The Fourier Transform

Size: px
Start display at page:

Download "The Fourier Transform"

Transcription

1 /9/ Th ourr Transform Jan Baptst Josph ourr Effcnt Data Rprsntaton Data can b rprsntd n many ways. Advantag usng an approprat rprsntaton. Eampls: osy ponts along a ln Color spac rd/grn/blu v.s. Hu/Brghtnss Why do w nd rprsntaton n th frquncy doman? ourr Transform Orgnal Problm Dffcult soluton Soluton of Orgnal Problm Invrs ourr Transform Problm n rquncy Spac Rlatvly asy soluton Soluton n rquncy Spac 3 4

2 /9/ How can w nhanc such an mag? Transforms. Bass unctons.. Mthod for fndng th mag gvn th transformcoffcnts. 3. Mthod for fndng th transform coffcnts gvn th mag. Y Coordnat Grayscal Imag V Coordnats Transformd Imag X Coordnat U Coordnats 6 Chang of Bass Th ourr bass functons u (a,a ) v (a 3,a 4 ) u Bass unctons ar sns and cosns v a 4 a v a 3 u sn() v a a, u u v v a a u u cos() sn(4) whr c, b c T* b c () b() * 7

3 /9/ Th ourr bass functons Th transform coffcnts dtrmn th ampltud and phas: Evry functon quals a sum of sns and cosns 3 sn() A a sn() a sn() -a sn(+φ) + sn(3) B A+B +.8 sn() C A+B+C +.4 sn(7) D A+B+C+D 9 Sum of cosns only symmtrc functons ourr Coffcnts Sum of sns only antsymmtrc functons f() C + C cos() + S sn() C cos() + S sn() +... Trms ar consdrd n pars: C cos() + S sn() R sn(+ θ ) whr R C + S S and θ tan C Usng Compl umbrs: cos(), sn() C cos() + S sn() R θ { { Ampltud+phas 3

4 /9/ Th D Contnuous ourr Transform Th Contnuous ourr Transformfnds () gvn th (cont.) sgnal f(): B π ( ) f() d Th Invrs Contnuous ourr Transform composs a sgnal f() gvn (): f ( ) s a compl wav functon for ach w. π ( ) d 3 Contnuous vs sampld Sgnals Mov from f() ( R) to f( j ) (j Z) by samplng at qualntrvals. f(j) f( +j ) j,,,..., - f() f(j) f( + j ) f( 4 f( + ) +3 ) f() f(3) 4 f() f( + ) 3 3 f( ) f() Th dscrt ourr bass functons ar Th D Dscrt ourr Transform (DT) b π ( ).... ( ) f( ), b( ) f ( ) π,,,..., - or frquncy th ourr coffcnt s: Matlab: fft(f); π π θ / ( R ) π C cos + S sn ( ) R θ Th Invrs Dscrt ourr Transform (IDT) s dfnd as: f ( ) ( ) π Matlab: fft(f);,,,..., - 6 4

5 /9/ Dscrt ourr Transform - Eampl () () () [ +3 -π +4 π +4-3π ] [--]- (3) f() [ 3 4 4] ( ) (f() + f() + f() + f(3)) (+3+4+4) 3 ( ) [ +3 -π/ +4 π +4-3π/ ] [-+] ( ) ( ) [ +3-3π/ +4 3π +4-9π/ ] [--] DT of [ 3 4 4] s [ 3 (-+) - (--) ] 7 Th ourr Transform - Summary () s th ourr transform of f(): { f( ) } ( ) f() s th nvrs ourr transform of (): { ( ) } f( ) f() and () ar a ourr par. f() s a rprsntaton of th sgnal n th Spatal Doman and () s a rprsntaton n th rquncy Doman. 8 Th ourr transform () s a functon ovr th compl numbrs: ( ) R θ Th rquncy Doman ( ) R θ R θ R tlls us how much of frquncy s ndd. θ tlls us th shft of th Sn wav wth frquncy. Altrnatvly: ( ) a b + f() Th sgnal f() Ampltud (spctrum) and Phas ( ) a b + Ral Imag a tlls us how much of cos wth frquncy s ndd. b tlls us how much of sn wth frquncy s ndd. Ral and Imagnary 9

6 /9/ R - s th ampltud of (). θ - s th phas of (). R * () () - s th powr spctrum of (). If f() has a lot of fn dtals, R wll b hgh for hgh. If f() s "smooth, R wll b low for hgh. Eampls 3 sn() + sn(3) +.8 sn() +.4 sn(7) Dmo Th Dlta uncton: f() ( ) g δ( π ( ) δ( ) d f( ) δ R θ lm δ( ) ; δ( ) d ( ) ( ) ) d g Th Constant uncton: f() f ( ) π ( ) d δ( ) R θ ourr ourr Ral Imag Ral Imag 3 4 6

