Signal,autocorrelation -0.6

Size: px
Start display at page:

Download "Signal,autocorrelation -0.6"

Transcription

1 Sgal,autocorrelato Phase ose p/ Tme Sgal,autocorrelato Phase ose p/ Tme Sgal,autocorrelato. Phase ose p/ Tme Sgal,autocorrelato. Phase ose p/ Tme

2 Sgal,autocorrelato 5 Addtve ose S/ Tme Sgal,autocorrelato 5 Addtve ose S/ Tme Eve o harmoc fuctos ca be etracted, but wth some dstorto 5.3 The Auto correlato of Uform, Sparse, Radom seres Let us start wth a radom occurrg square pulses (partcle detecto) Sgal Tme A pulse ampltude; T pulse wdth E A T Pulse Eergy. lm Average pulse desty T T R ( τ) ( t) ( t+ τ) R ( τ) for τ > T Whe τ > T Ether ( t) or ( t+ τ) R A T ( ) T τ ( τ ) lm E( ) T T T Detrmed by T of the sgle pulse!!! τ 3

3 For smplcty let us assume that T ; E - τ for τ < R ( τ ) for τ > ( ) ( ) ωτ ωτ S ( ω) R ( τ) e dτ τ e dτ ω s S ( ω) ω π Frst zero at ω! T S ( ω) τ cos( ωτ) dτ Sc(/)^ Tme. The wdth of the sparse seres s determed by that of a sgle pulse.. o correlated samples results f we sample at a rate of ω π T 4 Eamples:. A fast photomultpler wll geerate s pulse for each photo The ose spectrum wll reach GHz.. Electros coductors collde at femtosecod (average) The ose spectrum wll reach up to the IR rego. Usually we assume a dealzed Bad Lmted Whte ose S( ω) The power spectrum of the ose Ω Ω S( ω) for ω< Ω ad zero elsewhere ωt Ω s( Ωτ ) R( τ) S( ω) e dω π π ( Ωτ) π Frst zero at τ whch s the samplg rate for Ω o correlated ose samples 5 3

4 6. Geeralzed flter: Suppose we start from a sample of a measured sgal ad we wat to flter out some frequeces. We could geerate the FFT elemets: S( t) ( FFT ) G( ω) ( G( ω) H ( ω) )( flter) ( IFFT ) F( t ) ( Fltered ) ( ) { s } Istead of performg two fourer trasforms, wth ther Gbbs oscllato, we could fd the coeffcets drectly: ωt g( ω) s( t) e dt; F(t) deotes the fltered sgal. ωt Ft () g( ω) H( ω) e dω st ( τ) h( τ) dτ π Ft ( ) st ( t) ht ( ) Recursve flter the tme doma. "Memory" fucto. 6 A Eample- Low pass flter R C ωt ωt e H ( ω) ; h( τ) H( ω) e dω d + ωrc + ωrc ω for smplcty let us assume RC (tme uts) we have a smple pole at ω ; usg the resdue theorem h( τ ) e τ. for τ >. Usg causalty we kow that h( τ ) for τ < Ft ( ) st ( t) ht ( ) st ( ) ht ( t ) Memory fucto Tme 7 4

5 A eample: Idealzed Bad Pass flter H ( ω) ω < Ω ω >Ω Ω Ω ωt ωt s( Ωt) ht () H( ω) e dω e dω t Ωt Ω Ω s( Ωt ) h( t ) Flter coeffcets. Ω We ca dgtally sythesze ay flter we eed Matched flter What s the best set of weghtg factors ( the frequecy doma) to mamze sgal to ose? (If we are ot terested the sgal shape!) S( ω) ws W s the weghtg vector ad S s the sgal vector. each sample pot S has a ose compoet assocated wth t. the total measured ose varace (from ucorrelated samples) w ( ws ) ( w ) ; The measured (power) sgal to ose: S P P ma; w 4 ( Ss) S ( w ) s s w S S 9 5

