LINEAR SYSTEMS THEORY

Size: px
Start display at page:

Download "LINEAR SYSTEMS THEORY"

Transcription

1 Introducton to Mdcl Engnrng INEAR SYSTEMS THEORY Ho Kyung Km, Ph.D. School of Mchncl Engnrng Pun Ntonl Unvrty Evn / odd / prodc functon Thn bout con & n functon! Evn f - ; Odd f - -; d d o d Cn wrt ny gnl th um of n vn nd n odd prt: Prodc f X o

2 Compl functon In Crtn rprntton, y u, y v, y Imgnry In polr rprntton, y, y φ, y v, y, y whr, y u, y v, y modulu or mpltud φ v, y φ, y rctn u, y rgumnt or ph u Rl 3 Importnt gnl functon Eponntl p Compl ponntl or nuod A φ A [ co φ n φ ] A mpltud ptl frquncy φ ph 4

3 5 Rctngulr functon wdth Stp functon or Hvd functon > < for for for for for for u > < / 6 Trngulr functon b Normlzd Gun < Λ for for σ µ σ n G

4 Snc functon n nc Drc mpul δ for δ d δ d hftng A δ d A clng 7 nr ytm nput gnl ctton o { } o output gnl rpon.g., n mplfr wth gn A; { t } A t t o Modlng: th proc of fndng mthmtcl rltonhp btwn nput & output gnl nr ytm f th uprpoton prncpl hold; { c c } c { } c { } o.g., mplfr wth gn A; { c c } o A c { } c { } c A c c c A Nonlnr ytm; { c c } c c c c 8

5 Shft-nvrnt ytm Shft-nvrnt ytm f t proprt do not chng wth ptl poton; { } X X o hft hft hft nvrnt no chng SI ytm lnr & hft-nvrnt ytm Impul rpon, h, to Drc mpul hft vrnt chng wth poton PSF Pont prd functon For n rbtrry gnl; Thn, th rpon of n SI ytm; ξ δ ξ dξ { } ξ { δ ξ } dξ convoluton; o ξ h ξ dξ o h 9 Convoluton ξ ξ dξ ξ ξ dξ Procdur mrrorng bout ξ by chngng ξ to ξ trnltng th mrrord by ξ multplyng to th hftd & mrrord ntgrtng th rultng gnl rprntd by r rptng th prvou tp for ch vlu of

6 Convoluton for dgtl gnl Convoluton for multdmnonl gnl;, y, y ξ, y ζ ξ, ζ dξdζ th convoluton vlu r rprntd by volum. Proprt commuttvty: octvty: 3 3 dtrbutvty: 3 3

7 Rpon of n SI ytm For n nput gnl nuod; A Th rpon of n SI ytm; o A A { } A ξ ξ h ξ dξ h ξ dξ ξ h ξ dξ H H whr H ξ h ξ d ξ Fourr trnform of th PSF h trnfr functon or fltr Invr Fourr trnform; Thn, w hv; o S d S H d FT { S H } Dcu; h n domn v. S S H n domn o FT { } FT FT { S H } o [ ] Any nput gnl cn b wrttn n ntgrl of wghtd nuod wth dffrnt ptl frqunc o 3 Frquncy Rcll co φ n φ Normlzd Ampltud ncr, o do th frquncy of th ocllton th hghr, th hghr th gnl roluton, tht, on cn rprnt mllr gnl dtl gnl tht vr mor qucly 4

8 Sgnl ynth Any prodc gnl cn b crtd by combnton of wghtd nd hftd nuod t dffrnt frqunc. bo n /3 n3 um o A A A co φ n φ φ φ S d d d Invr FT!!! d bo n /3 n3 /5 n5 /7 n7 /9 n9 / n /3 n3 /5 n5 umf. bo n /3 n3 /5 n5 um Fourr trnform Forwrd trnform S r { r } r I r dr Invr trnform { S } r I S d r S r r I r r r dr { r } r { S } r r r I S d r r r Conjugt vrbl f r tm dmnon "cond", tmporl frquncy wth dmnon "hrtz" f r ptl poton wth dmnon "mm", ptl frquncy wth dmnon "mm - " 6

9 FT{rct} 7 A fnt gnl n th -domn crt n nfnt gnl n th -domn. th m tru vc vr nc n A A A d A d A A I A A A / / FT{tp p} 8 rl prt: mgnry prt: modulu: ph: { } 4 4 d d u u I rctn

10 FT{Drc mpul} 9 { } d δ δ I FT{con} Th pctrum cont of two mpul t ptl frqunc nd. A prodc functon h dcrt pctrum.., not ll ptl frqunc r prnt. An prodc functon h contnuou pctrum. { } co co d d d d δ δ I

11 Fourr trnform pr Img pc Fourr pc δ δ co n δ δ δ δ Λ nc nc G σ n Proprt nrty: c c cs cs Sclng: S Trnlton: S modfyng only t ph pctrum Convoluton: S S S S Prvl' thorm: Sprblty: I d S d { nc nc y } I{ nc } I{ nc y }

