( T) Blackbody Radiation. S hν. hν exp kt MODEL

Size: px
Start display at page:

Download "( T) Blackbody Radiation. S hν. hν exp kt MODEL"

Transcription

1 Let us use nsten's approach to relate gan and spontaneous emsson. ccordng to Plank:.The probablty o radaton takng place rom a black body decreased as the requency o the radaton ncreased.. The black body radaton low out n dscrete, lumpy quanttes hv lackbody Radaton MODL hν ( T) Planck postulated: Lecture 8b/ h J s hv ( hν) hν

2 Optcal gan spectrum measurements: rom T nsten's approach to gan and spontaneous emsson Lecture 8b/ Change o the photon numbers : hν M M ( ) ( ) ( ) Possble contrbuton o the varous electron transtons to emsson/absorpton o photons n energy nterval [hν,hνdhν] s accounted or n and constants. M M. qulbrum: 0 hv hν M The oscllators emttng lght n spectral range o nterest lackbody radaton: hv ( hν) hν k M M ( hν)

3 Lecture 8b/3 Comments hν ; 0 0: m qulbru M M M M M M M M M M M

4 Optcal gan spectrum measurements: rom T Gan and spontaneous emsson relaton Lecture 8b/4 No equlbrum:, 0. ( ) hν R R G hν x t x ( hν) υ υ ( - ) ( hν) G( hν) ( hv) ( g ) x ( ( - ) ) g x ( ) k I P ( hν) ( hν) G hν hν

5 Lecture 8b/5 Comments hν

6 Optcal gan spectrum measurements: rom T G ( hν) I P ( hν) ( hν) hν Lecture 8b/6 Laser pectrum nalyzer T Intensty (d) T5 o C 30 m m 6 m T Modal Optcal Gan (/cm) Modal gan I0 m T5 o C Wavelength (nm) Wavelength dvantage: measurable n wder spectral range comparng wth H-P. Complcaton: should be determned rom the ndependent erment. Needs calbraton to nd absolute value o the gan.

7 Lecture 8b/7 Determnaton o transparency energy: ndrekson technque Detecton propertes o laser dode I DC V DC bsorpton msson ( e ) ( e ) hν> hν< ( h ) ( h ), V, I, V, I NO VOLTG CHNG CRO DIOD WHN hν!

8 Lecture 8b/8 Determnaton o transparency energy: ndrekson technque Tunable modulated laser Dode laser under test I DC,V DC V C Induced C voltage (a.u.) VC Transparency wavelength at gven IDC, VDC Wavelength (nm) The transparency energy s equal to ( e ) -( h ). * Induced voltage s n phase wth or hν>( e ) -( h ) snce larger absorpton means larger voltage. or hν<( e ) -( h ) modes supported by resonator are ampled stronger and maxmum o corresponds to the voltage mnmum. The energy o voltage oscllaton phase change corresponds to transparency energy.

9 Measurements o optcal loss by Varable cavty length method Lecture 8b/9 ext α RROR α ext ext L m α α ln α αm α m L RR ssumpton: and α are cavty length ndependent m ext ln α R R L dvantage: mplcty o measurements hortcomng: or short cavtes or small nternal loss threshold s L-dependent and errors arse. lso a lot o laser materal s requred.

10 Measurements o optcal loss rom modal gan spectrum T TM Lecture 8b/0 g Modal Optcal Gan (/cm) T/TM 0 αtot λt T TM H T/TM T/TM T/TM ( hν) Γ G ( hν) α αtot T5 0 C I7 m Wavelength (nm) tot. T and TM modal gans ntersecton T comes rom C-HH transton; TM comes rom C-LH transton. Thus, spectra or T and TM gans are derent and correspondng gans and can be equal only when materal gan G0 (transparency pont).. aturaton or hν< g or photon energes below bandgap, materal gan s equal to zero. Modal (g) gan s equal to total loss wthn ths spectra regon. Usually, long wavelength tal o the modal gan spectra gves total loss value. When loss are determned, transparency energy (condton G0) can be estmated rom the gan spectrum.

Title: Radiative transitions and spectral broadening

Title: Radiative transitions and spectral broadening Lecture 6 Ttle: Radatve transtons and spectral broadenng Objectves The spectral lnes emtted by atomc vapors at moderate temperature and pressure show the wavelength spread around the central frequency.

