Section 6. Object-Image Relationships
|
|
- Egbert Wood
- 5 years ago
- Views:
Transcription
1 6-1 Section 6 Object-Image elationships
2 Object-Image elationships The purpose o this study is to examine the imaging properties o the general system that has been deined by its Gaussian properties and cardinal points. Dierent combinations o ront and rear ocal lengths can be studied: 0 0 ositive ocal System 0 0 Negative ocal System 0 0 ositive ocal System; Net elective Negative ocal System; Net elective
3 Object-Image elationships In Terms o the ront and ear ocal Lengths Newtonian Equations (Origins at, '): Gaussian Equations (Origins at, '): 1 m mm 1 2 m 1 1 1m m 1 m 6-3 mm 1 2 Aocal Systems: 2 2 m 1 1 n m m m 1 2 n
4 Object-Image Zones Newtonian Equations (Origins at, ') Case A: < 0 Object to the Let o m 1 m m 0 0 m0 0 m0 m 0 m mm
5 ositive ocal System Object to the Let o eal Object eal Image A B C D 0 ' 0 m ' 0 (Newtonian distances) A' B' C' D' 6-5 This representation hides the act that both the object and image spaces are separate and extend rom minus ininity to plus ininity. In addition, the physical relationship between the locations o the ront and ear rincipal lanes on the system coniguration (number and type o elements including relective, spacings and thicknesses, reractive indices, etc.). hysically, ' can be to the right o, to the let o or coincident with. It depends on the system coniguration.
6 ositive ocal System Object to the Let o eal Object eal Image A B C D 0 0 m (Newtonian distances) 6-6 ' ' A' B' C' D' Images are inverted Objects and images are in the same order
7 Object-Image Zones Newtonian Equations (Origins at, ') Case B: > 0 Object to the ight o m 1 m m 0 0 m0 0 m0 m 0 m mm
8 ositive ocal System Object between and eal Object Virtual Image 0 A B C 0 m0 ( m1) 0 0 ( ) (Newtonian distances) A' B' C' ' ' Images are erect and magniied Objects and images are in the same order
9 ositive ocal System Object to the ight o Virtual Object eal Image 0 m0 (0m1) 0 0 ( 0) (Newtonian distances) A B C ' A' B'C' ' Images are erect and miniied Objects and images are in the same order
10 ositive ocal System Zones A B C ' ' B' C' m 1 1m 0 A' m 0 Image Space Object Space
11 Negative ocal System Object to the Let o eal Object Virtual Image A B C D 0 m0 (0m1) 0 0 (0 ) (Newtonian distances) ' A' B' C' D' ' Images are erect and miniied Objects and images are in the same order
12 Negative ocal System Object Between and Virtual Object eal Image 0 A B 0 m0 ( m1) 0 0 ( ) (Newtonian distances) C ' ' A' B' C' Images are erect and magniied Objects and images are in the same order
13 Negative ocal System Object to the ight o Virtual Object Virtual Image 0 0 m (Newtonian distances) A B C D 6-13 A' B' C' D' ' ' Images are inverted Objects and images are in the same order
14 Negative ocal System Zones 6-14 A B C 0 0 ' ' C' A' B' Image Space Object Space m 0 0 m 1 m 1
15 ositive ocal System elective Object to the Let o eal Object eal Image A B C D 0 0 m (Newtonian distances) 6-15 D' C' B'A' ' ' Images are inverted Objects and images are in the opposite order
16 ositive ocal System elective Object between and eal Object Virtual Image 0 A B C 0 m0 ( m1) 0 0 ( ) (Newtonian distances) ' ' C' B' A' Images are erect and magniied Objects and images are in the opposite order
17 This image cannot currently be displayed. This image cannot currently be displayed. This image cannot currently be displayed. This image cannot currently be displayed. This image cannot currently be displayed. ositive ocal System elective Object to the ight o Virtual Object eal Image A B C 6-17 ' C'B' A' ' Images are erect and miniied Objects and images are in the opposite order
18 ositive ocal System elective Zones A B C ' ' A' C' B' Image Space Object Space m 0 0 m 1 m 1
19 Object-Image Zones Summary ositive ocal System A B B C m > 1 1 > m > 0 ositive ocal System elective A A C A A B C A B B A m > 1 1 > m > 0 C m < 0 0 < m < 1 m > 1 Negative ocal System m < 0 0 < m < 1 m > 1 B Negative ocal System - elective C B B A m < 0 C C 0 0 C m < eal Virtual The object-image ones show the general image properties as a unction o the object location relative to the cardinal points An object in Zone A will map to an image in Zone A, etc. All optical spaces extend rom to. A net relective system (an odd number o relections) inverts image space about.
