Seidel sums and a p p l p icat a ion o s f or o r s imp m l p e e c as a es
|
|
- Marjorie Dennis
- 6 years ago
- Views:
Transcription
1 Seidel sums ad applicatios for simple cases
2 Aspheric surface Geerally : o spherical rotatioally symmetric surfaces but ca be off-axis coic sectios Greatly help to improve performace, ad reduce the umber of compoets (hece less weight)
3 Aspheric surface : coic I I h A A S I I sag h R d + h R d ( ε + ) ε type ε< hyperboloid, focii o the axis ε paraboloid <ε<0 ellipsoid, focii o the axis ε0 sphere ε>0 ellipsoid, focii ot o the axis
4 Aspheric surface : geeral equatio h Rd I I sag + a 4h + a6h + a8h + a0h + ( ε + ) h Rd coic base to correct rd order to correct higher orders Drawbacks : - higher order terms lead to «umerical oscillatios» durig optimizatio, - it is almost impossible to tolerace the a k Solutio : Q-type polyomials from Forbes. See these papers : - G.. Forbes, "Shape specificatio for axially symmetric optical surfaces Opt. Express 5, (007) -Kevi P. Thompso, Floria Fourier, Jaick P. Rollad, G.. Forbes The Forbes Polyomial : a more predictable surface for fabricators IODC, paper OtuA6, 00 4
5 Set up for a sigle surface P i y α θ j k h C p x 5
6 Defiitios ad otatios Curvature of the surface: C Lagrageivariat : H déf yα R d démo facile P C Petzval curvature ( kα hθ ) 6
7 The Seidel sums are geerally P 0S 8 S S S I II III S 4 4 S IV All surf All surf All surf 8 ( i) ( i)( j) ( j) Seidel sums determied for the maximum aperture ad field. α h + C α h + C α h + C ( S + S ) ( j) V III All surf Lateral chromatic aberratio IV All surf 4 ( S + S ) Logitudial chromatic aberratio δ j i III δ C ε h 4 ε h ε h k k ( j) More details:"aberratios of optical systems", elford. IV α h + C + C 00 II ε h k C I All surf ε h All surf k h ( i) H h C 7
8 Total aberratio fuctio 4 040ρ + y ρ cosϕ + y ρ cos ϕ + 0 y S ρ + y ρ cosϕ (ormalized variables) 8
9 Special cases Object o the surface (h 0) 040 0, o chromatism Applicatios : field les, field corrector (Smyth les ) Object at the ceter of curvature (i 0), spherical surface 040 0, o logitudial chromatic aberratio Pupil at the ceter of curvature (j 0), spherical surface 0, o lateral chromatic aberratio Applicatios : Schmidt telescope 9
10 Special cases eierstrass poits ( (α/) 0), spherical surface Applicatios : letille ½ boule des objectifs de microscope à immersio Aspheric surface At least oe aberratio ca be corrected : 040,,, If the pupil is o the surface (k 0) : oly 040 ca be corrected Applicatios : paraboloid mirror, Ritchey-Chrétie telescope 0
11 Special cases Pupil shift (keepig costat aperture) j et k are chaged : 040 is ivariat,,, ad the lateral chromatic aberratio are modified Symetric optical system ad ray paths All the aberratios depedig o a odd power of the field are aught (,, lateral chromatic aberratio) If the system is symetric ad ot the ray paths (ex : /F), all the aberratios depedig o a odd power of the field remai weak Applicatios : double Gauss
12 Pupil shift D après «Optical system desig», R. Fisher
13 Pupil shift e assume that the aperture is costat (ie. h cte). k The pupil shift is characterized by : E h Oe ca show that : 040 0P 0 ( δ00 ) ( δ ) 4 E E 0 E E ( + ) E δ P + E To get more details, see for istace:" Aberratios of + 4 E 040 optical systems", elford
14 Pupil shift No effect o Spherical Aberratio or Petzval terms Aberratio «trasfer» : Coma itroduced if SA is preset Astigmatism itroduced if SA or Coma are preset Distortio itroduced if SA, Coma or Astigmatism are preset For a aberratio free system, shiftig the stop does ot itroduce aberratios. If the spherical aberratio is corrected, the the coma does ot deped o the positio of the pupil If the optical system is aplaetic, the the astigmatism does ot deped o the positio of the pupil If the logitudial chromatic aberratio is corrected, the the lateral chromatic aberratio does ot deped o the positio of the pupil 4
15 Sigle les (pupil o the les, objet at ifiity) To get more details, see:" Aberratios of Notatios: R P radius of Φ power of b shape factor c déf α + α α α the pupil the les déf C C + C C f ( )( C C ) optical systems", elford 5