More information

Rate of Absorption and Stimulated Emission

Rate of Absorption and Stimulated Emission MIT Department of Chemstry 5.74, Sprng 005: Introductory Quantum Mechancs II Instructor: Professor Andre Tokmakoff p. 81 Rate of Absorpton and Stmulated Emsson The rate of absorpton nduced by the feld

More information

5.04, Principles of Inorganic Chemistry II MIT Department of Chemistry Lecture 32: Vibrational Spectroscopy and the IR

5.04, Principles of Inorganic Chemistry II MIT Department of Chemistry Lecture 32: Vibrational Spectroscopy and the IR 5.0, Prncples of Inorganc Chemstry II MIT Department of Chemstry Lecture 3: Vbratonal Spectroscopy and the IR Vbratonal spectroscopy s confned to the 00-5000 cm - spectral regon. The absorpton of a photon

More information

Med Phys 4R06/6R03 Laboratory Experiment #6 MULTICHANNEL PULSE SPECTROMETRY

Med Phys 4R06/6R03 Laboratory Experiment #6 MULTICHANNEL PULSE SPECTROMETRY Med Phys 4R06/6R0 Laboratory Experment #6 MULICHANNEL PULSE SPECROMERY INRODUCION: In ths experment you wll use the technque o multchannel spectrometry to perorm quanttatve analyss o radoactve partculate

More information

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t 8.5: Many-body phenomena n condensed matter and atomc physcs Last moded: September, 003 Lecture. Squeezed States In ths lecture we shall contnue the dscusson of coherent states, focusng on ther propertes

More information

ECEN 5005 Crystals, Nanocrystals and Device Applications Class 19 Group Theory For Crystals

ECEN 5005 Crystals, Nanocrystals and Device Applications Class 19 Group Theory For Crystals ECEN 5005 Crystals, Nanocrystals and Devce Applcatons Class 9 Group Theory For Crystals Dee Dagram Radatve Transton Probablty Wgner-Ecart Theorem Selecton Rule Dee Dagram Expermentally determned energy

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Thermodynamics and statistical mechanics in materials modelling II

Thermodynamics and statistical mechanics in materials modelling II Course MP3 Lecture 8/11/006 (JAE) Course MP3 Lecture 8/11/006 Thermodynamcs and statstcal mechancs n materals modellng II A bref résumé of the physcal concepts used n materals modellng Dr James Ellott.1

More information

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations Quantum Physcs 量 理 Robert Esberg Second edton CH 09 Multelectron atoms ground states and x-ray exctatons 9-01 By gong through the procedure ndcated n the text, develop the tme-ndependent Schroednger equaton

More information

Level Crossing Spectroscopy

Level Crossing Spectroscopy Level Crossng Spectroscopy October 8, 2008 Contents 1 Theory 1 2 Test set-up 4 3 Laboratory Exercses 4 3.1 Hanle-effect for fne structure.................... 4 3.2 Hanle-effect for hyperfne structure.................

More information

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis Statstcal analyss usng matlab HY 439 Presented by: George Fortetsanaks Roadmap Probablty dstrbutons Statstcal estmaton Fttng data to probablty dstrbutons Contnuous dstrbutons Contnuous random varable X

More information

Why working at higher frequencies?

Why working at higher frequencies? Advanced course on ELECTRICAL CHARACTERISATION OF NANOSCALE SAMPLES & BIOCHEMICAL INTERFACES: methods and electronc nstrumentaton. MEASURING SMALL CURRENTS When speed comes nto play Why workng at hgher

More information

6. Stochastic processes (2)

6. Stochastic processes (2) Contents Markov processes Brth-death processes Lect6.ppt S-38.45 - Introducton to Teletraffc Theory Sprng 5 Markov process Consder a contnuous-tme and dscrete-state stochastc process X(t) wth state space

More information

6. Stochastic processes (2)

6. Stochastic processes (2) 6. Stochastc processes () Lect6.ppt S-38.45 - Introducton to Teletraffc Theory Sprng 5 6. Stochastc processes () Contents Markov processes Brth-death processes 6. Stochastc processes () Markov process

More information

Applied Nuclear Physics (Fall 2004) Lecture 23 (12/3/04) Nuclear Reactions: Energetics and Compound Nucleus

