Implementing Transformations. Why Transforms? Primitive Transformations: Translation. Affine Transform: Definition M = Q x Q y Q z

Size: px
Start display at page:

Download "Implementing Transformations. Why Transforms? Primitive Transformations: Translation. Affine Transform: Definition M = Q x Q y Q z"

Transcription

1 Why Tasfoms? Wat to aimate objects ad camea Taslatios Rotatios Sheas Ad moe.. Wat to be able to use pojectio tasfoms Implemetig Tasfomatios We use affie (liea) tasfoms Why? Ca be epeseted usig matices Ca be composed to fom complex tasfoms. Need to tasfom vetices oly, ad use (fast) scacovesio fo asteiatio. Ca do a lot (ot eveythig) with matices Use 3 3 ad 4 4 matices ITCS 3050:Game Egie Pogammig 1 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 2 Geometic Tasfomatios Pimitive Tasfomatios: Taslatio Affie Tasfom: Defiitio Q = M P + T Poit P (P x, P y, P ) is tasfomed ito Q(Q x, Q y, Q ) as follows: Q x Q y Q = ap x + dp y + gp + T x = bp x + ep y + hp + T y = cp x + fp y + kp + T Q x Q y = a d g b e h P x P y + T x T y Q c f k P T Q = M P + T ITCS 3050:Game Egie Pogammig 3 Geometic Tasfomatios Note: M = T = = I T x T y T Q x Q y = P x + T x P y + T y Q P + T (Idetity) Taslatio does ot fit withi a 3 3 matix epesetatio! ITCS 3050:Game Egie Pogammig 4 Geometic Tasfomatios

2 Pimitive Tasfomatios:Rotatio Pimitive Tasfomatios: Scale M = S x S y 0, T = [ ] 0 0 S Q x Q y = P xs x P y S y Q P S S x = S y = S : Uifom Scalig Else, Diffeetial Scalig ITCS 3050:Game Egie Pogammig 5 Geometic Tasfomatios M = cos si 0 Si Cos 0 T = [ ] Q = M P + T = P x Cos P y Si = P x Si + P y Cos = P Q x Q y Q Q y P y O R φ Execise: Deive the above 2D otatio matix. ITCS 3050:Game Egie Pogammig 6 Geometic Tasfomatios R Q Q x P P x Example: Tasfomig a Cicle Assume a cicle with oigi at (0,0,0) ad uit adius gltaslatef(8,0,0); RedeCicle(); gltaslatef(3,2,0); glscalef(2,2,2); RedeCicle(); gltaslatef(3,2,0) glscalef (2,2,2) Homogeeous Coodiate Repesetatio Motivatio Taslatio: Q = P + T (Tx, T y, T ) Rotatio: Q = R() P Scale: Q = S(Sx, S y, S ) P gltaslatef (8,0,0) Taslatio ivolves a vecto additio istead of a vecto-matix multiplicatio. Bette if all tasfoms use vecto-matix multiply. ITCS 3050:Game Egie Pogammig 7 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 8 Geometic Tasfomatios

3 Example: Homogeeous Coodiates i 2D Homogeeous Fom: Taslatio Add a thid coodiate w: P 2d = (x, y) = P H = (x, y, w) To covet fom P H to P 2D, poject to w = 1 (divide by w coodiate ad discad the 3d coodiate). w = 0 epesets poits at ifiity. W T H (T x, T y, T ) = T x T y T W = 1 Plae (x/w, y/w,1) P (x, y, w) Thus T x T y T P x P y P 1 P x + T x P = y + T y P + T 1 ITCS 3050:Game Egie Pogammig 9 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 10 Geometic Tasfomatios Homogeeous Fom: Rotatios Homogeeous Fom: Scale S x S S H (S x, S y, S ) = y S 0 Rotatio about,, Z R () = R x () = R y () = Cos Si 0 0 Si Cos Cos Si 0 0 Si Cos 0 Cos 0 Si Si 0 Cos 0 ITCS 3050:Game Egie Pogammig 11 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 12 Geometic Tasfomatios

4 Shea Tasfom Reflectio Tasfom 2D: 2D: M y = [ ] [ ] , M 0 1 x = 0 1 B B 3D: Shea (h > 0) (Q x Q y ) = (P x + hp y, gp x + P y ) = 1 h yx h x 0 h M sh = xy 1 h y 0 h x h y 1 0 [ ] [ ] 1 h Px g 1 P y whee h xy = shea alog axis due to, etc. ITCS 3050:Game Egie Pogammig 13 Geometic Tasfomatios 3D: (Reflectio about a plae) M = A C ITCS 3050:Game Egie Pogammig 14 Geometic Tasfomatios C A Affie Tasfom Iveses Tasfomatio About a Refeece Poit T 1 (T x, T y, T ) = T ( T x, T y, T ) x y M I geeal, S 1 (S x, S y, S ) = S(1/S x, 1/S y, 1/S ) Cos Si 0 0 R 1 () = R T Si Cos 0 0 = MM 1 = M 1 M = I Taslate (x, y, ) to the oigi. Tasfom. Ivet taslatio M = T (x, y, ).S(S x, S y, S ).T ( x, y, ) Fo otatio about a poit, eplace scale by a otatio tasfom. ITCS 3050:Game Egie Pogammig 15 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 16 Geometic Tasfomatios