20 Object Space/Image Space Mapping ocal Systems m 1 and m 2 are the lateral magniications or the two planes Newtonian Equations (distances measured rom, ') 1 m m mm 1 2 m m The magniication is proportional to the Newtonian image distance and inversely proportional to the Newtonian object distance When Δ is small, the longitudinal magniication is obtained m m m 1 2 m m 2 m m The image space spacing is proportional to the Newtonian image distance squared and inversely proportional to the Newtonian object distance squared.
21 Object Space/Image Space Mapping ositive ocal System A B C ' ' B' C' A'
22 5-22 Object Space/Image Space Mapping Negative ocal System 0 A B C 0 ' ' C' A' B'
23 Collinear Transormation A collinear transormation maps points to points, lines to lines, and planes to planes. The general mapping equations associated with a collinear transormation are: x y axbycd axbycd axbycd axbycd axbycd axbycd By applying the symmetries associated with a rotationally-symmetric system and the deinitions o the magniication and the cardinal points, all o the relationships o Gaussian imagery can be derived (or both ocal and aocal systems) rom these general mapping equations These derivations have been prepared by ro. oland Shack and are included as Appendix A to these notes.
Section 12. Afocal Systems
OPTI-0/0 Geoetrical and Instruental Optics Copyrigt 08 Jon E. Greivenkap - Section Aocal Systes Gaussian Optics Teores In te initial discussion o Gaussian optics, one o te teores deined te two dierent
More informationOPTI 201R Midterm 2 November 10, Problems 1-10 are worth 2.5 points each. Problems are worth 25 points each.
OPTI 20R Midterm 2 November 0, 205 Answer all questions Show your work Partial credit will be given Don t spend too much time on any one problem Use separate sheets o paper and don t cram your work into
More informationLecture 10. Lecture 10
Lecture 0 Telescope [Reading assignment: Hect 5.7.4, 5.7.7] A telescope enlarges te apparent size o a distant object so tat te image subtends a larger angle (rom te eye) tan does te object. Te telescope
More informationnr 2 nr 4 Correct Answer 1 Explanation If mirror is rotated by anglethan beeping incident ray fixed, reflected ray rotates by 2 Option 4
Q. No. A small plane mirror is placed at the centero a spherical screen o radius R. A beam o light is alling on the mirror. I the mirror makes n revolution per second, the speed o light on the screen ater
More informationNCERT-XII / Unit- 09 Ray Optics
REFLECTION OF LIGHT The laws o relection are.. (i) The incident ray, relected ray and the normal to the relecting surace at the point o incidence lie in the same plane (ii) The angle o relection (i.e.,
More informationAnalytical expressions for field astigmatism in decentered two mirror telescopes and application to the collimation of the ESO VLT
ASTRONOMY & ASTROPHYSICS MAY II 000, PAGE 57 SUPPLEMENT SERIES Astron. Astrophys. Suppl. Ser. 44, 57 67 000) Analytical expressions or ield astigmatism in decentered two mirror telescopes and application
More informationClicker questions. Clicker question 2. Clicker Question 1. Clicker question 2. Clicker question 1. the answers are in the lower right corner
licker questions the answers are in the lower right corner question wave on a string goes rom a thin string to a thick string. What picture best represents the wave some time ater hitting the boundary?
More informationA Fourier Transform Model in Excel #1
A Fourier Transorm Model in Ecel # -This is a tutorial about the implementation o a Fourier transorm in Ecel. This irst part goes over adjustments in the general Fourier transorm ormula to be applicable
More information( x) f = where P and Q are polynomials.
9.8 Graphing Rational Functions Lets begin with a deinition. Deinition: Rational Function A rational unction is a unction o the orm ( ) ( ) ( ) P where P and Q are polynomials. Q An eample o a simple rational
More information1. Interference condition. 2. Dispersion A B. As shown in Figure 1, the path difference between interfering rays AB and A B is a(sin
asic equations or astronomical spectroscopy with a diraction grating Jeremy Allington-Smith, University o Durham, 3 Feb 000 (Copyright Jeremy Allington-Smith, 000). Intererence condition As shown in Figure,
More informationGaussian imaging transformation for the paraxial Debye formulation of the focal region in a low-fresnel-number optical system
Zapata-Rodríguez et al. Vol. 7, No. 7/July 2000/J. Opt. Soc. Am. A 85 Gaussian imaging transormation or the paraxial Debye ormulation o the ocal region in a low-fresnel-number optical system Carlos J.
