Analysis of error propagation in profile measurement by using stitching

Size: px
Start display at page:

Download "Analysis of error propagation in profile measurement by using stitching"

Transcription

1 Ay o error propgto proe eureet y ug ttchg Ttuy KUME, Kzuhro ENAMI, Yuo HIGASHI, Kej UENO - Oho, Tuu, Ir, 35-8, JAPAN Atrct Sttchg techque whch ee oger eureet rge o proe ro eer eure proe hg prty oerppe eureet rge. Here, ccurcy o the proe ote y ttchg w yze ette coerg tht error ech eureet propgte to error o the ttche proe. A reut, error the ttche proe w expree ucto o eer eureet preter. t c optze the preter.. Itroucto Sttchg prog or hghy prece prooetry wth og eureet egth uch u-eter get o cceertor or ew te oeter o egth Iterto er Coer project [], [] t o ee oeter orer o prooetry X-ry rror hg eter orer o egth. Here, ttchg prooetry w oee ug eer eureet preter ccurcy o the proe ote through ttchg w yze coerg tht error cue ech prt eureet propgte to the ttche proe through ttchg.. Error y ttchg. Moeg o the ttchg Fgure how oe o -te o ttchg prooetry etgte th pper, where (x expre tot proe to e eure t egth, (x expre prty eure proe ro the tot proe ther egth ce ut eureet egth, * expre oerp o the eureet rge, where ee oerppg rto gt the ut eureet egth. Fgure how coecto chee o the two eure proe octe ext to ech other. Here, - (x (x rw y o e expre two eure proe otte e rw oth e o the expre tot proe to e eure. The eure proe re coecte ther et qure pproxto e, y - x - y x tch t oerp. Aug tht there o chge the eure proe except ther ope or oet urg eureet, the eure proe c rtuy e extee how Fg. 3 the retohp etwee two proe c e expree Eq. or etre eureet poto (x to. I Eq., t or ther t (ope th (oet orer coecet o the et qure pproxto e, expre ther erece etwee the two proe.

2 Fg.. Moe o -te o ttchg prooetry. Fg.. Proe coecto ug et qure pproxto e. Fg.3. Vrtu expo o the eure proe to the tot eureet rge (x to.

3 ( x ( x ( x ( Sce retohp expree y Eq. c e ppe or ( to, Eq. ere ( x ( x ( x ( x ( x ( x M ( x ( x ( x M ( x ( x ( x ( x ( x ( x. ( By g oth e o Eq., proe through -te o ttchg (x c e expree ( x ( x ( x, (3 where (x t or proe wthout ttchg. The eco ter o rght h e o Eq. 3 t or uto o the erece etwee two et qure pproxto e o eure proe t o oerp. Thu, proe ote through ttchg c e expree y proe wthout ttchg erece etwee the pproxto e t ech oerp.. Ay o error propgto towr the pproxto e Fg. 4 how eure proe t pproxto e t the oerp. Here, proe (x eure t o eureet pot p ( to wth pg ter et qure pproxto e y x ere ro the eure ue. There retohp og the 4 preter,,. (4 O the other h, ug tht error cue y oy or y-recto error t ech eureet pot,.e. there o error or x-recto, error o coecet or the et qure pproxto e ote ro eure ue t the o eureet pot p, p j,,p -, -p re expree

4 D, (5 D x, (6 repectey. Here, x e x-coorte o ech eureet pot D expree ( ( x x D. (7 Fg.4. Meure proe t pproxto e t the oerp. p ( to expre eureet pot. Sce x-coorte or ech eureet pot utpe o pg ter, Σx Σx re expree ( ( ( ( ( 6 x, (8 ( ( x, (9

5 repectey. Thereore, D expree D ( ( ( Thereore,, re expree, ( ( ( ( ( (. ( Here, ug retohp how Eq. 4, Eq. re trore (, (3 ~ ( ( ( ~ ( ( (, (4 repectey, ce the retohp expree Eq. re truth or, (~ re expree,..3 Ay o error propgto or the ttche proe Error propgto or ttche proe w ette y ug the ue t the e o eureet pot, x, where the ccuute error y ttchg expecte to e xu. Eq. 5 ote y uttutg or x Eq. 3. ( ( ( (5 Fro Eq. 5, error t the e o eureet pot x expree y to o, error (, error the eco ter o the rght h e, tht cue y ttchg. Sce the two error re epeet ech other, the tot error through ttchg expree y qure root o ther qure u how Eq. 6.

6 e (6 Error propgto,, or the two coecet Eq. 5, - -, - - re expree ( ~, (7 ( ~, (8 repectey. Here, -, - re repectey epeet, they re ere ro eeret eureet ue. O the other h, ce coecet or the et qure pproxto e re epeet o, error propgto or ther erece,, re o epeet o. u, error propgto or, whch expree erece etwee the two pproxto e t the oerppg re expree. (9 u ( u Here, re epeet, ce they re ote ro the etc eureet. O the other h, u epeet o. A reut,, error propgto through ttchg expree u (. ( ce ech u epeet. Eq. trore to 6 ( (, ( ug retohp expree Eq Here, uer o ttchg expree

7 ( ( Eq. trore to ( ( ( ( 6. (3 Thereore, the tot error propgto through ttchg e expree ( ( ( ( e 6 4 (4 ug 4 o eureet preter,,, error the ut eureet. 3. Etto or eect o the eureet preter Meureet preter cocerg wth eureet egth Eq. 4,,, re e to eoe y coecet u u, (5, (6 repectey. u e pg coecet t or uer o pg pot wth the ut eureet egth. e eureet egth expo coecet t or expo rto or eureet egth y ttchg. Eq. 4 trore ug u ( ( ( ( e e K u u u u 6 4,(7 where K e expree gcto o error cue y ttchg e error propgto