16 Seidel sums ( ) ( ) ( ) Φ Φ c b H R c c b R P P Φ Φ Φ R H H P P δ ν δ 6
17 40 Best shape les,5 ; c s b 7
18 Chage of the idex of refractio c- 40, s b 8
19 40 5 Variatios of 040 et,5 ; c- S S b 9
20 Spherical mirror (whatever the positios of the object ad the pupil) Notatio : C 040 0P i h 4 ijh C j j i h H C C R d C. e get : ( + ) 0P 0
21 Spherical mirror Cases of iterest: Pupil o the mirror (j θ) Pupil at the ceter of curvature (j 0) : coma, astigmatism ad distortio are corrected
22 Plae parallel plate (pupil o the plate, ay positio of the object) α d 040 0P δ δ 00 0 ( ) 8 ( ) 4 α d α θd ( ) d ( ) α θ αθ d α d αθd
23 Plae parallel plate These formulas are depedig o α et θ, ad ot h et k : the aberratios do ot deped o the positio of the plate, with respect to the beam The logitudial chromatic aberratio, the spherical aberratio ad the astigmatism are all over-corrected If the object is at ifiity (α 0), the all the aberratios are aught If the pupil is at ifiity (θ 0, telecetric system), the all the field aberratios are aught.
Overview of Aberrations
Overview of Aberratios Les Desig OPTI 57 Aberratio From the Lati, aberrare, to wader from; Lati, ab, away, errare, to wader. Symmetry properties Overview of Aberratios (Departures from ideal behavior)
More informationAstigmatism Field Curvature Distortion
Astigmatism Field Curvature Distortio Les Desig OPTI 57 . Phil Earliest through focus images.t. Youg, O the mechaism of the eye, Tras Royal Soc Lod 80; 9: 3 88 ad plates. Astigmatism through focus Astigmatism
More informationComa aberration. Lens Design OPTI 517. Prof. Jose Sasian
Coma aberratio Les Desig OPTI 517 Coma 0.5 wave 1.0 wave.0 waves 4.0 waves Spot diagram W W W... 040 0 H,, W 4 H W 131 W 00 311 H 3 H H cos W 3 W 00 W H cos W 400 111 H H cos cos 4 Coma though focus Cases
More informationZernike polynomials. Frederik Zernike, 1953 Nobel prize
erike poloials Frederik erike, 95 Nobel prize eider (,, ϕ Thierr Lépie - Optical desig... cos cos cos cos ϕ ϕ ϕ ϕ S Advatages erike poloials are of great iterest i a fields : optical desig optical etrolog
More informationSummary of formulas Summary of optical systems
Summar of formulas Summar of optical sstems Les Desig OPTI 57 Imagig: cetral projectio X' Y' Z' a X b Y c Z d axbyczd 0 0 0 0 a X b Y c Z d axbyczd 0 0 0 0 a X byczd axbyczd 3 3 3 3 0 0 0 0 Colliear trasformatio
More information62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +
62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of
More informationCork Institute of Technology Bachelor of Science (Honours) in Applied Physics and Instrumentation-Award - (NFQ Level 8)
ork Istitute of Techology Bachelor of Sciece (Hoours) i Applied Physics ad Istrumetatio-Award - (NFQ Level 8) Istructios Aswer Four questios, at least TWO questios from each Sectio. Use separate aswer
More informationAstro 500 A500/L-7 1
Astro 500 1 Telescopes & Optics Outline Defining the telescope & observatory Mounts Foci Optical designs Geometric optics Aberrations Conceptually separate Critical for understanding telescope and instrument
More informationMa 530 Introduction to Power Series
Ma 530 Itroductio to Power Series Please ote that there is material o power series at Visual Calculus. Some of this material was used as part of the presetatio of the topics that follow. What is a Power
More informationIntroduction to aberrations OPTI 518 Lecture 14
Introduction to aberrations Lecture 14 Topics Structural aberration coefficients Examples Structural coefficients Ж Requires a focal system Afocal systems can be treated with Seidel sums Structural stop
More information3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,
3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [
More informationCALCULUS BASIC SUMMER REVIEW
CALCULUS BASIC SUMMER REVIEW NAME rise y y y Slope of a o vertical lie: m ru Poit Slope Equatio: y y m( ) The slope is m ad a poit o your lie is, ). ( y Slope-Itercept Equatio: y m b slope= m y-itercept=
More informationRay Optics Theory and Mode Theory. Dr. Mohammad Faisal Dept. of EEE, BUET
Ray Optics Theory ad Mode Theory Dr. Mohammad Faisal Dept. of, BUT Optical Fiber WG For light to be trasmitted through fiber core, i.e., for total iteral reflectio i medium, > Ray Theory Trasmissio Ray
