Hadamard Matrices

Decomposition of Hadamard Matrices Chapter 7 Decomposto of Hadamard Matrces We hae see Chapter that Hadamard s orgal costructo of Hadamard matrces states that the Kroecer product of Hadamard matrces of orders m ad s a Hadamard matrx of

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

Functions of Random Variables

Functions 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 information

Wavelet Basics. (A Beginner s Introduction) J. S. Marron Department of Statistics University of North Carolina

Wavelet Basics. (A Beginner s Introduction) J. S. Marron Department of Statistics University of North Carolina Wavelet Bascs (A Beger s Itroducto) J. S. Marro Departmet o Statstcs Uversty o North Carola Some reereces: Marro, J. S. (999) Spectral vew o wavelets ad olear regresso, Bayesa Ierece Wavelet-Based Models,

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS 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 information

Investigating Cellular Automata

Investigating Cellular Automata Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 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 information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 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 information

A NEW NUMERICAL APPROACH FOR SOLVING HIGH-ORDER LINEAR AND NON-LINEAR DIFFERANTIAL EQUATIONS

A NEW NUMERICAL APPROACH FOR SOLVING HIGH-ORDER LINEAR AND NON-LINEAR DIFFERANTIAL EQUATIONS Secer, A., et al.: A New Numerıcal Approach for Solvıg Hıgh-Order Lıear ad No-Lıear... HERMAL SCIENCE: Year 8, Vol., Suppl., pp. S67-S77 S67 A NEW NUMERICAL APPROACH FOR SOLVING HIGH-ORDER LINEAR AND NON-LINEAR

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

MOLECULAR VIBRATIONS

MOLECULAR 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 information

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction Computer Aded Geometrc Desg 9 79 78 www.elsever.com/locate/cagd Applcato of Legedre Berste bass trasformatos to degree elevato ad degree reducto Byug-Gook Lee a Yubeom Park b Jaechl Yoo c a Dvso of Iteret

More information

A Study on Generalized Generalized Quasi hyperbolic Kac Moody algebra QHGGH of rank 10

A Study on Generalized Generalized Quasi hyperbolic Kac Moody algebra QHGGH of rank 10 Global Joural of Mathematcal Sceces: Theory ad Practcal. ISSN 974-3 Volume 9, Number 3 (7), pp. 43-4 Iteratoal Research Publcato House http://www.rphouse.com A Study o Geeralzed Geeralzed Quas (9) hyperbolc

More information

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler Mathematcal ad Computatoal Applcatos, Vol. 8, No. 3, pp. 293-300, 203 BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS Aysegul Ayuz Dascoglu ad Nese Isler Departmet of Mathematcs,

More information

18.413: Error Correcting Codes Lab March 2, Lecture 8

18.413: 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 information

Analyzing Fuzzy System Reliability Using Vague Set Theory

Analyzing Fuzzy System Reliability Using Vague Set Theory Iteratoal Joural of Appled Scece ad Egeerg 2003., : 82-88 Aalyzg Fuzzy System Relablty sg Vague Set Theory Shy-Mg Che Departmet of Computer Scece ad Iformato Egeerg, Natoal Tawa versty of Scece ad Techology,

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS

DIFFERENTIAL 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 information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov Iteratoal Boo Seres "Iformato Scece ad Computg" 97 MULTIIMNSIONAL HTROGNOUS VARIABL PRICTION BAS ON PRTS STATMNTS Geady Lbov Maxm Gerasmov Abstract: I the wors [ ] we proposed a approach of formg a cosesus

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Some Notes on the Probability Space of Statistical Surveys

Some Notes on the Probability Space of Statistical Surveys Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation 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 information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter A Robust otal east Mea Square Algorthm For Nolear Adaptve Flter Ruxua We School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049, P.R. Cha rxwe@chare.com Chogzhao Ha, azhe u School of Electroc

More information

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations Lecture 7 3. Parametrc ad No-Parametrc Ucertates, Radal Bass Fuctos ad Neural Network Approxmatos he parameter estmato algorthms descrbed prevous sectos were based o the assumpto that the system ucertates

More information

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK Ram Rzayev Cyberetc Isttute of the Natoal Scece Academy of Azerbaa Republc ramrza@yahoo.com Aygu Alasgarova Khazar

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 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 information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations. III- G. Bref evew of Grad Orthogoalty Theorem ad mpact o epresetatos ( ) GOT: h [ () m ] [ () m ] δδ δmm ll GOT puts great restrcto o form of rreducble represetato also o umber: l h umber of rreducble