More informationPart I: Thin Converging Lens
Laboratory 1 PHY431 Fall 011 Part I: Thin Converging Lens This eperiment is a classic eercise in geometric optics. The goal is to measure the radius o curvature and ocal length o a single converging lens
More informationZERO-DISTANCE PULSE FRONTS OF STRETCHER AND ITS OPTICAL SYSTEM
ERODISTANCE PULSE RONTS O STRETCHER AND ITS OPTICAL SYSTEM Author: DOI: 10.12684/alt.1.70 Corresponding author: email: agitin@mbiberlin.de erodistance Pulse ronts o a Stretcher and its Optical System Max
More informationYour Comments. I was not sure about the angle at which the beam reflects from a concave mirror.
Your Comments This pre-lecture was almost a "mirror" image o the last one! This stu makes sense!!! Yay!!! I was not sure about the angle at which the beam relects rom a concave mirror. Keeping all these
More informationRelating axial motion of optical elements to focal shift
Relating aial motion o optical elements to ocal shit Katie Schwertz and J. H. Burge College o Optical Sciences, University o Arizona, Tucson AZ 857, USA katie.schwertz@gmail.com ABSTRACT In this paper,
More informationLEONARD EVENS. 2. Some History
c 2008 Leonard Evens VIEW CAMERA GEOMETRY LEONARD EVENS 1. introduction This article started as an exposition or mathematicians o the relation between projective geometry and view camera photography. The
More informationChapter 10 Light- Reflectiion & Refraction
Capter 0 Ligt- Relectiion & Reraction Intext Questions On Page 68 Question : Deine te principal ocus o a concae irror. Principal ocus o te concae irror: A point on principal axis on wic parallel ligt rays
More informationRelating axial motion of optical elements to focal shift
Relating aial motion o optical elements to ocal shit Katie Schwertz and J. H. Burge College o Optical Sciences, University o Arizona, Tucson AZ 857, USA katie.schwertz@gmail.com ABSTRACT In this paper,
More informationMaximum Flow. Reading: CLRS Chapter 26. CSE 6331 Algorithms Steve Lai
Maximum Flow Reading: CLRS Chapter 26. CSE 6331 Algorithms Steve Lai Flow Network A low network G ( V, E) is a directed graph with a source node sv, a sink node tv, a capacity unction c. Each edge ( u,
More informationFeasibility of a Multi-Pass Thomson Scattering System with Confocal Spherical Mirrors
Plasma and Fusion Research: Letters Volume 5, 044 200) Feasibility o a Multi-Pass Thomson Scattering System with Conocal Spherical Mirrors Junichi HIRATSUKA, Akira EJIRI, Yuichi TAKASE and Takashi YAMAGUCHI
More informationREFLECTION AND REFRACTION OF LIGHT
Relection and Reraction o Light MODULE - 6 20 REFLECTION AND REFRACTION OF LIGHT Light makes us to see things and is responsible or our visual contact with our immediate environment. It enables us to admire
More informationFunctions: Review of Algebra and Trigonometry
Sec. and. Functions: Review o Algebra and Trigonoetry A. Functions and Relations DEFN Relation: A set o ordered pairs. (,y) (doain, range) DEFN Function: A correspondence ro one set (the doain) to anther
More informationEffective Fresnel-number concept for evaluating the relative focal shift in focused beams
Martíne-Corral et al. Vol. 15, No. / February 1998/ J. Opt. Soc. Am. A 449 Eective Fresnel-number concept or evaluating the relative ocal shit in ocused beams Manuel Martíne-Corral, Carlos J. Zapata-Rodrígue,
More informationSingular Frégier Conics in Non-Euclidean Geometry
Singular Frégier onics in on-euclidean Geometry Hans-Peter Schröcker University o Innsbruck, Austria arxiv:1605.07437v1 [math.mg] 24 May 2016 May 25, 2016 The hyotenuses o all right triangles inscribed
More informationSaturday X-tra X-Sheet: 8. Inverses and Functions
Saturda X-tra X-Sheet: 8 Inverses and Functions Ke Concepts In this session we will ocus on summarising what ou need to know about: How to ind an inverse. How to sketch the inverse o a graph. How to restrict
More informationSection 7. Gaussian Reduction
7- Sectio 7 Gaussia eductio Paraxial aytrace Equatios eractio occurs at a iterace betwee two optical spaces. The traser distace t' allows the ray height y' to be determied at ay plae withi a optical space
More informationTLT-5200/5206 COMMUNICATION THEORY, Exercise 3, Fall TLT-5200/5206 COMMUNICATION THEORY, Exercise 3, Fall Problem 1.
