Introduction to Digital Signal Processing(DSP)

Size: px
Start display at page:

Download "Introduction to Digital Signal Processing(DSP)"

Transcription

1 Forth Clss Commictio II Electricl Dept Nd Nsih Itrodctio to Digitl Sigl ProcessigDSP Recet developmets i digitl compters ope the wy to this sject The geerl lock digrm of DSP system is show elow: Bd limited filter ADC Compter DAC Smoothig filter xt f s xt s yt s yt The ipt sigl xt is loge sigl speech, video, This sigl is first d limited sig LPF hvig ct-off freqecy f mx The d limited sigl is the coverted ito digitl xt s sig smpler with smplig freqecy f s f mx d qtier The discrete sigl xt s or simply writte x is etered to digitl compter with sitle iterfce crd sod crd, video crd, Iside this compter progrm high or low level d rel time or off time is writte to perform y sort of sigl lysis to x sch s lier mplifictio, log, expoetil, covoltio, correltio, d filterig The reslt of the digitl processig is yt s this sigl is the coverted to loge form sig DAC, de-smpler, d filly smoothig filter to remove the stir cse shpe of y Geerl cocepts i DSP: Lierity : A DSP system is clled lier if sperpositio theory pplies For exmple if y=x, d x=x +x the the system is lier sice: y=x=x +x = x +x = y +y where y d y re the otpts de to x d x

2 Forth Clss Commictio II Electricl Dept Nd Nsih x y x y If y= y +y the system is lier If y # y +y the system is ot lier Cslity: A DSP system is sid to e csl if the preset vle of the otpt is ot the fctio of ftre vle of the ipt x y memory-less csl x y - memory csl x y + csl Stility: A DSP system is sid to e stle if the otpt is oded for oded ipt For exmple, if y=x-5x-, x <G where G is fiite, the y <G-5G hece if x is oded y G the y is lso oded ie the system is stle 4 Time Vrit d Time Ivrit: A system is sid to e time vrit if its chrcteristics depeds o the time idex For exmple, y=x is time vrit system A system is sid to e time ivrit if its chrcteristics does't chge with time idex For exmple, y=e -x- is time ivrit system

3 Forth Clss Commictio II Ipt/Otpt reltios of the lier systems: Electricl Dept Nd Nsih Aloge cotios systems: If ht is the implse respose of the system the Hw is the trsfer fctio which is the Forier trsform of ht yt=xt ht where is the covoltio t x h t d h x t y t d t xt Xw ht Hw yt Yw Also Yw=XwHw d Yw = Xw Hw Or G y w=g x w Hw where G y w d G x w re spectrl desities of x d y respectively Also otpt power y t H w Gx w dw Discrete digitl systems: Here h is the implse respose of the system, d H is its trsfer fctio: H=Y/X xt ht yt Ad Xw Hw Yw y=x h or y x k h k h k x k which is clled the discrete covoltio k k DSP systems re clssified ccordig to their resposes h ito: i FIR Fiite Implse Respose: where h hs fiite mer of elemets sch s : h={,,4,,,} where the crsor idictes the positio where =

4 Forth Clss Commictio II Electricl Dept Nd Nsih ii IIR Ifiite Implse Respose: where h hs ifiite mer of elemets sch s : h=/ where is the it step fctio: = for =,,,, else where 4 5 h Discrete covoltio Methods: Grphic Method: This iclde the sic covoltio steps :- reversig i time sig k s time idex shiftig y smples c Mltiplictio of the correspodig smples d Addig 4

5 Forth Clss Commictio II Electricl Dept Nd Nsih Ex: fid the vle of y sig grphicl method h={,-,}, x={,,-,} y x k h k k y x k h k 6 k y x k h k 5 k y x k h k k Ad so o, shiftig of h left d right til the over lppig etwee xk d h-k disppers givig 's t the otpt y h x h- - - h-- h