8 coecet. u,,, Meureet egth expo coecet: Error propgto coecet: Ke Fg. 5. Error propgto coecet K e ucto o eureet egth expo coecet ug pg coecet u preter ce.5 Fg. 5 how K e ucto o ce.5, or u,,,, preter, ug the reto how Eq. 7. Here or expe, error the ttche proe hg tot eureet egth o -te o the ut eureet ce ug u.5 expree to e pproxtey -te o the error the ut eureet. Fgure 6 how K e ucto o ce, or u,,,, preter, ug the reto how Eq. 7. Here or expe, error the ttche proe hg tot eureet egth o -te o the ut eureet ug u expree to e u ce.7 to.8 the error ecoe pproxtey -te o the error the ut eureet.

9 u,,, Oerppg rto: Error propgto coecet: Ke Fg. 6. Error propgto coecet K e ucto o oerppg rto ug pg coecet u preter ce. 4. Cocuo Here, orer to optze eureet preter ce ug ttchg or ppyg u-eter get o cceertor hg ew te oeter o egth Iterto er Coer project. Error ttche proe w yze ug tht error cue y ech ut eureet propgte oeyg error propgto rue. A reut error propgte towr ttche proe w expree y 4 eureet preter (,,,. The reto w geerze y 3 eoe preter (u,,, howg tht eureet coto c e optze y eectg pproprte coto o the preter. It how tht th tuy c o e ppe or prooetry or hghy prece X-ry rror ( ccurcy or oger th egth ot oy or get o er coer. Reerece [] [] 6 prg JSPE u eetg N5 ( Jpee.

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

1. Linear second-order circuits

1. Linear second-order circuits ear eco-orer crcut Sere R crcut Dyamc crcut cotag two capactor or two uctor or oe uctor a oe capactor are calle the eco orer crcut At frt we coer a pecal cla of the eco-orer crcut, amely a ere coecto of

More information

Methods for solving the radiative transfer equation. Part 3: Discreteordinate. 1. Discrete-ordinate method for the case of isotropic scattering.

Methods for solving the radiative transfer equation. Part 3: Discreteordinate. 1. Discrete-ordinate method for the case of isotropic scattering. ecture Metos for sov te rtve trsfer equto. rt 3: Dscreteorte eto. Obectves:. Dscrete-orte eto for te cse of sotropc sctter..geerzto of te screte-orte eto for ooeeous tospere. 3. uerc peetto of te screte-orte

More information

DETAIL MEASURE EVALUATE

DETAIL MEASURE EVALUATE MEASURE EVALUATE B I M E q u i t y BIM Workflow Guide MEASURE EVALUATE Introduction We o e to ook 2 i t e BIM Workflow Guide i uide wi tr i you i re ti ore det i ed ode d do u e t tio u i r i d riou dd

More information

( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x)

( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x) Objectve fucto f() : he optzato proble cossts of fg a vector of ecso varables belogg to the feasble set of solutos R such that It s eote as: Nolear optzato proble wthout costrats NPP: R f ( ) : R R f f

More information

Review of Linear Algebra

Review of Linear Algebra PGE 30: Forulto d Soluto Geosstes Egeerg Dr. Blhoff Sprg 0 Revew of Ler Alger Chpter 7 of Nuercl Methods wth MATLAB, Gerld Recktewld Vector s ordered set of rel (or cople) uers rrged s row or colu sclr

More information

Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17

Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17 Itroucto to Ecoometrcs (3 r Upate Eto) by James H. Stock a Mark W. Watso Solutos to O-Numbere E-of-Chapter Exercses: Chapter 7 (Ths erso August 7, 04) 05 Pearso Eucato, Ic. Stock/Watso - Itroucto to Ecoometrcs

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

Mathematical Induction (selected questions)

Mathematical Induction (selected questions) Mtheticl Iductio (selected questios). () Let P() e the propositio : For P(), L.H.S. R.H.S., P() is true. Assue P() is true for soe turl uer, tht is, () For P( ),, y () By the Priciple of Mtheticl Iductio,

More information

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ] Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow

More information

Fredholm Type Integral Equations with Aleph-Function. and General Polynomials

Fredholm Type Integral Equations with Aleph-Function. and General Polynomials Iteto Mthetc Fou Vo. 8 3 o. 989-999 HIKI Ltd.-h.co Fedho Te Iteg uto th eh-fucto d Gee Poo u J K.J. o Ittute o Mgeet tude & eech Mu Id u5@g.co Kt e K.J. o Ittute o Mgeet tude & eech Mu Id dehuh_3@hoo.co

More information

CS 4758 Robot Kinematics. Ashutosh Saxena

CS 4758 Robot Kinematics. Ashutosh Saxena CS 4758 Rt Kemt Ahuth Se Kemt tude the mt f de e re tereted tw emt tp Frwrd Kemt (ge t pt ht u re gve: he egth f eh he ge f eh t ht u fd: he pt f pt (.e. t (,, rdte Ivere Kemt (pt t ge ht u re gve: he