12 h H Trnfr functon nd mpul rpon or PSF r n FT pr In mgng, th FT of th PSF nown th optcl trnfr functon OTF. th modulu of th OTF th modulton trnfr functon MTF Th PSF mm nd OTF mm - or lp/mm chrctrz th roluton of th ytm. 3 Not If th gnl dcrt but nfnt, thn th frquncy pctrum contnuou but prodc h l. If th gnl dcrt nd fnt N mpl, thn th frquncy pctrum dcrt nd prodc n N. 4

13 FT n polr coordnt Forwrd trnform S, y r coθ yr n θ, y r, θ r, θ y y ddy r coθ yr n θ rdrdθ r drdθ Not: r J r θ coθ n θ rco θ n θ r y y n θ r coθ r θ Invr trnform co φ y n φ, φ, y ddφ 5 Smplng Smpld gnl: n III comb functon or mpul trn III δ n n mplng dtnc nformton my b lot by mplng cn w rcovr contnuou gnl compltly from t mpl? Smplng thorm Nyqut crtron f th FT of gvn gnl bnd-lmtd nd f th mplng frquncy lrgr thn twc th m. ptl frquncy prnt n th gnl, thn th mpl unquly dfn th gnl S > m If > m thn n unquly dfn S I{ III } { } & I III K δ lk wth l Hnc, K S S K S K S K S K S S Not tht KS S K bcu S K K 6

14 Infnt ptl tnt t bnd-lmtd FT 7 Fnt ptl tnt t not-bnd-lmtd FT If th gnl S not bnd lmtd, or f t bnd lmtd but / m, th hftd rplc of S wll ovrlp. Thrfor, th pctrum of S cnnot b rcovrd by multplcton wth rctngulr pul. Known lng nd unvodbl f th orgnl gnl not bnd lmtd. Ptnt lwy hv lmtd ptl tnt! 8

15 Alng: A commonly obrvd phnomnon Tn from Dr. K. Mullr Sld 9 Ant-lng Tn from Dr. K. Mullr Sld 3

16 Dcrt FT 3 Forwrd trnform Invr trnform Ft Fourr trnform FFT for th numbr of mpl n powr of two nlogn flop n D n logn flop n D,, N q M p N nq M mp y y q p n m S,, N n M m N nq M mp y n m S y q p

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

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

The Fourier Transform

The Fourier Transform /9/ Th ourr Transform Jan Baptst Josph ourr 768-83 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.

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

Generalized Half Linear Canonical Transform And Its Properties

Generalized Half Linear Canonical Transform And Its Properties Gnrlz Hl Lnr Cnoncl Trnorm An I Propr A S Guh # A V Joh* # Gov Vrh Inu o Scnc n Humn, Amrv M S * Shnkrll Khnlwl Collg, Akol - 444 M S Arc: A gnrlzon o h Frconl Fourr rnorm FRFT, h lnr cnoncl rnorm LCT

More information

TOPIC 5: INTEGRATION

TOPIC 5: INTEGRATION TOPIC 5: INTEGRATION. Th indfinit intgrl In mny rspcts, th oprtion of intgrtion tht w r studying hr is th invrs oprtion of drivtion. Dfinition.. Th function F is n ntidrivtiv (or primitiv) of th function

More information

(A) the function is an eigenfunction with eigenvalue Physical Chemistry (I) First Quiz

(A) the function is an eigenfunction with eigenvalue Physical Chemistry (I) First Quiz 96- Physcl Chmstry (I) Frst Quz lctron rst mss m 9.9 - klogrm, Plnck constnt h 6.66-4 oul scon Sp of lght c. 8 m/s, lctron volt V.6-9 oul. Th functon F() C[cos()+sn()] s n gnfuncton of /. Th gnvlu s (A)

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

Preview. Graph. Graph. Graph. Graph Representation. Graph Representation 12/3/2018. Graph Graph Representation Graph Search Algorithms

Preview. Graph. Graph. Graph. Graph Representation. Graph Representation 12/3/2018. Graph Graph Representation Graph Search Algorithms /3/0 Prvw Grph Grph Rprsntton Grph Srch Algorthms Brdth Frst Srch Corrctnss of BFS Dpth Frst Srch Mnmum Spnnng Tr Kruskl s lgorthm Grph Drctd grph (or dgrph) G = (V, E) V: St of vrt (nod) E: St of dgs

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

8. INVERSE Z-TRANSFORM

8. INVERSE Z-TRANSFORM 8. INVERSE Z-TRANSFORM The proce by whch Z-trnform of tme ere, nmely X(), returned to the tme domn clled the nvere Z-trnform. The nvere Z-trnform defned by: Computer tudy Z X M-fle trn.m ued to fnd nvere

More information

INTEGRALS. Chapter 7. d dx. 7.1 Overview Let d dx F (x) = f (x). Then, we write f ( x)

INTEGRALS. Chapter 7. d dx. 7.1 Overview Let d dx F (x) = f (x). Then, we write f ( x) Chptr 7 INTEGRALS 7. Ovrviw 7.. Lt d d F () f (). Thn, w writ f ( ) d F () + C. Ths intgrls r clld indfinit intgrls or gnrl intgrls, C is clld constnt of intgrtion. All ths intgrls diffr y constnt. 7..