7 /9/ A Bass uncton: f () π Th Cosnuncton: ( ) f ( ) cos π f() π π ( ) d ourr ( ) π d δ( ) R θ f() δ + δ + π π π ( ) ( + ) d [ ( ) ( )] ourr - R θ Ral Imag Ral Imag - 6 Th Sn uncton: ( ) f ( ) sn π δ + δ π π π ( ) ( ) d [ ( ) ( )] f() R θ ourr π/ - -π/ Ral Imag Th Wndow uncton (rct): f() -.. ourr ( ) f < rct ( ) othrws. π. ( π) sn d π R snc ( π)

8 /9/ Proof: -/ / f() rct / () { / othrws Th Gaussan uncton: f ( ) ( ) π π ( ) f ( ) π π π π π [ ] / / d π π [ ] / π / d [ cos( π) sn( π) cos( π) sn( π) ] () f() ourr R sn( π ) SIC ( ) π 9 3 Th Comb uncton: () c δ ( mod ) { c } δ mod C ( ) Proprts of Th ourr Transform Lnarty: [ α f] α [ f] Dstrbutv (addtvty): f() c () ourr R C / () / DC (avrag): Symmtrc: If f() s ral thn, [ f + f ] [ f] [ ] + f ( ) f( ) d * ( ) ( ) thus ( ) ( ) 3 3 8

9 /9/ Dstrbutv: f() { f + g} { f} + { g} g() + f+g Translaton: Transformatons π [ f( )] ( ) Th ourr Spctrum rmans unchangd undr translaton: () + G() π ( ) ( ) ()+G() 33 Scalng: [ f( a ) ] a a 34 Eampl Translaton:.8.6 D Imag ral((u)) mag((u)) (u) 8 6 Chang of Scal: f() f { f( ) } ( ) thn { f( a) } () a a Translatd - - f() () f() () Dffrncs:

10 /9/ Chang of Scal: f() () End f(). (/) f(/) () 37 38

The Fourier Transform

The Fourier Transform e Processng ourer Transform D The ourer Transform Effcent Data epresentaton Dscrete ourer Transform - D Contnuous ourer Transform - D Eamples + + + Jean Baptste Joseph ourer Effcent Data epresentaton Data

More information

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

Fourier Transform: Overview. The Fourier Transform. Why Fourier Transform? What is FT? FT of a pulse function. FT maps a function to its frequencies

Fourier Transform: Overview. The Fourier Transform. Why Fourier Transform? What is FT? FT of a pulse function. FT maps a function to its frequencies .5.3..9.7.5.3. -. -.3 -.5.8.6.4. -. -.4 -.6 -.8 -. 8. 6. 4. -. -. 4 -. 6 -. 8 -.8.6.4. -. -.4 -.6 -.8 - orr Transform: Ovrvw Th orr Transform Wh T s sfl D T, DT, D DT T proprts Lnar ltrs Wh orr Transform?

More information

Jones vector & matrices

Jones 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 information

Outlier-tolerant parameter estimation

Outlier-tolerant parameter estimation Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln

More information

Announce. ECE 2026 Summer LECTURE OBJECTIVES READING. LECTURE #3 Complex View of Sinusoids May 21, Complex Number Review

Announce. ECE 2026 Summer LECTURE OBJECTIVES READING. LECTURE #3 Complex View of Sinusoids May 21, Complex Number Review ECE 06 Summr 018 Announc HW1 du at bginning of your rcitation tomorrow Look at HW bfor rcitation Lab 1 is Thursday: Com prpard! Offic hours hav bn postd: LECTURE #3 Complx Viw of Sinusoids May 1, 018 READIG

More information

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm . Grundlgnd Vrfahrn zur Übrtragung dgtalr Sgnal (Zusammnfassung) wt Dc. 5 Transmsson of Dgtal Sourc Sgnals Sourc COD SC COD MOD MOD CC dg RF s rado transmsson mdum Snk DC SC DC CC DM dg DM RF g physcal

More information

LECTURE 5 Guassian Wave Packet

LECTURE 5 Guassian Wave Packet LECTURE 5 Guassian Wav Pact 1.5 Eampl f a guassian shap fr dscribing a wav pact Elctrn Pact ψ Guassian Assumptin Apprimatin ψ As w hav sn in QM th wav functin is ftn rprsntd as a Furir transfrm r sris.

More information

LINEAR SYSTEMS THEORY

LINEAR SYSTEMS THEORY Fall Introduton to Mdal Engnrng INEAR SYSTEMS THEORY Ho Kung Km Ph.D. houng@puan.a.r Shool of Mhanal Engnrng Puan Natonal Unvrt Evn / odd / prod funton Thn about on & n funton! Evn f - = ; Odd f - = -;

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Lecture 3: Phasor notation, Transfer Functions. Context

Lecture 3: Phasor notation, Transfer Functions. Context EECS 5 Fall 4, ctur 3 ctur 3: Phasor notaton, Transfr Functons EECS 5 Fall 3, ctur 3 Contxt In th last lctur, w dscussd: how to convrt a lnar crcut nto a st of dffrntal quatons, How to convrt th st of