6 Forth Clss Commictio II Electricl Dept Nd Nsih Tlr Method: this is very simple method sed for FIR systems with fiite mer of smples x A rectglr tle with N rows mer of elemets i h d N colms mer elemets of x, or vis vers, is rrged The the cross mltiplictios re crried ot The sm of the mltiplictios digolly will give the vle of y Ex: Repet previos exmple sig tlr method h={,-,}, x={,,-,} y-= y-=- sm sm sm sm y-= y=6 y=-5 y=6 sm sm The y={,-,,6,-5,6} Note tht N=N +N -= mer of elemets i y = +4-=6 Ad tht O d O re positios of the crsors i h d x from the left, the O= O +O -=+-=4 which is the positio of y Add-overlp Method: This is modified method from the tlr method, whe either h d x hs lrge mer of elemets, the this c e divided ito s-segmets of smller legth 6

7 Forth Clss Commictio II Ex: fid the discrete covoltio etwee: h={,-,}, x={,,-,,4,-,,} Electricl Dept Nd Nsih Here x is divided ito three segmets, first two segmets of legth, d lst segmet of legth Hece previos tlr method will e repeted times y ={,,-,5,-} y ={,,,9,-} y ={,,-,6} The, dd y, y, d y, with y shifted to the left y elemets legth of ech segmet d y shifted to the left y 6 elemets: y y 9 - y - 6 y y={,,-,8,-,,9,,-,6} with the crsor for y t O=+4-=5 7

8 Forth Clss Electricl Dept Commictio II Nd Nsih 8 4 Mtrix Method: Here, Y=Ah, where y=otpt, h=h d the A mtrix hs N=N +N - rows d N colms mer of elemets of h The st row i A is the st elemet i x from the left d the remiig elemets re 's the d row i A is the d elemet i x, the the st elemet i x d the remiig elemets re 's d so o til the lst elemet i x is etered t the N th row N is the mer of elemets i x After tht 's re pplied isted of the elemets of x til the lst row t N=N +N - Ex: fid the vle of y sig mtrix method h={,-,}, x={,,-,} h A the Y The y={,-,,6,-5,6} where O=+-= 5 the Z-trsform Method: This is geerl method sed either or oth of h d x hs ifiite elemets The procedre here is to tke the -trsform of x d h, the mltiply to fid Y from which y is fod sig iverse -trsform

9 Forth Clss Electricl Dept Commictio II Nd Nsih 9 Ex: fid the vle of y where: h={,-,}, x=/ Note, sice x hs ifiite mer of elemets, the we mst se -trsform method Now, tkig the -trsform of oth h d x: h H 5 5 x X 5 5 X The Y=HX=-+ - / Ad y= Ex: Fid sig the -trsform where d re costts d,the: Z Z Hece: = d,the: Z Z Hece: = c d Z Z

10 Forth Clss Commictio II Discrete Decovoltio: Electricl Dept Nd Nsih To fid h if oth x d y re give, we se the -trsform method sice it is vlid for oth FIR d IIR DSP system H=Z - H=Z - Y/X Ex: Fid h if x={,-,} d y={,,-,7,-6} X= divisio d Y= the we se the log Hece H= or h={,5,-} d there is o remider, the the system is FIR Ex: Fid h if x= d y=-5 X The H= d Y By tkig the iverse -trsform we get : h=5

11 Forth Clss Commictio II Electricl Dept Nd Nsih DSP System Implemettios: FIR systems: Here h hs fiite mer of elemets: h={h, h, h, hm} with m+ elemets H= h+h - + h -, hm -m d if: H=Y/X, the: y= hx+ hx-+ hx- hmx-m ie, y is otied from x y the weighted sm weighted y h of the delyed smples of x these delyed smples of x re otied sig tpped dely lie with m-tps d with T s time dely per tp x Z - Z - Z Z - h h hm Z - T s Ʃ y Note tht the FIR system is lwys stle, where the system is ope loop withot feed ck d tht is why it is clled o-recrsive