More information

Wedge clamp, double-acting for dies with tapered clamping edge

Wedge clamp, double-acting for dies with tapered clamping edge Wg c, ou-ctg or th tr cg g Acto: cg o th tr cg g or cg o o r or cg o jcto oug ch A B Hr g cg rt Buhg Dg: Dou-ctg g c or cg o r or or or cg jcto oug ch. Th g c cot o hyruc oc cyr to gu houg. Th cg ot ro

More information

-HYBRID LAPLACE TRANSFORM AND APPLICATIONS TO MULTIDIMENSIONAL HYBRID SYSTEMS. PART II: DETERMINING THE ORIGINAL

-HYBRID LAPLACE TRANSFORM AND APPLICATIONS TO MULTIDIMENSIONAL HYBRID SYSTEMS. PART II: DETERMINING THE ORIGINAL UPB Sc B See A Vo 72 I 3 2 ISSN 223-727 MUTIPE -HYBRID APACE TRANSORM AND APPICATIONS TO MUTIDIMENSIONA HYBRID SYSTEMS PART II: DETERMININ THE ORIINA Ve PREPEIŢĂ Te VASIACHE 2 Ace co copeeă oă - pce he

More information

Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate

More information

Strategies for the AP Calculus Exam

Strategies for the AP Calculus Exam Strteges for the AP Clculus Em Strteges for the AP Clculus Em Strtegy : Kow Your Stuff Ths my seem ovous ut t ees to e metoe. No mout of cochg wll help you o the em f you o t kow the mterl. Here s lst

More information

Current Programmed Control (i.e. Peak Current-Mode Control) Lecture slides part 2 More Accurate Models

Current Programmed Control (i.e. Peak Current-Mode Control) Lecture slides part 2 More Accurate Models Curret Progred Cotrol.e. Pek Curret-Mode Cotrol eture lde prt More Aurte Model ECEN 5807 Drg Mkovć Sple Frt-Order CPM Model: Sury Aupto: CPM otroller operte delly, Ueful reult t low frequee, well uted

More information

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

More information

Statistical Modeling and Analysis of the Correlation between the Gross Domestic Product per Capita and the Life Expectancy

Statistical Modeling and Analysis of the Correlation between the Gross Domestic Product per Capita and the Life Expectancy Als of Dure de Jos Uerst of Glt Fsccle. Ecoocs d Appled fortcs Yers XX o /5 N-L 584-49 N-Ole 44-44X www.e.fe.ugl.ro ttstcl Mode d Alss of the Correlto etwee the Gross Doestc Product per Cpt d the Lfe Epectc

More information

H STO RY OF TH E SA NT

H STO RY OF TH E SA NT O RY OF E N G L R R VER ritten for the entennial of th e Foundin g of t lair oun t y on ay 8 82 Y EEL N E JEN K RP O N! R ENJ F ] jun E 3 1 92! Ph in t ed b y h e t l a i r R ep u b l i c a n O 4 1922

More information

Pre-Calculus - Chapter 3 Sections Notes

Pre-Calculus - Chapter 3 Sections Notes Pre-Clculus - Chpter 3 Sectios 3.1-3.4- Notes Properties o Epoets (Review) 1. ( )( ) = + 2. ( ) =, (c) = 3. 0 = 1 4. - = 1/( ) 5. 6. c Epoetil Fuctios (Sectio 3.1) Deiitio o Epoetil Fuctios The uctio deied

More information

An Introduction to Robot Kinematics. Renata Melamud

An Introduction to Robot Kinematics. Renata Melamud A Itrdut t Rt Kemt Ret Memud Kemt tude the mt f de A Empe -he UMA 56 3 he UMA 56 hsirevute t A revute t h E degree f freedm ( DF tht defed t ge 4 here re tw mre t the ed effetr (the grpper ther t Revute

More information

Complex Variables. Chapter 19 Series and Residues. March 26, 2013 Lecturer: Shih-Yuan Chen

Complex Variables. Chapter 19 Series and Residues. March 26, 2013 Lecturer: Shih-Yuan Chen omplex Vrble hpter 9 Sere d Redue Mrch 6, Lecturer: Shh-Yu he Except where otherwe oted, cotet lceed uder BY-N-SA. TW Lcee. otet Sequece & ere Tylor ere Luret ere Zero & pole Redue & redue theorem Evluto

More information

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions HK Km Slghtly moded //9 /8/6 Frstly wrtte t Mrch 5 Itertve Methods er Systems: Guss-Sedel Noler Systems Cse Study: Chemcl Rectos Itertve or ppromte methods or systems o equtos cosst o guessg vlue d the

More information

5. Lighting & Shading

5. Lighting & Shading 3 4 Rel-Worl vs. eg Rel worl comlex comuttos see otcs textoos, hotorelstc reerg eg smlfe moel met, ffuse seculr lght sources reflectos esy to tue fst to comute ght sources ght reflecto ght source: Reflecto

More information

lower lower median upper upper proportions. 1 cm= 10 mm extreme quartile quartile extreme 28mm =?cm I I I

lower lower median upper upper proportions. 1 cm= 10 mm extreme quartile quartile extreme 28mm =?cm I I I Sxth Grde Buld # 7 :!l. ':S.,. (6)()=_ 66 + () = 6 + ()= 88(6)= e :: : : c f So! G) Use the box d whsk plot to sw questos bout the dt ovt the ut of esure usg jjj [ low low ed upp upp proportos. c= 8 =?c

More information

ON THE CHROMATIC NUMBER OF GENERALIZED STABLE KNESER GRAPHS

ON THE CHROMATIC NUMBER OF GENERALIZED STABLE KNESER GRAPHS ON THE CHROMATIC NUMBER OF GENERALIZED STABLE KNESER GRAPHS JAKOB JONSSON Abstract. For each teger trple (, k, s) such that k 2, s 2, a ks, efe a graph the followg maer. The vertex set cossts of all k-subsets

More information

Chapter 4: Linear Momentum and Collisions

Chapter 4: Linear Momentum and Collisions Chater 4: Lear oetu ad Collsos 4.. The Ceter o ass, Newto s Secod Law or a Syste o artcles 4.. Lear oetu ad Its Coserato 4.3. Collso ad Iulse 4.4. oetu ad Ketc Eergy Collsos 4.. The Ceter o ass. Newto

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

THE LOWELL LEDGER, INDEPENDENT NOT NEUTRAL.

