Power Spectrum Estimation of Stochastic Stationary Signals
|
|
- Brooke Baldwin
- 5 years ago
- Views:
Transcription
1 ag of 6 or Spctru stato of Stochastc Statoary Sgas Lt s cosr a obsrvato of a stochastc procss (). Ay obsrvato s a ft rcor of th ra procss. Thrfor, ca say: <,... ˆ hr s,... a a rctag o. o t s s hat s th ffct of ths tato or trucato of th sga (), o ts SD. By fto () FFT SD ˆ. Thrfor, ) (. { } { } o o Lt Rag of : [, ] rag of [ ] : Rag of : [, ] { } () () but ()
2 ag of 6 a thrfor { () } { } o 3 WB ( ) ( ) () { } () () () Tru SD of () Thrfor, th ffct of trucato o th por spctru of th sga s that gt a asyptotcay ubas stator of th tru SD of th sga. ( ) s ca roogra. As sa, t s a asyptotcay ubas stator but ts varac s ot cosstt or o bcaus var ˆ ( ) { } ( ) a t os t go to zro. That s hy roogra s rfrr as a osy stator of th tru SD of (). Thr ar a f thos to ry ths varac prob. Bartt (Avragg roogra) tho I orr to ruc th varac of SD stators, Bartt sgt th ata to sgts ach th gth. (, hr s th ubr of ata saps), coput th roogra of ach sgt a th got th avrag of th as th SD stator. ata of ach sgt: () ( ),,., # of sgts. sgt,.., gth of ach roogra of ach sgt:
3 ag 3 of 6 ( ) B Th th avrag roogra s: p ( ) Lts s ts statstca proprts: B { ( )} p ( ) ( { } { p ( )}., hch s th sa as bas of roogra () ) a hc, asyptotcay ubas stator. 3 WB Th bas s, hch go to f W () B s ca Bartt o, hch s a hrt o as a s rsut of trucato. Lt's oo at th varac of Bartt tho: var B { ( )} var { } var{ ( )} Thrfor, th varac s ruc by th factor. So, t ss as cras th ubr of sgts (), ruc th varac of stator or. But, ths s at th cost of osg frqucy rsouto. Wch tho Wch tho s fact th of Bartt tho by th foog to ofcatos:. Sgts hav a ovrap. Appyg a o rathr tha a Rctag o to ach sgt. Thrfor, () ata saps ach sgt ( D), hr,.., - gth of ach sgt,.., - ubr of sgts To o sgtato atab: For :
4 ag 4 of 6 : ; th 5% ovrap D/ ; If D, th thr s o ovrap. If, D thr s a 5% ovrap bt th succssv sgts. Aso, thr s a o for sgtato ca Data Wo () s a o such that f f W. Thr ar ay ffrt chocs for () such as Hag, Hag, Baca, arz, tc., os. Th por spctru stato Wch tho s th f as. hr,...,, U a U s th orazato factor to rsut f f W o, { } { } { } a { } { } U U but, Γ { } U Γ But S ( ) SD of a U S { } W W S S Γ Γ
5 ag 5 of 6 So th bas of SD th th Wch tho ps o th shap of ata o. You ca pct th arror th a ob of S W (), th sar th bas. But aga as usua, th ssr bas ay caus a argr varac of th stator. var { } { ( )} var Γ 9 Γ 8 ( ) ( ) th o ovrap th 5% ovrap ot that th cas of o ovrap abov s ot th sa as th o th cas of 5% ovrap. Suary I suary a goo ay to stat th por spctru of a statoary stochastc sga s to sgt t to qua gth ovrappg (5%) sgts a th cacuat th FFT of ach sgt, ta ts agtu to th por of a f th avrag of t bt th sgts. Spctrogra of Stochastc o-statoary Sgas by Short-T Fourr Trasfor (STFT) O tho to stat por spctru of a rao sga that s o-statoary gra ( ay auo sga) s to v th sga to f gth short-urato sgts a cacuat th FFT of ach sgt a sho th spctrogra. I ths tchqu, th urato of th sgts ust b short ough to sur that th sga ras statoary th that urato. I orr to hy o ust o sgtato for o-statoary sgas (h for statoary sgas t s bttr to o but o t hav to), t's o th foog prts. Dooa th av f "sash.av" a typ th foog co a ru atab. %% Aaptv sgtato