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

Using SIP techniques to verify the trade-off between SNR and information capacity of a sigma delta modulator

Using SIP techniques to verify the trade-off between SNR and information capacity of a sigma delta modulator Audo Engnrng Socty Convnton Ppr 6697 Prsntd t th 0th Convnton 006 y 0 3 Prs, Frnc hs convnton ppr hs bn rproducd from th uthor's dvnc mnuscrpt, wthout dtng, corrctons, or consdrton by th Rvw Bord. h AES

More information

Rate of Molecular Exchange Through the Membranes of Ionic Liquid Filled. Polymersomes Dispersed in Water

Rate of Molecular Exchange Through the Membranes of Ionic Liquid Filled. Polymersomes Dispersed in Water Supportng Informton for: Rt of Molculr Exchng hrough th Mmrns of Ionc Lqud Flld olymrsoms Dsprsd n Wtr Soonyong So nd mothy. Lodg *,, Dprtmnt of Chmcl Engnrng & Mtrls Scnc nd Dprtmnt of Chmstry, Unvrsty

More information

Announcements. Image Formation: Outline. The course. Image Formation and Cameras (cont.)

Announcements. Image Formation: Outline. The course. Image Formation and Cameras (cont.) nnouncements Imge Formton nd Cmers (cont.) ssgnment : Cmer & Lenses, gd Trnsformtons, nd Homogrph wll be posted lter tod. CSE 5 Lecture 5 CS5, Fll CS5, Fll CS5, Fll The course rt : The phscs of mgng rt

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

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1)

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1) Conrgnc Thors for Two Itrt Mthods A sttonry trt thod for solng th lnr syst: Ax = b (.) ploys n trton trx B nd constnt ctor c so tht for gn strtng stt x of x for = 2... x Bx c + = +. (.2) For such n trton

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

5. 5. Detection of of Signals in in Noise

5. 5. Detection of of Signals in in Noise 5. 5. Dtton of of Sgnl n n o Dtton probl: Gvn th obrvton vtor, w, thn, prfor ppng dodng fro to n ttd ˆ of th trnttd ybol, n wy tht would nz th probblty of rror n th don kng pro. W wll how tht, n th tht

More information

CONTINUITY AND DIFFERENTIABILITY

CONTINUITY AND DIFFERENTIABILITY MCD CONTINUITY AND DIFFERENTIABILITY NCERT Solvd mpls upto th sction 5 (Introduction) nd 5 (Continuity) : Empl : Chck th continuity of th function f givn by f() = + t = Empl : Emin whthr th function f

More information

MASTER CLASS PROGRAM UNIT 4 SPECIALIST MATHEMATICS SEMESTER TWO 2014 WEEK 11 WRITTEN EXAMINATION 1 SOLUTIONS

MASTER CLASS PROGRAM UNIT 4 SPECIALIST MATHEMATICS SEMESTER TWO 2014 WEEK 11 WRITTEN EXAMINATION 1 SOLUTIONS MASTER CLASS PROGRAM UNIT SPECIALIST MATHEMATICS SEMESTER TWO WEEK WRITTEN EXAMINATION SOLUTIONS FOR ERRORS AND UPDATES, PLEASE VISIT WWW.TSFX.COM.AU/MC-UPDATES QUESTION () Lt p ( z) z z z If z i z ( is

More information

APPLICATIONS OF THE LAPLACE-MELLIN INTEGRAL TRANSFORM TO DIFFERNTIAL EQUATIONS

APPLICATIONS OF THE LAPLACE-MELLIN INTEGRAL TRANSFORM TO DIFFERNTIAL EQUATIONS Intrntionl Journl o Sintii nd Rrh Publition Volum, Iu 5, M ISSN 5-353 APPLICATIONS OF THE LAPLACE-MELLIN INTEGRAL TRANSFORM TO DIFFERNTIAL EQUATIONS S.M.Khirnr, R.M.Pi*, J.N.Slun** Dprtmnt o Mthmti Mhrhtr

More information

8. Linear Contracts under Risk Neutrality

8. Linear Contracts under Risk Neutrality 8. Lnr Contrcts undr Rsk Nutrlty Lnr contrcts r th smplst form of contrcts nd thy r vry populr n pplctons. Thy offr smpl ncntv mchnsm. Exmpls of lnr contrcts r mny: contrctul jont vnturs, quty jont vnturs,

More information

Fundamentals of Continuum Mechanics. Seoul National University Graphics & Media Lab

Fundamentals of Continuum Mechanics. Seoul National University Graphics & Media Lab Fndmntls of Contnm Mchncs Sol Ntonl Unvrsty Grphcs & Md Lb Th Rodmp of Contnm Mchncs Strss Trnsformton Strn Trnsformton Strss Tnsor Strn T + T ++ T Strss-Strn Rltonshp Strn Enrgy FEM Formlton Lt s Stdy

More information

Limits Indeterminate Forms and L Hospital s Rule

Limits Indeterminate Forms and L Hospital s Rule Limits Indtrmint Forms nd L Hospitl s Rul I Indtrmint Form o th Tp W hv prviousl studid its with th indtrmint orm s shown in th ollowin mpls: Empl : Empl : tn [Not: W us th ivn it ] Empl : 8 h 8 [Not:

More information

1.9 Cartesian Tensors

1.9 Cartesian Tensors Scton.9.9 Crtsn nsors s th th ctor, hghr ordr) tnsor s mthmtc obct hch rprsnts mny physc phnomn nd hch xsts ndpndnty of ny coordnt systm. In ht foos, Crtsn coordnt systm s sd to dscrb tnsors..9. Crtsn