Applied Nuclear Physics (Fall 2004) Lecture 23 (12/3/04) Nuclear Reactions: Energetics and Compound Nucleus .101 Appled Nuclear Physcs (Fall 004) Lecture 3 (1/3/04) Nuclear Reactons: Energetcs and Compound Nucleus References: W. E. Meyerhof, Elements of Nuclear Physcs (McGraw-Hll, New York, 1967), Chap 5. Among

More information

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential Open Systems: Chemcal Potental and Partal Molar Quanttes Chemcal Potental For closed systems, we have derved the followng relatonshps: du = TdS pdv dh = TdS + Vdp da = SdT pdv dg = VdP SdT For open systems,

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

On resolving the optical spectra of the edge plasma radiation against a strong background of the divertor stray light

On resolving the optical spectra of the edge plasma radiation against a strong background of the divertor stray light Journal of Physcs: Conference Seres PAPER OPEN ACCESS On resolvng the optcal spectra of the edge plasma radaton aganst a strong background of the dvertor stray lght To cte ths artcle: L Ognev and V S Lstsa

More information

FEEDBACK AMPLIFIERS. v i or v s v 0

FEEDBACK AMPLIFIERS. v i or v s v 0 FEEDBCK MPLIFIERS Feedback n mplers FEEDBCK IS THE PROCESS OF FEEDING FRCTION OF OUTPUT ENERGY (VOLTGE OR CURRENT) BCK TO THE INPUT CIRCUIT. THE CIRCUIT EMPLOYED FOR THIS PURPOSE IS CLLED FEEDBCK NETWORK.

More information

Lecture 16 Statistical Analysis in Biomaterials Research (Part II)

Lecture 16 Statistical Analysis in Biomaterials Research (Part II) 3.051J/0.340J 1 Lecture 16 Statstcal Analyss n Bomaterals Research (Part II) C. F Dstrbuton Allows comparson of varablty of behavor between populatons usng test of hypothess: σ x = σ x amed for Brtsh statstcan

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

5.60 Thermodynamics & Kinetics Spring 2008

5.60 Thermodynamics & Kinetics Spring 2008 MIT OpenCourseWare http://ocw.mt.edu 5.60 Thermodynamcs & Knetcs Sprng 2008 For nformaton about ctng these materals or our Terms of Use, vst: http://ocw.mt.edu/terms. 5.60 Sprng 2008 Lecture #29 page 1

More information

SIMPLE LINEAR REGRESSION

SIMPLE LINEAR REGRESSION Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two

More information

Appendix II Summary of Important Equations

Appendix II Summary of Important Equations W. M. Whte Geochemstry Equatons of State: Ideal GasLaw: Coeffcent of Thermal Expanson: Compressblty: Van der Waals Equaton: The Laws of Thermdynamcs: Frst Law: Appendx II Summary of Important Equatons

More information

4. Blackbody Radiation, Boltzmann Statistics, Temperature, and Thermodynamic Equilibrium

4. Blackbody Radiation, Boltzmann Statistics, Temperature, and Thermodynamic Equilibrium 4. Blackbody Radaton, Boltzmann Statstcs, Temperature, and Thermodynamc Equlbrum Blackbody radaton, temperature, and thermodynamc equlbrum gve a tghtly coupled descrpton of systems (atmospheres, volumes,

More information

Röntgen s experiment in X-ray Spectroscopy. Röntgen s experiment. Interaction of x-rays x. x-rays. with matter. Wavelength: m

Röntgen s experiment in X-ray Spectroscopy. Röntgen s experiment. Interaction of x-rays x. x-rays. with matter. Wavelength: m X-ray Spectroscopy Röntgen s experment n 1895 Lecture 1: Introducton & expermental aspects Lecture : Atomc Multplet Theory Crystal Feld Theory CTM4XAS program Lecture 3: Charge Transfer Multplet Theory

More information

The middle point of each range is used to calculated the sample mean and sample variance as follows:

The middle point of each range is used to calculated the sample mean and sample variance as follows: 7.0 (a 50 Number of Observato 00 50 00 50 0 3 4 5 6 7 8 9 0 Acceptance Gap G, (sec (b The mddle pont of each range s used to calculated the sample mean and sample varance as follows: No. of G ng observaton(