6 ag 6 of 6 [y fs]avra('sash'); sousc(y,5) %% stg to th sou of sga y(5:63); %% ths s th part that sous "sh"! fgur subpot(,,) pot(), ho, bar([ ],[- - ]); %% pottg th bouars (:5); (5:35); 3(35:gth()); subpot(,,), tt(' spctru') ps(,gth(),5,[],) fgur subpot(,,), tt(' spctru') ps(,gth(),5,[],) subpot(,,), tt('3 spctru') ps(3,gth(3),5,[],) Obsrv that th thr spctra hav ffrt frqucy charactrstcs. o, t's prt th f sgtato tchqu. Ra th hp of atab for th coa "spcgra". It s a fucto to pot th spctrogra of a sga D. Th agtu s bg sho by coor. You ca choos th gth of sgt a aso choos to put ovrap bt aact sgts or ot. Ru th foog co a obsrv th pots. %% f sgtato fgur spcgra(,56,5,[],) tt('urato of ach sgt56 saps3 sc, ovrap bt th sgts') fgur spcgra(,56,5,[],8) tt('urato of ach sgt56 saps3 sc, ovrap bt th sgts5%') Thrfor, for th o-statoary sgas ay boogca sgas or ay auo sga, shou tr th STFT hch s cacuat by th foog stps: - Sgt th ata to qua gth ovrappg sgts (5% ovrap). - Cacuat th FFT of th sga ach sgt. 3- Ta thr th og (abs(fft(sga))) or abs(fft(sga)).^ a that s th por spctru of vry sgt. Of cours you oy haf of th gth of th FFT.
Digital Signal Processing, Fall 2006
Digital Sigal Procssig, Fall 6 Lctur 9: Th Discrt Fourir Trasfor Zhg-Hua Ta Dpartt of Elctroic Systs Aalborg Uivrsity, Dar zt@o.aau.d Digital Sigal Procssig, I, Zhg-Hua Ta, 6 Cours at a glac MM Discrt-ti
More informationCorrelation in tree The (ferromagnetic) Ising model
5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.
More informationUnbalanced Panel Data Models
Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr
More information3.4 Properties of the Stress Tensor
cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato
More informationHomework 1: Solutions
Howo : Solutos No-a Fals supposto tst but passs scal tst lthouh -f th ta as slowss [S /V] vs t th appaac of laty alty th path alo whch slowss s to b tat to obta tavl ts ps o th ol paat S o V as a cosquc
More informationUsing Nonlinear Filter for Adaptive Blind Channel Equalization
HAMDRZA BAKHSH Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Us oar Ftr or Adaptv Bd Cha quazato MOHAMMAD POOYA Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Abstract: trsybo trrc
More informationEstimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek
Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd
More informationGRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?
5 9 Bt Ft L # 8 7 6 5 GRAPH IN CIENCE O of th thg ot oft a rto of a xrt a grah of o k. A grah a vual rrtato of ural ata ollt fro a xrt. o of th ty of grah you ll f ar bar a grah. Th o u ot oft a l grah,
More information11/8/2002 CS 258 HW 2
/8/ CS 58 HW. G o a a qc of aa h < fo a I o goa o co a C cc c F ch ha F fo a I A If cc - c a co h aoa coo o ho o choo h o qc? I o g o -coa o o-coa? W ca choo h o qc o h a a h aa a. Tha f o o a h o h a:.
More informationChemistry 342 Spring, The Hydrogen Atom.