More information

Introduction to Laplace Transforms October 25, 2017

Introduction to Laplace Transforms October 25, 2017 Iroduco o Lplc Trform Ocobr 5, 7 Iroduco o Lplc Trform Lrr ro Mchcl Egrg 5 Smr Egrg l Ocobr 5, 7 Oul Rvw l cl Wh Lplc rform fo of Lplc rform Gg rform b gro Fdg rform d vr rform from bl d horm pplco o dffrl

More information

COMPLEX NUMBER & QUADRATIC EQUATION

COMPLEX NUMBER & QUADRATIC EQUATION MCQ COMPLEX NUMBER & QUADRATIC EQUATION Syllus : Comple numers s ordered prs of rels, Representton of comple numers n the form + nd ther representton n plne, Argnd dgrm, lger of comple numers, modulus

More information

Lecture 4: Piecewise Cubic Interpolation

Lecture 4: Piecewise Cubic Interpolation Lecture notes on Vrtonl nd Approxmte Methods n Appled Mthemtcs - A Perce UBC Lecture 4: Pecewse Cubc Interpolton Compled 6 August 7 In ths lecture we consder pecewse cubc nterpolton n whch cubc polynoml

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

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black Stf-Boltzm lw stts tht th powr mttd pr ut r of th surfc of blck body s proportol to th fourth powr of th bsolut tmprtur: 4 S T whr T s th bsolut tmprtur d th Stf-Boltzm costt= 5 4 k B 3 5c h ( Clcult 5

More information

ScienceDirect. ScienceDirec

ScienceDirect. ScienceDirec CRX_ / CR X_ / Q Q Q Q : R J : / // / J : / / N K * Jk k G U U U U N k U NC U : R k R J R H k - - - - - H - K: R - H - - V V V R - V V V - - L L H H - - C L H j q C L H j q k k k X k R k k X L k k k -

More information

2. Work each problem on the exam booklet in the space provided.

2. Work each problem on the exam booklet in the space provided. ECE470 EXAM # SOLUTIONS SPRING 08 Intructon:. Cloed-book, cloed-note, open-mnd exm.. Work ech problem on the exm booklet n the pce provded.. Wrte netly nd clerly for prtl credt. Cro out ny mterl you do

More information

Inverse Fourier Transform. Properties of Continuous time Fourier Transform. Review. Linearity. Reading Assignment Oppenheim Sec pp.289.

Inverse Fourier Transform. Properties of Continuous time Fourier Transform. Review. Linearity. Reading Assignment Oppenheim Sec pp.289. Convrgnc of ourir Trnsform Rding Assignmn Oppnhim Sc 42 pp289 Propris of Coninuous im ourir Trnsform Rviw Rviw or coninuous-im priodic signl x, j x j d Invrs ourir Trnsform 2 j j x d ourir Trnsform Linriy

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

FSA. CmSc 365 Theory of Computation. Finite State Automata and Regular Expressions (Chapter 2, Section 2.3) ALPHABET operations: U, concatenation, *

FSA. CmSc 365 Theory of Computation. Finite State Automata and Regular Expressions (Chapter 2, Section 2.3) ALPHABET operations: U, concatenation, * CmSc 365 Thory of Computtion Finit Stt Automt nd Rgulr Exprssions (Chptr 2, Sction 2.3) ALPHABET oprtions: U, conctntion, * otin otin Strings Form Rgulr xprssions dscri Closd undr U, conctntion nd * (if

More information

Section 3: Antiderivatives of Formulas

Section 3: Antiderivatives of Formulas Chptr Th Intgrl Appli Clculus 96 Sction : Antirivtivs of Formuls Now w cn put th is of rs n ntirivtivs togthr to gt wy of vluting finit intgrls tht is ct n oftn sy. To vlut finit intgrl f(t) t, w cn fin

More information

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

Minimum Spanning Trees

Minimum Spanning Trees Mnmum Spnnng Trs Spnnng Tr A tr (.., connctd, cyclc grph) whch contns ll th vrtcs of th grph Mnmum Spnnng Tr Spnnng tr wth th mnmum sum of wghts 1 1 Spnnng forst If grph s not connctd, thn thr s spnnng

More information

Active Vibration Control of Satellite Panels using Piezoelectric Actuators and Sensors

Active Vibration Control of Satellite Panels using Piezoelectric Actuators and Sensors Rcnt Advnc n Automtc Control, odllng nd Smulton Actv Vbrton Control of Stllt Pnl ung Pzolctrc Actutor nd Snor.. ELADANY, K.A. ALSAIF,.A. FODA, A.A. ALEDAH Dprtmnt of chncl Engnrng Kng Sud Unvrty Rydh,

More information

Wave Phenomena Physics 15c

Wave Phenomena Physics 15c Wv hnon hyscs 5c cur 4 Coupl Oscllors! H& con 4. Wh W D s T " u forc oscllon " olv h quon of oon wh frcon n foun h sy-s soluon " Oscllon bcos lr nr h rsonnc frquncy " hs chns fro 0 π/ π s h frquncy ncrss

More information

Chapter 3. The Fourier Series

Chapter 3. The Fourier Series Chpr 3 h Fourir Sris Signls in h im nd Frquny Domin INC Signls nd Sysms Chpr 3 h Fourir Sris Eponnil Funion r j ros jsin ) INC Signls nd Sysms Chpr 3 h Fourir Sris Odd nd Evn Evn funion : Odd funion :

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

The Derivative of the Natural Logarithmic Function. Derivative of the Natural Exponential Function. Let u be a differentiable function of x.