TLT-5/56 COMMUNICATION THEORY, Exercise 3, Fall Problem. The "random walk" was modelled as a random sequence [ n] where W[i] are binary i.i.d. random variables with P[W[i] = s] = p (orward step with probability
More informationMath-3 Lesson 1-4. Review: Cube, Cube Root, and Exponential Functions
Math- Lesson -4 Review: Cube, Cube Root, and Eponential Functions Quiz - Graph (no calculator):. y. y ( ) 4. y What is a power? vocabulary Power: An epression ormed by repeated Multiplication o the same
More informationExample: When describing where a function is increasing, decreasing or constant we use the x- axis values.
Business Calculus Lecture Notes (also Calculus With Applications or Business Math II) Chapter 3 Applications o Derivatives 31 Increasing and Decreasing Functions Inormal Deinition: A unction is increasing
More informationProblem Set. Problems on Unordered Summation. Math 5323, Fall Februray 15, 2001 ANSWERS
Problem Set Problems on Unordered Summation Math 5323, Fall 2001 Februray 15, 2001 ANSWERS i 1 Unordered Sums o Real Terms In calculus and real analysis, one deines the convergence o an ininite series
More informationFORMAL LANGUAGES, AUTOMATA AND COMPUTABILITY. FLAC (15-453) Spring l. Blum TIME COMPLEXITY AND POLYNOMIAL TIME;
15-453 TIME COMPLEXITY AND POLYNOMIAL TIME; FORMAL LANGUAGES, AUTOMATA AND COMPUTABILITY NON DETERMINISTIC TURING MACHINES AND NP THURSDAY Mar 20 COMPLEXITY THEORY Studies what can and can t be computed
More informationLecture 18. Waves and Sound
Lecture 18 Waves and Sound Today s Topics: Nature o Waves Periodic Waves Wave Speed The Nature o Sound Speed o Sound Sound ntensity The Doppler Eect Disturbance Wave Motion DEMO: Rope A wave is a traveling
More informationMesa College Math SAMPLES
Mesa College Math 6 - SAMPLES Directions: NO CALCULATOR. Write neatly, show your work and steps. Label your work so it s easy to ollow. Answers without appropriate work will receive NO credit. For inal
More informationWe would now like to turn our attention to a specific family of functions, the one to one functions.
9.6 Inverse Functions We would now like to turn our attention to a speciic amily o unctions, the one to one unctions. Deinition: One to One unction ( a) (b A unction is called - i, or any a and b in the
More informationENSC327 Communications Systems 2: Fourier Representations. School of Engineering Science Simon Fraser University
ENSC37 Communications Systems : Fourier Representations School o Engineering Science Simon Fraser University Outline Chap..5: Signal Classiications Fourier Transorm Dirac Delta Function Unit Impulse Fourier
More informationEEO 401 Digital Signal Processing Prof. Mark Fowler
EEO 401 Digital Signal Processing Pro. Mark Fowler Note Set #10 Fourier Analysis or DT Signals eading Assignment: Sect. 4.2 & 4.4 o Proakis & Manolakis Much o Ch. 4 should be review so you are expected
More informationSpatial Vector Algebra
A Short Course on The Easy Way to do Rigid Body Dynamics Roy Featherstone Dept. Inormation Engineering, RSISE The Australian National University Spatial vector algebra is a concise vector notation or describing
More information( ) x y z. 3 Functions 36. SECTION D Composite Functions
3 Functions 36 SECTION D Composite Functions By the end o this section you will be able to understand what is meant by a composite unction ind composition o unctions combine unctions by addition, subtraction,
More informationBeam Propagation Hazard Calculations for Telescopic Viewing of Laser Beams
ILSC 003 Conerence Proceeings Beam Propagation Hazar Calculations or Telescopic Vieing o Laser Beams Ulrie Grabner, Georg Vees an Karl Schulmeister Please register to receive our Laser, LED & Lamp Saety
More informationReview of Prerequisite Skills for Unit # 2 (Derivatives) U2L2: Sec.2.1 The Derivative Function
UL1: Review o Prerequisite Skills or Unit # (Derivatives) Working with the properties o exponents Simpliying radical expressions Finding the slopes o parallel and perpendicular lines Simpliying rational
More informationObjectives. By the time the student is finished with this section of the workbook, he/she should be able
FUNCTIONS Quadratic Functions......8 Absolute Value Functions.....48 Translations o Functions..57 Radical Functions...61 Eponential Functions...7 Logarithmic Functions......8 Cubic Functions......91 Piece-Wise
More informationAnalog Computing Technique
Analog Computing Technique by obert Paz Chapter Programming Principles and Techniques. Analog Computers and Simulation An analog computer can be used to solve various types o problems. It solves them in
More informationSection 3.4: Concavity and the second Derivative Test. Find any points of inflection of the graph of a function.