Th Hyrogn Ato. Th quation. Th first quation w want to sov is φ This quation is of faiiar for; rca that for th fr partic, w ha ψ x for which th soution is Sinc k ψ ψ(x) a cos kx a / k sin kx ± ix cos x
More informationThe Exile Began. Family Journal Page. God Called Jeremiah Jeremiah 1. Preschool. below. Tell. them too. Kids. Ke Passage: Ezekiel 37:27
Faily Jo Pag Th Exil Bg io hy u c prof b jo ou Shar ab ou job ab ar h o ay u Yo ra u ar u r a i A h ) ar par ( grp hav h y y b jo i crib blo Tll ri ir r a r gro up Allo big u r a i Rvi h b of ha u ha a
More informationChapter 7. A Quantum Mechanical Model for the Vibration and Rotation of Molecules
Chaptr 7. A Quantu Mchanica Mo for th Vibration an Rotation of Mocus Haronic osciator: Hook s aw: F k is ispacnt Haronic potntia: V F k k is forc constant: V k curvatur of V at quiibriu Nwton s quation:
More informationEECE 301 Signals & Systems Prof. Mark Fowler
EECE 301 Signals & Systms Prof. Mark Fowlr ot St #21 D-T Signals: Rlation btwn DFT, DTFT, & CTFT 1/16 W can us th DFT to implmnt numrical FT procssing This nabls us to numrically analyz a signal to find
More informationChapter 6. pn-junction diode: I-V characteristics
Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc
More information3) Use the average steady-state equation to determine the dose. Note that only 100 mg tablets of aminophylline are available here.
PHA 5127 Dsigning A Dosing Rgimn Answrs provi by Jry Stark Mr. JM is to b start on aminophyllin or th tratmnt o asthma. H is a non-smokr an wighs 60 kg. Dsign an oral osing rgimn or this patint such that
More informationY 0. Standing Wave Interference between the incident & reflected waves Standing wave. A string with one end fixed on a wall
Staning Wav Intrfrnc btwn th incint & rflct wavs Staning wav A string with on n fix on a wall Incint: y, t) Y cos( t ) 1( Y 1 ( ) Y (St th incint wav s phas to b, i.., Y + ral & positiv.) Rflct: y, t)
More informationWindowing in FIR Filter Design. Design Summary and Examples
Lctur 3 Outi: iowig i FIR Fitr Dsig. Dsig Summary a Exams Aoucmts: Mitrm May i cass. i covr through FIR Fitr Dsig. 4 ost, 5% ogr tha usua, 4 xtra ays to comt (u May 8) Mor tais o say Thr wi b o aitioa
More informationIntro to QM due: February 8, 2019 Problem Set 12
Intro to QM du: Fbruary 8, 9 Prob St Prob : Us [ x i, p j ] i δ ij to vrify that th anguar ontu oprators L i jk ɛ ijk x j p k satisfy th coutation rations [ L i, L j ] i k ɛ ijk Lk, [ L i, x j ] i k ɛ
More informationReliability of time dependent stress-strength system for various distributions
IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,
More informationME 501A Seminar in Engineering Analysis Page 1
St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp
More informationA Simple Representation of the Weighted Non-Central Chi-Square Distribution
SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch
More informationIntroduction to logistic regression
Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data
More informationWeights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )
Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also
More informationON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS
ACENA Vo.. 03-08 005 03 ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS Rub A. CERUTTI RESUMEN: Cosrao os úcos Rsz coo casos artcuars úco causa
More informationA L A BA M A L A W R E V IE W
A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N
More informationAPPENDIX: STATISTICAL TOOLS
I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.