More information

Simulation and Random Number Generation

Simulation and Random Number Generation Smulaton and Random Number Generaton Summary Dscrete Tme vs Dscrete Event Smulaton Random number generaton Generatng a random sequence Generatng random varates from a Unform dstrbuton Testng the qualty

More information

Statistics II Final Exam 26/6/18

Statistics II Final Exam 26/6/18 Statstcs II Fnal Exam 26/6/18 Academc Year 2017/18 Solutons Exam duraton: 2 h 30 mn 1. (3 ponts) A town hall s conductng a study to determne the amount of leftover food produced by the restaurants n the

More information

Chapters 18 & 19: Themodynamics review. All macroscopic (i.e., human scale) quantities must ultimately be explained on the microscopic scale.

Chapters 18 & 19: Themodynamics review. All macroscopic (i.e., human scale) quantities must ultimately be explained on the microscopic scale. Chapters 18 & 19: Themodynamcs revew ll macroscopc (.e., human scale) quanttes must ultmately be explaned on the mcroscopc scale. Chapter 18: Thermodynamcs Thermodynamcs s the study o the thermal energy

More information

Force = F Piston area = A

Force = F Piston area = A CHAPTER III Ths chapter s an mportant transton between the propertes o pure substances and the most mportant chapter whch s: the rst law o thermodynamcs In ths chapter, we wll ntroduce the notons o heat,

More information

Department of Chemistry Purdue University Garth J. Simpson

Department of Chemistry Purdue University Garth J. Simpson Objectves: 1. Develop a smple conceptual 1D model for NLO effects. Extend to 3D and relate to computatonal chemcal calculatons of adabatc NLO polarzabltes. 2. Introduce Sum-Over-States (SOS) approaches

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA 4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

4. INTERACTION OF LIGHT WITH MATTER

4. INTERACTION OF LIGHT WITH MATTER Andre Tokmakoff, MIT Department of Chemstry, /8/7 4-1 4. INTERACTION OF LIGHT WITH MATTER One of the most mportant topcs n tme-dependent quantum mechancs for chemsts s the descrpton of spectroscopy, whch

More information

NAME and Section No.

NAME and Section No. Chemstry 391 Fall 2007 Exam I KEY (Monday September 17) 1. (25 Ponts) ***Do 5 out of 6***(If 6 are done only the frst 5 wll be graded)*** a). Defne the terms: open system, closed system and solated system

More information

Chapter Eight. Review and Summary. Two methods in solid mechanics ---- vectorial methods and energy methods or variational methods

Chapter Eight. Review and Summary. Two methods in solid mechanics ---- vectorial methods and energy methods or variational methods Chapter Eght Energy Method 8. Introducton 8. Stran energy expressons 8.3 Prncpal of statonary potental energy; several degrees of freedom ------ Castglano s frst theorem ---- Examples 8.4 Prncpal of statonary

More information

Measurement of Radiation: Exposure. Purpose. Quantitative description of radiation

Measurement of Radiation: Exposure. Purpose. Quantitative description of radiation Measurement of Radaton: Exposure George Starkschall, Ph.D. Department of Radaton Physcs U.T. M.D. Anderson Cancer Center Purpose To ntroduce the concept of radaton exposure and to descrbe and evaluate

More information

8.592J: Solutions for Assignment 7 Spring 2005

8.592J: Solutions for Assignment 7 Spring 2005 8.59J: Solutons for Assgnment 7 Sprng 5 Problem 1 (a) A flament of length l can be created by addton of a monomer to one of length l 1 (at rate a) or removal of a monomer from a flament of length l + 1

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

4. INTERACTION OF LIGHT WITH MATTER

4. INTERACTION OF LIGHT WITH MATTER Andre Tokmakoff, MIT Department of Chemstry, 3/8/7 4-1 4. INTERACTION OF LIGHT WITH MATTER One of the most mportant topcs n tme-dependent quantum mechancs for chemsts s the descrpton of spectroscopy, whch

More information

PHYS 705: Classical Mechanics. Calculus of Variations II

PHYS 705: Classical Mechanics. Calculus of Variations II 1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary

More information

G4023 Mid-Term Exam #1 Solutions

G4023 Mid-Term Exam #1 Solutions Exam1Solutons.nb 1 G03 Md-Term Exam #1 Solutons 1-Oct-0, 1:10 p.m to :5 p.m n 1 Pupn Ths exam s open-book, open-notes. You may also use prnt-outs of the homework solutons and a calculator. 1 (30 ponts,

More information

and Statistical Mechanics Material Properties

and Statistical Mechanics Material Properties Statstcal Mechancs and Materal Propertes By Kuno TAKAHASHI Tokyo Insttute of Technology, Tokyo 15-855, JAPA Phone/Fax +81-3-5734-3915 takahak@de.ttech.ac.jp http://www.de.ttech.ac.jp/~kt-lab/ Only for

More information

Lecture 5: Quantitative Emission/Absorption

Lecture 5: Quantitative Emission/Absorption Lecture 5: Quanttatve Emsson/bsorpton. Eqn. of radatve transfer / Beer s Law o (ν) Gas (ν). Ensten theory of radaton 3. pectral absorpton coeffcent Collmated lht @ ν L 4. Radatve lfetme 5. Lne strenths

More information

Exercises of Fundamentals of Chemical Processes

Exercises of Fundamentals of Chemical Processes Department of Energ Poltecnco d Mlano a Lambruschn 4 2056 MILANO Exercses of undamentals of Chemcal Processes Prof. Ganpero Gropp Exercse 7 ) Estmaton of the composton of the streams at the ext of an sothermal

More information

1.4 Small-signal models of BJT

1.4 Small-signal models of BJT 1.4 Small-sgnal models of J Analog crcuts often operate wth sgnal levels that are small compared to the bas currents and voltages n the crcut. Under ths condton, ncremental or small-sgnal models can be

More information

Some basic statistics and curve fitting techniques

Some basic statistics and curve fitting techniques Some basc statstcs and curve fttng technques Statstcs s the dscplne concerned wth the study of varablty, wth the study of uncertanty, and wth the study of decsonmakng n the face of uncertanty (Lndsay et

More information

Optimising MAD & SAD Experiments in MX

Optimising MAD & SAD Experiments in MX Optmsng MAD & SAD Experments n MX Gordon Leonard Structural Bology Group European Synchrotron Radaton Faclty, Grenoble, France. Page 1 l 18 th September 2014 l Gordon Leonard CONTENTS 1. Anomalous Scatterng

More information

The Schrödinger Equation

The Schrödinger Equation Chapter 1 The Schrödnger Equaton 1.1 (a) F; () T; (c) T. 1. (a) Ephoton = hν = hc/ λ =(6.66 1 34 J s)(.998 1 8 m/s)/(164 1 9 m) = 1.867 1 19 J. () E = (5 1 6 J/s)( 1 8 s) =.1 J = n(1.867 1 19 J) and n

More information

Course 395: Machine Learning - Lectures

Course 395: Machine Learning - Lectures Course 395: Machne Learnng - Lectures Lecture 1-2: Concept Learnng (M. Pantc Lecture 3-4: Decson Trees & CC Intro (M. Pantc Lecture 5-6: Artfcal Neural Networks (S.Zaferou Lecture 7-8: Instance ased Learnng

More information

Chapter 1. Probability

Chapter 1. Probability Chapter. Probablty Mcroscopc propertes of matter: quantum mechancs, atomc and molecular propertes Macroscopc propertes of matter: thermodynamcs, E, H, C V, C p, S, A, G How do we relate these two propertes?

More information

Boundaries, Near-field Optics

Boundaries, Near-field Optics Boundares, Near-feld Optcs Fve boundary condtons at an nterface Fresnel Equatons : Transmsson and Reflecton Coeffcents Transmttance and Reflectance Brewster s condton a consequence of Impedance matchng

More information

Physics 30 Lesson 31 The Bohr Model of the Atom

Physics 30 Lesson 31 The Bohr Model of the Atom Physcs 30 Lesson 31 The Bohr Model o the Atom I. Planetary models o the atom Ater Rutherord s gold ol scatterng experment, all models o the atom eatured a nuclear model wth electrons movng around a tny,

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

Introduction to Interfacial Segregation. Xiaozhe Zhang 10/02/2015

Introduction to Interfacial Segregation. Xiaozhe Zhang 10/02/2015 Introducton to Interfacal Segregaton Xaozhe Zhang 10/02/2015 Interfacal egregaton Segregaton n materal refer to the enrchment of a materal conttuent at a free urface or an nternal nterface of a materal.