Unit 3: Applications o Dierentiation Section 3.4: Concavity and the second Derivative Test Determine intervals on which a unction is concave upward or concave downward. Find any points o inlection o the
More informationFFTs in Graphics and Vision. Rotational and Reflective Symmetry Detection
FFTs in Grahics and Vision Rotational and Relectie Symmetry Detection Outline Reresentation Theory Symmetry Detection Rotational Symmetry Relectie Symmetry Reresentation Theory Recall: A rou is a set o
More information9.1 The Square Root Function
Section 9.1 The Square Root Function 869 9.1 The Square Root Function In this section we turn our attention to the square root unction, the unction deined b the equation () =. (1) We begin the section
More information1. Definition: Order Statistics of a sample.
AMS570 Order Statistics 1. Deinition: Order Statistics o a sample. Let X1, X2,, be a random sample rom a population with p.d.. (x). Then, 2. p.d.. s or W.L.O.G.(W thout Loss o Ge er l ty), let s ssu e
More information10. Joint Moments and Joint Characteristic Functions
10. Joint Moments and Joint Characteristic Functions Following section 6, in this section we shall introduce various parameters to compactly represent the inormation contained in the joint p.d. o two r.vs.
More informationIn many diverse fields physical data is collected or analysed as Fourier components.
1. Fourier Methods In many diverse ields physical data is collected or analysed as Fourier components. In this section we briely discuss the mathematics o Fourier series and Fourier transorms. 1. Fourier
More information1 Categories, Functors, and Natural Transformations. Discrete categories. A category is discrete when every arrow is an identity.
MacLane: Categories or Working Mathematician 1 Categories, Functors, and Natural Transormations 1.1 Axioms or Categories 1.2 Categories Discrete categories. A category is discrete when every arrow is an
More information8.4 Inverse Functions
Section 8. Inverse Functions 803 8. Inverse Functions As we saw in the last section, in order to solve application problems involving eponential unctions, we will need to be able to solve eponential equations
More informationPHYSICS 231 Sound PHY 231
PHYSICS 231 Sound 1 Sound: longitudinal waves A sound wave consist o longitudinal oscillations in the pressure o the medium that carries the sound wave. Thereore, in vacuum: there is no sound. 2 Relation
More informationOptical determination of field angular correlation for transmission through three-dimensional turbid media
1040 J. Opt. Soc. Am. A/Vol. 16, No. 5/May 1999 Brian G. Hoover Optical determination o ield angular correlation or transmission through three-dimensional turbid media Brian G. Hoover Department o Electrical
More informationFunctions. Essential Question What are some of the characteristics of the graph of a logarithmic function?
5. Logarithms and Logarithmic Functions Essential Question What are some o the characteristics o the graph o a logarithmic unction? Ever eponential unction o the orm () = b, where b is a positive real
More informationz-axis SUBMITTED BY: Ms. Harjeet Kaur Associate Professor Department of Mathematics PGGCG 11, Chandigarh y-axis x-axis
z-ais - - SUBMITTED BY: - -ais - - - - - - -ais Ms. Harjeet Kaur Associate Proessor Department o Mathematics PGGCG Chandigarh CONTENTS: Function o two variables: Deinition Domain Geometrical illustration
More informationOPTIMAL PLACEMENT AND UTILIZATION OF PHASOR MEASUREMENTS FOR STATE ESTIMATION
OPTIMAL PLACEMENT AND UTILIZATION OF PHASOR MEASUREMENTS FOR STATE ESTIMATION Xu Bei, Yeo Jun Yoon and Ali Abur Teas A&M University College Station, Teas, U.S.A. abur@ee.tamu.edu Abstract This paper presents
More informationEstimation of the Vehicle Sideslip Angle by Means of the State Dependent Riccati Equation Technique
Proceedings o the World Congress on Engineering 7 Vol II WCE 7, Jul 5-7, 7, London, U.K. Estimation o the Vehicle Sideslip Angle b Means o the State Dependent Riccati Equation Technique S. Strano, M. Terzo
More information8. INTRODUCTION TO STATISTICAL THERMODYNAMICS
n * D n d Fluid z z z FIGURE 8-1. A SYSTEM IS IN EQUILIBRIUM EVEN IF THERE ARE VARIATIONS IN THE NUMBER OF MOLECULES IN A SMALL VOLUME, SO LONG AS THE PROPERTIES ARE UNIFORM ON A MACROSCOPIC SCALE 8. INTRODUCTION
More informationLongitudinal Waves. Reading: Chapter 17, Sections 17-7 to Sources of Musical Sound. Pipe. Closed end: node Open end: antinode
Longitudinal Waes Reading: Chapter 7, Sections 7-7 to 7-0 Sources o Musical Sound Pipe Closed end: node Open end: antinode Standing wae pattern: Fundamental or irst harmonic: nodes at the ends, antinode
More informationDifferentiation. The main problem of differential calculus deals with finding the slope of the tangent line at a point on a curve.