5 Composig Affie Tasfoms If M 1, M 2,..., M ae affie tasfoms to be applied i successio to a poit P. Q = (M.(M 1...(M 2.(M 1.P )...) = (M.M 1...M 3.M 2.M 1 )P Fo tasfomig lage umbes of poits, the matix poduct (M 1.M 2.M 3...M 1.M ) eeds to be pefomed oly oce. M comp Tasfomig Diectio ectos Motivatio: Need to tasfom (vetex) omals, light diectio Matices used to tasfom poits, lies caot be used to tasfom diectio vectos. Must use the taspose of the ivese of geomety tasfomig matices. Taslatios do ot affect omals, ad otatios ae othogoal (R 1 = R T ) N = (M 1 ) T Oigial Icoect Coect poly omal M comp = T (T x, T y, T ).S(S x, S y, S ).R().T ( T x, T y ) 0 ITCS 3050:Game Egie Pogammig 17 Geometic Tasfomatios Scale by 0.5 alog ITCS 3050:Game Egie Pogammig 18 Geometic Tasfomatios The Eule Tasfom The Eule Tasfom (cotd) Gimbal lock, the loss of a degee of feedom may occu. Fo istace, h = 0, p = π/2adias, cos( + h) 0 si( + h) E(h, π/2, ) = si( + h) 0 cos( + h) 010 Eule Tasfom : a meas to descibe oietatios Give a view dow the egative Z axis, with up i the ad to the ight, E(h, p, ) = R ()R x (p)r y (h) with (h, p, ) efeig to the head, pitch, oll agles, ad E 1 = E T ITCS 3050:Game Egie Pogammig 19 Geometic Tasfomatios which is a fuctio of oly oe agle. Useful to extact the Eule agles fom a composite otatio matix, F = f 00 f 01 f 02 f 10 f 11 f 12 = R ()R x (p)r y (h) f 20 f 21 f 22 Expad RHS ad solve (details, Sectio 3.2.2) ITCS 3050:Game Egie Pogammig 20 Geometic Tasfomatios

6 Quateios: Defiitios Quateios Iveted by Si William Rowa Hamilto i 1843 Extesio of complex umbes Itoduced to compute gaphics by Ke Shoemake i 1985 A compact, efficiet meas to epesetig ad itepolatig oietatios. Supeio to both Eule agles ad matices, especially fo otatios. A quateio ˆq has 4 compoets, ˆq = (q v, q w ) = iq x + jq y + kq + q w = q v + q w i 2 = j 2 = k 2 = 1, jk = kj = i, ki = ik = j, ij = ji = k q w is the eal pat, ad q v the imagiay pat. All omal vecto opeatios (additio, scale, dot ad coss poduces) ca be pefomed o q v. Multiplicatio: ˆqˆ = (iq x + jq y + kq + q w )(i x + j y + k + w ) = (q v v + w q v + q w v, q w w q v v ) ITCS 3050:Game Egie Pogammig 21 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 22 Geometic Tasfomatios Quateios: Defiitios (cotd) Additio: ˆq + ˆ = (q v + v, q w + w ) Cojugate:ˆq = (q v, q w ) = ( q v, q w ) Nom: (ˆq) = ˆqˆq = ˆq ˆq = q v q v + q 2 w = q 2 x + q 2 y + q 2 + q 2 w Idetity: î = (0, 1) Multiplicative Ivese: (ˆq) 2 = ˆqˆq ˆqˆq (ˆq) = 1 2 ˆq 1 1 = (ˆq) 2 ˆq A quateio ˆq, with (ˆq) = 1. ˆq ca be witte as Give u q = 1, Uit Quateios ˆq = (siφu q, cosφ) = siφu q + cosφ ( ˆq) = si 2 φ(u q u q ) + cos 2 φ = 1 Quateios ae pefect fo ceatig otatios ad oietatios. Othe Popeties: Cojugate, Nom ules, Lieaity, Associativity, log, powe. ITCS 3050:Game Egie Pogammig 23 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 24 Geometic Tasfomatios