12 Forth Clss Commictio II Electricl Dept Nd Nsih IIR systems: For IIR system hvig m-eros d r-poles, the: Y H X m m r r Where we c lwys set the first term t the deomitor to ity y dividig with sitle costt Fro which : y= x+ x-+ x-+ + m x-m- y-- y-- - r y-r Note tht y depeds o preset d previos vles of x d previos vles of y Hece: IIR system cotis feedck from the otpt to the ipt possiility of istility if some poles of H lies otside the it circle to implemet the IIR system, the tpped dely lies re reqired, oe with m-tps for the x ipt d the other with r-tps for the feedck from the y otpt x Ʃ y Z - Z - Z - Z - Z - Z - Z - Z - Z - Z - Z - m r Z -

13 Forth Clss Commictio II Ex: implemet the DSP system: H 4 Electricl Dept Nd Nsih This is IIR system First we divide y to set the first term t the domitor to ity, the: H This eeds tpped dely lie with tps for x d tpped dely lie with tps for y x Ʃ y Z - 5 Z - Z Z - 5 Z -

The z Transform. The Discrete LTI System Response to a Complex Exponential

The z Transform. The Discrete LTI System Response to a Complex Exponential The Trsform The trsform geerlies the Discrete-time Forier Trsform for the etire complex ple. For the complex vrible is sed the ottio: jω x+ j y r e ; x, y Ω rg r x + y {} The Discrete LTI System Respose

More information

Frequency-domain Characteristics of Discrete-time LTI Systems

Frequency-domain Characteristics of Discrete-time LTI Systems requecy-domi Chrcteristics of Discrete-time LTI Systems Prof. Siripog Potisuk LTI System descriptio Previous bsis fuctio: uit smple or DT impulse The iput sequece is represeted s lier combitio of shifted

More information

Discrete-Time Signals & Systems

Discrete-Time Signals & Systems Chpter 2 Discrete-Time Sigls & Systems 清大電機系林嘉文 cwli@ee.thu.edu.tw 03-57352 Discrete-Time Sigls Sigls re represeted s sequeces of umbers, clled smples Smple vlue of typicl sigl or sequece deoted s x =

More information

DEPARTMENT OF ELECTRICAL &ELECTRONICS ENGINEERING SIGNALS AND SYSTEMS. Assoc. Prof. Dr. Burak Kelleci. Spring 2018

DEPARTMENT OF ELECTRICAL &ELECTRONICS ENGINEERING SIGNALS AND SYSTEMS. Assoc. Prof. Dr. Burak Kelleci. Spring 2018 DEPARTMENT OF ELECTRICAL &ELECTRONICS ENGINEERING SIGNALS AND SYSTEMS Assoc. Prof. Dr. Bur Kelleci Sprig 8 OUTLINE The Z-Trsform The Regio of covergece for the Z-trsform The Iverse Z-Trsform Geometric

More information

Digital Signal Processing, Fall 2006

Digital Signal Processing, Fall 2006 Digitl Sigl Processig, Fll 6 Lecture 6: Sstem structures for implemettio Zeg-u T Deprtmet of Electroic Sstems Alorg Uiversit, Demr t@om.u.d Digitl Sigl Processig, VI, Zeg-u T, 6 Course t glce Discrete-time

More information

=> PARALLEL INTERCONNECTION. Basic Properties LTI Systems. The Commutative Property. Convolution. The Commutative Property. The Distributive Property

=> PARALLEL INTERCONNECTION. Basic Properties LTI Systems. The Commutative Property. Convolution. The Commutative Property. The Distributive Property Lier Time-Ivrit Bsic Properties LTI The Commuttive Property The Distributive Property The Associtive Property Ti -6.4 / Chpter Covolutio y ] x ] ] x ]* ] x ] ] y] y ( t ) + x( τ ) h( t τ ) dτ x( t) * h(