The Derivative of the Natural Logarithmic Function. Derivative of the Natural Exponential Function. Let u be a differentiable function of x. Th Ntrl Logrithmic n Eponntil Fnctions: : Diffrntition n Intgrtion Objctiv: Fin rivtivs of fnctions involving th ntrl logrithmic fnction. Th Drivtiv of th Ntrl Logrithmic Fnction Lt b iffrntibl fnction

More information

Position and Speed Control. Industrial Electrical Engineering and Automation Lund University, Sweden

Position and Speed Control. Industrial Electrical Engineering and Automation Lund University, Sweden Poton nd Speed Control Lund Unverty, Seden Generc Structure R poer Reference Sh tte Voltge Current Control ytem M Speed Poton Ccde Control * θ Poton * Speed * control control - - he ytem contn to ntegrton.

More information

Electrical Systems Peter Avitabile Mechanical Engineering Department University of Massachusetts Lowell

Electrical Systems Peter Avitabile Mechanical Engineering Department University of Massachusetts Lowell Elctrcl Systms Ptr Avtbl Mchncl Engnrng Dprtmnt Unvrsty of Msschustts owll.45 Dynmc Systms Elctrcl Dr. Ptr Avtbl Modl Anlyss & Controls bortory Elctrcl Systms Pssv Elctrcl Elmnts sstor, Inductor, Cpctor

More information

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture:

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture: Lctur 11 Wvs in Priodic Potntils Tody: 1. Invrs lttic dfinition in 1D.. rphicl rprsnttion of priodic nd -priodic functions using th -xis nd invrs lttic vctors. 3. Sris solutions to th priodic potntil Hmiltonin

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

Principle Component Analysis

Principle Component Analysis Prncple Component Anlyss Jng Go SUNY Bufflo Why Dmensonlty Reducton? We hve too mny dmensons o reson bout or obtn nsghts from o vsulze oo much nose n the dt Need to reduce them to smller set of fctors

More information

h Summary Chapter 7.

h Summary Chapter 7. Summry Chptr 7. In Chptr 7 w dscussd byond th fr lctron modl of chptr 6. In prtculrly w focusd on th nflunc of th prodc potntl of th on cors on th nrgy lvl dgrm of th outr lctrons of th toms. It wll hlp

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

Fractions. Mathletics Instant Workbooks. Simplify. Copyright

Fractions. Mathletics Instant Workbooks. Simplify. Copyright Frctons Stunt Book - Srs H- Smplfy + Mthltcs Instnt Workbooks Copyrht Frctons Stunt Book - Srs H Contnts Topcs Topc - Equvlnt frctons Topc - Smplfyn frctons Topc - Propr frctons, mpropr frctons n mx numbrs

More information

Phys 2310 Wed. Sept. 13, 2017 Today s Topics

Phys 2310 Wed. Sept. 13, 2017 Today s Topics Phys 3 Wd. Spt. 3, 7 Tody s Topcs Chptr 5: Th Suprposton of Wvs Intrfrnc Mthods for Addng Wvs Boundry Condtons Stndng Wvs & Norl Mods Supplntry Mtrl: Fourr Anlyss Rdng for Nxt T Howork ths Wk SZ Chptr

More information

DISCRETE TIME FOURIER TRANSFORM (DTFT)

DISCRETE TIME FOURIER TRANSFORM (DTFT) DISCRETE TIME FOURIER TRANSFORM (DTFT) Th dicrt-tim Fourir Tranform x x n xn n n Th Invr dicrt-tim Fourir Tranform (IDTFT) x n Not: ( ) i a complx valud continuou function = π f [rad/c] f i th digital

More information

FL/VAL ~RA1::1. Professor INTERVI of. Professor It Fr recru. sor Social,, first of all, was. Sys SDC? Yes, as a. was a. assumee.

FL/VAL ~RA1::1. Professor INTERVI of. Professor It Fr recru. sor Social,, first of all, was. Sys SDC? Yes, as a. was a. assumee. B Pror NTERV FL/VAL ~RA1::1 1 21,, 1989 i n or Socil,, fir ll, Pror Fr rcru Sy Ar you lir SDC? Y, om um SM: corr n 'd m vry ummr yr. Now, y n y, f pr my ry for ummr my 1 yr Un So vr ummr cour d rr o l

More information

The Mathematics of Harmonic Oscillators

The Mathematics of Harmonic Oscillators Th Mhcs of Hronc Oscllors Spl Hronc Moon In h cs of on-nsonl spl hronc oon (SHM nvolvng sprng wh sprng consn n wh no frcon, you rv h quon of oon usng Nwon's scon lw: con wh gvs: 0 Ths s sos wrn usng h

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Lecture contents. Bloch theorem k-vector Brillouin zone Almost free-electron model Bands Effective mass Holes. NNSE 508 EM Lecture #9