More information

ENGR-4300 Electronic Instrumentation Quiz 4 Fall 2010 Name Section. Question Value Grade I 20 II 20 III 20 IV 20 V 20. Total (100 points)

ENGR-4300 Electronic Instrumentation Quiz 4 Fall 2010 Name Section. Question Value Grade I 20 II 20 III 20 IV 20 V 20. Total (100 points) ENGR-43 Electronc Instrumentaton Quz 4 Fall 21 Name Secton Queston Value Grade I 2 II 2 III 2 IV 2 V 2 Total (1 ponts) On all questons: SHOW LL WORK. EGIN WITH FORMULS, THEN SUSTITUTE VLUES ND UNITS. No

More information

THERMAL DISTRIBUTION IN THE HCL SPECTRUM OBJECTIVE

THERMAL DISTRIBUTION IN THE HCL SPECTRUM OBJECTIVE ame: THERMAL DISTRIBUTIO I THE HCL SPECTRUM OBJECTIVE To nvestgate a system s thermal dstrbuton n dscrete states; specfcally, determne HCl gas temperature from the relatve occupatons of ts rotatonal states.

More information

Waveguides and resonant cavities

Waveguides and resonant cavities Wavegudes and resonant cavtes February 8, 014 Essentally, a wavegude s a conductng tube of unform cross-secton and a cavty s a wavegude wth end caps. The dmensons of the gude or cavty are chosen to transmt,

More information

Lesson 16: Basic Control Modes

Lesson 16: Basic Control Modes 0/8/05 Lesson 6: Basc Control Modes ET 438a Automatc Control Systems Technology lesson6et438a.tx Learnng Objectves Ater ths resentaton you wll be able to: Descrbe the common control modes used n analog

More information

1. Fundamentals 1.1 Probability Theory Sample Space and Probability Random Variables Limit Theories

1. Fundamentals 1.1 Probability Theory Sample Space and Probability Random Variables Limit Theories 1. undamentals 1.1 robablt Theor 1.1.1 Sample Space and robablt 1.1.2 Random Varables 1.1.3 Lmt Theores 1.1 robablt Theor A statstcal model probablt model deals wth eperment whose outcome s not precsel

More information

Frequency dependence of the permittivity

Frequency dependence of the permittivity Frequency dependence of the permttvty February 7, 016 In materals, the delectrc constant and permeablty are actually frequency dependent. Ths does not affect our results for sngle frequency modes, but

More information

Introduction to Statistical Methods

Introduction to Statistical Methods Introducton to Statstcal Methods Physcs 4362, Lecture #3 hermodynamcs Classcal Statstcal Knetc heory Classcal hermodynamcs Macroscopc approach General propertes of the system Macroscopc varables 1 hermodynamc

More information

Tracking with Kalman Filter

Tracking with Kalman Filter Trackng wth Kalman Flter Scott T. Acton Vrgna Image and Vdeo Analyss (VIVA), Charles L. Brown Department of Electrcal and Computer Engneerng Department of Bomedcal Engneerng Unversty of Vrgna, Charlottesvlle,

More information

Color Rendering Uncertainty

Color Rendering Uncertainty Australan Journal of Basc and Appled Scences 4(10): 4601-4608 010 ISSN 1991-8178 Color Renderng Uncertanty 1 A.el Bally M.M. El-Ganany 3 A. Al-amel 1 Physcs Department Photometry department- NIS Abstract:

More information

( ) = ( ) + ( 0) ) ( )

( ) = ( ) + ( 0) ) ( ) EETOMAGNETI OMPATIBIITY HANDBOOK 1 hapter 9: Transent Behavor n the Tme Doman 9.1 Desgn a crcut usng reasonable values for the components that s capable of provdng a tme delay of 100 ms to a dgtal sgnal.

More information

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Maxmum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models

More information

1 Rabi oscillations. Physical Chemistry V Solution II 8 March 2013

1 Rabi oscillations. Physical Chemistry V Solution II 8 March 2013 Physcal Chemstry V Soluton II 8 March 013 1 Rab oscllatons a The key to ths part of the exercse s correctly substtutng c = b e ωt. You wll need the followng equatons: b = c e ωt 1 db dc = dt dt ωc e ωt.