More information

Auto-correlation. Window Selection: Hamming. Hamming Filtered Power Spectrum. White Noise Auto-Covariance vs. Hamming Filtered Noise

Auto-correlation. Window Selection: Hamming. Hamming Filtered Power Spectrum. White Noise Auto-Covariance vs. Hamming Filtered Noise Correltio d Spectrl Alsis Applictio 4 Review of covrice idepedece cov cov with vrice : ew rdom vrile forms. d For idepedet rdom vriles - Autocorreltio Autocovrice cptures covrice where I geerl. for oise

More information

The z Transform. The Discrete LTI System Response to a Complex Exponential

The z Transform. The Discrete LTI System Response to a Complex Exponential The Trasform The trasform geeralies the Discrete-time Forier Trasform for the etire complex plae. For the complex variable is sed the otatio: jω x+ j y r e ; x, y Ω arg r x + y {} The Discrete LTI System

More information

INFINITE SERIES. ,... having infinite number of terms is called infinite sequence and its indicated sum, i.e., a 1

INFINITE SERIES. ,... having infinite number of terms is called infinite sequence and its indicated sum, i.e., a 1 Appedix A.. Itroductio As discussed i the Chpter 9 o Sequeces d Series, sequece,,...,,... hvig ifiite umber of terms is clled ifiite sequece d its idicted sum, i.e., + + +... + +... is clled ifite series

More information

Fig. 1. I a. V ag I c. I n. V cg. Z n Z Y. I b. V bg

Fig. 1. I a. V ag I c. I n. V cg. Z n Z Y. I b. V bg ymmetricl Compoets equece impedces Although the followig focuses o lods, the results pply eqully well to lies, or lies d lods. Red these otes together with sectios.6 d.9 of text. Cosider the -coected lced

More information

2.1.1 Definition The Z-transform of a sequence x [n] is simply defined as (2.1) X re x k re x k r

2.1.1 Definition The Z-transform of a sequence x [n] is simply defined as (2.1) X re x k re x k r Z-Trsforms. INTRODUCTION TO Z-TRANSFORM The Z-trsform is coveiet d vluble tool for represetig, lyig d desigig discrete-time sigls d systems. It plys similr role i discrete-time systems to tht which Lplce

More information

Section IV.6: The Master Method and Applications

Section IV.6: The Master Method and Applications Sectio IV.6: The Mster Method d Applictios Defiitio IV.6.1: A fuctio f is symptoticlly positive if d oly if there exists rel umer such tht f(x) > for ll x >. A cosequece of this defiitio is tht fuctio

More information

ELEG 5173L Digital Signal Processing Ch. 2 The Z-Transform

ELEG 5173L Digital Signal Processing Ch. 2 The Z-Transform Deprtmet of Electricl Egieerig Uiversity of Arkss ELEG 573L Digitl Sigl Processig Ch. The Z-Trsform Dr. Jigxi Wu wuj@urk.edu OUTLINE The Z-Trsform Properties Iverse Z-Trsform Z-Trsform of LTI system Z-TRANSFORM

More information

Chapter 10: The Z-Transform Adapted from: Lecture notes from MIT, Binghamton University Hamid R. Rabiee Arman Sepehr Fall 2010

Chapter 10: The Z-Transform Adapted from: Lecture notes from MIT, Binghamton University Hamid R. Rabiee Arman Sepehr Fall 2010 Sigls & Systems Chpter 0: The Z-Trsform Adpted from: Lecture otes from MIT, Bighmto Uiversity Hmid R. Riee Arm Sepehr Fll 00 Lecture 5 Chpter 0 Outlie Itroductio to the -Trsform Properties of the ROC of

More information

Section 7.3, Systems of Linear Algebraic Equations; Linear Independence, Eigenvalues, Eigenvectors (the variable vector of the system) and