Lecture contents. Bloch theorem k-vector Brillouin zone Almost free-electron model Bands Effective mass Holes. NNSE 508 EM Lecture #9 Lctur contnts Bloch thorm -vctor Brillouin zon Almost fr-lctron modl Bnds ffctiv mss Hols Trnsltionl symmtry: Bloch thorm On-lctron Schrödingr qution ch stt cn ccommo up to lctrons: If Vr is priodic function:

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

CSE303 - Introduction to the Theory of Computing Sample Solutions for Exercises on Finite Automata

CSE303 - Introduction to the Theory of Computing Sample Solutions for Exercises on Finite Automata CSE303 - Introduction to th Thory of Computing Smpl Solutions for Exrciss on Finit Automt Exrcis 2.1.1 A dtrministic finit utomton M ccpts th mpty string (i.., L(M)) if nd only if its initil stt is finl

More information

INTRODUCTION TO COMPLEX NUMBERS

INTRODUCTION TO COMPLEX NUMBERS INTRODUCTION TO COMPLEX NUMBERS The numers -4, -3, -, -1, 0, 1,, 3, 4 represent the negtve nd postve rel numers termed ntegers. As one frst lerns n mddle school they cn e thought of s unt dstnce spced

More information

Chapter 4 A First Analysis of F edback edbac

Chapter 4 A First Analysis of F edback edbac Chr 4 A Fr Anly of Fbck 4. h Bc quon of Conrol On-loo ym - Ouu - rror - On-loo rnfr funconolf Clo-loo ym U Uny fbck rucur hr xrnl nu: - : rfrnc h ouu xc o rck - W: urbnc - V : nor no Ouu: ffc by boh nu

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

ELEG 413 Lecture #6. Mark Mirotznik, Ph.D. Professor The University of Delaware

ELEG 413 Lecture #6. Mark Mirotznik, Ph.D. Professor The University of Delaware LG 43 Lctur #6 Mrk Mirtnik, Ph.D. Prfssr Th Univrsity f Dlwr mil: mirtni@c.udl.du Wv Prpgtin nd Plritin TM: Trnsvrs lctrmgntic Wvs A md is prticulr fild cnfigurtin. Fr givn lctrmgntic bundry vlu prblm,

More information

Logistic Regression I. HRP 261 2/10/ am

Logistic Regression I. HRP 261 2/10/ am Logstc Rgrsson I HRP 26 2/0/03 0- am Outln Introducton/rvw Th smplst logstc rgrsson from a 2x2 tabl llustrats how th math works Stp-by-stp xampls to b contnud nxt tm Dummy varabls Confoundng and ntracton

More information

More on FT. Lecture 10 4CT.5 3CT.3-5,7,8. BME 333 Biomedical Signals and Systems - J.Schesser

More on FT. Lecture 10 4CT.5 3CT.3-5,7,8. BME 333 Biomedical Signals and Systems - J.Schesser Mr n FT Lcur 4CT.5 3CT.3-5,7,8 BME 333 Bimdicl Signls nd Sysms - J.Schssr 43 Highr Ordr Diffrniin d y d x, m b Y b X N n M m N M n n n m m n m n d m d n m Y n d f n [ n ] F d M m bm m X N n n n n n m p

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

22.615, MHD Theory of Fusion Systems Prof. Freidberg Lecture 2: Derivation of Ideal MHD Equation

22.615, MHD Theory of Fusion Systems Prof. Freidberg Lecture 2: Derivation of Ideal MHD Equation .65, MHD Thory of Fuson Systms Prof. Frdbrg Lctur : Drvton of Idl MHD Equton Rvw of th Drvton of th Momnt Equton. Strtng Pont: Boltzmnn Equton for lctrons, ons nd Mxwll Equtons. Momnts of Boltzmnn Equton:

More information

Minimum Spanning Trees

Minimum Spanning Trees Minimum Spnning Trs Minimum Spnning Trs Problm A town hs st of houss nd st of rods A rod conncts nd only houss A rod conncting houss u nd v hs rpir cost w(u, v) Gol: Rpir nough (nd no mor) rods such tht:

More information

Jens Siebel (University of Applied Sciences Kaiserslautern) An Interactive Introduction to Complex Numbers

Jens Siebel (University of Applied Sciences Kaiserslautern) An Interactive Introduction to Complex Numbers Jens Sebel (Unversty of Appled Scences Kserslutern) An Interctve Introducton to Complex Numbers 1. Introducton We know tht some polynoml equtons do not hve ny solutons on R/. Exmple 1.1: Solve x + 1= for

More information

SAMPLE CSc 340 EXAM QUESTIONS WITH SOLUTIONS: part 2

SAMPLE CSc 340 EXAM QUESTIONS WITH SOLUTIONS: part 2 AMPLE C EXAM UETION WITH OLUTION: prt. It n sown tt l / wr.7888l. I Φ nots orul or pprotng t vlu o tn t n sown tt t trunton rror o ts pproton s o t or or so onstnts ; tt s Not tt / L Φ L.. Φ.. /. /.. Φ..787.