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

Math 656 March 10, 2011 Midterm Examination Solutions

Math 656 March 10, 2011 Midterm Examination Solutions Math 656 March 0, 0 Mdtrm Eamnaton Soltons (4pts Dr th prsson for snh (arcsnh sng th dfnton of snh w n trms of ponntals, and s t to fnd all als of snh (. Plot ths als as ponts n th compl plan. Mak sr or

More information

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION CHAPTER 7d. DIFFERENTIATION AND INTEGRATION A. J. Clark School o Engnrng Dpartmnt o Cvl and Envronmntal Engnrng by Dr. Ibrahm A. Assakka Sprng ENCE - Computaton Mthods n Cvl Engnrng II Dpartmnt o Cvl and

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

More information

Mathematics. Complex Number rectangular form. Quadratic equation. Quadratic equation. Complex number Functions: sinusoids. Differentiation Integration

Mathematics. Complex Number rectangular form. Quadratic equation. Quadratic equation. Complex number Functions: sinusoids. Differentiation Integration Mathmatics Compl numbr Functions: sinusoids Sin function, cosin function Diffrntiation Intgration Quadratic quation Quadratic quations: a b c 0 Solution: b b 4ac a Eampl: 1 0 a= b=- c=1 4 1 1or 1 1 Quadratic

More information

Basic Electrical Engineering for Welding [ ] --- Introduction ---

Basic Electrical Engineering for Welding [ ] --- Introduction --- Basc Elctrcal Engnrng for Wldng [] --- Introducton --- akayosh OHJI Profssor Ertus, Osaka Unrsty Dr. of Engnrng VIUAL WELD CO.,LD t-ohj@alc.co.jp OK 15 Ex. Basc A.C. crcut h fgurs n A-group show thr typcal

More information

Anglo-Chinese Junior College H2 Mathematics JC 2 PRELIM PAPER 1 Solutions

Anglo-Chinese Junior College H2 Mathematics JC 2 PRELIM PAPER 1 Solutions Anglo-Chnese Junor College H Mathematcs 97 8 JC PRELIM PAPER Solutons ( ( + + + + + 8 range of valdty: > or < but snce number, reject < wll result n the sq rt of a negatve . no. of

More information

CHAPTER 33: PARTICLE PHYSICS

CHAPTER 33: PARTICLE PHYSICS Collg Physcs Studnt s Manual Chaptr 33 CHAPTER 33: PARTICLE PHYSICS 33. THE FOUR BASIC FORCES 4. (a) Fnd th rato of th strngths of th wak and lctromagntc forcs undr ordnary crcumstancs. (b) What dos that

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time. Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss

More information

Theoretical Seismology

Theoretical Seismology Thorcal Ssmology Lcur 9 Sgnal Procssng Fourr analyss Fourr sudd a h Écol Normal n Pars, augh by Lagrang, who Fourr dscrbd as h frs among Europan mn of scnc, Laplac, who Fourr rad lss hghly, and by Mong.

More information

Image Filtering: Noise Removal, Sharpening, Deblurring. Yao Wang Polytechnic University, Brooklyn, NY11201

Image Filtering: Noise Removal, Sharpening, Deblurring. Yao Wang Polytechnic University, Brooklyn, NY11201 Imag Filtring: Nois Rmoval, Sharpning, Dblurring Yao Wang Polytchnic Univrsity, Brooklyn, NY http://wb.poly.du/~yao Outlin Nois rmoval by avraging iltr Nois rmoval by mdian iltr Sharpning Edg nhancmnt

More information

IMGS-261 Solutions to Homework #9

IMGS-261 Solutions to Homework #9 IMGS-6 Solutons to Homework #9. For f [] SINC [] sn[π], use the modulaton theorem to evaluate and sketch π the Fourer transform of f [] f [] f [] (f []) Soluton: We know that F{RECT []} SINC [] so we use

More information

From Structural Analysis to FEM. Dhiman Basu

From Structural Analysis to FEM. Dhiman Basu From Structural Analyss to FEM Dhman Basu Acknowldgmnt Followng txt books wr consultd whl prparng ths lctur nots: Znkwcz, OC O.C. andtaylor Taylor, R.L. (000). Th FntElmnt Mthod, Vol. : Th Bass, Ffth dton,

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

The graph of y = x (or y = ) consists of two branches, As x 0, y + ; as x 0, y +. x = 0 is the

The graph of y = x (or y = ) consists of two branches, As x 0, y + ; as x 0, y +. x = 0 is the Copyright itutcom 005 Fr download & print from wwwitutcom Do not rproduc by othr mans Functions and graphs Powr functions Th graph of n y, for n Q (st of rational numbrs) y is a straight lin through th

More information

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline Introucton to Ornar Dffrntal Equatons Sptmbr 7, 7 Introucton to Ornar Dffrntal Equatons Larr artto Mchancal Engnrng AB Smnar n Engnrng Analss Sptmbr 7, 7 Outln Rvw numrcal solutons Bascs of ffrntal quatons