7 Rotatio About Axis Rotatio About Axis: Taditioal Method Goal: To otate the vecto by about, R = R(, ) R Uit quateios ca epeset ay 3D otatio ey compact, efficiet otatio To otate p about u q, put p = (p x p y p p w ) T ito a uit quateio, siφu q, cosφ Result: ˆq ˆpˆq 1 otates ˆp aoud u q by the agle 2φ adias Note: q 1 = ˆq. ITCS 3050:Game Egie Pogammig 25 Geometic Tasfomatios R Decompose ito ad, paallel ad pepedicula to = ( ) = ( ) ITCS 3050:Game Egie Pogammig 26 Geometic Tasfomatios Rotatio About Axis: Taditioal Method Rotatio About Axis: Taditioal Method R R R R Detemie R by computig, othogoal to ad : = Thus, R = (cos) + (si) ITCS 3050:Game Egie Pogammig 27 Geometic Tasfomatios Thus R = R + R = R + (cos) + (si) = ( ) + cos( ( )) + (si) = (cos) + (1 cos)( ) + (si) ITCS 3050:Game Egie Pogammig 28 Geometic Tasfomatios

8 Rotatio Usig Quateios T he same otatio, R = R(2, ) ca be pefomed usig the (uit) quateio poduct ˆq ˆpˆq 1 Assume ˆq = (q v, q w ) ˆq is a uit quateio ˆq = (si, cos), = 1 Give Compute pq 1 : Remembe Deivatio: ˆq ˆpˆq 1 p = (0, ) q = (s, v) q 1 = ˆq = (s, v) ˆp(s, v)ˆq(s, v ) = s 2 vv, v v + sv + s v Compute the poduct ˆq ˆpˆq 1. Ca be show to be ˆq ˆpˆq 1 = (q 2 w v v) + 2v(v ) + 2s(v, 0) = (cos 2 si 2 ) + 2si 2 ( ) + 2cossi( )) = (cos2 + (1 cos2)( ) + si2( ), 0) Thus, pq 1 = (0, )(s, v) = (0 + v), ( v) + s. ITCS 3050:Game Egie Pogammig 29 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 30 Geometic Tasfomatios Deivatio: ˆq ˆpˆq 1 (cotd) Compute q(pq 1 ): q(pq 1 ) = (s, v)( v, ( v) + s) = (s( v) v ( v) s(v )), (v ( v)) + s(v ) + s( v) + s 2 + ( v) v = (0, (v v) ( ) + (v ) v + s(v ) + s( v) + s 2 + ( v) v = (s 2 v v) + 2s(v ) + 2(v )v Fo uit quateios, s = cos, v = si, thus q(pq 1 ) = (cos 2 si 2 ) + 2si(si) + 2cos(si ) = cos(2) + (1 cos(2)) + si(2)( v) Idetical to the taditioal method, except fo a facto of 2, easily adjusted by edefiig q = (si 2, cos 2 ) ITCS 3050:Game Egie Pogammig 31 Geometic Tasfomatios Taditioal Rotatio vs Quateio Rotatio: Summay Expessio obtaied usig taditioal method: R = (cos) + (1 cos)( ) + (si) Expessio obtaied usig quateio otatio: R = (cos(2) + (1 cos(2))( ) + si(2)( )) ITCS 3050:Game Egie Pogammig 32 Geometic Tasfomatios

9 Quateios i Matix Fom 1 s(qy 2 + q) 2 s(q x q y q w q ) s(q x q + q w q y ) 0 M q = s(q x q y + q w q ) 1 s(qx 2 + q) 2 s(q y q q w q x ) 0 s(q x q q w q y ) s(q y q + q w q x ) 1 s(qx 2 + qy) 2 0 Fo uit quateios, s = 2/(ˆq), 1 2(qy 2 + q) 2 2(q x q y q w q ) 2(q x q + q w q y ) 0 M q = 2(q x q y + q w q ) 1 2(qx 2 + q) 2 2(q y q q w q x ) 0 2(q x q q w q y ) 2(q y q + q w q x ) 1 2(qx 2 + qy) 2 0 ITCS 3050:Game Egie Pogammig 33 Geometic Tasfomatios Rotatio fom Oe ecto to Aothe To otate fom vecto s to vecto t Method: Nomalie s ad t. Compute uit otatio axis, u = s t s t e = s t = cos(2), s t = si(2), 2φ, the agle betwee the vectos Quateio ˆq = (siφu, cosφ) With optimiatios (efe text) e + hv 2 x hv x v y v hv x v + v y 0 hv R(s, t) = x v y + v e + hvy 2 hv y v v x 0 hv x v v y hv y v + v x e + hv 2 0 whee h = 1 1 e ITCS 3050:Game Egie Pogammig 34 Geometic Tasfomatios Spheical Liea Itepolatio Smooth itepolatio of quateios, fom ˆq to ˆ. Useful i aimatig objects This is doe by the followig: ŝ(ˆq, ˆ, t) = (ˆˆq 1 ) tˆq, Algebaic Fom si(φ(1 t)) ŝ(ˆq, ˆ, t) = slep(ˆq, ˆ, t) = ˆq + si(φt) siφ siφ ˆ The secod fom is moe useful, effectively itepolatig ove a 4D sphee at fixed costat speed (geodesic itepolatio). Applicatio: Camea Positioig ad Oietatio Give camea positioed at (0, 0, 0) T ad lookig dow v = (0, 0, 1) T Goal: To move lookat diectio to w ad move camea to a ew positio p Accomplished by = T(p)R(v, w) Usually, will eed to otate the camea up diectio to somethig moe desiable. ITCS 3050:Game Egie Pogammig 35 Geometic Tasfomatios ITCS 3050:Game Egie Pogammig 36 Geometic Tasfomatios