Dierentiation The main problem o dierential calculus deals with inding the slope o the tangent line at a point on a curve. deinition() : The slope o a curve at a point p is the slope, i it eists, o the
More informationf sends 1 to 2, so f 1 sends 2 back to 1. f sends 2 to 4, so f 1 sends 4 back to 2. f 1 = { (2,1), (4,2), (6,3), (1,4), (3,5), (5,6) }.
.3 Inverse Functions an their Derivatives In this unit you ll review inverse unctions, how to in them, an how the graphs o unctions an their inverses are relate geometrically. Not all unctions can be unone,
More informationMath 2412 Activity 1(Due by EOC Sep. 17)
Math 4 Activity (Due by EOC Sep. 7) Determine whether each relation is a unction.(indicate why or why not.) Find the domain and range o each relation.. 4,5, 6,7, 8,8. 5,6, 5,7, 6,6, 6,7 Determine whether
More informationFunction Operations. I. Ever since basic math, you have been combining numbers by using addition, subtraction, multiplication, and division.
Function Operations I. Ever since basic math, you have been combining numbers by using addition, subtraction, multiplication, and division. Add: 5 + Subtract: 7 Multiply: (9)(0) Divide: (5) () or 5 II.
More informationAP Calculus AB Summer Assignment
AP Calculus AB Summer Assignment As Advanced placement students, our irst assignment or the 07-08 school ear is to come to class the ver irst da in top mathematical orm. Calculus is a world o change. While
More informationChapter 8 Conservation of Energy and Potential Energy
Chapter 8 Conservation o Energy and Potential Energy So ar we have analyzed the motion o point-like bodies under the action o orces using Newton s Laws o Motion. We shall now use the Principle o Conservation
More informationSpecial types of Riemann sums
Roberto s Notes on Subject Chapter 4: Deinite integrals and the FTC Section 3 Special types o Riemann sums What you need to know already: What a Riemann sum is. What you can learn here: The key types o
More informationRELIABILITY OF BURIED PIPELINES WITH CORROSION DEFECTS UNDER VARYING BOUNDARY CONDITIONS
REIABIITY OF BURIE PIPEIES WITH CORROSIO EFECTS UER VARYIG BOUARY COITIOS Ouk-Sub ee 1 and ong-hyeok Kim 1. School o Mechanical Engineering, InHa University #53, Yonghyun-ong, am-ku, Incheon, 40-751, Korea
More informationNew Functions from Old Functions
.3 New Functions rom Old Functions In this section we start with the basic unctions we discussed in Section. and obtain new unctions b shiting, stretching, and relecting their graphs. We also show how
More informationThu June 16 Lecture Notes: Lattice Exercises I
Thu June 6 ecture Notes: attice Exercises I T. Satogata: June USPAS Accelerator Physics Most o these notes ollow the treatment in the class text, Conte and MacKay, Chapter 6 on attice Exercises. The portions
More informationDouble-slit interference of biphotons generated in spontaneous parametric downconversion from a thick crystal
INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF OPTICS B: QUANTUM AND SEMICLASSICAL OPTICS J. Opt. B: Quantum Semiclass. Opt. 3 (2001 S50 S54 www.iop.org/journals/ob PII: S1464-4266(0115159-1 Double-slit intererence
More informationCategories and Natural Transformations
Categories and Natural Transormations Ethan Jerzak 17 August 2007 1 Introduction The motivation or studying Category Theory is to ormalise the underlying similarities between a broad range o mathematical
More informationMath-Essentials Unit 3 Review. Equations and Transformations of the Linear, Quadratic, Absolute Value, Square Root, and Cube Functions
Math-Essentials Unit Review Equations and Transormations o the Linear, Quadratic, Absolute Value, Square Root, and Cube Functions Vocabulary Relation: A mapping or pairing o input values to output values.