More informationComplex Analysis Spring 2001 Homework I Solution
Complex Aalysis Sprig 2001 Homework I Solutio 1. Coway, Chapter 1, sectio 3, problem 3. Describe the set of poits satisfyig the equatio z a z + a = 2c, where c > 0 ad a R. To begi, we see from the triagle
More informationInfinite Sequences and Series
Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet
More informationProblem Cosider the curve give parametrically as x = si t ad y = + cos t for» t» ß: (a) Describe the path this traverses: Where does it start (whe t =
Mathematics Summer Wilso Fial Exam August 8, ANSWERS Problem 1 (a) Fid the solutio to y +x y = e x x that satisfies y() = 5 : This is already i the form we used for a first order liear differetial equatio,
More information10 Lecture, 5 October 1999
10 Lecture, 5 October 1999 10.1 Aberration compensation for spherical primaries: the Schmidt camera All-reflecting optical systems are called catoptric; all-refracting systems are called dioptric. Mixed
More informationOverview of Aberrations
Overview of Aberrations Lens Design OPTI 57 Aberration From the Latin, aberrare, to wander from; Latin, ab, away, errare, to wander. Symmetry properties Overview of Aberrations (Departures from ideal behavior)
More informationChapter 4. Fourier Series
Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,
More informationSection 19. Dispersing Prisms
Sectio 9 Dispersig Prisms 9- Dispersig Prism 9- The et ray deviatio is the sum of the deviatios at the two surfaces. The ray deviatio as a fuctio of the iput agle : si si si cossi Prism Deviatio - Derivatio
More informationSection 19. Dispersing Prisms
19-1 Sectio 19 Dispersig Prisms Dispersig Prism 19-2 The et ray deviatio is the sum of the deviatios at the two surfaces. The ray deviatio as a fuctio of the iput agle : 1 2 2 si si si cossi Prism Deviatio
More informationThe z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j
The -Trasform 7. Itroductio Geeralie the complex siusoidal represetatio offered by DTFT to a represetatio of complex expoetial sigals. Obtai more geeral characteristics for discrete-time LTI systems. 7.
More informationSECTION 2 Electrostatics
SECTION Electrostatics This sectio, based o Chapter of Griffiths, covers effects of electric fields ad forces i static (timeidepedet) situatios. The topics are: Electric field Gauss s Law Electric potetial
More information(s)h(s) = K( s + 8 ) = 5 and one finite zero is located at z 1
ROOT LOCUS TECHNIQUE 93 should be desiged differetly to eet differet specificatios depedig o its area of applicatio. We have observed i Sectio 6.4 of Chapter 6, how the variatio of a sigle paraeter like
More informationWe will conclude the chapter with the study a few methods and techniques which are useful
Chapter : Coordiate geometry: I this chapter we will lear about the mai priciples of graphig i a dimesioal (D) Cartesia system of coordiates. We will focus o drawig lies ad the characteristics of the graphs
More informationMATH 31B: MIDTERM 2 REVIEW
MATH 3B: MIDTERM REVIEW JOE HUGHES. Evaluate x (x ) (x 3).. Partial Fractios Solutio: The umerator has degree less tha the deomiator, so we ca use partial fractios. Write x (x ) (x 3) = A x + A (x ) +
More informationChapter 1. Complex Numbers. Dr. Pulak Sahoo
Chapter 1 Complex Numbers BY Dr. Pulak Sahoo Assistat Professor Departmet of Mathematics Uiversity Of Kalyai West Begal, Idia E-mail : sahoopulak1@gmail.com 1 Module-2: Stereographic Projectio 1 Euler
More informationChapter 9: Numerical Differentiation
178 Chapter 9: Numerical Differetiatio Numerical Differetiatio Formulatio of equatios for physical problems ofte ivolve derivatives (rate-of-chage quatities, such as velocity ad acceleratio). Numerical
More informationMa 530 Infinite Series I
Ma 50 Ifiite Series I Please ote that i additio to the material below this lecture icorporated material from the Visual Calculus web site. The material o sequeces is at Visual Sequeces. (To use this li
More informationCourse Outline. Designing Control Systems. Proportional Controller. Amme 3500 : System Dynamics and Control. Root Locus. Dr. Stefan B.