10 Rotatio about a Abitay Axis:Methodology Rotatio about a Abitay Axis s y t x M Sometimes, useful to otate about a abitay axis, t s y Need two additioal axes to fom a basis, the chage bases. Chage fom stadad basis to ew basis, otate by give agle, ad tasfom back ITCS 3050:Game Egie Pogammig 37 Geometic Tasfomatios M T x s y t x Fid othoomal axes of basis: Detemiig s: Set smallest compoet of to eo, swap the emaiig two tems ad egate the fist, (0, x, y ) x <, x < s = (, 0, x ) y < x, y < ( y, x, 0) < x, < y s = s/ s t = s Rotatio to stadad basis: M = Fial Tasfom: = M T R x (α)m T s T t T ITCS 3050:Game Egie Pogammig 38 Geometic Tasfomatios

Two degree of freedom systems. Equations of motion for forced vibration Free vibration analysis of an undamped system

Two degree of freedom systems. Equations of motion for forced vibration Free vibration analysis of an undamped system wo degee of feedom systems Equatios of motio fo foced vibatio Fee vibatio aalysis of a udamped system Itoductio Systems that equie two idepedet d coodiates to descibe thei motio ae called two degee of

More information

THE PRINCIPLE OF THE ACTIVE JMC SCATTERER. Seppo Uosukainen

THE PRINCIPLE OF THE ACTIVE JMC SCATTERER. Seppo Uosukainen THE PRINCIPLE OF THE ACTIVE JC SCATTERER Seppo Uoukaie VTT Buildig ad Tapot Ai Hadlig Techology ad Acoutic P. O. Bo 1803, FIN 02044 VTT, Filad Seppo.Uoukaie@vtt.fi ABSTRACT The piciple of fomulatig the

More information

Understanding Financial Management: A Practical Guide Guideline Answers to the Concept Check Questions

Understanding Financial Management: A Practical Guide Guideline Answers to the Concept Check Questions Udestadig Fiacial Maagemet: A Pactical Guide Guidelie Aswes to the Cocept Check Questios Chapte 4 The Time Value of Moey Cocept Check 4.. What is the meaig of the tems isk-etu tadeoff ad time value of

More information

Derivation of Annuity and Perpetuity Formulae. A. Present Value of an Annuity (Deferred Payment or Ordinary Annuity)

Derivation of Annuity and Perpetuity Formulae. A. Present Value of an Annuity (Deferred Payment or Ordinary Annuity) Aity Deivatios 4/4/ Deivatio of Aity ad Pepetity Fomlae A. Peset Vale of a Aity (Defeed Paymet o Odiay Aity 3 4 We have i the show i the lecte otes ad i ompodi ad Discoti that the peset vale of a set of

More information

Periodic Review Probabilistic Multi-Item Inventory System with Zero Lead Time under Constraints and Varying Order Cost

Periodic Review Probabilistic Multi-Item Inventory System with Zero Lead Time under Constraints and Varying Order Cost Ameica Joual of Applied Scieces (8: 3-7, 005 ISS 546-939 005 Sciece Publicatios Peiodic Review Pobabilistic Multi-Item Ivetoy System with Zeo Lead Time ude Costaits ad Vayig Ode Cost Hala A. Fegay Lectue

More information

Vector Calculus: Are you ready? Vectors in 2D and 3D Space: Review

Vector Calculus: Are you ready? Vectors in 2D and 3D Space: Review Vecto Calculus: Ae you eady? Vectos in D and 3D Space: Review Pupose: Make cetain that you can define, and use in context, vecto tems, concepts and fomulas listed below: Section 7.-7. find the vecto defined

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

On the Optimality and Interconnection of Valiant Load-Balancing Networks

On the Optimality and Interconnection of Valiant Load-Balancing Networks O the Optimality ad Itecoectio of Valiat Load-Balacig Netwoks Moshe Babaioff ad Joh Chuag School of Ifomatio Uivesity of Califoia at Bekeley Bekeley, Califoia 94720 4600 {moshe,chuag}@sims.bekeley.edu

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

Learning Objectives. Chapter 2 Pricing of Bonds. Future Value (FV)

Learning Objectives. Chapter 2 Pricing of Bonds. Future Value (FV) Leaig Objectives Chapte 2 Picig of Bods time value of moey Calculate the pice of a bod estimate the expected cash flows detemie the yield to discout Bod pice chages evesely with the yield 2-1 2-2 Leaig

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required.