More information

Introduction to Medical Imaging. Lecture 4: Fourier Theory = = ( ) 2sin(2 ) Introduction

Introduction to Medical Imaging. Lecture 4: Fourier Theory = = ( ) 2sin(2 ) Introduction Introduction Introduction to Mdical aging Lctur 4: Fourir Thory Thory dvlopd by Josph Fourir (768-83) Th Fourir transform of a signal s() yilds its frquncy spctrum S(k) Klaus Mullr s() forward transform

More information

Physics 256: Lecture 2. Physics

Physics 256: Lecture 2. Physics Physcs 56: Lctur Intro to Quantum Physcs Agnda for Today Complx Numbrs Intrfrnc of lght Intrfrnc Two slt ntrfrnc Dffracton Sngl slt dffracton Physcs 01: Lctur 1, Pg 1 Constructv Intrfrnc Ths wll occur

More information

10. The Discrete-Time Fourier Transform (DTFT)

10. The Discrete-Time Fourier Transform (DTFT) Th Discrt-Tim Fourir Transform (DTFT Dfinition of th discrt-tim Fourir transform Th Fourir rprsntation of signals plays an important rol in both continuous and discrt signal procssing In this sction w

More information

Review - Probabilistic Classification

Review - 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 information

Frequency Response. Response of an LTI System to Eigenfunction

Frequency Response. Response of an LTI System to Eigenfunction Frquncy Rsons Las m w Rvsd formal dfnons of lnary and m-nvaranc Found an gnfuncon for lnar m-nvaran sysms Found h frquncy rsons of a lnar sysm o gnfuncon nu Found h frquncy rsons for cascad, fdbac, dffrnc

More information

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors Dfnton Th nvr of an n by n atrx A an n by n atrx B whr, Not: nar Algbra Matrx Invron atrc on t hav an nvr. If a atrx ha an nvr, thn t call. Proprt of Matrx Invr. If A an nvrtbl atrx thn t nvr unqu.. (A

More information

Solutions to Problem Set 6

Solutions to Problem Set 6 Solutons to Problem Set 6 Problem 6. (Resdue theory) a) Problem 4.7.7 Boas. n ths problem we wll solve ths ntegral: x sn x x + 4x + 5 dx: To solve ths usng the resdue theorem, we study ths complex ntegral:

More information

18th European Signal Processing Conference (EUSIPCO-2010) Aalborg, Denmark, August 23-27, 2010

18th European Signal Processing Conference (EUSIPCO-2010) Aalborg, Denmark, August 23-27, 2010 8th Europan Sgnal Procssng Conrnc EUSIPCO- Aalorg Dnmark August 3-7 EIGEFUCTIOS EIGEVALUES AD FRACTIOALIZATIO OF THE QUATERIO AD BIQUATERIO FOURIER TRASFORS Soo-Chang P Jan-Jun Dng and Kuo-W Chang Dpartmnt

More information

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko

SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko SGNose and AGDas - tools for processng of superconductng and absolute gravty data Vojtech Pálnkáš and Mloš Vaľko 1 Research Insttute of Geodesy, Topography and Cartography, Czech Republc SGNose Web tool

More information

DSP-First, 2/e. LECTURE # CH2-3 Complex Exponentials & Complex Numbers TLH MODIFIED. Aug , JH McClellan & RW Schafer

DSP-First, 2/e. LECTURE # CH2-3 Complex Exponentials & Complex Numbers TLH MODIFIED. Aug , JH McClellan & RW Schafer DSP-First, / TLH MODIFIED LECTURE # CH-3 Complx Exponntials & Complx Numbrs Aug 016 1 READING ASSIGNMENTS This Lctur: Chaptr, Scts. -3 to -5 Appndix A: Complx Numbrs Complx Exponntials Aug 016 LECTURE

More information

Analyzing Frequencies

Analyzing Frequencies Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5

More information

Phys 2310 Fri. Nov. 7, 2014 Today s Topics. Begin Chapter 15: The Superposition of Waves Reading for Next Time

Phys 2310 Fri. Nov. 7, 2014 Today s Topics. Begin Chapter 15: The Superposition of Waves Reading for Next Time Phys 3 Fr. Nov. 7, 4 Today s Topcs Bgn Chaptr 5: Th Suprposton of Wavs Radng for Nxt T Radng ths Wk By Frday: Bgn Ch. 5 (5. 5.3 Addton of Wavs of th Sa Frquncy, Addton of Wavs of Dffrnt Frquncy, Rad Supplntary

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

Homework: Due

Homework: Due hw-.nb: //::9:5: omwok: Du -- Ths st (#7) s du on Wdnsday, //. Th soluton fom Poblm fom th xam s found n th mdtm solutons. ü Sakua Chap : 7,,,, 5. Mbach.. BJ 6. ü Mbach. Th bass stats of angula momntum