More information

Quantization in Dynamic Smarandache Multi-Space

Quantization in Dynamic Smarandache Multi-Space Quatzato Dyamc Smaradache Mult-Space Fu Yuhua Cha Offshore Ol Research Ceter, Beg, 7, Cha (E-mal: fuyh@cooc.com.c ) Abstract: Dscussg the applcatos of Dyamc Smaradache Mult-Space (DSMS) Theory. Supposg

More information

Principal Components. Analysis. Basic Intuition. A Method of Self Organized Learning

Principal Components. Analysis. Basic Intuition. A Method of Self Organized Learning Prcpal Compoets Aalss A Method of Self Orgazed Learg Prcpal Compoets Aalss Stadard techque for data reducto statstcal patter matchg ad sgal processg Usupervsed learg: lear from examples wthout a teacher

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

PROJECTION PROBLEM FOR REGULAR POLYGONS Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c

More information

The Necessarily Efficient Point Method for Interval Molp Problems

The Necessarily Efficient Point Method for Interval Molp Problems ISS 6-69 Eglad K Joural of Iformato ad omputg Scece Vol. o. 9 pp. - The ecessarly Effcet Pot Method for Iterval Molp Problems Hassa Mshmast eh ad Marzeh Alezhad + Mathematcs Departmet versty of Ssta ad

More information

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific CIS 54 - Iterpolato Roger Crawfs Basc Scearo We are able to prod some fucto, but do ot kow what t really s. Ths gves us a lst of data pots: [x,f ] f(x) f f + x x + August 2, 25 OSU/CIS 54 3 Taylor s Seres

More information

Chapter 11 Systematic Sampling

Chapter 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 information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

On the convergence of derivatives of Bernstein approximation

On the convergence of derivatives of Bernstein approximation O the covergece of dervatves of Berste approxmato Mchael S. Floater Abstract: By dfferetatg a remader formula of Stacu, we derve both a error boud ad a asymptotc formula for the dervatves of Berste approxmato.

More information

CS5620 Intro to Computer Graphics

CS5620 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 information

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Sebasta Starz COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Abstract The am of the work s to preset a method of rakg a fte set of dscrete radom varables. The proposed method s based o two approaches:

More information

3D Geometry for Computer Graphics. Lesson 2: PCA & SVD

3D Geometry for Computer Graphics. Lesson 2: PCA & SVD 3D Geometry for Computer Graphcs Lesso 2: PCA & SVD Last week - egedecomposto We wat to lear how the matrx A works: A 2 Last week - egedecomposto If we look at arbtrary vectors, t does t tell us much.

More information

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013 ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport

More information

Research Article Gauss-Lobatto Formulae and Extremal Problems

Research Article Gauss-Lobatto Formulae and Extremal Problems Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 2008 Artcle ID 624989 0 pages do:055/2008/624989 Research Artcle Gauss-Lobatto Formulae ad Extremal Problems wth Polyomals Aa Mara Acu ad

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds 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

Newton s Power Flow algorithm

Newton s Power Flow algorithm Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other

More information

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers. PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

Point Estimation: definition of estimators

Point 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 information

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i. CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

to the estimation of total sensitivity indices

to the estimation of total sensitivity indices Applcato of the cotrol o varate ate techque to the estmato of total sestvty dces S KUCHERENKO B DELPUECH Imperal College Lodo (UK) skuchereko@mperalacuk B IOOSS Electrcté de Frace (Frace) S TARANTOLA Jot

More information

QR Factorization and Singular Value Decomposition COS 323

QR 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 information

Generalization of the Dissimilarity Measure of Fuzzy Sets

Generalization of the Dissimilarity Measure of Fuzzy Sets Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra

More information

Descriptive Statistics

Descriptive Statistics Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people

More information

Numerical Analysis Formulae Booklet

Numerical 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 information

Convergence of the Desroziers scheme and its relation to the lag innovation diagnostic

Convergence of the Desroziers scheme and its relation to the lag innovation diagnostic Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING

0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING CONVEX OPIMIZAION AND INERIOR POIN MEHODS FINAL PROJEC / INEGER PROGRAMMING AND SEMIDEFINE PROGRAMMING b Luca Buch ad Natala Vktorova CONENS:.Itroducto.Formulato.Applcato to Kapsack Problem 4.Cuttg Plaes

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

Hybrid Wavelet and Chaos Theory for Runoff Forecasting

Hybrid Wavelet and Chaos Theory for Runoff Forecasting Proceedgs of the 5th WSEAS/IASME It. Cof. o SYSTEMS THEORY ad SCIENTIFIC COMPUTATION, Malta, September 5-7, 5 (pp75-79) Hybrd Wavelet ad Chaos Theory for Ruoff Forecastg Che X, Jag Chuawe, Wag Yu, Zhou

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

Pascal-Interpolation-Based Noninteger Delay Filter and Low-Complexity Realization