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required. S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,

More information

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

More information

580.439 Course Notes: Nonlinear Dynamics and Hodgkin-Huxley Equations

580.439 Course Notes: Nonlinear Dynamics and Hodgkin-Huxley Equations 58.439 Couse Notes: Noliea Dyamics ad Hodgki-Huxley Equatios Readig: Hille (3 d ed.), chapts 2,3; Koch ad Segev (2 d ed.), chapt 7 (by Rizel ad Emetout). Fo uthe eadig, S.H. Stogatz, Noliea Dyamics ad

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu> (March 16, 004) Factorig x 1: cyclotomic ad Aurifeuillia polyomials Paul Garrett Polyomials of the form x 1, x 3 1, x 4 1 have at least oe systematic factorizatio x 1 = (x 1)(x 1

More information

Annuities and loan. repayments. Syllabus reference Financial mathematics 5 Annuities and loan. repayments

Annuities and loan. repayments. Syllabus reference Financial mathematics 5 Annuities and loan. repayments 8 8A Futue value of a auity 8B Peset value of a auity 8C Futue ad peset value tables 8D Loa epaymets Auities ad loa epaymets Syllabus efeece Fiacial mathematics 5 Auities ad loa epaymets Supeauatio (othewise

More information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

Section 8.3 : De Moivre s Theorem and Applications

Section 8.3 : De Moivre s Theorem and Applications The Sectio 8 : De Moivre s Theorem ad Applicatios Let z 1 ad z be complex umbers, where z 1 = r 1, z = r, arg(z 1 ) = θ 1, arg(z ) = θ z 1 = r 1 (cos θ 1 + i si θ 1 ) z = r (cos θ + i si θ ) ad z 1 z =

More information

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern. 5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Multicomponent Systems

Multicomponent Systems CE 6333, Levicky 1 Multicompoet Systems MSS TRNSFER. Mass tasfe deals with situatios i which thee is moe tha oe compoet peset i a system; fo istace, situatios ivolvig chemical eactios, dissolutio, o mixig

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

NURBS Drawing Week 5, Lecture 10

NURBS Drawing Week 5, Lecture 10 CS 43/585 Compute Gaphics I NURBS Dawing Week 5, Lectue 1 David Been, William Regli and Maim Pesakhov Geometic and Intelligent Computing Laboato Depatment of Compute Science Deel Univesit http://gicl.cs.deel.edu

More information

Money Math for Teens. Introduction to Earning Interest: 11th and 12th Grades Version

Money Math for Teens. Introduction to Earning Interest: 11th and 12th Grades Version Moey Math fo Tees Itoductio to Eaig Iteest: 11th ad 12th Gades Vesio This Moey Math fo Tees lesso is pat of a seies ceated by Geeatio Moey, a multimedia fiacial liteacy iitiative of the FINRA Ivesto Educatio

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

Negotiation Programs

Negotiation Programs Negotiatio Pogams Javie Espaza 1 ad Jög Desel 2 1 Fakultät fü Ifomatik, Techische Uivesität Müche, Gemay espaza@tum.de 2 Fakultät fü Mathematik ud Ifomatik, FeUivesität i Hage, Gemay joeg.desel@feui-hage.de

More information

Finance Practice Problems

Finance Practice Problems Iteest Fiace Pactice Poblems Iteest is the cost of boowig moey. A iteest ate is the cost stated as a pecet of the amout boowed pe peiod of time, usually oe yea. The pevailig maket ate is composed of: 1.

More information

Network Theorems - J. R. Lucas. Z(jω) = jω L

Network Theorems - J. R. Lucas. Z(jω) = jω L Netwo Theoems - J.. Lucas The fudametal laws that gove electic cicuits ae the Ohm s Law ad the Kichoff s Laws. Ohm s Law Ohm s Law states that the voltage vt acoss a esisto is diectly ootioal to the cuet

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

The dinner table problem: the rectangular case

The dinner table problem: the rectangular case The ie table poblem: the ectagula case axiv:math/009v [mathco] Jul 00 Itouctio Robeto Tauaso Dipatimeto i Matematica Uivesità i Roma To Vegata 00 Roma, Italy tauaso@matuiomait Decembe, 0 Assume that people

More information

Symmetric polynomials and partitions Eugene Mukhin

Symmetric polynomials and partitions Eugene Mukhin Symmetic polynomials and patitions Eugene Mukhin. Symmetic polynomials.. Definition. We will conside polynomials in n vaiables x,..., x n and use the shotcut p(x) instead of p(x,..., x n ). A pemutation

More information

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k.