More informationNotes on Wavelets- Sandra Chapman (MPAGS: Time series analysis) # $ ( ) = G f. y t
Wavelets Recall: we can choose! t ) as basis on which we expand, ie: ) = y t ) = G! t ) y t! may be orthogonal chosen or appropriate properties. This is equivalent to the transorm: ) = G y t )!,t )d 2
More informationAcoustic forcing of flexural waves and acoustic fields for a thin plate in a fluid
Acoustic orcing o leural waves and acoustic ields or a thin plate in a luid Darryl MCMAHON Maritime Division, Deence Science and Technology Organisation, HMAS Stirling, WA Australia ABSTACT Consistency
More informationPhysic 231 Lecture 35
Physic 31 Lecture 35 Main points o last lecture: Waves transverse longitudinal traveling waves v wave λ Wave speed or a string v F µ Superposition and intererence o waves; wave orms interere. Relection
More information(One Dimension) Problem: for a function f(x), find x 0 such that f(x 0 ) = 0. f(x)
Solving Nonlinear Equations & Optimization One Dimension Problem: or a unction, ind 0 such that 0 = 0. 0 One Root: The Bisection Method This one s guaranteed to converge at least to a singularity, i not
More informationSemicontinuous filter limits of nets of lattice groupvalued
Semicontinuous ilter limits o nets o lattice grouvalued unctions THEMATIC UNIT: MATHEMATICS AND APPLICATIONS A Boccuto, Diartimento di Matematica e Inormatica, via Vanvitelli, I- 623 Perugia, Italy, E-mail:
More informationELEG 3143 Probability & Stochastic Process Ch. 4 Multiple Random Variables
Department o Electrical Engineering University o Arkansas ELEG 3143 Probability & Stochastic Process Ch. 4 Multiple Random Variables Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Two discrete random variables
More informationEquidistant Polarizing Transforms
DRAFT 1 Equidistant Polarizing Transorms Sinan Kahraman Abstract arxiv:1708.0133v1 [cs.it] 3 Aug 017 This paper presents a non-binary polar coding scheme that can reach the equidistant distant spectrum
More informationEx x xf xdx. Ex+ a = x+ a f x dx= xf x dx+ a f xdx= xˆ. E H x H x H x f x dx ˆ ( ) ( ) ( ) μ is actually the first moment of the random ( )
Fall 03 Analysis o Eperimental Measurements B Eisenstein/rev S Errede The Epectation Value o a Random Variable: The epectation value E[ ] o a random variable is the mean value o, ie ˆ (aa μ ) For discrete
More informationNumerical Calculation of Coupling Efficiency for an Elegant Hermite-Cosh-Gaussian Beams
International Journal o Optics and Photonics (IJOP) Vol. 6, No., Summer-Fall Numerical Calculation o Coupling Eiciency or an Elegant Hermite-Cosh-Gaussian Beams A. Keshavarz* and M. Kazempour Department
More informationFlow of aerosol in 3D alveolated bifurcations: experimental measurements by Particle Image Velocimetry and Particle Tracking Velocimetry
Flow o aerosol in 3D alveolated biurcations: experimental measurements by Particle Image Velocimetry and Particle Tracking Velocimetry Vincent Ruwet, Patricia Corieri, Ra Theunissen, Baoshun Ma, Michel
More informationAPPENDIX B MATRIX NOTATION. The Definition of Matrix Notation is the Definition of Matrix Multiplication B.1 INTRODUCTION
APPENDIX B MAIX NOAION he Deinition o Matrix Notation is the Deinition o Matrix Mltiplication B. INODUCION { XE "Matrix Mltiplication" }{ XE "Matrix Notation" }he se o matrix notations is not necessary
More informationExtreme Values of Functions
Extreme Values o Functions When we are using mathematics to model the physical world in which we live, we oten express observed physical quantities in terms o variables. Then, unctions are used to describe
More informationMath 60. Rumbos Spring Solutions to Assignment #17
Math 60. Rumbos Spring 2009 1 Solutions to Assignment #17 a b 1. Prove that if ad bc 0 then the matrix A = is invertible and c d compute A 1. a b Solution: Let A = and assume that ad bc 0. c d First consider
More informationMath Review and Lessons in Calculus
Math Review and Lessons in Calculus Agenda Rules o Eponents Functions Inverses Limits Calculus Rules o Eponents 0 Zero Eponent Rule a * b ab Product Rule * 3 5 a / b a-b Quotient Rule 5 / 3 -a / a Negative
More informationBasic mathematics of economic models. 3. Maximization