THE LOWELL LEDGER, INDEPENDENT NOT NEUTRAL. E OE EDGER DEEDE O EUR FO X O 2 E RUO OE G DY OVEER 0 90 O E E GE ER E ( - & q \ G 6 Y R OY F EEER F YOU q --- Y D OVER D Y? V F F E F O V F D EYR DE OED UDER EDOOR OUE RER (E EYEV G G R R R :; - 90 R

More information

Note 7 Root-Locus Techniques

Note 7 Root-Locus Techniques Lecture Note of Cotrol Syte I - ME 43/Alyi d Sythei of Lier Cotrol Syte - ME862 Note 7 Root-Locu Techique Deprtet of Mechicl Egieerig, Uiverity Of Sktchew, 57 Cpu Drive, Sktoo, S S7N 5A9, Cd Lecture Note

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

jfljjffijffgy^^^ ^--"/.' -'V^^^V'^NcxN^*-'..( -"->"'-;':'-'}^l 7-'- -:-' ""''-' :-- '-''. '-'"- ^ " -.-V-'.'," V'*-irV^'^^amS.

jfljjffijffgy^^^ ^--/.' -'V^^^V'^NcxN^*-'..( -->'-;':'-'}^l 7-'- -:-' ''-' :-- '-''. '-'- ^  -.-V-'.', V'*-irV^'^^amS. x } < 5 RY REOR RY OOBER 0 930 EER ORE PBE EEEY RY ERE Z R E 840 EG PGE O XXER O 28 R 05 OOG E ERE OOR GQE EOEE Y O RO Y OY E OEY PRE )Q» OY OG OORRO EROO OORRO G 4 B E B E?& O E O EE OY R z B 4 Y R PY

More information

Black or White Video. Lecture 3: Face Detection. Face Detection. Why is Face Detection Difficult? Automated Face Detection Why is it Difficult?

Black or White Video. Lecture 3: Face Detection. Face Detection. Why is Face Detection Difficult? Automated Face Detection Why is it Difficult? Back or Whte Veo ecture : Face Detecto Reag: Egeaces oe paper FP pgs 55-5 Haouts: Course Descrpto P Assge Face Detecto Face ocazato egmetato Face rackg Faca eatures ocazato Faca eatures trackg orphg wwwyoutubecom/watch?vzi9oyrwq

More information

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1

More information

A Dynamical Quasi-Boolean System

A Dynamical Quasi-Boolean System ULETNUL Uestăţ Petol Gze Ploeşt Vol LX No / - 9 Se Mtetă - otă - Fză l Qs-oole Sste Gel Mose Petole-Gs Uest o Ploest ots etet est 39 Ploest 68 o el: ose@-loesto stt Ths e oes the esto o ol theoetl oet:

More information

Asymptotic Dominance Problems. is not constant but for n 0, f ( n) 11. 0, so that for n N f

Asymptotic Dominance Problems. is not constant but for n 0, f ( n) 11. 0, so that for n N f Asymptotc Domce Prolems Dsply ucto : N R tht s Ο( ) ut s ot costt 0 = 0 The ucto ( ) = > 0 s ot costt ut or 0, ( ) Dee the relto " " o uctos rom N to R y g d oly = Ο( g) Prove tht s relexve d trstve (Recll:

More information

Certain Expansion Formulae Involving a Basic Analogue of Fox s H-Function

Certain Expansion Formulae Involving a Basic Analogue of Fox s H-Function vlle t htt:vu.edu l. l. Mth. ISSN: 93-9466 Vol. 3 Iue Jue 8. 8 36 Pevouly Vol. 3 No. lcto d led Mthetc: Itetol Joul M Cet Exo Foule Ivolvg c logue o Fox -Fucto S.. Puoht etet o c-scece Mthetc College o

More information

If a is any non zero real or imaginary number and m is the positive integer, then a...

If a is any non zero real or imaginary number and m is the positive integer, then a... Idices d Surds.. Defiitio of Idices. If is o ero re or igir uer d is the positive iteger the...... ties. Here is ced the se d the ide power or epoet... Lws of Idices. 0 0 0. where d re rtio uers where

More information

Mean Cordial Labeling of Certain Graphs

Mean Cordial Labeling of Certain Graphs J Comp & Math Sc Vol4 (4), 74-8 (03) Mea Cordal Labelg o Certa Graphs ALBERT WILLIAM, INDRA RAJASINGH ad S ROY Departmet o Mathematcs, Loyola College, Chea, INDIA School o Advaced Sceces, VIT Uversty,

More information

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder Collapg to Saple ad Reader Mea Ed Staek Collapg to Saple ad Reader Average order to collape the expaded rado varable to weghted aple ad reader average, we pre-ultpled by ( M C C ( ( M C ( M M M ( M M M,