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k. 18.409 A Algorithmist s Toolkit September 17, 009 Lecture 3 Lecturer: Joatha Keler Scribe: Adre Wibisoo 1 Outlie Today s lecture covers three mai parts: Courat-Fischer formula ad Rayleigh quotiets The

More information

Physics 235 Chapter 5. Chapter 5 Gravitation

Physics 235 Chapter 5. Chapter 5 Gravitation Chapte 5 Gavitation In this Chapte we will eview the popeties of the gavitational foce. The gavitational foce has been discussed in geat detail in you intoductoy physics couses, and we will pimaily focus

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis Ruig Time ( 3.) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

PY1052 Problem Set 8 Autumn 2004 Solutions

PY1052 Problem Set 8 Autumn 2004 Solutions PY052 Poblem Set 8 Autumn 2004 Solutions H h () A solid ball stats fom est at the uppe end of the tack shown and olls without slipping until it olls off the ight-hand end. If H 6.0 m and h 2.0 m, what

More information

Skills Needed for Success in Calculus 1

Skills Needed for Success in Calculus 1 Skills Needed fo Success in Calculus Thee is much appehension fom students taking Calculus. It seems that fo man people, "Calculus" is snonmous with "difficult." Howeve, an teache of Calculus will tell

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

Estimating Surface Normals in Noisy Point Cloud Data

Estimating Surface Normals in Noisy Point Cloud Data Estiatig Suface Noals i Noisy Poit Cloud Data Niloy J. Mita Stafod Gaphics Laboatoy Stafod Uivesity CA, 94305 iloy@stafod.edu A Nguye Stafod Gaphics Laboatoy Stafod Uivesity CA, 94305 aguye@cs.stafod.edu

More information

Coordinate Systems L. M. Kalnins, March 2009

Coordinate Systems L. M. Kalnins, March 2009 Coodinate Sstems L. M. Kalnins, Mach 2009 Pupose of a Coodinate Sstem The pupose of a coodinate sstem is to uniquel detemine the position of an object o data point in space. B space we ma liteall mean

More information

The Binomial Multi- Section Transformer

The Binomial Multi- Section Transformer 4/15/21 The Bioial Multisectio Matchig Trasforer.doc 1/17 The Bioial Multi- Sectio Trasforer Recall that a ulti-sectio atchig etwork ca be described usig the theory of sall reflectios as: where: Γ ( ω

More information

Displacement, Velocity And Acceleration

Displacement, Velocity And Acceleration Displacement, Velocity And Acceleation Vectos and Scalas Position Vectos Displacement Speed and Velocity Acceleation Complete Motion Diagams Outline Scala vs. Vecto Scalas vs. vectos Scala : a eal numbe,

More information

AP Calculus BC 2003 Scoring Guidelines Form B

AP Calculus BC 2003 Scoring Guidelines Form B AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

Heat (or Diffusion) equation in 1D*

Heat (or Diffusion) equation in 1D* Heat (or Diffusio) equatio i D* Derivatio of the D heat equatio Separatio of variables (refresher) Worked eamples *Kreysig, 8 th Ed, Sectios.4b Physical assumptios We cosider temperature i a log thi wire

More information

TruStore: The storage. system that grows with you. Machine Tools / Power Tools Laser Technology / Electronics Medical Technology

TruStore: The storage. system that grows with you. Machine Tools / Power Tools Laser Technology / Electronics Medical Technology TruStore: The storage system that grows with you Machie Tools / Power Tools Laser Techology / Electroics Medical Techology Everythig from a sigle source. Cotets Everythig from a sigle source. 2 TruStore

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

THE problem of fitting a circle to a collection of points

THE problem of fitting a circle to a collection of points IEEE TRANACTION ON INTRUMENTATION AND MEAUREMENT, VOL. XX, NO. Y, MONTH 000 A Few Methods for Fittig Circles to Data Dale Umbach, Kerry N. Joes Abstract Five methods are discussed to fit circles to data.