6 w If we remember that S ca be comple, ad S SS * tha we get s cost * ; For Whte ose all are equal ad the flter should trasmt all frequeces that the sgal carres. For o whte ose we should frst whte the ose! ad the tme doma, for Whte ose: ht ( ) cost s( t ); ( ) fltered( τ) s( t + τ) + ( t + τ) s( t) 6. Sources of ose. Thermal ose (also kow as Johso or yqust ose). Based o thermodyamcs: equpartto law, secod law of thermodyamcs ad eumeratg cavty modes coected to a resstor V 4 k TRΩ B for M Ω resstor, at room temperature, V 3V Hz 6. Matched flter What s the best set of weghtg factors ( the frequecy doma) to mamze sgal to ose? (If we are ot terested the sgal shape!) S( ω) ws W s the weghtg vector ad S s the sgal vector. each sample pot S has a ose compoet assocated wth t. the total measured ose varace (from ucorrelated samples) w ( ws ) ( w ) ; The measured (power) sgal to ose: S P P ma; w 4 ( Ss) S ( w ) s s w S S 6

7 . Shot ose. Resultg from the dscrete flow of electros, (or os or photos). f the average flow cossts of partcles, the fluctuato the umber s eiω For ao-ampere curret 8 fa Hz 3. Flcker or /f ose. Theoretcal bass s stll shaky, but s foud every case, ad domates at low frequeces (<Hz). 4. Pck up ose (Iterferece ose). Maly at 5 ad Hz ad hgher harmocs of le frequecy. at AM ad FM frequeces (-Mhz) at dmmer le swtchg frequeces ( KHz) TV, Radar ad Moble phoes (.3-3GHz) 6.3 Trasverse hardwred flters: Surface Acoustc Wave All our flterg procedures ca be dagramed as follows: Delay Delay Delay Delay Multply Multply Multply Multply Add 3 7

8 7. Cross correlato fuctos: If we ecte a lear system, we kow that ts respose s determed by the Trasfer fucto, ad ectato fucto, eve whe t s hdde ose. 7. Let us defe a Cross correlato fucto : S ( ) ( ) ( ) lm y τ t y t + τ T ( t ) y ( t + τ) dt Some mmedate propertes:. Sy ( τ) Sy ( τ) From statoary propertes of the system.. Sy ( τ ) T T R () R yy () Mootoc decay tme. 3. If ether t ( ) or yt ( ) are perodc tha S y ( τ ) s perodc too. Sy ( τ + T) t ( ) yt ( + τ + T) t ( T) yt ( + τ) Sy ( τ) 4. If both t ( ) ad yt ( ) are perodc wth the same perod tha: 4 ω t ( ) ae ; yt ( ) be ωt mω( t+ τ) ( + m) ωt m S ( ) abe e abe e S τ y m m m, m, ( τ ), m a y t mωt m m b e mωτ m + m ab e δ ωτ S wll have large compoets whe both ad y have large compoets at the same! 6. Whe or y are ot perodc we ca epad them Fourer tegral: ωt ω( t+ τ) t ( ) ( ω) e dω; yt ( τ) y( ω) e dω π + π ωτ 5 8

9 ( τ ) ( ω ) ( ω) ω ω ( π ) ( ω+ ω) t ωτ S y e e d d y ( ω ) ( ω) ω ω ( π ) ( ω+ ω) t ωτ y e e d d d ωτ ( ω) y( ω) e δ( ω ω) dωdω π + ωτ Sy( τ) ( ω) y( ω) e dω π * S ( ω) ( ω) y( ω) ( ω) y( ω) y t Both ad y cotrbute to the cross correlato To mamze the cross correlato fucto t s advsable correlate the system respose wth a replca of the ectato fucto Matched flter may be realzed by cross correlato (whte ose) The output of a matched flter at tme t f f ( ) ( τ ) S( T) st ( ) + ht ( ) s( ) + ht ( τ) dτ for a matched flter h s kow: st ( t),... t T ht () elswhere T ( τ ) S( T) s( ) + s( T + τ) dτ T t s: Cross correlate the sgal wth addtve ose wth a replca of the ectato sgal T 7 9

10 Sgal,autocorrelato Adtve ose S/ Tme Sgal,crosscorrelato Addtve ose S/ Tme Auto ad Cross correlato of Addtve ose Sgal() t s( ω t) + radom S ( t) (s( ω t) + radom) s( ω t) y ( ω ) S ( t ) Sgal( t )s ( t t ) y 8 Multplcatve ose S/ Sgal,autocorrelato Tme Multplcatve ose S/ Sgal,crosscorrelato Tme Auto ad Cross correlato of Multplcatve ose Sgal() t s( ω t)* radom y S ( t) (s( ω t) * radom) s( ω t) 9