Amme 3500 : System Dyamics ad Cotrol Root Locus Course Outlie Week Date Cotet Assigmet Notes Mar Itroductio 8 Mar Frequecy Domai Modellig 3 5 Mar Trasiet Performace ad the s-plae 4 Mar Block Diagrams Assig
More informationPrecalculus MATH Sections 3.1, 3.2, 3.3. Exponential, Logistic and Logarithmic Functions
Precalculus MATH 2412 Sectios 3.1, 3.2, 3.3 Epoetial, Logistic ad Logarithmic Fuctios Epoetial fuctios are used i umerous applicatios coverig may fields of study. They are probably the most importat group
More informationSection 1.1. Calculus: Areas And Tangents. Difference Equations to Differential Equations
Differece Equatios to Differetial Equatios Sectio. Calculus: Areas Ad Tagets The study of calculus begis with questios about chage. What happes to the velocity of a swigig pedulum as its positio chages?
More informationSignal Processing. Lecture 02: Discrete Time Signals and Systems. Ahmet Taha Koru, Ph. D. Yildiz Technical University.
Sigal Processig Lecture 02: Discrete Time Sigals ad Systems Ahmet Taha Koru, Ph. D. Yildiz Techical Uiversity 2017-2018 Fall ATK (YTU) Sigal Processig 2017-2018 Fall 1 / 51 Discrete Time Sigals Discrete
More informationSigma notation. 2.1 Introduction
Sigma otatio. Itroductio We use sigma otatio to idicate the summatio process whe we have several (or ifiitely may) terms to add up. You may have see sigma otatio i earlier courses. It is used to idicate
More informationA second look at separation of variables
A secod look at separatio of variables Assumed that u(x,t)x(x)t(t) Pluggig this ito the wave, diffusio, or Laplace equatio gave two ODEs The solutios to those ODEs subject to boudary ad iitial coditios
More informationMTH Assignment 1 : Real Numbers, Sequences
MTH -26 Assigmet : Real Numbers, Sequeces. Fid the supremum of the set { m m+ : N, m Z}. 2. Let A be a o-empty subset of R ad α R. Show that α = supa if ad oly if α is ot a upper boud of A but α + is a
More informationSection 11.8: Power Series
Sectio 11.8: Power Series 1. Power Series I this sectio, we cosider geeralizig the cocept of a series. Recall that a series is a ifiite sum of umbers a. We ca talk about whether or ot it coverges ad i
More informationAssignment 1 : Real Numbers, Sequences. for n 1. Show that (x n ) converges. Further, by observing that x n+2 + x n+1
Assigmet : Real Numbers, Sequeces. Let A be a o-empty subset of R ad α R. Show that α = supa if ad oly if α is ot a upper boud of A but α + is a upper boud of A for every N. 2. Let y (, ) ad x (, ). Evaluate
More information3. Newton s Rings. Background. Aim of the experiment. Apparatus required. Theory. Date : Newton s Rings
ate : Newto s igs 3. Newto s igs Backgroud Coheret light Phase relatioship Path differece Iterferece i thi fil Newto s rig apparatus Ai of the experiet To study the foratio of Newto s rigs i the air-fil
More informationSequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018
CSE 353 Discrete Computatioal Structures Sprig 08 Sequeces, Mathematical Iductio, ad Recursio (Chapter 5, Epp) Note: some course slides adopted from publisher-provided material Overview May mathematical
More informationNotes 8 Singularities
ECE 6382 Fall 27 David R. Jackso Notes 8 Sigularities Notes are from D. R. Wilto, Dept. of ECE Sigularity A poit s is a sigularity of the fuctio f () if the fuctio is ot aalytic at s. (The fuctio does
More information-ORDER CONVERGENCE FOR FINDING SIMPLE ROOT OF A POLYNOMIAL EQUATION
NEW NEWTON-TYPE METHOD WITH k -ORDER CONVERGENCE FOR FINDING SIMPLE ROOT OF A POLYNOMIAL EQUATION R. Thukral Padé Research Cetre, 39 Deaswood Hill, Leeds West Yorkshire, LS7 JS, ENGLAND ABSTRACT The objective
More informationTwo or more points can be used to describe a rigid body. This will eliminate the need to define rotational coordinates for the body!