More information

E40M Device Models, Resistors, Voltage and Current Sources, Diodes, Solar Cells. M. Horowitz, J. Plummer, R. Howe 1

E40M Device Models, Resistors, Voltage and Current Sources, Diodes, Solar Cells. M. Horowitz, J. Plummer, R. Howe 1 E40M Devce Models, Resstors, Voltage and Current Sources, Dodes, Solar Cells M. Horowtz, J. Plummer, R. Howe 1 Understandng the Solar Charger Lab Project #1 We need to understand how: 1. Current, voltage

More information

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2015

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2015 Lecture 2. 1/07/15-1/09/15 Unversty of Washngton Department of Chemstry Chemstry 453 Wnter Quarter 2015 We are not talkng about truth. We are talkng about somethng that seems lke truth. The truth we want

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

THEOREMS OF QUANTUM MECHANICS

THEOREMS OF QUANTUM MECHANICS THEOREMS OF QUANTUM MECHANICS In order to develop methods to treat many-electron systems (atoms & molecules), many of the theorems of quantum mechancs are useful. Useful Notaton The matrx element A mn

More information

Markov Chain Monte Carlo (MCMC), Gibbs Sampling, Metropolis Algorithms, and Simulated Annealing Bioinformatics Course Supplement

Markov Chain Monte Carlo (MCMC), Gibbs Sampling, Metropolis Algorithms, and Simulated Annealing Bioinformatics Course Supplement Markov Chan Monte Carlo MCMC, Gbbs Samplng, Metropols Algorthms, and Smulated Annealng 2001 Bonformatcs Course Supplement SNU Bontellgence Lab http://bsnuackr/ Outlne! Markov Chan Monte Carlo MCMC! Metropols-Hastngs

More information

Machine learning: Density estimation

Machine learning: Density estimation CS 70 Foundatons of AI Lecture 3 Machne learnng: ensty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square ata: ensty estmaton {.. n} x a vector of attrbute values Objectve: estmate the model of

More information

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University PHYS 45 Sprng semester 7 Lecture : Dealng wth Expermental Uncertantes Ron Refenberger Brck anotechnology Center Purdue Unversty Lecture Introductory Comments Expermental errors (really expermental uncertantes)

More information

Computing MLE Bias Empirically

Computing MLE Bias Empirically Computng MLE Bas Emprcally Kar Wa Lm Australan atonal Unversty January 3, 27 Abstract Ths note studes the bas arses from the MLE estmate of the rate parameter and the mean parameter of an exponental dstrbuton.

More information

Chapter 3 Describing Data Using Numerical Measures

Chapter 3 Describing Data Using Numerical Measures Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The

More information

Marginal Models for categorical data.

Marginal Models for categorical data. Margnal Models for categorcal data Applcaton to condtonal ndependence and graphcal models Wcher Bergsma 1 Marcel Croon 2 Jacques Hagenaars 2 Tamas Rudas 3 1 London School of Economcs and Poltcal Scence

More information

Statistical Energy Analysis for High Frequency Acoustic Analysis with LS-DYNA

Statistical Energy Analysis for High Frequency Acoustic Analysis with LS-DYNA 14 th Internatonal Users Conference Sesson: ALE-FSI Statstcal Energy Analyss for Hgh Frequency Acoustc Analyss wth Zhe Cu 1, Yun Huang 1, Mhamed Soul 2, Tayeb Zeguar 3 1 Lvermore Software Technology Corporaton

More information

Lecture 6 More on Complete Randomized Block Design (RBD)

Lecture 6 More on Complete Randomized Block Design (RBD) Lecture 6 More on Complete Randomzed Block Desgn (RBD) Multple test Multple test The multple comparsons or multple testng problem occurs when one consders a set of statstcal nferences smultaneously. For

More information

Aerodynamics. Finite Wings Lifting line theory Glauert s method

Aerodynamics. Finite Wings Lifting line theory Glauert s method α ( y) l Γ( y) r ( y) V c( y) β b 4 V Glauert s method b ( y) + r dy dγ y y dy Soluton procedure that transforms the lftng lne ntegro-dfferental equaton nto a system of algebrac equatons - Restrcted to

More information

Chapter 6. Operational Amplifier. inputs can be defined as the average of the sum of the two signals.