Pascal-Interpolation-Based Noninteger Delay Filter and Low-Complexity Realization SOONTORNWONG, S CHIVAREECHA, ASCAL-INTEROLATION-BASE NONINTEGER ELAY FILTER ascal-iterpolato-based Noteger elay Flter ad Low-Complety Realzato arya SOONTORNWONG, Sorawat CHIVAREECHA ept of Telecommucato

More information

Research Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix

Research Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix Mathematcal Problems Egeerg Volume 05 Artcle ID 94757 7 pages http://ddoorg/055/05/94757 Research Artcle A New Dervato ad Recursve Algorthm Based o Wroska Matr for Vadermode Iverse Matr Qu Zhou Xja Zhag

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Fourth 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 information

DKA method for single variable holomorphic functions

DKA method for single variable holomorphic functions DKA method for sgle varable holomorphc fuctos TOSHIAKI ITOH Itegrated Arts ad Natural Sceces The Uversty of Toushma -, Mamhosama, Toushma, 770-8502 JAPAN Abstract: - Durad-Kerer-Aberth (DKA method for

More information

Solving Interval and Fuzzy Multi Objective. Linear Programming Problem. by Necessarily Efficiency Points

Solving Interval and Fuzzy Multi Objective. Linear Programming Problem. by Necessarily Efficiency Points Iteratoal Mathematcal Forum, 3, 2008, o. 3, 99-06 Solvg Iterval ad Fuzzy Mult Obectve ear Programmg Problem by Necessarly Effcecy Pots Hassa Mshmast Neh ad Marzeh Aleghad Mathematcs Departmet, Faculty

More information

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems [ype text] [ype text] [ype text] ISSN : 0974-7435 Volume 0 Issue 6 Boechology 204 Ida Joural FULL PPER BIJ, 0(6, 204 [927-9275] Research o scheme evaluato method of automato mechatroc systems BSRC Che

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary 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 information

Computer Graphics. Shi-Min Hu. Tsinghua University

Computer 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 information

A Markov Chain Competition Model

A Markov Chain Competition Model Academc Forum 3 5-6 A Marov Cha Competto Model Mchael Lloyd, Ph.D. Mathematcs ad Computer Scece Abstract A brth ad death cha for two or more speces s examed aalytcally ad umercally. Descrpto of the Model

More information

x y exp λ'. x exp λ 2. x exp 1.

x 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 information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Nonlinear Piecewise-Defined Difference Equations with Reciprocal Quadratic Terms

Nonlinear 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 information

A Collocation Method for Solving Abel s Integral Equations of First and Second Kinds

A 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 information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

4 Inner Product Spaces

4 Inner Product Spaces 11.MH1 LINEAR ALGEBRA Summary Notes 4 Ier Product Spaces Ier product s the abstracto to geeral vector spaces of the famlar dea of the scalar product of two vectors or 3. I what follows, keep these key

More information

Chapter 5. Curve fitting

Chapter 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 information

Lecture 5: Interpolation. Polynomial interpolation Rational approximation

Lecture 5: Interpolation. Polynomial interpolation Rational approximation Lecture 5: Iterpolato olyomal terpolato Ratoal appromato Coeffcets of the polyomal Iterpolato: Sometme we kow the values of a fucto f for a fte set of pots. Yet we wat to evaluate f for other values perhaps

More information

SPECTRAL ANALYSIS OF A SIGNAL DRIVEN SAMPLING SCHEME

SPECTRAL ANALYSIS OF A SIGNAL DRIVEN SAMPLING SCHEME 4th Europea Sgal Processg Coferece (EUSIPCO 006), Florece, Italy, September 4-8, 006, copyrght by EURASIP SPECTRAL AALYSIS OF A SIGAL DRIVE SAMPLIG SCHEME Saeed Ma Qasar, Lauret Fesquet, Marc Reaud TIMA,

More information

13. Artificial Neural Networks for Function Approximation

13. Artificial Neural Networks for Function Approximation Lecture 7 3. Artfcal eural etworks for Fucto Approxmato Motvato. A typcal cotrol desg process starts wth modelg, whch s bascally the process of costructg a mathematcal descrpto (such as a set of ODE-s)

More information

On Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros

On Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros It. Joural of Math. Aalyss, Vol. 7, 2013, o. 20, 983-988 HIKARI Ltd, www.m-hkar.com O Modfed Iterval Symmetrc Sgle-Step Procedure ISS2-5D for the Smultaeous Icluso of Polyomal Zeros 1 Nora Jamalud, 1 Masor

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A 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 information

A New Method for Decision Making Based on Soft Matrix Theory

A New Method for Decision Making Based on Soft Matrix Theory Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer

More information