OINTCOORDINATE FORMULATION Two or more poits ca be used to describe a rigid body. This will elimiate the eed to defie rotatioal coordiates for the body i z r i i, j r j j rimary oits: The coordiates of
More informationDesign and Correction of Optical Systems
Desig ad Correctio of Optical Systems Lecture : Materials ad compoets 07-04-4 Herbert Gross Summer term 07 www.iap.ui-jea.de Prelimiary Schedule - DCS 07 07.04. Basics.04. Materials ad Compoets 3 8.04.
More informationIntroductions to HarmonicNumber2
Itroductios to HarmoicNumber2 Itroductio to the differetiated gamma fuctios Geeral Almost simultaeously with the developmet of the mathematical theory of factorials, biomials, ad gamma fuctios i the 8th
More informationAppendix: The Laplace Transform
Appedix: The Laplace Trasform The Laplace trasform is a powerful method that ca be used to solve differetial equatio, ad other mathematical problems. Its stregth lies i the fact that it allows the trasformatio
More informationMATH spring 2008 lecture 3 Answers to selected problems. 0 sin14 xdx = x dx. ; (iv) x +
MATH - sprig 008 lecture Aswers to selected problems INTEGRALS. f =? For atiderivatives i geeral see the itegrals website at http://itegrals.wolfram.com. (5-vi (0 i ( ( i ( π ; (v π a. This is example
More informationCS 270 Algorithms. Oliver Kullmann. Growth of Functions. Divide-and- Conquer Min-Max- Problem. Tutorial. Reading from CLRS for week 2
Geeral remarks Week 2 1 Divide ad First we cosider a importat tool for the aalysis of algorithms: Big-Oh. The we itroduce a importat algorithmic paradigm:. We coclude by presetig ad aalysig two examples.
More informationSection A assesses the Units Numerical Analysis 1 and 2 Section B assesses the Unit Mathematics for Applied Mathematics
X0/70 NATIONAL QUALIFICATIONS 005 MONDAY, MAY.00 PM 4.00 PM APPLIED MATHEMATICS ADVANCED HIGHER Numerical Aalysis Read carefully. Calculators may be used i this paper.. Cadidates should aswer all questios.
More information1988 AP Calculus BC: Section I
988 AP Calculus BC: Sectio I 9 Miutes No Calculator Notes: () I this eamiatio, l deotes the atural logarithm of (that is, logarithm to the base e). () Uless otherwise specified, the domai of a fuctio f
More informationProbability and statistics: basic terms
Probability ad statistics: basic terms M. Veeraraghava August 203 A radom variable is a rule that assigs a umerical value to each possible outcome of a experimet. Outcomes of a experimet form the sample
More informationMath 257: Finite difference methods
Math 257: Fiite differece methods 1 Fiite Differeces Remember the defiitio of a derivative f f(x + ) f(x) (x) = lim 0 Also recall Taylor s formula: (1) f(x + ) = f(x) + f (x) + 2 f (x) + 3 f (3) (x) +...