More information

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges Physcs of Vry Hgh Frquncy (VHF) Capactvly Coupld Plasma Dschargs Shahd Rauf, Kallol Bra, Stv Shannon, and Kn Collns Appld Matrals, Inc., Sunnyval, CA AVS 54 th Intrnatonal Symposum Sattl, WA Octobr 15-19,

More information

An Overview of Markov Random Field and Application to Texture Segmentation

An Overview of Markov Random Field and Application to Texture Segmentation An Ovrvw o Markov Random Fld and Applcaton to Txtur Sgmntaton Song-Wook Joo Octobr 003. What s MRF? MRF s an xtnson o Markov Procss MP (D squnc o r.v. s unlatral (causal: p(x t x,

More information

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d)

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d) Ερωτήσεις και ασκησεις Κεφ 0 (για μόρια ΠΑΡΑΔΟΣΗ 9//06 Th coffcnt A of th van r Waals ntracton s: (a A r r / ( r r ( (c a a a a A r r / ( r r ( a a a a A r r / ( r r a a a a A r r / ( r r 4 a a a a 0 Th

More information

Digital Signal Processing

Digital Signal Processing Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over

More information

ECE Spring Prof. David R. Jackson ECE Dept. Notes 25

ECE Spring Prof. David R. Jackson ECE Dept. Notes 25 ECE 6345 Sprng 2015 Prof. Davd R. Jackson ECE Dept. Notes 25 1 Overvew In ths set of notes we use the spectral-doman method to fnd the nput mpedance of a rectangular patch antenna. Ths method uses the

More information

COMPLEX NUMBERS AND QUADRATIC EQUATIONS

COMPLEX NUMBERS AND QUADRATIC EQUATIONS COMPLEX NUMBERS AND QUADRATIC EQUATIONS INTRODUCTION We know that x 0 for all x R e the square of a real number (whether postve, negatve or ero) s non-negatve Hence the equatons x, x, x + 7 0 etc are not

More information

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University xtrnal quvalnt 5 Analyss of Powr Systms Chn-Chng Lu, ong Dstngushd Profssor Washngton Stat Unvrsty XTRNAL UALNT ach powr systm (ara) s part of an ntrconnctd systm. Montorng dvcs ar nstalld and data ar

More information

Quadratic speedup for unstructured search - Grover s Al-

Quadratic speedup for unstructured search - Grover s Al- Quadratc speedup for unstructured search - Grover s Al- CS 94- gorthm /8/07 Sprng 007 Lecture 11 001 Unstructured Search Here s the problem: You are gven a boolean functon f : {1,,} {0,1}, and are promsed

More information

Digital Modems. Lecture 2

Digital Modems. Lecture 2 Dgtal Modems Lecture Revew We have shown that both Bayes and eyman/pearson crtera are based on the Lkelhood Rato Test (LRT) Λ ( r ) < > η Λ r s called observaton transformaton or suffcent statstc The crtera

More information

EE 570: Location and Navigation: Theory & Practice

EE 570: Location and Navigation: Theory & Practice EE 570: Locaton and Navgaton: Thor & Practc Navgaton Snsors and INS Mchanaton Thursda 8 F 013 NMT EE 570: Locaton and Navgaton: Thor & Practc Sld 1 of 10 Navgaton Snsors and INS Mchanaton Navgaton Equatons

More information

ECE Spring Prof. David R. Jackson ECE Dept. Notes 41

ECE Spring Prof. David R. Jackson ECE Dept. Notes 41 ECE 634 Sprng 6 Prof. Davd R. Jackson ECE Dept. Notes 4 Patch Antenna In ths set of notes we do the followng: Fnd the feld E produced by the patch current on the nterface Fnd the feld E z nsde the substrate

More information

Math 34A. Final Review

Math 34A. Final Review Math A Final Rviw 1) Us th graph of y10 to find approimat valus: a) 50 0. b) y (0.65) solution for part a) first writ an quation: 50 0. now tak th logarithm of both sids: log() log(50 0. ) pand th right

More information

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13)

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13) Pag- Econ7 Appld Economtrcs Topc : Dummy Dpndnt Varabl (Studnmund, Chaptr 3) I. Th Lnar Probablty Modl Suppos w hav a cross scton of 8-24 yar-olds. W spcfy a smpl 2-varabl rgrsson modl. Th probablty of

More information

Text: WMM, Chapter 5. Sections , ,

Text: WMM, Chapter 5. Sections , , Lcturs 6 - Continuous Probabilit Distributions Tt: WMM, Chaptr 5. Sctions 6.-6.4, 6.6-6.8, 7.-7. In th prvious sction, w introduc som of th common probabilit distribution functions (PDFs) for discrt sampl

More information

ELEN E4830 Digital Image Processing

ELEN E4830 Digital Image Processing ELEN E48 Dgal Imag Procssng Mrm Eamnaon Sprng Soluon Problm Quanzaon and Human Encodng r k u P u P u r r 6 6 6 6 5 6 4 8 8 4 P r 6 6 P r 4 8 8 6 8 4 r 8 4 8 4 7 8 r 6 6 6 6 P r 8 4 8 P r 6 6 8 5 P r /

More information

Lecture 2: Discrete-Time Signals & Systems. Reza Mohammadkhani, Digital Signal Processing, 2015 University of Kurdistan eng.uok.ac.