More information

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as. Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o

More information

On Several Inequalities Deduced Using a Power Series Approach

On Several Inequalities Deduced Using a Power Series Approach It J Cotemp Mth Sceces, Vol 8, 203, o 8, 855-864 HIKARI Ltd, wwwm-hrcom http://dxdoorg/02988/jcms2033896 O Severl Iequltes Deduced Usg Power Seres Approch Lored Curdru Deprtmet of Mthemtcs Poltehc Uversty

More information

On the energy of complement of regular line graphs

On the energy of complement of regular line graphs MATCH Coucato Matheatcal ad Coputer Chetry MATCH Cou Math Coput Che 60 008) 47-434 ISSN 0340-653 O the eergy of copleet of regular le graph Fateeh Alaghpour a, Baha Ahad b a Uverty of Tehra, Tehra, Ira

More information

Sensorless A.C. Drive with Vector Controlled Synchronous Motor

Sensorless A.C. Drive with Vector Controlled Synchronous Motor Seole A.C. Dve wth Vecto Cotolle Sychoo Moto Ořej Fše VŠB-echcal Uvety of Otava, Faclty of Electcal Egeeg a Ifomatc, Deatmet of Powe Electoc a Electcal Dve, 17.ltoa 15, 78 33 Otava-Poba, Czech eblc oej.fe@vb.cz

More information

The z-transform. LTI System description. Prof. Siripong Potisuk

The z-transform. LTI System description. Prof. Siripong Potisuk The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put

More information

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I

More information

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix. Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof

More information

State The position of school d i e t i c i a n, a created position a t S t a t e,

State The position of school d i e t i c i a n, a created position a t S t a t e, P G E 0 E C O E G E E FRDY OCOBER 3 98 C P && + H P E H j ) ) C jj D b D x b G C E Ob 26 C Ob 6 R H E2 7 P b 2 b O j j j G C H b O P G b q b? G P P X EX E H 62 P b 79 P E R q P E x U C Ob ) E 04 D 02 P

More information

20.2. The Transform and its Inverse. Introduction. Prerequisites. Learning Outcomes

20.2. The Transform and its Inverse. Introduction. Prerequisites. Learning Outcomes The Trnform nd it Invere 2.2 Introduction In thi Section we formlly introduce the Lplce trnform. The trnform i only pplied to cul function which were introduced in Section 2.1. We find the Lplce trnform

More information

Load Relief Control System for Launch Vehicle based on Acceleration Feedback

Load Relief Control System for Launch Vehicle based on Acceleration Feedback IOSR Jour of Eetr Eetro Egeerg IOSR-JEEE e-issn: 78-676,p-ISSN: 30-333 PP -0 www.orjour.org o Reef Cotro Syte for uh Vehe be o Aeerto Feebk Joy Joh Deprtet of Eetr Egeerg, Coege of Egeerg rvru, I Abtrt:

More information

MA 524 Homework 6 Solutions

MA 524 Homework 6 Solutions MA 524 Homework 6 Solutos. Sce S(, s the umber of ways to partto [] to k oempty blocks, ad c(, s the umber of ways to partto to k oempty blocks ad also the arrage each block to a cycle, we must have S(,

More information

Mathematical Notation Math Calculus & Analytic Geometry I

Mathematical Notation Math Calculus & Analytic Geometry I Mthemticl Nottio Mth - Clculus & Alytic Geometry I Nme : Use Wor or WorPerect to recrete the ollowig ocumets. Ech rticle is worth poits c e prite give to the istructor or emile to the istructor t jmes@richl.eu.

More information

Homework Assignment Number Eight Solutions

Homework Assignment Number Eight Solutions D Keer MSE 0 Dept o Materals Scece & Egeerg Uversty o Teessee Kovlle Homework Assgmet Number Eght Solutos Problem Fd the soluto to the ollowg system o olear algebrac equatos ear () Soluto: s Sce ths s

More information

Hypergeometric Functions and Lucas Numbers

Hypergeometric Functions and Lucas Numbers IOSR Jourl of Mthetis (IOSR-JM) ISSN: 78-78. Volue Issue (Sep-Ot. ) PP - Hypergeoetri utios d us Nuers P. Rjhow At Kur Bor Deprtet of Mthetis Guhti Uiversity Guwhti-78Idi Astrt: The i purpose of this pper

More information

Mathematical Notation Math Calculus & Analytic Geometry I

Mathematical Notation Math Calculus & Analytic Geometry I Mthemticl Nottio Mth - Clculus & Alytic Geometry I Use Wor or WorPerect to recrete the ollowig ocumets. Ech rticle is worth poits shoul e emile to the istructor t jmes@richl.eu. Type your me t the top

More information

Maximum Walk Entropy Implies Walk Regularity

Maximum Walk Entropy Implies Walk Regularity Maxmum Walk Etropy Imples Walk Regularty Eresto Estraa, a José. e la Peña Departmet of Mathematcs a Statstcs, Uversty of Strathclye, Glasgow G XH, U.K., CIMT, Guaajuato, Mexco BSTRCT: The oto of walk etropy

More information

for each of its columns. A quick calculation will verify that: thus m < dim(v). Then a basis of V with respect to which T has the form: A

for each of its columns. A quick calculation will verify that: thus m < dim(v). Then a basis of V with respect to which T has the form: A Desty of dagoalzable square atrces Studet: Dael Cervoe; Metor: Saravaa Thyagaraa Uversty of Chcago VIGRE REU, Suer 7. For ths etre aer, we wll refer to V as a vector sace over ad L(V) as the set of lear