More informationMarkov Decision Processes
Markov Decisio Processes Defiitios; Statioary policies; Value improvemet algorithm, Policy improvemet algorithm, ad liear programmig for discouted cost ad average cost criteria. Markov Decisio Processes
More informationOptics. n n. sin. 1. law of rectilinear propagation 2. law of reflection = 3. law of refraction
Optics What is light? Visible electromagetic radiatio Geometrical optics (model) Light-ray: extremely thi parallel light beam Usig this model, the explaatio of several optical pheomea ca be give as the
More informationChapter 6 Overview: Sequences and Numerical Series. For the purposes of AP, this topic is broken into four basic subtopics:
Chapter 6 Overview: Sequeces ad Numerical Series I most texts, the topic of sequeces ad series appears, at first, to be a side topic. There are almost o derivatives or itegrals (which is what most studets
More informationNotes 12 Asymptotic Series
ECE 6382 Fall 207 David R. Jackso otes 2 Asymptotic Series Asymptotic Series A asymptotic series (as ) is of the form a ( ) f as = 0 or f a + a a + + ( ) 2 0 2 ote the asymptotically equal to sig. The
More informationMechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter
Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,
More informationChapter 7: Numerical Series
Chapter 7: Numerical Series Chapter 7 Overview: Sequeces ad Numerical Series I most texts, the topic of sequeces ad series appears, at first, to be a side topic. There are almost o derivatives or itegrals
More informationRotationally invariant integrals of arbitrary dimensions
September 1, 14 Rotatioally ivariat itegrals of arbitrary dimesios James D. Wells Physics Departmet, Uiversity of Michiga, A Arbor Abstract: I this ote itegrals over spherical volumes with rotatioally
More informationChapter 4 : Laplace Transform
4. Itroductio Laplace trasform is a alterative to solve the differetial equatio by the complex frequecy domai ( s = σ + jω), istead of the usual time domai. The DE ca be easily trasformed ito a algebraic
More informationOptics.
Optics www.optics.rochester.edu/classes/opt100/opt100page.html Course outline Light is a Ray (Geometrical Optics) 1. Nature of light 2. Production and measurement of light 3. Geometrical optics 4. Matrix
More information1.3 Convergence Theorems of Fourier Series. k k k k. N N k 1. With this in mind, we state (without proof) the convergence of Fourier series.
.3 Covergece Theorems of Fourier Series I this sectio, we preset the covergece of Fourier series. A ifiite sum is, by defiitio, a limit of partial sums, that is, a cos( kx) b si( kx) lim a cos( kx) b si(
More informationGoal. Adaptive Finite Element Methods for Non-Stationary Convection-Diffusion Problems. Outline. Differential Equation
Goal Adaptive Fiite Elemet Methods for No-Statioary Covectio-Diffusio Problems R. Verfürth Ruhr-Uiversität Bochum www.ruhr-ui-bochum.de/um1 Tübige / July 0th, 017 Preset space-time adaptive fiite elemet
More informationChapter 10: Power Series
Chapter : Power Series 57 Chapter Overview: Power Series The reaso series are part of a Calculus course is that there are fuctios which caot be itegrated. All power series, though, ca be itegrated because
More informationLaw of the sum of Bernoulli random variables
Law of the sum of Beroulli radom variables Nicolas Chevallier Uiversité de Haute Alsace, 4, rue des frères Lumière 68093 Mulhouse icolas.chevallier@uha.fr December 006 Abstract Let be the set of all possible
More informationDesign and Correction of Optical Systems
Desig ad Correctio of Optical Sstems Lecture 3: Paraial optics 207-04-28 Herbert Gross Summer term 207 www.iap.ui-ea.de 2 Prelimiar Schedule - DCS 207 07.04. Basics 2 2.04. Materials ad Compoets 3 28.04.