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

1- I. M. ALGHROUZ: A New Approach To Fractional Derivatives, J. AOU, V. 10, (2007), pp

1- I. M. ALGHROUZ: A New Approach To Fractional Derivatives, J. AOU, V. 10, (2007), pp Jourl o Al-Qus Op Uvrsy or Rsrch Sus - No.4 - Ocobr 8 Rrcs: - I. M. ALGHROUZ: A Nw Approch To Frcol Drvvs, J. AOU, V., 7, pp. 4-47 - K.S. Mllr: Drvvs o or orr: Mh M., V 68, 995 pp. 83-9. 3- I. PODLUBNY:

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

Floating Point Number System -(1.3)

Floating Point Number System -(1.3) Floting Point Numbr Sstm -(.3). Floting Point Numbr Sstm: Comutrs rrsnt rl numbrs in loting oint numbr sstm: F,k,m,M 0. 3... k ;0, 0 i, i,...,k, m M. Nottions: th bs 0, k th numbr o igts in th bs xnsion

More information

Floating Point Number System -(1.3)

Floating Point Number System -(1.3) Floting Point Numbr Sstm -(.3). Floting Point Numbr Sstm: Comutrs rrsnt rl numbrs in loting oint numbr sstm: F,k,m,M 0. 3... k ;0, 0 i, i,...,k, m M. Nottions: th bs 0, k th numbr o igits in th bs xnsion

More information

UNIT # 08 (PART - I)

UNIT # 08 (PART - I) . r. d[h d[h.5 7.5 mol L S d[o d[so UNIT # 8 (PRT - I CHEMICL INETICS EXERCISE # 6. d[ x [ x [ x. r [X[C ' [X [[B r '[ [B [C. r [NO [Cl. d[so d[h.5 5 mol L S d[nh d[nh. 5. 6. r [ [B r [x [y r' [x [y r'

More information

The Wave Excitation Forces on a Floating Vertical Cylinder in Water of Infinite Depth

The Wave Excitation Forces on a Floating Vertical Cylinder in Water of Infinite Depth Th Wv Exctton Forcs on Flotng Vrtcl Cylnr n Wtr of Infnt Dpth Wll Fnngn, Mrtn Mr, J Goggns 3,* Collg of Engnrng n Infortcs, Ntonl Unvrsty of Irln, Glwy, Irln chool of Mthtcs, ttstcs n Appl Mthtcs, Ntonl

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

Bayesian belief networks: Inference

Bayesian belief networks: Inference C 740 Knowd rprntton ctur 0 n f ntwork: nfrnc o ukrcht o@c.ptt.du 539 nnott qur C 750 chn rnn n f ntwork. 1. Drctd ccc rph Nod rndo vr nk n nk ncod ndpndnc. urr rthquk r ohnc rc C 750 chn rnn n f ntwork.

More information

x=0 x=0 Positive Negative Positions Positions x=0 Positive Negative Positions Positions

x=0 x=0 Positive Negative Positions Positions x=0 Positive Negative Positions Positions Knemtc Quntte Lner Moton Phyc 101 Eyre Tme Intnt t Fundmentl Tme Interl Defned Poton x Fundmentl Dplcement Defned Aerge Velocty g Defned Aerge Accelerton g Defned Knemtc Quntte Scler: Mgntude Tme Intnt,

More information

Walk Like a Mathematician Learning Task:

Walk Like a Mathematician Learning Task: Gori Dprtmnt of Euction Wlk Lik Mthmticin Lrnin Tsk: Mtrics llow us to prform mny usful mthmticl tsks which orinrily rquir lr numbr of computtions. Som typs of problms which cn b on fficintly with mtrics

More information

ECE507 - Plasma Physics and Applications

ECE507 - Plasma Physics and Applications ECE57 - Plasa Physcs and Applcatons Lctur Prof. Jorg Rocca and Dr. Frnando Toasl Dpartnt of Elctrcal and Coputr Engnrng Introducton: What s a plasa? A quas-nutral collcton of chargd (and nutral) partcls

More information

MECH321 Dynamics of Engineering System Week 4 (Chapter 6)

MECH321 Dynamics of Engineering System Week 4 (Chapter 6) MH3 Dynamc of ngnrng Sytm Wk 4 (haptr 6). Bac lctrc crcut thor. Mathmatcal Modlng of Pav rcut 3. ompl mpdanc Approach 4. Mchancal lctrcal analogy 5. Modllng of Actv rcut: Opratonal Amplfr rcut Bac lctrc

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

Steady-state tracking & sys. types

Steady-state tracking & sys. types Sty-tt trcking & y. ty Unity fck control: um CL tl lnt r C y - r - o.l. y y r ol ol o.l. m m n n n N N N N N, N,, ut N N, m, ol.. clo-loo: y r ol.. trcking rror: r y r ty-tt trcking: t r ol.. ol.. For

More information

Math 2142 Homework 2 Solutions. Problem 1. Prove the following formulas for Laplace transforms for s > 0. a s 2 + a 2 L{cos at} = e st.