More information

Mechanics of Materials CIVL 3322 / MECH 3322

Mechanics of Materials CIVL 3322 / MECH 3322 Mechacs of Materals CVL / MECH Cetrods ad Momet of erta Calculatos Cetrods = A = = = A = = Cetrod ad Momet of erta Calculatos z= z A = = Parallel As Theorem f ou kow the momet of erta about a cetrodal

More information

Introduction to Modern Control Theory

Introduction to Modern Control Theory Itroductio to Moder Cotrol Theory MM : Itroductio to Stte-Spce Method MM : Cotrol Deig for Full Stte Feedck MM 3: Etitor Deig MM 4: Itroductio of the Referece Iput MM 5: Itegrl Cotrol d Rout Trckig //4

More information

/ / MET Day 000 NC1^ INRTL MNVR I E E PRE SLEEP K PRE SLEEP R E

/ / MET Day 000 NC1^ INRTL MNVR I E E PRE SLEEP K PRE SLEEP R E 05//0 5:26:04 09/6/0 (259) 6 7 8 9 20 2 22 2 09/7 0 02 0 000/00 0 02 0 04 05 06 07 08 09 0 2 ay 000 ^ 0 X Y / / / / ( %/ ) 2 /0 2 ( ) ^ 4 / Y/ 2 4 5 6 7 8 9 2 X ^ X % 2 // 09/7/0 (260) ay 000 02 05//0

More information

E-Companion: Mathematical Proofs

E-Companion: Mathematical Proofs E-omnon: Mthemtcl Poo Poo o emm : Pt DS Sytem y denton o t ey to vey tht t ncee n wth d ncee n We dene } ] : [ { M whee / We let the ttegy et o ech etle n DS e ]} [ ] [ : { M w whee M lge otve nume oth

More information

6.6 Moments and Centers of Mass

6.6 Moments and Centers of Mass th 8 www.tetodre.co 6.6 oets d Ceters of ss Our ojectve here s to fd the pot P o whch th plte of gve shpe lces horzotll. Ths pot s clled the ceter of ss ( or ceter of grvt ) of the plte.. We frst cosder

More information

Objectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method)

Objectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method) Ojectves 7 Statcs 7. Cete of Gavty 7. Equlum of patcles 7.3 Equlum of g oes y Lew Sau oh Leag Outcome (a) efe cete of gavty () state the coto whch the cete of mass s the cete of gavty (c) state the coto

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

Algebra 2 Important Things to Know Chapters bx c can be factored into... y x 5x. 2 8x. x = a then the solutions to the equation are given by

Algebra 2 Important Things to Know Chapters bx c can be factored into... y x 5x. 2 8x. x = a then the solutions to the equation are given by Alger Iportt Thigs to Kow Chpters 8. Chpter - Qudrtic fuctios: The stdrd for of qudrtic fuctio is f ( ) c, where 0. c This c lso e writte s (if did equl zero, we would e left with The grph of qudrtic fuctio

More information

Dopant Compensation. Lecture 2. Carrier Drift. Types of Charge in a Semiconductor

Dopant Compensation. Lecture 2. Carrier Drift. Types of Charge in a Semiconductor Lecture OUTLIE Bc Semcoductor Phycs (cot d) rrer d uo P ucto odes Electrosttcs ctce ot omesto tye semcoductor c be coverted to P tye mterl by couter dog t wth ccetors such tht >. comested semcoductor mterl

More information

Analele Universităţii din Oradea, Fascicula: Protecţia Mediului, Vol. XIII, 2008

Analele Universităţii din Oradea, Fascicula: Protecţia Mediului, Vol. XIII, 2008 Alele Uverstăţ d Orde Fsul: Proteţ Medulu Vol. XIII 00 THEORETICAL AND COMPARATIVE STUDY REGARDING THE MECHANICS DISPLASCEMENTS UNDER THE STATIC LOADINGS FOR THE SQUARE PLATE MADE BY WOOD REFUSE AND MASSIF

More information

A Shunt Connected DC-Motor Feedback Linearization Technique with On-Line Parameters Estimation

A Shunt Connected DC-Motor Feedback Linearization Technique with On-Line Parameters Estimation A Shut Coecte DC-Motor Feebac Learzato Techque wth O-Le Paraeters Estato M.S.IBBINI Al Huso Uversty Collee Al Balqa Apple Uversty P.O.Box 5, Al Huso-Jora Abstract The es o syste cotrollers s usually accoplshe

More information

Centroids Method of Composite Areas

Centroids Method of Composite Areas Cetrods Method of Composte reas small boy swallowed some cos ad was take to a hosptal. Whe hs gradmother telephoed to ask how he was a urse sad 'No chage yet'. Cetrods Prevously, we developed a geeral

More information

Instruction Sheet COOL SERIES DUCT COOL LISTED H NK O. PR D C FE - Re ove r fro e c sed rea. I Page 1 Rev A

Instruction Sheet COOL SERIES DUCT COOL LISTED H NK O. PR D C FE - Re ove r fro e c sed rea. I Page 1 Rev A Instruction Sheet COOL SERIES DUCT COOL C UL R US LISTED H NK O you or urc s g t e D C t oroug y e ore s g / as e OL P ea e rea g product PR D C FE RES - Re ove r fro e c sed rea t m a o se e x o duct

More information

DYNAMICS. Systems of Particles VECTOR MECHANICS FOR ENGINEERS: Seventh Edition CHAPTER. Ferdinand P. Beer E. Russell Johnston, Jr.