More informationThe target reliability and design working life
Safety ad Security Egieerig IV 161 The target reliability ad desig workig life M. Holický Kloker Istitute, CTU i Prague, Czech Republic Abstract Desig workig life ad target reliability levels recommeded
More informationStatistics 511 Additional Materials
Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability
More informationProperties and Tests of Zeros of Polynomial Functions
Properties ad Tests of Zeros of Polyomial Fuctios The Remaider ad Factor Theorems: Sythetic divisio ca be used to fid the values of polyomials i a sometimes easier way tha substitutio. This is show by
More informationDesign of multiplexed phase diffractive optical elements for focal depth extension
Desig of multiplexed phase diffractive optical elemets for focal depth extesio Hua Liu, Zhewu Lu,,* Qiag Su ad Hu Zhag,,2 Opto_electroics techology ceter, Chagchu Istitute of Optics ad Fie Mechaics ad
More informationLinearly Independent Sets, Bases. Review. Remarks. A set of vectors,,, in a vector space is said to be linearly independent if the vector equation
Liearly Idepedet Sets Bases p p c c p Review { v v vp} A set of vectors i a vector space is said to be liearly idepedet if the vector equatio cv + c v + + c has oly the trivial solutio = = { v v vp} The
More informationProf. Jose Sasian OPTI 518. Introduction to aberrations OPTI 518 Lecture 14
Introduction to aberrations Lecture 14 Topics Structural aberration coefficients Examples Structural coefficients Ж Requires a focal system Afocal systems can be treated with Seidel sums Structural stop
More informationLesson 10: Limits and Continuity
www.scimsacademy.com Lesso 10: Limits ad Cotiuity SCIMS Academy 1 Limit of a fuctio The cocept of limit of a fuctio is cetral to all other cocepts i calculus (like cotiuity, derivative, defiite itegrals
More informationCHAPTER I: Vector Spaces
CHAPTER I: Vector Spaces Sectio 1: Itroductio ad Examples This first chapter is largely a review of topics you probably saw i your liear algebra course. So why cover it? (1) Not everyoe remembers everythig
More informationApplication to Random Graphs
A Applicatio to Radom Graphs Brachig processes have a umber of iterestig ad importat applicatios. We shall cosider oe of the most famous of them, the Erdős-Réyi radom graph theory. 1 Defiitio A.1. Let
More informationAppendix K. The three-point correlation function (bispectrum) of density peaks
Appedix K The three-poit correlatio fuctio (bispectrum) of desity peaks Cosider the smoothed desity field, ρ (x) ρ [ δ (x)], with a geeral smoothig kerel W (x) δ (x) d yw (x y)δ(y). (K.) We defie the peaks
More information( 1) n (4x + 1) n. n=0
Problem 1 (10.6, #). Fid the radius of covergece for the series: ( 1) (4x + 1). For what values of x does the series coverge absolutely, ad for what values of x does the series coverge coditioally? Solutio.
More informationSolving equations (incl. radical equations) involving these skills, but ultimately solvable by factoring/quadratic formula (no complex roots)
Evet A: Fuctios ad Algebraic Maipulatio Factorig Square of a sum: ( a + b) = a + ab + b Square of a differece: ( a b) = a ab + b Differece of squares: a b = ( a b )(a + b ) Differece of cubes: a 3 b 3
More informationAH Checklist (Unit 3) AH Checklist (Unit 3) Matrices
AH Checklist (Uit 3) AH Checklist (Uit 3) Matrices Skill Achieved? Kow that a matrix is a rectagular array of umbers (aka etries or elemets) i paretheses, each etry beig i a particular row ad colum Kow
More informationEllipsoid Method for Linear Programming made simple
Ellipsoid Method for Liear Programmig made simple Sajeev Saxea Dept. of Computer Sciece ad Egieerig, Idia Istitute of Techology, Kapur, INDIA-08 06 December 3, 07 Abstract I this paper, ellipsoid method
More informationSubject: Differential Equations & Mathematical Modeling -III. Lesson: Power series solutions of Differential Equations. about ordinary points
Power series solutio of Differetial equatios about ordiary poits Subject: Differetial Equatios & Mathematical Modelig -III Lesso: Power series solutios of Differetial Equatios about ordiary poits Lesso
More informationExponential Moving Average Pieter P
Expoetial Movig Average Pieter P Differece equatio The Differece equatio of a expoetial movig average lter is very simple: y[] x[] + (1 )y[ 1] I this equatio, y[] is the curret output, y[ 1] is the previous