Lecture 2: Discrete-Time Signals & Systems. Reza Mohammadkhani, Digital Signal Processing, 2015 University of Kurdistan eng.uok.ac. Lctur 2: Discrt-Tim Signals & Systms Rza Mohammadkhani, Digital Signal Procssing, 2015 Univrsity of Kurdistan ng.uok.ac.ir/mohammadkhani 1 Signal Dfinition and Exampls 2 Signal: any physical quantity that

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

Filter Design Techniques

Filter Design Techniques Fltr Dsgn chnqus Fltr Fltr s systm tht psss crtn frquncy componnts n totlly rcts ll othrs Stgs of th sgn fltr Spcfcton of th sr proprts of th systm ppromton of th spcfcton usng cusl scrt-tm systm Rlzton

More information

CHAPTER 24 HYPERBOLIC FUNCTIONS

CHAPTER 24 HYPERBOLIC FUNCTIONS EXERCISE 00 Pag 5 CHAPTER HYPERBOLIC FUNCTIONS. Evaluat corrct to significant figurs: (a) sh 0.6 (b) sh.8 0.686, corrct to significant figurs (a) sh 0.6 0.6 0.6 ( ) Altrnativly, using a scintific calculator,

More information

Solution Set #1

Solution Set #1 05-78-0 Soluton Set #. Fnd epressons and setch the results of the followng operatons: (a) COMB RECT The spacng of the elements of the COMB functon matches the wdth of the rectangle; we can do ths n ether

More information

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr

More information

The Concept of Beamforming

The Concept of Beamforming ELG513 Smart Antennas S.Loyka he Concept of Beamformng Generc representaton of the array output sgnal, 1 where w y N 1 * = 1 = w x = w x (4.1) complex weghts, control the array pattern; y and x - narrowband

More information

2F1120 Spektrala transformer för Media Solutions to Steiglitz, Chapter 1

2F1120 Spektrala transformer för Media Solutions to Steiglitz, Chapter 1 F110 Spktrala transformr för Mdia Solutions to Stiglitz, Chaptr 1 Prfac This documnt contains solutions to slctd problms from Kn Stiglitz s book: A Digital Signal Procssing Primr publishd by Addison-Wsly.

More information

Introduction to the Fourier transform. Computer Vision & Digital Image Processing. The Fourier transform (continued) The Fourier transform (continued)

Introduction to the Fourier transform. Computer Vision & Digital Image Processing. The Fourier transform (continued) The Fourier transform (continued) Introduction to th Fourir transform Computr Vision & Digital Imag Procssing Fourir Transform Lt f(x) b a continuous function of a ral variabl x Th Fourir transform of f(x), dnotd by I {f(x)} is givn by:

More information

INTEGRATION BY PARTS

INTEGRATION BY PARTS Mathmatics Rvision Guids Intgration by Parts Pag of 7 MK HOME TUITION Mathmatics Rvision Guids Lvl: AS / A Lvl AQA : C Edcl: C OCR: C OCR MEI: C INTEGRATION BY PARTS Vrsion : Dat: --5 Eampls - 6 ar copyrightd

More information

Exercises for lectures 7 Steady state, tracking and disturbance rejection

Exercises for lectures 7 Steady state, tracking and disturbance rejection Exrc for lctur 7 Stady tat, tracng and dturbanc rjcton Martn Hromčí Automatc control 06-3-7 Frquncy rpon drvaton Automatcé řízní - Kybrnta a robota W lad a nuodal nput gnal to th nput of th ytm, gvn by

More information

Fourier Transform. Additive noise. Fourier Tansform. I = S + N. Noise doesn t depend on signal. We ll consider:

Fourier Transform. Additive noise. Fourier Tansform. I = S + N. Noise doesn t depend on signal. We ll consider: Flterng Announcements HW2 wll be posted later today Constructng a mosac by warpng mages. CSE252A Lecture 10a Flterng Exampel: Smoothng by Averagng Kernel: (From Bll Freeman) m=2 I Kernel sze s m+1 by m+1

More information

Lightening Summary of Fourier Analysis

Lightening Summary of Fourier Analysis Lghtnng Suary of Fourr Analyss D. Ad. Coponnts n Vctor Spacs You ar falar wth th fact that, n so vctor spac of your choosng, any vctor can b dcoposd nto coponnts along th drctons of so gvn bass: aˆ+ bj

More information

ECE 472/572 - Digital Image Processing. Roadmap. Questions. Lecture 6 Geometric and Radiometric Transformation 09/27/11