More informationPriority Search Trees - Part I
.S. 252 Pro. Rorto Taassa oputatoal otry S., 1992 1993 Ltur 9 at: ar 8, 1993 Sr: a Q ol aro Prorty Sar Trs - Part 1 trouto t last ltur, w loo at trval trs. or trval pot losur prols, ty us lar spa a optal
More informationFrequency Measurement in Noise
Frqucy Masurmt i ois Porat Sctio 6.5 /4 Frqucy Mas. i ois Problm Wat to o look at th ct o ois o usig th DFT to masur th rqucy o a siusoid. Cosidr sigl complx siusoid cas: j y +, ssum Complx Whit ois Gaussia,
More informationBinary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit
(c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,
More informationCS 491 G Combinatorial Optimization
CS 49 G Cobinatorial Optiization Lctur Nots Junhui Jia. Maiu Flow Probls Now lt us iscuss or tails on aiu low probls. Thor. A asibl low is aiu i an only i thr is no -augnting path. Proo: Lt P = A asibl
More informationThe Matrix Exponential
Th Matrix Exponntial (with xrciss) by D. Klain Vrsion 207.0.05 Corrctions and commnts ar wlcom. Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix A A k I + A + k!
More informationc. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f
Essential Skills Chapter f ( x + h) f ( x ). Simplifying the difference quotient Section. h f ( x + h) f ( x ) Example: For f ( x) = 4x 4 x, find and simplify completely. h Answer: 4 8x 4 h. Finding the
More informationThe Matrix Exponential
Th Matrix Exponntial (with xrciss) by Dan Klain Vrsion 28928 Corrctions and commnts ar wlcom Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix () A A k I + A + k!
More informationEstimation of Population Variance Using a Generalized Double Sampling Estimator
r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst
More informationUNTYPED LAMBDA CALCULUS (II)
1 UNTYPED LAMBDA CALCULUS (II) RECALL: CALL-BY-VALUE O.S. Basic rul Sarch ruls: (\x.) v [v/x] 1 1 1 1 v v CALL-BY-VALUE EVALUATION EXAMPLE (\x. x x) (\y. y) x x [\y. y / x] = (\y. y) (\y. y) y [\y. y /
More informationFirst order differential equation Linear equation; Method of integrating factors
First orr iffrntial quation Linar quation; Mtho of intgrating factors Exampl 1: Rwrit th lft han si as th rivativ of th prouct of y an som function by prouct rul irctly. Solving th first orr iffrntial
More informationMultiple Short Term Infusion Homework # 5 PHA 5127
Multipl Short rm Infusion Homwork # 5 PHA 527 A rug is aministr as a short trm infusion. h avrag pharmacokintic paramtrs for this rug ar: k 0.40 hr - V 28 L his rug follows a on-compartmnt boy mol. A 300
More informationReview - Probabilistic Classification
Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw
More informationExecutive Committee and Officers ( )
Gifted and Talented International V o l u m e 2 4, N u m b e r 2, D e c e m b e r, 2 0 0 9. G i f t e d a n d T a l e n t e d I n t e r n a t i o n a2 l 4 ( 2), D e c e m b e r, 2 0 0 9. 1 T h e W o r
More informationGilbert the Green Tree Frog
Gbrt th Gr Tr Frog A org Kpr Kd book Wrtt by Stph Jk Iutrtd by T Eor ONCE UPON A TIME thr w frog md Gbrt. Gbrt w Gr Tr Frog. H fmy w gr. H hom w gr. Ad omtm v h food w gr. Gbrt w ck d trd of bg o gr th
More informationOn Hamiltonian Tetrahedralizations Of Convex Polyhedra
O Ht Ttrrzts O Cvx Pyr Frs C 1 Q-Hu D 2 C A W 3 1 Dprtt Cputr S T Uvrsty H K, H K, C. E: @s.u. 2 R & TV Trsss Ctr, Hu, C. E: q@163.t 3 Dprtt Cputr S, Mr Uvrsty Nwu St. J s, Nwu, C A1B 35. E: w@r.s.u. Astrt
More informationDifferentiation of Exponential Functions
Calculus Modul C Diffrntiation of Eponntial Functions Copyright This publication Th Northrn Albrta Institut of Tchnology 007. All Rights Rsrvd. LAST REVISED March, 009 Introduction to Diffrntiation of
More informationVISUALIZATION OF TRIVARIATE NURBS VOLUMES
ISUALIZATIO OF TRIARIATE URS OLUMES SAMUELČÍK Mat SK Abstact. I ths pap fcs patca st f f-f bcts a ts sazat. W xt appach f g cs a sfacs a ppa taat s bas z a -sp xpsss. O a ga s t saz g paatc s. Th sazat
More informationLecture 1: Empirical economic relations
Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.