More information

Math 113 HW #11 Solutions

Math 113 HW #11 Solutions Math 3 HW # Solutios 5. 4. (a) Estimate the area uder the graph of f(x) = x from x = to x = 4 usig four approximatig rectagles ad right edpoits. Sketch the graph ad the rectagles. Is your estimate a uderestimate

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

Time Value of Money. First some technical stuff. HP10B II users

Time Value of Money. First some technical stuff. HP10B II users Time Value of Moey Basis for the course Power of compoud iterest $3,600 each year ito a 401(k) pla yields $2,390,000 i 40 years First some techical stuff You will use your fiacial calculator i every sigle

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

12. Rolling, Torque, and Angular Momentum

12. Rolling, Torque, and Angular Momentum 12. olling, Toque, and Angula Momentum 1 olling Motion: A motion that is a combination of otational and tanslational motion, e.g. a wheel olling down the oad. Will only conside olling with out slipping.

More information

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

Saturated and weakly saturated hypergraphs

Saturated and weakly saturated hypergraphs Satuated and weakly satuated hypegaphs Algebaic Methods in Combinatoics, Lectues 6-7 Satuated hypegaphs Recall the following Definition. A family A P([n]) is said to be an antichain if we neve have A B

More information

Gauss Law. Physics 231 Lecture 2-1

Gauss Law. Physics 231 Lecture 2-1 Gauss Law Physics 31 Lectue -1 lectic Field Lines The numbe of field lines, also known as lines of foce, ae elated to stength of the electic field Moe appopiately it is the numbe of field lines cossing

More information

Notes on Power System Load Flow Analysis using an Excel Workbook

Notes on Power System Load Flow Analysis using an Excel Workbook Notes o owe System Load Flow Aalysis usig a Excel Woboo Abstact These otes descibe the featues of a MS-Excel Woboo which illustates fou methods of powe system load flow aalysis. Iteative techiques ae epeseted

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

Domain 1: Designing a SQL Server Instance and a Database Solution

Domain 1: Designing a SQL Server Instance and a Database Solution Maual SQL Server 2008 Desig, Optimize ad Maitai (70-450) 1-800-418-6789 Domai 1: Desigig a SQL Server Istace ad a Database Solutio Desigig for CPU, Memory ad Storage Capacity Requiremets Whe desigig a

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

More information

CHAPTER 4: NET PRESENT VALUE

CHAPTER 4: NET PRESENT VALUE EMBA 807 Corporate Fiace Dr. Rodey Boehe CHAPTER 4: NET PRESENT VALUE (Assiged probles are, 2, 7, 8,, 6, 23, 25, 28, 29, 3, 33, 36, 4, 42, 46, 50, ad 52) The title of this chapter ay be Net Preset Value,

More information

Chapter 22. Outside a uniformly charged sphere, the field looks like that of a point charge at the center of the sphere.

Chapter 22. Outside a uniformly charged sphere, the field looks like that of a point charge at the center of the sphere. Chapte.3 What is the magnitude of a point chage whose electic field 5 cm away has the magnitude of.n/c. E E 5.56 1 11 C.5 An atom of plutonium-39 has a nuclea adius of 6.64 fm and atomic numbe Z94. Assuming

More information

Now here is the important step

Now here is the important step LINEST i Excel The Excel spreadsheet fuctio "liest" is a complete liear least squares curve fittig routie that produces ucertaity estimates for the fit values. There are two ways to access the "liest"

More information

Figure 2. So it is very likely that the Babylonians attributed 60 units to each side of the hexagon. Its resulting perimeter would then be 360!

Figure 2. So it is very likely that the Babylonians attributed 60 units to each side of the hexagon. Its resulting perimeter would then be 360! 1. What ae angles? Last time, we looked at how the Geeks intepeted measument of lengths. Howeve, as fascinated as they wee with geomety, thee was a shape that was much moe enticing than any othe : the

More information

The LCOE is defined as the energy price ($ per unit of energy output) for which the Net Present Value of the investment is zero.

The LCOE is defined as the energy price ($ per unit of energy output) for which the Net Present Value of the investment is zero. Poject Decision Metics: Levelized Cost of Enegy (LCOE) Let s etun to ou wind powe and natual gas powe plant example fom ealie in this lesson. Suppose that both powe plants wee selling electicity into the

More information

between Modern Degree Model Logistics Industry in Gansu Province 2. Measurement Model 1. Introduction 2.1 Synergetic Degree

between Modern Degree Model Logistics Industry in Gansu Province 2. Measurement Model 1. Introduction 2.1 Synergetic Degree www.ijcsi.og 385 Calculatio adaalysis alysis of the Syegetic Degee Model betwee Mode Logistics ad Taspotatio Idusty i Gasu Povice Ya Ya 1, Yogsheg Qia, Yogzhog Yag 3,Juwei Zeg 4 ad Mi Wag 5 1 School of