Chapter 6. Operational Amplifier.  inputs can be defined as the average of the sum of the two signals. 6 Operatonal mpler Chapter 6 Operatonal mpler CC Symbol: nput nput Output EE () Non-nvertng termnal, () nvertng termnal nput mpedance : Few mega (ery hgh), Output mpedance : Less than (ery low) Derental

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Chapter 7 Channel Capacty and Codng Contents 7. Channel models and channel capacty 7.. Channel models Bnary symmetrc channel Dscrete memoryless channels Dscrete-nput, contnuous-output channel Waveform

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Dynamics of a Superconducting Qubit Coupled to an LC Resonator

Dynamics of a Superconducting Qubit Coupled to an LC Resonator Dynamcs of a Superconductng Qubt Coupled to an LC Resonator Y Yang Abstract: We nvestgate the dynamcs of a current-based Josephson juncton quantum bt or qubt coupled to an LC resonator. The Hamltonan of

More information

Google PageRank with Stochastic Matrix

Google PageRank with Stochastic Matrix Google PageRank wth Stochastc Matrx Md. Sharq, Puranjt Sanyal, Samk Mtra (M.Sc. Applcatons of Mathematcs) Dscrete Tme Markov Chan Let S be a countable set (usually S s a subset of Z or Z d or R or R d

More information

Chemical Equilibrium. Chapter 6 Spontaneity of Reactive Mixtures (gases) Taking into account there are many types of work that a sysem can perform

Chemical Equilibrium. Chapter 6 Spontaneity of Reactive Mixtures (gases) Taking into account there are many types of work that a sysem can perform Ths chapter deals wth chemcal reactons (system) wth lttle or no consderaton on the surroundngs. Chemcal Equlbrum Chapter 6 Spontanety of eactve Mxtures (gases) eactants generatng products would proceed

More information

Determination of Dose Factors for External Gamma Radiation in Dwellings

Determination of Dose Factors for External Gamma Radiation in Dwellings Determnaton of Dose Factors for External Gamma daton n Dwellngs M.F. Maduar and G. Hromoto Insttuto de Pesqusas Energétcas e Nucleares, CP 049, 05422-970 São Paulo, Brazl INTRODUCTION e largest contrbuton

More information

Traffic Signal Timing: Basic Principles. Development of a Traffic Signal Phasing and Timing Plan. Two Phase and Three Phase Signal Operation

Traffic Signal Timing: Basic Principles. Development of a Traffic Signal Phasing and Timing Plan. Two Phase and Three Phase Signal Operation Traffc Sgnal Tmng: Basc Prncples 2 types of sgnals Pre-tmed Traffc actuated Objectves of sgnal tmng Reduce average delay of all vehcles Reduce probablty of accdents by mnmzng possble conflct ponts Objectves

More information

Flyback Converter in DCM

Flyback Converter in DCM Flyback Converter n CM m 1:n V O V S m I M m 1 1 V CCM: wth O V I I n and S 2 1 R L M m M m s m 1 CM: IM 2 m 1 1 V 1 Borderlne: O VS I n wth V nv 2 1 R 2 L 1 M m s O S m CM f R > R 2n crt 2 L m 2 (1 )

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

Lecture 3. Interaction of radiation with surfaces. Upcoming classes

Lecture 3. Interaction of radiation with surfaces. Upcoming classes Radaton transfer n envronmental scences Lecture 3. Interacton of radaton wth surfaces Upcomng classes When a ray of lght nteracts wth a surface several nteractons are possble: 1. It s absorbed. 2. It s

More information

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n!

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n! 8333: Statstcal Mechancs I Problem Set # 3 Solutons Fall 3 Characterstc Functons: Probablty Theory The characterstc functon s defned by fk ep k = ep kpd The nth coeffcent of the Taylor seres of fk epanded

More information

Randomness and Computation

Randomness and Computation Randomness and Computaton or, Randomzed Algorthms Mary Cryan School of Informatcs Unversty of Ednburgh RC 208/9) Lecture 0 slde Balls n Bns m balls, n bns, and balls thrown unformly at random nto bns usually

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information