More informationBeechwood Music Department Staff
Beechwood Music Department Staff MRS SARAH KERSHAW - HEAD OF MUSIC S a ra h K e rs h a w t r a i n e d a t t h e R oy a l We ls h C o l le g e of M u s i c a n d D ra m a w h e re s h e ob t a i n e d
More information8(4 m0) ( θ ) ( ) Solutions for HW 8. Chapter 25. Conceptual Questions
Solutios for HW 8 Captr 5 Cocptual Qustios 5.. θ dcrass. As t crystal is coprssd, t spacig d btw t plas of atos dcrass. For t first ordr diffractio =. T Bragg coditio is = d so as d dcrass, ust icras for
More informationECE602 Exam 1 April 5, You must show ALL of your work for full credit.
ECE62 Exam April 5, 27 Nam: Solution Scor: / This xam is closd-book. You must show ALL of your work for full crdit. Plas rad th qustions carfully. Plas chck your answrs carfully. Calculators may NOT b
More informationSelf-Adjointness and Its Relationship to Quantum Mechanics. Ronald I. Frank 2016
Ronald I. Frank 06 Adjoint https://n.wikipdia.org/wiki/adjoint In gnral thr is an oprator and a procss that dfin its adjoint *. It is thn slf-adjoint if *. Innr product spac https://n.wikipdia.org/wiki/innr_product_spac
More informationThe Real Hydrogen Atom
T Ra Hydog Ato ov ad i fist od gt iddt of :.6V a us tubatio toy to dti: agti ffts si-obit ad yfi -A ativisti otios Aso av ab sift du to to sfitatio. Nd QD Dia q. ad dds o H wavfutio at sou of ti fid. Vy
More informationOption 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges.
Optio Chaptr Ercis. Covrgs to Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Divrgs 8 Divrgs Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Covrgs to Covrgs to 8 Proof Covrgs to π l 8 l a b Divrgt π Divrgt
More informationCS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples
CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht los@cs.tt.eu 59 Seott Square Suervse learg Data: D { D D.. D} a set of eales D s a ut vector of sze s the esre outut gve b a teacher Obectve: lear
More informationMotivation. We talk today for a more flexible approach for modeling the conditional probabilities.
Baysia Ntworks Motivatio Th coditioal idpdc assuptio ad by aïv Bays classifirs ay s too rigid spcially for classificatio probls i which th attributs ar sowhat corrlatd. W talk today for a or flibl approach
More informationWorksheet: Taylor Series, Lagrange Error Bound ilearnmath.net
Taylor s Thorm & Lagrag Error Bouds Actual Error This is th ral amout o rror, ot th rror boud (worst cas scario). It is th dirc btw th actual () ad th polyomial. Stps:. Plug -valu ito () to gt a valu.
More informationCOMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES
COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld
More informationa 1and x is any real number.
Calcls Nots Eponnts an Logarithms Eponntial Fnction: Has th form y a, whr a 0, a an is any ral nmbr. Graph y, Graph y ln y y Th Natral Bas (Elr s nmbr): An irrational nmbr, symboliz by th lttr, appars
More information22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.
Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M
More informationWedge clamp, double-acting for dies with tapered clamping edge
Wg c, ou-ctg or th tr cg g Acto: cg o th tr cg g or cg o o r or cg o jcto oug ch A B Hr g cg rt Buhg Dg: Dou-ctg g c or cg o r or or or cg jcto oug ch. Th g c cot o hyruc oc cyr to gu houg. Th cg ot ro
More informationWashington State University
he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us
More informationEEO 401 Digital Signal Processing Prof. Mark Fowler
EEO 401 Digital Signal Procssing Prof. Mark Fowlr Dtails of th ot St #19 Rading Assignmnt: Sct. 7.1.2, 7.1.3, & 7.2 of Proakis & Manolakis Dfinition of th So Givn signal data points x[n] for n = 0,, -1
More informationI N A C O M P L E X W O R L D
IS L A M I C E C O N O M I C S I N A C O M P L E X W O R L D E x p l o r a t i o n s i n A g-b eanste d S i m u l a t i o n S a m i A l-s u w a i l e m 1 4 2 9 H 2 0 0 8 I s l a m i c D e v e l o p m e
More informationThey must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei.
37 1 How may utros ar i a uclus of th uclid l? 20 37 54 2 crtai lmt has svral isotops. Which statmt about ths isotops is corrct? Thy must hav diffrt umbrs of lctros orbitig thir ucli. Thy must hav th sam
More information176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s
A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps
More informationDepartment of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis
Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..
More informationTotal Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are
Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,
More informationFace Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction
F Dtto Roto Lr Alr F Roto C Y I Ursty O solto: tto o l trs s s ys os ot. Dlt to t to ltpl ws. F Roto Aotr ppro: ort y rry s tor o so E.. 56 56 > pot 6556- stol sp A st o s t ps to ollto o pots ts sp. F
More informationME311 Machine Design
ME311 Machin Dsign Lctur 4: Strss Concntrations; Static Failur W Dornfld 8Sp017 Fairfild Univrsit School of Enginring Strss Concntration W saw that in a curvd bam, th strss was distortd from th uniform
More informationExamples and applications on SSSP and MST
Exampls an applications on SSSP an MST Dan (Doris) H & Junhao Gan ITEE Univrsity of Qunslan COMP3506/7505, Uni of Qunslan Exampls an applications on SSSP an MST Dijkstra s Algorithm Th algorithm solvs
More information[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then
SYSTEM PERFORMANCE Lctur 0: Stady-tat Error Stady-tat Error Lctur 0: Stady-tat Error Dr.alyana Vluvolu Stady-tat rror can b found by applying th final valu thorm and i givn by lim ( t) lim E ( ) t 0 providd
More informationData Assimilation 1. Alan O Neill National Centre for Earth Observation UK
Data Assimilation 1 Alan O Nill National Cntr for Earth Obsrvation UK Plan Motivation & basic idas Univariat (scalar) data assimilation Multivariat (vctor) data assimilation 3d-Variational Mthod (& optimal
More informationLecture contents. Density of states Distribution function Statistic of carriers. Intrinsic Extrinsic with no compensation Compensation
Ltur otts Dsity of stats Distributio futio Statisti of arrirs Itrisi trisi with o ompsatio ompsatio S 68 Ltur #7 Dsity of stats Problm: alulat umbr of stats pr uit rgy pr uit volum V() Larg 3D bo (L is
More informationProblem Set 4 Solutions Distributed: February 26, 2016 Due: March 4, 2016
Probl St 4 Solutions Distributd: Fbruary 6, 06 Du: March 4, 06 McQuarri Probls 5-9 Ovrlay th two plots using Excl or Mathatica. S additional conts blow. Th final rsult of Exapl 5-3 dfins th forc constant
More informationME 321 Kinematics and Dynamics of Machines S. Lambert Winter 2002
3.4 Forc Analysis of Linkas An undrstandin of forc analysis of linkas is rquird to: Dtrmin th raction forcs on pins, tc. as a consqunc of a spcifid motion (don t undrstimat th sinificanc of dynamic or
More information2. Finite Impulse Response Filters (FIR)
.. Mthos for FIR filtrs implmntation. Finit Impuls Rspons Filtrs (FIR. Th winow mtho.. Frquncy charactristic uniform sampling. 3. Maximum rror minimizing. 4. Last-squars rror minimizing.. Mthos for FIR
More information- Prefixes 'mono', 'uni', 'bi' and 'du' - Why are there no asprins in the jungle? Because the parrots ate them all.