John Riley 1 January 16 Basic mathematics o economic models 3 Maimization 31 Single variable maimization 1 3 Multi variable maimization 6 33 Concave unctions 9 34 Maimization with non-negativity constraints
More informationGood Practice Guide Mueller Polarimetry measurements on PV Films. Søren A. Jensen and Poul-Erik Hansen
Good Practice Guide Mueller Polarimetry measurements on PV Films Søren. ensen and Poul-Erik Hansen une 7 Contents ntroduction... 3 Ellipsometry... 3 Müller matrix ormalism... 3 Experimental setup... 4
More informationPower Spectral Analysis of Elementary Cellular Automata
Power Spectral Analysis o Elementary Cellular Automata Shigeru Ninagawa Division o Inormation and Computer Science, Kanazawa Institute o Technology, 7- Ohgigaoka, Nonoichi, Ishikawa 92-850, Japan Spectral
More informationComputer-Generated Holographic Gratings in Soft Matter
Computer-Generated Holographic Gratings in Sot Matter Gianluigi Zito 1,2, Antigone Marino 1,3, Bruno Piccirillo 1,2, Volodymyr Tkachenko 3, Enrico Santamato 1,2, Giancarlo Abbate 1,3,4 1 University Federico
More informationLecture 13: Applications of Fourier transforms (Recipes, Chapter 13)
Lecture 13: Applications o Fourier transorms (Recipes, Chapter 13 There are many applications o FT, some o which involve the convolution theorem (Recipes 13.1: The convolution o h(t and r(t is deined by
More informationChapter 8 Laminar Flows with Dependence on One Dimension
Chapter 8 Laminar Flows with Dependence on One Dimension Couette low Planar Couette low Cylindrical Couette low Planer rotational Couette low Hele-Shaw low Poiseuille low Friction actor and Reynolds number
More informationStanding Waves If the same type of waves move through a common region and their frequencies, f, are the same then so are their wavelengths, λ.
Standing Waves I the same type o waves move through a common region and their requencies,, are the same then so are their wavelengths,. This ollows rom: v=. Since the waves move through a common region,
More informationFeedback Linearization
Feedback Linearization Peter Al Hokayem and Eduardo Gallestey May 14, 2015 1 Introduction Consider a class o single-input-single-output (SISO) nonlinear systems o the orm ẋ = (x) + g(x)u (1) y = h(x) (2)
More informationCHAPTER 8: Matrices and Determinants
(Exercises for Chapter 8: Matrices and Determinants) E.8.1 CHAPTER 8: Matrices and Determinants (A) means refer to Part A, (B) means refer to Part B, etc. Most of these exercises can be done without a
More informationTwo-photon absorption coefficient determination with a differential F-scan technique
Two-photon absorption coeicient determination with a dierential F-scan technique E RUEDA, 1 J H SERNA, A HAMAD AND H GARCIA 3,* 1 Grupo de Óptica y Fotónica, Instituto de Física, U de A, Calle 70 No. 5-1,
More informationMOMENT OF A COUPLE. Today s Objectives: Students will be able to. a) define a couple, and, b) determine the moment of a couple.
Today s Objectives: Students will be able to MOMENT OF A COUPLE a) define a couple, and, b) determine the moment of a couple. In-Class activities: Check Homework Reading Quiz Applications Moment of a Couple
More information17 Groups of Matrices
17 Groups o Matrices In this paragraph, we examine some groups whose elements are matrices. The reader proaly knows matrices (whose entries are real or complex numers), ut this is not a prerequisite or
More informationME 328 Machine Design Vibration handout (vibrations is not covered in text)
ME 38 Machine Design Vibration handout (vibrations is not covered in text) The ollowing are two good textbooks or vibrations (any edition). There are numerous other texts o equal quality. M. L. James,
More informationCS 361 Meeting 28 11/14/18
CS 361 Meeting 28 11/14/18 Announcements 1. Homework 9 due Friday Computation Histories 1. Some very interesting proos o undecidability rely on the technique o constructing a language that describes the
More information9.3 Graphing Functions by Plotting Points, The Domain and Range of Functions
9. Graphing Functions by Plotting Points, The Domain and Range o Functions Now that we have a basic idea o what unctions are and how to deal with them, we would like to start talking about the graph o
More information