More informationChapter 6 Principles of Data Reduction
Chapter 6 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 0 Chapter 6 Priciples of Data Reductio Sectio 6. Itroductio Goal: To summarize or reduce the data X, X,, X to get iformatio about a
More informationA Quantitative Lusin Theorem for Functions in BV
A Quatitative Lusi Theorem for Fuctios i BV Adrás Telcs, Vicezo Vespri November 19, 013 Abstract We exted to the BV case a measure theoretic lemma previously proved by DiBeedetto, Giaazza ad Vespri ([1])
More informationEfficient GMM LECTURE 12 GMM II
DECEMBER 1 010 LECTURE 1 II Efficiet The estimator depeds o the choice of the weight matrix A. The efficiet estimator is the oe that has the smallest asymptotic variace amog all estimators defied by differet
More information6.867 Machine learning, lecture 7 (Jaakkola) 1
6.867 Machie learig, lecture 7 (Jaakkola) 1 Lecture topics: Kerel form of liear regressio Kerels, examples, costructio, properties Liear regressio ad kerels Cosider a slightly simpler model where we omit
More informationECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations
ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows
More informationZeros of Polynomials
Math 160 www.timetodare.com 4.5 4.6 Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered with fidig the solutios of polyomial equatios of ay degree
More informationFormation of A Supergain Array and Its Application in Radar
Formatio of A Supergai Array ad ts Applicatio i Radar Tra Cao Quye, Do Trug Kie ad Bach Gia Duog. Research Ceter for Electroic ad Telecommuicatios, College of Techology (Coltech, Vietam atioal Uiversity,
More informationCHAPTER 1 SEQUENCES AND INFINITE SERIES
CHAPTER SEQUENCES AND INFINITE SERIES SEQUENCES AND INFINITE SERIES (0 meetigs) Sequeces ad limit of a sequece Mootoic ad bouded sequece Ifiite series of costat terms Ifiite series of positive terms Alteratig
More informationApply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.
Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α
More informationINF-GEO Solutions, Geometrical Optics, Part 1
INF-GEO430 20 Solutios, Geometrical Optics, Part Reflectio by a symmetric triagular prism Let be the agle betwee the two faces of a symmetric triagular prism. Let the edge A where the two faces meet be
More informationMATH 1080: Calculus of One Variable II Fall 2017 Textbook: Single Variable Calculus: Early Transcendentals, 7e, by James Stewart.
MATH 1080: Calculus of Oe Variable II Fall 2017 Textbook: Sigle Variable Calculus: Early Trascedetals, 7e, by James Stewart Uit 3 Skill Set Importat: Studets should expect test questios that require a
More informationCHAPTER 10 INFINITE SEQUENCES AND SERIES
CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece
More informationMA Lesson 26 Notes Graphs of Rational Functions (Asymptotes) Limits at infinity
MA 1910 Lesso 6 Notes Graphs of Ratioal Fuctios (Asymptotes) Limits at ifiity Defiitio of a Ratioal Fuctio: If P() ad Q() are both polyomial fuctios, Q() 0, the the fuctio f below is called a Ratioal Fuctio.
More informationOptimally Sparse SVMs
A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but
More informationf(w) w z =R z a 0 a n a nz n Liouville s theorem, we see that Q is constant, which implies that P is constant, which is a contradiction.
Theorem 3.6.4. [Liouville s Theorem] Every bouded etire fuctio is costat. Proof. Let f be a etire fuctio. Suppose that there is M R such that M for ay z C. The for ay z C ad R > 0 f (z) f(w) 2πi (w z)
More informationOptical/IR Observational Astronomy Telescopes I: Telescope Basics. David Buckley, SAAO
David Buckley, SAAO 17 Feb 2010 1 Some other Telescope Parameters 1. Plate Scale This defines the scale of an image at the telescopes focal surface For a focal plane, with no distortion, this is just related
More informationChapter 7 Isoperimetric problem
Chapter 7 Isoperimetric problem Recall that the isoperimetric problem (see the itroductio its coectio with ido s proble) is oe of the most classical problem of a shape optimizatio. It ca be formulated
More informationA Note on the Symmetric Powers of the Standard Representation of S n
A Note o the Symmetric Powers of the Stadard Represetatio of S David Savitt 1 Departmet of Mathematics, Harvard Uiversity Cambridge, MA 0138, USA dsavitt@mathharvardedu Richard P Staley Departmet of Mathematics,
More information