- Prfs '', '', 'b' a '' - Na: Wrsar 27 Dat: W ar tr asrs t? Bas t arrts at t a. At t btt f t a s a st f wrs. Ts wrs ar t. T wrs av b a rta (ra arss), vrta (ra w) r aa (fr rr t rr). W f a wr, raw a at r
More informationBayesian Decision Theory
Baysian Dcision Thory Baysian Dcision Thory Know probabiity distribution of th catgoris Amost nvr th cas in ra if! Nvrthss usfu sinc othr cass can b rducd to this on aftr som work Do not vn nd training
More informationChapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University
Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod
More informationsignal amplification; design of digital logic; memory circuits
hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll
More informationChapter Discrete Fourier Transform
haptr.4 Dscrt Fourr Trasform Itroducto Rcad th xpota form of Fourr srs s Equatos 8 ad from haptr., wt f t 8, h.. T w t f t dt T Wh th abov tgra ca b usd to comput, h.., t s mor prfrab to hav a dscrtzd
More informationLinear Prediction Analysis of Speech Sounds
Lr Prdcto Alyss of Sch Souds Brl Ch 4 frcs: X Hug t l So Lgug Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus sch rcogto rocdgs of th I Stbr 993 5-47 Lr Prdctv
More informationShortest Paths in Graphs. Paths in graphs. Shortest paths CS 445. Alon Efrat Slides courtesy of Erik Demaine and Carola Wenk
S 445 Shortst Paths n Graphs lon frat Sls courtsy of rk man an arola Wnk Paths n raphs onsr a raph G = (V, ) wth -wht functon w : R. Th wht of path p = v v v k s fn to xampl: k = w ( p) = w( v, v + ).
More informationInference on Stress-Strength Reliability for Weighted Weibull Distribution
Arca Joural of Mathatcs a Statstcs 03, 3(4: 0-6 DOI: 0.593/j.ajs.030304.06 Ifrc o Strss-Strgth Rlablty for Wght Wbull Dstrbuto Hay M. Sal Dpartt of Statstcs, Faculty of Corc, Al-Azhr Uvrsty, Egypt & Qass
More informationthe output is Thus, the output lags in phase by θ( ωo) radians Rewriting the above equation we get
Th output y[ of a frquncy-sctiv LTI iscrt-tim systm with a frquncy rspons H ( xhibits som ay rativ to th input caus by th nonro phas rspons θ( ω arg{ H ( } of th systm For an input A cos( ωo n + φ, < n
More informationFOR MORE PAPERS LOGON TO
IT430 - E-Commerce Quesion No: 1 ( Marks: 1 )- Please choose one MAC sand for M d a A ss Conro a M d a A ss Consor M r of As an Co n on of s Quesion No: 2 ( Marks: 1 )- Please choose one C oos orr HTML
More informationNuclear Chemistry -- ANSWERS
Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay
More informationExiting from QE. Fumio Hayashi and Junko Koeda. for presentation at SF Fed Conference. March 28, 2014
Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 1 / 29 Exiting from QE Fumio Hayashi an Junko Koa for prsntation at SF F Confrnc March 28, 214 To gt start... h^ : Fumio Hayashi an Junko Koa Exiting
More informationLECTURE 6 TRANSFORMATION OF RANDOM VARIABLES
LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt
More informationLinear Prediction Analysis of
Lr Prdcto Alyss of Sch Souds Brl Ch Drtt of Coutr Scc & Iforto grg Ntol Tw Norl Uvrsty frcs: X Hug t l So Lgug g Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus
More informationChapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional
Chaptr 13 GMM for Linar Factor Modls in Discount Factor form GMM on th pricing rrors givs a crosssctional rgrssion h cas of xcss rturns Hors rac sting for charactristic sting for pricd factors: lambdas
More informationP a g e 5 1 of R e p o r t P B 4 / 0 9
P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e
More informationIV. First Law of Thermodynamics. Cooler. IV. First Law of Thermodynamics
D. Applcatons to stady flow dvcs. Hat xchangrs - xampl: Clkr coolr for cmnt kln Scondary ar 50 C, 57,000 lbm/h Clkr? C, 5 ton/h Coolr Clkr 400 C, 5 ton/h Scondary ar 0 C, 57,000 lbm/h a. Assumptons. changs
More informationJones vector & matrices
Jons vctor & matrcs PY3 Colást na hollscol Corcagh, Ér Unvrst Collg Cork, Irland Dpartmnt of Phscs Matr tratmnt of polarzaton Consdr a lght ra wth an nstantanous -vctor as shown k, t ˆ k, t ˆ k t, o o
More informationProbability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers Roy D. Yates and David J.
Probability and Stochastic Procsss: A Frindly Introduction for Elctrical and Computr Enginrs Roy D. Yats and David J. Goodman Problm Solutions : Yats and Goodman,4.3. 4.3.4 4.3. 4.4. 4.4.4 4.4.6 4.. 4..7
More informationChapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS
Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)
More informationHeat/Di usion Equation. 2 = 0 k constant w(x; 0) = '(x) initial condition. ( w2 2 ) t (kww x ) x + k(w x ) 2 dx. (w x ) 2 dx 0.
Hat/Di usion Equation @w @t k @ w @x k constant w(x; ) '(x) initial condition w(; t) w(l; t) boundary conditions Enrgy stimat: So w(w t kw xx ) ( w ) t (kww x ) x + k(w x ) or and thrfor E(t) R l Z l Z
More informationECE594I Notes set 6: Thermal Noise
C594I ots, M. odwll, copyrightd C594I Nots st 6: Thrmal Nois Mark odwll Uivrsity of Califoria, ata Barbara rodwll@c.ucsb.du 805-893-344, 805-893-36 fax frcs ad Citatios: C594I ots, M. odwll, copyrightd
More informationExam 1. It is important that you clearly show your work and mark the final answer clearly, closed book, closed notes, no calculator.
Exam N a m : _ S O L U T I O N P U I D : I n s t r u c t i o n s : It is important that you clarly show your work and mark th final answr clarly, closd book, closd nots, no calculator. T i m : h o u r
More informationThe probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1
Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th
More informationPrevious knowlegde required. Spherical harmonics and some of their properties. Angular momentum. References. Angular momentum operators
// vious owg ui phica haoics a so o thi poptis Goup thoy Quatu chaics pctoscopy H. Haga 8 phica haoics Rcs Bia. iv «Iucib Tso thos A Itouctio o chists» Acaic ss D.A. c Quai.D. io «hii hysiu Appoch oécuai»
More informationCATAVASII LA NAȘTEREA DOMNULUI DUMNEZEU ȘI MÂNTUITORULUI NOSTRU, IISUS HRISTOS. CÂNTAREA I-A. Ήχος Πα. to os se e e na aș te e e slă ă ă vi i i i i
CATAVASII LA NAȘTEREA DOMNULUI DUMNEZEU ȘI MÂNTUITORULUI NOSTRU, IISUS HRISTOS. CÂNTAREA I-A Ήχος α H ris to os s n ș t slă ă ă vi i i i i ți'l Hris to o os di in c ru u uri, în tâm pi i n ți i'l Hris
More informationEstimation Theory. Chapter 4
Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,
More information