11 7. Uses of cross correlato fuctos 7. Iput-Output cross correlato () t H( ω) y() t S t y t S y * y( τ ) ( ) ( + τ ; y( ω) ( ω) ( ω) [ ] S H * y( ω) ( ω) ( ω) ( ω) f ( ω) cost (Whte ose) S ( ω) cost* H( ω) y We ca measure the trasfer fucto of a commucato etwork o le whe there are o sgals, oly ose, by computg the put output cross correlato

Transforms that are commonly used are separable

Transforms that are commonly used are separable Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )

More information

CODING & MODULATION Prof. Ing. Anton Čižmár, PhD.

CODING & MODULATION Prof. Ing. Anton Čižmár, PhD. CODING & MODULATION Prof. Ig. Ato Čžmár, PhD. also from Dgtal Commucatos 4th Ed., J. G. Proaks, McGraw-Hll It. Ed. 00 CONTENT. PROBABILITY. STOCHASTIC PROCESSES Probablty ad Stochastc Processes The theory

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel

More information

Lecture Notes Forecasting the process of estimating or predicting unknown situations

Lecture Notes Forecasting the process of estimating or predicting unknown situations Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Objectives of Multiple Regression

Objectives of Multiple Regression Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations. III- G. Bref evew of Grad Orthogoalty Theorem ad mpact o epresetatos ( ) GOT: h [ () m ] [ () m ] δδ δmm ll GOT puts great restrcto o form of rreducble represetato also o umber: l h umber of rreducble

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

x y exp λ'. x exp λ 2. x exp 1.

x y exp λ'. x exp λ 2. x exp 1. egecosmcd Egevalue-egevector of the secod dervatve operator d /d hs leads to Fourer seres (se, cose, Legedre, Bessel, Chebyshev, etc hs s a eample of a systematc way of geeratg a set of mutually orthogoal

More information

Investigating Cellular Automata

Investigating Cellular Automata Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte

More information

Module 7. Lecture 7: Statistical parameter estimation

Module 7. Lecture 7: Statistical parameter estimation Lecture 7: Statstcal parameter estmato Parameter Estmato Methods of Parameter Estmato 1) Method of Matchg Pots ) Method of Momets 3) Mamum Lkelhood method Populato Parameter Sample Parameter Ubased estmato

More information

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise OISE Thermal oe ktb (T abolute temperature kelv, B badwdth, k Boltzama cotat) 3 k.38 0 joule / kelv ( joule /ecod watt) ( ) v ( freq) 4kTB Thermal oe refer to the ketc eergy of a body of partcle a a reult

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004. MASSACHUSETTS INSTITUTE OF TECHNOLOGY Departmet of Electrical Egieerig ad Computer Sciece 6.34 Discrete Time Sigal Processig Fall 24 BACKGROUND EXAM September 3, 24. Full Name: Note: This exam is closed

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Ahmed Elgamal. MDOF Systems & Modal Analysis

Ahmed Elgamal. MDOF Systems & Modal Analysis DOF Systems & odal Aalyss odal Aalyss (hese otes cover sectos from Ch. 0, Dyamcs of Structures, Al Chopra, Pretce Hall, 995). Refereces Dyamcs of Structures, Al K. Chopra, Pretce Hall, New Jersey, ISBN

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices.

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices. 4.3 - Modal Aalyss Physcal coordates are ot always the easest to work Egevectors provde a coveet trasformato to modal coordates Modal coordates are lear combato of physcal coordates Say we have physcal

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

Algorithms Theory, Solution for Assignment 2

Algorithms Theory, Solution for Assignment 2 Juor-Prof. Dr. Robert Elsässer, Marco Muñz, Phllp Hedegger WS 2009/200 Algorthms Theory, Soluto for Assgmet 2 http://lak.formatk.u-freburg.de/lak_teachg/ws09_0/algo090.php Exercse 2. - Fast Fourer Trasform