ECE 472/572 - Digital Image Processing. Roadmap. Questions. Lecture 6 Geometric and Radiometric Transformation 09/27/11 ECE 472/572 - Dgtal Image Processng Lecture 6 Geometrc and Radometrc Transformaton 09/27/ Roadmap Introducton Image format vector vs. btmap IP vs. CV vs. CG HLIP vs. LLIP Image acquston Percepton Structure

More information

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY Stud of Dnamc Aprtur for PETRA III Rng K. Balws, W. Brfld, W. Dcng, Y. L DESY FLS6 Hamburg PETRA III Yong-Jun L t al. Ovrvw Introducton Dnamcs of dampng wgglrs hoc of machn tuns, and optmzaton of stupol

More information

Why Monte Carlo Integration? Introduction to Monte Carlo Method. Continuous Probability. Continuous Probability

Why Monte Carlo Integration? Introduction to Monte Carlo Method. Continuous Probability. Continuous Probability Introducton to Monte Carlo Method Kad Bouatouch IRISA Emal: kad@rsa.fr Wh Monte Carlo Integraton? To generate realstc lookng mages, we need to solve ntegrals of or hgher dmenson Pel flterng and lens smulaton

More information

Quasi-Classical States of the Simple Harmonic Oscillator

Quasi-Classical States of the Simple Harmonic Oscillator Quasi-Classical Stats of th Simpl Harmonic Oscillator (Draft Vrsion) Introduction: Why Look for Eignstats of th Annihilation Oprator? Excpt for th ground stat, th corrspondnc btwn th quantum nrgy ignstats

More information

Self-Adjointness and Its Relationship to Quantum Mechanics. Ronald I. Frank 2016

Self-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 information

Math 656 Midterm Examination March 27, 2015 Prof. Victor Matveev

Math 656 Midterm Examination March 27, 2015 Prof. Victor Matveev Math 656 Mdtrm Examnatn March 7, 05 Prf. Vctr Matvv ) (4pts) Fnd all vals f n plar r artsan frm, and plt thm as pnts n th cmplx plan: (a) Snc n-th rt has xactly n vals, thr wll b xactly =6 vals, lyng n

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

Chapter 4: Root Finding

Chapter 4: Root Finding Chapter 4: Root Fndng Startng values Closed nterval methods (roots are search wthn an nterval o Bsecton Open methods (no nterval o Fxed Pont o Newton-Raphson o Secant Method Repeated roots Zeros of Hgher-Dmensonal

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

1 1 1 p q p q. 2ln x x. in simplest form. in simplest form in terms of x and h.

1 1 1 p q p q. 2ln x x. in simplest form. in simplest form in terms of x and h. NAME SUMMER ASSIGNMENT DUE SEPTEMBER 5 (FIRST DAY OF SCHOOL) AP CALC AB Dirctions: Answr all of th following qustions on a sparat sht of papr. All work must b shown. You will b tstd on this matrial somtim

More information

A general N-dimensional vector consists of N values. They can be arranged as a column or a row and can be real or complex.

A general N-dimensional vector consists of N values. They can be arranged as a column or a row and can be real or complex. Lnr lgr Vctors gnrl -dmnsonl ctor conssts of lus h cn rrngd s column or row nd cn rl or compl Rcll -dmnsonl ctor cn rprsnt poston, loct, or cclrton Lt & k,, unt ctors long,, & rspctl nd lt k h th componnts

More information

Random Process Part 1

Random Process Part 1 Random Procss Part A random procss t (, ζ is a signal or wavform in tim. t : tim ζ : outcom in th sampl spac Each tim w rapat th xprimnt, a nw wavform is gnratd. ( W will adopt t for short. Tim sampls

More information

5. B To determine all the holes and asymptotes of the equation: y = bdc dced f gbd

5. B To determine all the holes and asymptotes of the equation: y = bdc dced f gbd 1. First you chck th domain of g x. For this function, x cannot qual zro. Thn w find th D domain of f g x D 3; D 3 0; x Q x x 1 3, x 0 2. Any cosin graph is going to b symmtric with th y-axis as long as

More information

GPC From PeakSimple Data Acquisition

GPC From PeakSimple Data Acquisition GPC From PakSmpl Data Acquston Introducton Th follong s an outln of ho PakSmpl data acquston softar/hardar can b usd to acqur and analyz (n conjuncton th an approprat spradsht) gl prmaton chromatography

More information

1973 AP Calculus AB: Section I

1973 AP Calculus AB: Section I 97 AP Calculus AB: Sction I 9 Minuts No Calculator Not: In this amination, ln dnots th natural logarithm of (that is, logarithm to th bas ).. ( ) d= + C 6 + C + C + C + C. If f ( ) = + + + and ( ), g=

More information

Special Random Variables: Part 1

Special Random Variables: Part 1 Spcl Rndom Vrbls: Prt Dscrt Rndom Vrbls Brnoull Rndom Vrbl (wth prmtr p) Th rndom vrbl x dnots th succss from trl. Th probblty mss functon of th rndom vrbl X s gvn by p X () p X () p p ( E[X ]p Th momnt

More information

Numbering Systems Basic Building Blocks Scaling and Round-off Noise. Number Representation. Floating vs. Fixed point. DSP Design.