More information

Chapter 12 Static Equilibrium and Elasticity

Chapter 12 Static Equilibrium and Elasticity Chapte Static Equilibium ad Elaticity Coceptual Poblem [SSM] Tue o fale: (a) i 0 i ufficiet fo tatic equilibium to eit. i (b) i 0 i eceay fo tatic equilibium to eit. i (c) I tatic equilibium, the et toque

More information

A Guide to the Pricing Conventions of SFE Interest Rate Products

A Guide to the Pricing Conventions of SFE Interest Rate Products A Guide to the Pricig Covetios of SFE Iterest Rate Products SFE 30 Day Iterbak Cash Rate Futures Physical 90 Day Bak Bills SFE 90 Day Bak Bill Futures SFE 90 Day Bak Bill Futures Tick Value Calculatios

More information

CHAPTER 11 Financial mathematics

CHAPTER 11 Financial mathematics CHAPTER 11 Fiacial mathematics I this chapter you will: Calculate iterest usig the simple iterest formula ( ) Use the simple iterest formula to calculate the pricipal (P) Use the simple iterest formula

More information

Multiplexers and Demultiplexers

Multiplexers and Demultiplexers I this lesso, you will lear about: Multiplexers ad Demultiplexers 1. Multiplexers 2. Combiatioal circuit implemetatio with multiplexers 3. Demultiplexers 4. Some examples Multiplexer A Multiplexer (see

More information

Spirotechnics! September 7, 2011. Amanda Zeringue, Michael Spannuth and Amanda Zeringue Dierential Geometry Project

Spirotechnics! September 7, 2011. Amanda Zeringue, Michael Spannuth and Amanda Zeringue Dierential Geometry Project Spiotechnics! Septembe 7, 2011 Amanda Zeingue, Michael Spannuth and Amanda Zeingue Dieential Geomety Poject 1 The Beginning The geneal consensus of ou goup began with one thought: Spiogaphs ae awesome.

More information

Formulae and Tables for use in the State Examinations

Formulae and Tables for use in the State Examinations Fomulae ad Tables fo use i the State Examiatios Page PDF Watemak Remove DEMO : Puchase fom www.pdfwatemakrem Obsevatios ae ivited o this daft booklet of Fomulae ad Tables, which is iteded to eplace the

More information

Class Meeting # 16: The Fourier Transform on R n

Class Meeting # 16: The Fourier Transform on R n MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,

More information

Chapter 30: Magnetic Fields Due to Currents

Chapter 30: Magnetic Fields Due to Currents d Chapte 3: Magnetic Field Due to Cuent A moving electic chage ceate a magnetic field. One of the moe pactical way of geneating a lage magnetic field (.1-1 T) i to ue a lage cuent flowing though a wie.

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

Magnetic Field and Magnetic Forces. Young and Freedman Chapter 27

Magnetic Field and Magnetic Forces. Young and Freedman Chapter 27 Magnetic Field and Magnetic Foces Young and Feedman Chapte 27 Intoduction Reiew - electic fields 1) A chage (o collection of chages) poduces an electic field in the space aound it. 2) The electic field

More information

Complex Numbers. where x represents a root of Equation 1. Note that the ± sign tells us that quadratic equations will have

Complex Numbers. where x represents a root of Equation 1. Note that the ± sign tells us that quadratic equations will have Comple Numbers I spite of Calvi s discomfiture, imagiar umbers (a subset of the set of comple umbers) eist ad are ivaluable i mathematics, egieerig, ad sciece. I fact, i certai fields, such as electrical

More information

The force between electric charges. Comparing gravity and the interaction between charges. Coulomb s Law. Forces between two charges

The force between electric charges. Comparing gravity and the interaction between charges. Coulomb s Law. Forces between two charges The foce between electic chages Coulomb s Law Two chaged objects, of chage q and Q, sepaated by a distance, exet a foce on one anothe. The magnitude of this foce is given by: kqq Coulomb s Law: F whee

More information

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value Cocept 9: Preset Value Is the value of a dollar received today the same as received a year from today? A dollar today is worth more tha a dollar tomorrow because of iflatio, opportuity cost, ad risk Brigig

More information

2 r2 θ = r2 t. (3.59) The equal area law is the statement that the term in parentheses,

2 r2 θ = r2 t. (3.59) The equal area law is the statement that the term in parentheses, 3.4. KEPLER S LAWS 145 3.4 Keple s laws You ae familia with the idea that one can solve some mechanics poblems using only consevation of enegy and (linea) momentum. Thus, some of what we see as objects

More information

Swaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps

Swaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps Swaps: Costat maturity swaps (CMS) ad costat maturity reasury (CM) swaps A Costat Maturity Swap (CMS) swap is a swap where oe of the legs pays (respectively receives) a swap rate of a fixed maturity, while

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information