Math 2142 Homework 2 Solutions. Problem 1. Prove the following formulas for Laplace transforms for s > 0. a s 2 + a 2 L{cos at} = e st. Mth 2142 Homework 2 Solution Problem 1. Prove the following formul for Lplce trnform for >. L{1} = 1 L{t} = 1 2 L{in t} = 2 + 2 L{co t} = 2 + 2 Solution. For the firt Lplce trnform, we need to clculte:

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX 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 information

Theories of Vowel Systems: Quantal Theory and Adaptive Dispersion

Theories of Vowel Systems: Quantal Theory and Adaptive Dispersion L105/205 Phontcs Scrborough Hndout 8 10/25/05 Rdng: Stvns, Lljncrnts & Lndblom (tody) Thors of Vowl Systms: Quntl Thory nd Adptv Dsprson 1. Thr r hug (vn nfnt) numbr of dffrnt rtcultons tht cn b md. Obvously,

More information

Slide 1. Slide 2. Slide 3 DIGITAL SIGNAL PROCESSING CLASSIFICATION OF SIGNALS

Slide 1. Slide 2. Slide 3 DIGITAL SIGNAL PROCESSING CLASSIFICATION OF SIGNALS Slid DIGITAL SIGAL PROCESSIG UIT I DISCRETE TIME SIGALS AD SYSTEM Slid Rviw of discrt-tim signals & systms Signal:- A signal is dfind as any physical quantity that varis with tim, spac or any othr indpndnt

More information

Multi-Section Coupled Line Couplers

Multi-Section Coupled Line Couplers /0/009 MultiSction Coupld Lin Couplrs /8 Multi-Sction Coupld Lin Couplrs W cn dd multipl coupld lins in sris to incrs couplr ndwidth. Figur 7.5 (p. 6) An N-sction coupld lin l W typiclly dsign th couplr

More information

Signal Manipulation for the Hearing Impaired

Signal Manipulation for the Hearing Impaired Sgnl Mnpulton for th rng Imprd Fnl Rport Sprng Smtr 6 Prtl Rport y Ryn Grn Lrry Lowry Stvn Ml Dprtmnt of Elctrcl nd Computr Engnrng Colordo Stt Unvrty Fort Colln Colordo 853 Proct Advor: Dr. J. Rocy Luo

More information

Cycles and Simple Cycles. Paths and Simple Paths. Trees. Problem: There is No Completely Standard Terminology!

Cycles and Simple Cycles. Paths and Simple Paths. Trees. Problem: There is No Completely Standard Terminology! Outlin Computr Sin 331, Spnnin, n Surphs Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #30 1 Introution 2 3 Dinition 4 Spnnin 5 6 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 1 / 20 Mik

More information

The Theory of Small Reflections

The Theory of Small Reflections Jim Stils Th Univ. of Knss Dt. of EECS 4//9 Th Thory of Smll Rflctions /9 Th Thory of Smll Rflctions Rcll tht w nlyzd qurtr-wv trnsformr usg th multil rflction viw ot. V ( z) = + β ( z + ) V ( z) = = R

More information

Quantitative Genomics and Genetics BTRY 4830/6830; PBSB

Quantitative Genomics and Genetics BTRY 4830/6830; PBSB Quntttv Gnomcs n Gntcs BTRY 4830/6830; BSB.520.0 Lctur8: Logstc rgrsson II Json Mzy jgm45@cornll.u Aprl 0, 208 (T 8:40-9:55 Announcmnts Grng (homworks n mtrm rojct wll b ssgn NEXT WEEK (!! Schul for th

More information

Chapter 16. 1) is a particular point on the graph of the function. 1. y, where x y 1

Chapter 16. 1) is a particular point on the graph of the function. 1. y, where x y 1 Prctic qustions W now tht th prmtr p is dirctl rltd to th mplitud; thrfor, w cn find tht p. cos d [ sin ] sin sin Not: Evn though ou might not now how to find th prmtr in prt, it is lws dvisl to procd

More information

Kinematics Quantities. Linear Motion. Coordinate System. Kinematics Quantities. Velocity. Position. Don t Forget Units!

Kinematics Quantities. Linear Motion. Coordinate System. Kinematics Quantities. Velocity. Position. Don t Forget Units! Knemtc Quntte Lner Phyc 11 Eyre Tme Intnt t Fundmentl Tme Interl t Dened Poton Fundmentl Dplcement Dened Aerge g Dened Aerge Accelerton g Dened Knemtc Quntte Scler: Mgntude Tme Intnt, Tme Interl nd Speed

More information

2 T. or T. DSP First, 2/e. This Lecture: Lecture 7C Fourier Series Examples: Appendix C, Section C-2 Various Fourier Series

2 T. or T. DSP First, 2/e. This Lecture: Lecture 7C Fourier Series Examples: Appendix C, Section C-2 Various Fourier Series DSP Firs, Lcur 7C Fourir Sris Empls: Common Priodic Signls READIG ASSIGMES his Lcur: Appndi C, Scion C- Vrious Fourir Sris Puls Wvs ringulr Wv Rcifid Sinusoids lso in Ch. 3, Sc. 3-5 Aug 6 3-6, JH McCllln

More information

CONIC SECTIONS. MODULE-IV Co-ordinate Geometry OBJECTIVES. Conic Sections

CONIC SECTIONS. MODULE-IV Co-ordinate Geometry OBJECTIVES. Conic Sections Conic Sctions 16 MODULE-IV Co-ordint CONIC SECTIONS Whil cutting crrot ou might hv noticd diffrnt shps shown th dgs of th cut. Anlticll ou m cut it in thr diffrnt ws, nml (i) (ii) (iii) Cut is prlll to

More information