DYNAMICS. Systems of Particles VECTOR MECHANICS FOR ENGINEERS: Seventh Edition CHAPTER. Ferdinand P. Beer E. Russell Johnston, Jr. Seeth Edto CHPTER 4 VECTOR MECHNICS FOR ENINEERS: DYNMICS Ferdad P. eer E. Russell Johsto, Jr. Systes of Partcles Lecture Notes: J. Walt Oler Texas Tech Uersty 003 The Mcraw-Hll Copaes, Ic. ll rghts resered.

More information

SYSTEMS OF NON-LINEAR EQUATIONS. Introduction Graphical Methods Close Methods Open Methods Polynomial Roots System of Multivariable Equations

SYSTEMS OF NON-LINEAR EQUATIONS. Introduction Graphical Methods Close Methods Open Methods Polynomial Roots System of Multivariable Equations SYSTEMS OF NON-LINEAR EQUATIONS Itoduto Gaphal Method Cloe Method Ope Method Polomal Root Stem o Multvaale Equato Chapte Stem o No-Lea Equato /. Itoduto Polem volvg o-lea equato egeeg lude optmato olvg

More information

t r ès s r â 2s ré t s r té s s s s r é é ér t s 2 ï s t 1 s à r

t r ès s r â 2s ré t s r té s s s s r é é ér t s 2 ï s t 1 s à r P P r t r t tr t r ès s rs té P rr t r r t t é t q s q é s Prés té t s t r r â 2s ré t s r té s s s s r é é ér t s 2 ï s t 1 s à r ès r é r r t ît P rt ré ré t à r P r s q rt s t t r r2 s rtí 3 Pr ss r

More information

under the curve in the first quadrant.

under the curve in the first quadrant. NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )

More information

Phys 2310 Fri. Oct. 23, 2017 Today s Topics. Begin Chapter 6: More on Geometric Optics Reading for Next Time

Phys 2310 Fri. Oct. 23, 2017 Today s Topics. Begin Chapter 6: More on Geometric Optics Reading for Next Time Py F. Oct., 7 Today Topc Beg Capte 6: Moe o Geometc Optc eadg fo Next Tme Homewok t Week HW # Homewok t week due Mo., Oct. : Capte 4: #47, 57, 59, 6, 6, 6, 6, 67, 7 Supplemetal: Tck ee ad e Sytem Pcple

More information

Chapter 3. Differentiation 3.3 Differentiation Rules

Chapter 3. Differentiation 3.3 Differentiation Rules 3.3 Dfferetato Rules 1 Capter 3. Dfferetato 3.3 Dfferetato Rules Dervatve of a Costat Fucto. If f as te costat value f(x) = c, te f x = [c] = 0. x Proof. From te efto: f (x) f(x + ) f(x) o c c 0 = 0. QED

More information

Answer: First, I ll show how to find the terms analytically then I ll show how to use the TI to find them.

Answer: First, I ll show how to find the terms analytically then I ll show how to use the TI to find them. . CHAPTER 0 SEQUENCE, SERIES, d INDUCTION Secto 0. Seqece A lst of mers specfc order. E / Fd the frst terms : of the gve seqece: Aswer: Frst, I ll show how to fd the terms ltcll the I ll show how to se

More information

Union, Intersection, Product and Direct Product of Prime Ideals

Union, Intersection, Product and Direct Product of Prime Ideals Globl Jourl of Pure d Appled Mthemtcs. ISSN 0973-1768 Volume 11, Number 3 (2015), pp. 1663-1667 Reserch Id Publctos http://www.rpublcto.com Uo, Itersecto, Product d Drect Product of Prme Idels Bdu.P (1),

More information

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. 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 information

SCHOOL OF MATHEMATICS AND STATISTICS. Mathematics II (Materials)

SCHOOL OF MATHEMATICS AND STATISTICS. Mathematics II (Materials) Dt proie: Form Sheet MAS5 SCHOOL OF MATHEMATICS AND STATISTICS Mthemtics II (Mteris) Atm Semester -3 hors Mrks wi e wre or swers to qestios i Sectio A or or est THREE swers to qestios i Sectio. Sectio

More information

CS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples

CS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht los@cs.tt.eu 59 Seott Square Suervse learg Data: D { D D.. D} a set of eales D s a ut vector of sze s the esre outut gve b a teacher Obectve: lear

More information

AP Physics Momentum AP Wrapup

AP Physics Momentum AP Wrapup AP Phyic Moentu AP Wrapup There are two, and only two, equation that you get to play with: p Thi i the equation or oentu. J Ft p Thi i the equation or ipule. The equation heet ue, or oe reaon, the ybol

More information

Linear Open Loop Systems

Linear Open Loop Systems Colordo School of Me CHEN43 Trfer Fucto Ler Ope Loop Sytem Ler Ope Loop Sytem... Trfer Fucto for Smple Proce... Exmple Trfer Fucto Mercury Thermometer... 2 Derblty of Devto Vrble... 3 Trfer Fucto for Proce

More information

LAWS OF INDICES M.K. HOME TUITION. Mathematics Revision Guides Level: GCSE Higher Tier

LAWS OF INDICES M.K. HOME TUITION. Mathematics Revision Guides Level: GCSE Higher Tier Mthetics Revisio Guides Lws of Idices Pge of 7 Author: Mrk Kudlowski M.K. HOME TUITION Mthetics Revisio Guides Level: GCSE Higher Tier LAWS OF INDICES Versio:. Dte: 0--0 Mthetics Revisio Guides Lws of