More information

Centroids & Moments of Inertia of Beam Sections

Centroids & Moments of Inertia of Beam Sections RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

ε. Therefore, the estimate

ε. Therefore, the estimate Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model

More information

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations Lecture 7 3. Parametrc ad No-Parametrc Ucertates, Radal Bass Fuctos ad Neural Network Approxmatos he parameter estmato algorthms descrbed prevous sectos were based o the assumpto that the system ucertates

More information

G S Power Flow Solution

G S Power Flow Solution G S Power Flow Soluto P Q I y y * 0 1, Y y Y 0 y Y Y 1, P Q ( k) ( k) * ( k 1) 1, Y Y PQ buses * 1 P Q Y ( k1) *( k) ( k) Q Im[ Y ] 1 P buses & Slack bus ( k 1) *( k) ( k) Y 1 P Re[ ] Slack bus 17 Calculato

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon EE105 - Srg 007 Mcroelectroc Devces ad Crcuts Perodc Table of Elemets Lecture Semcoductor Bascs Electroc Proertes of Slco Slco s Grou IV (atomc umber 14) Atom electroc structure: 1s s 6 3s 3 Crystal electroc

More information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information : Maru Jutt Overvew he propertes of adlmted Gaussa chaels are further studed, parallel Gaussa chaels ad Gaussa chaels wth feedac are solved. Source he materal s maly ased o Sectos.4.6 of the course oo

More information

Chapter 10 Two Stage Sampling (Subsampling)

Chapter 10 Two Stage Sampling (Subsampling) Chapter 0 To tage amplg (usamplg) I cluster samplg, all the elemets the selected clusters are surveyed oreover, the effcecy cluster samplg depeds o sze of the cluster As the sze creases, the effcecy decreases

More information

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i ENUMERATON OF FNTE GROUPS OF THE FORM R ( 2 = (RfR^'u =1. 21 THE COMPLETE ENUMERATON OF FNTE GROUPS OF THE FORM R 2 ={R R j ) k -= H. S. M. COXETER*. ths paper, we vestgate the abstract group defed by

More information

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis) We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the

More information

6.4.5 MOS capacitance-voltage analysis

6.4.5 MOS capacitance-voltage analysis 6.4.5 MOS capactace-voltage aalyss arous parameters of a MOS devce ca be determed from the - characterstcs.. Type of substrate dopg. Isulator capactace = /d sulator thckess d 3. The mmum depleto capactace

More information

Statistics MINITAB - Lab 5

Statistics MINITAB - Lab 5 Statstcs 10010 MINITAB - Lab 5 PART I: The Correlato Coeffcet Qute ofte statstcs we are preseted wth data that suggests that a lear relatoshp exsts betwee two varables. For example the plot below s of

More information

COV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic.

COV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic. c Pogsa Porchawseskul, Faculty of Ecoomcs, Chulalogkor Uversty olato of costat varace of s but they are stll depedet. C,, he error term s sad to be heteroscedastc. c Pogsa Porchawseskul, Faculty of Ecoomcs,

More information

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever. 9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,

More information

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

We have already referred to a certain reaction, which takes place at high temperature after rich combustion. ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a

More information

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..

More information

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty

More information

Chapter Two. An Introduction to Regression ( )

Chapter Two. An Introduction to Regression ( ) ubject: A Itroducto to Regresso Frst tage Chapter Two A Itroducto to Regresso (018-019) 1 pg. ubject: A Itroducto to Regresso Frst tage A Itroducto to Regresso Regresso aalss s a statstcal tool for the

More information

D. VQ WITH 1ST-ORDER LOSSLESS CODING

D. VQ WITH 1ST-ORDER LOSSLESS CODING VARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING) Varable-Rate VQ = Quatzato + Lossless Varable-Legth Bary Codg A rage of optos -- from smple to complex A. Uform scalar quatzato wth varable-legth codg, oe

More information

Chapter 3 Sampling For Proportions and Percentages

Chapter 3 Sampling For Proportions and Percentages Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

Multiple Linear Regression Analysis

Multiple Linear Regression Analysis LINEA EGESSION ANALYSIS MODULE III Lecture - 4 Multple Lear egresso Aalyss Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Cofdece terval estmato The cofdece tervals multple