Numbering Systems Basic Building Blocks Scaling and Round-off Noise. Number Representation. Floating vs. Fixed point. DSP Design. Numbring Systms Basic Building Blocks Scaling and Round-off Nois Numbr Rprsntation Viktor Öwall viktor.owall@it.lth.s Floating vs. Fixd point In floating point a valu is rprsntd by mantissa dtrmining th

More information

Laboratory associate professor Radu Damian Wednesday 12-14, II.12 odd weeks L 25% final grade P 25% final grade

Laboratory associate professor Radu Damian Wednesday 12-14, II.12 odd weeks L 25% final grade P 25% final grade ctur 8/9 C/, MDC Attndanc at mnmum 7 sssons (cours + laboratory) cturs- assocat profssor adu Daman Frday 9-,? III.34, II.3 E 5% fnal grad problms + (p attn. lct.) + (3 tsts) + (bonus actvty) 3p=+.5p all

More information

Error Bars in both X and Y

Error Bars in both X and Y Error Bars n both X and Y Wrong ways to ft a lne : 1. y(x) a x +b (σ x 0). x(y) c y + d (σ y 0) 3. splt dfference between 1 and. Example: Prmordal He abundance: Extrapolate ft lne to [ O / H ] 0. [ He

More information

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing-

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing- Outlin Advancd Multimdia Signal Procssing #5:Spch Signal Procssing -Procssing- Intllignt Elctronic Systms Group Dpt. of Elctronic Enginring, UEC Basis of Spch Procssing Nois Rmoval Spctral Subtraction

More information

Digital Filter Examples

Digital Filter Examples Power Systems Protecton and Relayng Sesson ; Page /22 Fall 28 Defne samplng rate per cycle 6 Dgtal Flter Examples Defne length of sample data set, n cycles CY 8 Total number of samples: M CY n M Δt 6Hz

More information

MSLC Math 151 WI09 Exam 2 Review Solutions

MSLC Math 151 WI09 Exam 2 Review Solutions Eam Rviw Solutions. Comput th following rivativs using th iffrntiation ruls: a.) cot cot cot csc cot cos 5 cos 5 cos 5 cos 5 sin 5 5 b.) c.) sin( ) sin( ) y sin( ) ln( y) ln( ) ln( y) sin( ) ln( ) y y

More information

As the matrix of operator B is Hermitian so its eigenvalues must be real. It only remains to diagonalize the minor M 11 of matrix B.

As the matrix of operator B is Hermitian so its eigenvalues must be real. It only remains to diagonalize the minor M 11 of matrix B. 7636S ADVANCED QUANTUM MECHANICS Solutions Spring. Considr a thr dimnsional kt spac. If a crtain st of orthonormal kts, say, and 3 ar usd as th bas kts, thn th oprators A and B ar rprsntd by a b A a and

More information

Logarithms. Secondary Mathematics 3 Page 164 Jordan School District

Logarithms. Secondary Mathematics 3 Page 164 Jordan School District Logarithms Sondary Mathmatis Pag 6 Jordan Shool Distrit Unit Clustr 6 (F.LE. and F.BF.): Logarithms Clustr 6: Logarithms.6 For ponntial modls, prss as a arithm th solution to a and d ar numrs and th as

More information

AP Calculus Multiple-Choice Question Collection

AP Calculus Multiple-Choice Question Collection AP Calculus Multipl-Coic Qustion Collction 985 998 . f is a continuous function dfind for all ral numbrs and if t maimum valu of f () is 5 and t minimum valu of f () is 7, tn wic of t following must b

More information

Designing Information Devices and Systems II Spring 2018 J. Roychowdhury and M. Maharbiz Discussion 3A

Designing Information Devices and Systems II Spring 2018 J. Roychowdhury and M. Maharbiz Discussion 3A EECS 16B Desgnng Informaton Devces and Systems II Sprng 018 J. Roychowdhury and M. Maharbz Dscusson 3A 1 Phasors We consder snusodal voltages and currents of a specfc form: where, Voltage vt) = V 0 cosωt

More information

innovations shocks white noise

innovations shocks white noise Innovaons Tm-srs modls ar consrucd as lnar funcons of fundamnal forcasng rrors, also calld nnovaons or shocks Ths basc buldng blocks sasf var σ Srall uncorrlad Ths rrors ar calld wh nos In gnral, f ou

More information

Davisson Germer experiment Announcements:

Davisson Germer experiment Announcements: Davisson Grmr xprimnt Announcmnts: Homwork st 7 is du Wdnsday. Problm solving sssions M3-5, T3-5. Th 2 nd midtrm will b April 7 in MUEN E0046 at 7:30pm. BFFs: Davisson and Grmr. Today w will go ovr th

More information