More information

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS . REVIEW OF PROPERTIES OF EIGENVLUES ND EIGENVECTORS. EIGENVLUES ND EIGENVECTORS We hll ow revew ome bc fct from mtr theory. Let be mtr. clr clled egevlue of f there et ozero vector uch tht Emle: Let 9

More information

Coding Theorems on New Fuzzy Information Theory of Order α and Type β

Coding Theorems on New Fuzzy Information Theory of Order α and Type β Progress Noear yamcs ad Chaos Vo 6, No, 28, -9 ISSN: 232 9238 oe Pubshed o 8 February 28 wwwresearchmathscorg OI: http://ddoorg/22457/pdacv6a Progress Codg Theorems o New Fuzzy Iormato Theory o Order ad

More information

RESEARCH ON THE RADIANT CEILING COOLING SYSTEM

RESEARCH ON THE RADIANT CEILING COOLING SYSTEM RESEARCH ON THE RADIANT CEILING COOLING SYSTEM Y Ya,*, ZX 2 a W Wag 3 Departmet o Urba Costructo Egeerg, Bejg Isttute o Cvl Egeerg a Archtecture Bejg 00044, Cha 2 Cha Natoal Real Developmet Group Ne Techology

More information

Solutions for HW4. x k n+1. k! n(n + 1) (n + k 1) =.

Solutions for HW4. x k n+1. k! n(n + 1) (n + k 1) =. Exercse 13 (a Proe Soutos for HW4 (1 + x 1 + x 2 1 + (1 + x 2 + x 2 2 + (1 + x + x 2 + by ducto o M(Sν x S x ν(x Souto: Frst ote that sce the mutsets o {x 1 } are determed by ν(x 1 the set of mutsets o

More information

Lecture 24 Outline: Z Transforms. Will be 1 more HW, may be short, no late HW8s

Lecture 24 Outline: Z Transforms. Will be 1 more HW, may be short, no late HW8s Lecture 4 Outie: Z Trsfors ouceets: HW 7 to e oste Friy, ue Jue Wi e ore HW, y e short, o te HW8s Lst ecture Jue 6 wi icue course review Fi ex Jue t 3:306:30 i this roo; i fterwrs ore fi ex ouceets ext

More information

Parallel Programming: Speedups and Amdahl s law. Definition of Speedup

Parallel Programming: Speedups and Amdahl s law. Definition of Speedup Programmg: Seedus ad Amdahl s law Mke Baley mjb@cs.oregostate.edu Orego State Uversty Orego State Uversty Comuter Grahcs seedus.ad.amdahls.law.tx Defto of Seedu 2 If you are usg rocessors, your Seedu s:

More information

Review Exam II Complex Analysis

Review Exam II Complex Analysis Revew Exam II Complex Aalyss Uderled Propostos or Theorems: Proofs May Be Asked for o Exam Chapter 3. Ifte Seres Defto: Covergece Defto: Absolute Covergece Proposto. Absolute Covergece mples Covergece

More information

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE Joural o Mathematcs ad tatstcs 9 (3): 49-55, 03 I: 549-3644 03 cece Publcatos do:0.3844/jmssp.03.49.55 Publshed Ole 9 (3) 03 (http://www.thescpub.com/jmss.toc) ADAPTIVE CLUTER AMPLIG UIG AUXILIARY VARIABLE

More information

An Ising model on 2-D image

An Ising model on 2-D image School o Coputer Scence Approte Inerence: Loopy Bele Propgton nd vrnts Prolstc Grphcl Models 0-708 Lecture 4, ov 7, 007 Receptor A Knse C Gene G Receptor B Knse D Knse E 3 4 5 TF F 6 Gene H 7 8 Hetunndn

More information

ENGI 4430 Numerical Integration Page 5-01

ENGI 4430 Numerical Integration Page 5-01 ENGI 443 Numercal Itegrato Page 5-5. Numercal Itegrato I some o our prevous work, (most otaly the evaluato o arc legth), t has ee dcult or mpossle to d the dete tegral. Varous symolc algera ad calculus

More information

THE LOWELL LEDGER. X

THE LOWELL LEDGER. X * : V : ~ E E EGER X Y X 22 E Y BK E G U P B - ; * -K R B BY K E BE YU YU RE EE «> BE B F F P B * q UR V BB«56 x YU 88»* 00 E PU P B P B P V F P EPEE EUR E G URY VEBER

More information

He e e some s nd t icks o emoving g om v iety o su ces

He e e some s nd t icks o emoving g om v iety o su ces T B W d w T y The Br ke Wi w The ry tte tht whe re trt t l k u ightly, y r ke wi w tht e t get xe r gr tht t le e up, the re will ue t eteri rte. Th rete illu i i iffere e tht r t ri i l u e ir le eh i

More information

ANALYTICAL NUMBER THEORY. MM-504 and 505 (Option-P 5

ANALYTICAL NUMBER THEORY. MM-504 and 505 (Option-P 5 ANALYTICAL NUMBER THEORY M.A./M.Sc. Mthetc (Fl) MM-504 505 (Oto-P 5 ) Drectorte of Dtce Eucto Mhrh Dy Uverty ROHTAK 4 00 Coyrght 004, Mhrh Dy Uverty, ROHTAK All Rght Reerve. No rt of th ulcto y e rerouce

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information