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law Module : The equato of cotuty Lecture 5: Coservato of Mass for each speces & Fck s Law NPTEL, IIT Kharagpur, Prof. Sakat Chakraborty, Departmet of Chemcal Egeerg 2 Basc Deftos I Mass Trasfer, we usually

More information

Uniform DFT Filter Banks 1/27

Uniform DFT Filter Banks 1/27 .. Ufor FT Flter Baks /27 Ufor FT Flter Baks We ll look at 5 versos of FT-based flter baks all but the last two have serous ltatos ad are t practcal. But they gve a ce trasto to the last two versos whch

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013 ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport

More information

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Mean is only appropriate for interval or ratio scales, not ordinal or nominal. Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot

More information

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss

More information

Uniform magnetic susceptibilities

Uniform magnetic susceptibilities Uform magetc susceptbltes Typcal behavors ad measuremet techques SUSCPTIILIT UNIFORM & MAGNTOMTRIS page 1 - M ICFP- lectroc propertes of solds (Fabrce ert Varous behavors of M(H magetzato M Lear respose

More information

ρ < 1 be five real numbers. The

ρ < 1 be five real numbers. The Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:

More information

EP2200 Queueing theory and teletraffic systems. Queueing networks. Viktoria Fodor KTH EES/LCN KTH EES/LCN

EP2200 Queueing theory and teletraffic systems. Queueing networks. Viktoria Fodor KTH EES/LCN KTH EES/LCN EP2200 Queueg theory ad teletraffc systems Queueg etworks Vktora Fodor Ope ad closed queug etworks Queug etwork: etwork of queug systems E.g. data packets traversg the etwork from router to router Ope

More information

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter A Robust otal east Mea Square Algorthm For Nolear Adaptve Flter Ruxua We School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049, P.R. Cha rxwe@chare.com Chogzhao Ha, azhe u School of Electroc

More information

3. Basic Concepts: Consequences and Properties

3. Basic Concepts: Consequences and Properties : 3. Basc Cocepts: Cosequeces ad Propertes Markku Jutt Overvew More advaced cosequeces ad propertes of the basc cocepts troduced the prevous lecture are derved. Source The materal s maly based o Sectos.6.8

More information

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

The E vs k diagrams are in general a function of the k -space direction in a crystal

The E vs k diagrams are in general a function of the k -space direction in a crystal vs dagram p m m he parameter s called the crystal mometum ad s a parameter that results from applyg Schrödger wave equato to a sgle-crystal lattce. lectros travelg dfferet drectos ecouter dfferet potetal

More information

Model Fitting, RANSAC. Jana Kosecka

Model Fitting, RANSAC. Jana Kosecka Model Fttg, RANSAC Jaa Kosecka Fttg: Issues Prevous strateges Le detecto Hough trasform Smple parametrc model, two parameters m, b m + b Votg strateg Hard to geeralze to hgher dmesos a o + a + a 2 2 +

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

Integral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy

Integral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy Itroducto to Smulato - Lecture 22 Itegral Equato ethods Jacob Whte Thaks to Deepak Ramaswamy, chal Rewesk, X Wag ad Kare Veroy Outle Itegral Equato ethods Exteror versus teror problems Start wth usg pot

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

More information

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Random Variables and Probability Distributions

Random Variables and Probability Distributions Radom Varables ad Probablty Dstrbutos * If X : S R s a dscrete radom varable wth rage {x, x, x 3,. } the r = P (X = xr ) = * Let X : S R be a dscrete radom varable wth rage {x, x, x 3,.}.If x r P(X = x

More information

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..

More information

Lecture 8: Linear Regression

Lecture 8: Linear Regression Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE

More information

Exercises for Square-Congruence Modulo n ver 11

Exercises for Square-Congruence Modulo n ver 11 Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced

More information

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs CLASS NOTES for PBAF 58: Quattatve Methods II SPRING 005 Istructor: Jea Swaso Dael J. Evas School of Publc Affars Uversty of Washgto Ackowledgemet: The structor wshes to thak Rachel Klet, Assstat Professor,

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

Some Notes on the Probability Space of Statistical Surveys

Some Notes on the Probability Space of Statistical Surveys Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty

More information