Section 7.3, Systems of Linear Algebraic Equations; Linear Independence, Eigenvalues, Eigenvectors (the variable vector of the system) and Sec. 7., Boyce & DiPrim, p. Sectio 7., Systems of Lier Algeric Equtios; Lier Idepedece, Eigevlues, Eigevectors I. Systems of Lier Algeric Equtios.. We c represet the system...... usig mtrices d vectors

More information

Week 13 Notes: 1) Riemann Sum. Aim: Compute Area Under a Graph. Suppose we want to find out the area of a graph, like the one on the right:

Week 13 Notes: 1) Riemann Sum. Aim: Compute Area Under a Graph. Suppose we want to find out the area of a graph, like the one on the right: Week 1 Notes: 1) Riem Sum Aim: Compute Are Uder Grph Suppose we wt to fid out the re of grph, like the oe o the right: We wt to kow the re of the red re. Here re some wys to pproximte the re: We cut the

More information

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Lecture 17

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Lecture 17 CS 70 Discrete Mthemtics d Proility Theory Sprig 206 Ro d Wlrd Lecture 7 Vrice We hve see i the previous ote tht if we toss coi times with is p, the the expected umer of heds is p. Wht this mes is tht

More information

The Reimann Integral is a formal limit definition of a definite integral

The Reimann Integral is a formal limit definition of a definite integral MATH 136 The Reim Itegrl The Reim Itegrl is forml limit defiitio of defiite itegrl cotiuous fuctio f. The costructio is s follows: f ( x) dx for Reim Itegrl: Prtitio [, ] ito suitervls ech hvig the equl

More information

Chapter 2 Infinite Series Page 1 of 9

Chapter 2 Infinite Series Page 1 of 9 Chpter Ifiite eries Pge of 9 Chpter : Ifiite eries ectio A Itroductio to Ifiite eries By the ed of this sectio you will be ble to uderstd wht is met by covergece d divergece of ifiite series recogise geometric

More information

CHAPTER 3 NETWORK ADMITTANCE AND IMPEDANCE MATRICES

CHAPTER 3 NETWORK ADMITTANCE AND IMPEDANCE MATRICES CHAPTER NETWORK ADTTANCE AND PEDANCE ATRCES As we hve see i Chter tht ower system etwor c e coverted ito equivlet imedce digrm. This digrm forms the sis of ower flow (or lod flow) studies d short circuit

More information

The total number of permutations of S is n!. We denote the set of all permutations of S by

The total number of permutations of S is n!. We denote the set of all permutations of S by DETERMINNTS. DEFINITIONS Def: Let S {,,, } e the set of itegers from to, rrged i scedig order. rerrgemet jjj j of the elemets of S is clled permuttio of S. S. The totl umer of permuttios of S is!. We deote

More information

GRAPHING LINEAR EQUATIONS. Linear Equations. x l ( 3,1 ) _x-axis. Origin ( 0, 0 ) Slope = change in y change in x. Equation for l 1.

GRAPHING LINEAR EQUATIONS. Linear Equations. x l ( 3,1 ) _x-axis. Origin ( 0, 0 ) Slope = change in y change in x. Equation for l 1. GRAPHING LINEAR EQUATIONS Qudrt II Qudrt I ORDERED PAIR: The first umer i the ordered pir is the -coordite d the secod umer i the ordered pir is the y-coordite. (, ) Origi ( 0, 0 ) _-is Lier Equtios Qudrt

More information

SM2H. Unit 2 Polynomials, Exponents, Radicals & Complex Numbers Notes. 3.1 Number Theory

SM2H. Unit 2 Polynomials, Exponents, Radicals & Complex Numbers Notes. 3.1 Number Theory SMH Uit Polyomils, Epoets, Rdicls & Comple Numbers Notes.1 Number Theory .1 Addig, Subtrctig, d Multiplyig Polyomils Notes Moomil: A epressio tht is umber, vrible, or umbers d vribles multiplied together.

More information

Introduction. Question: Why do we need new forms of parametric curves? Answer: Those parametric curves discussed are not very geometric.

Introduction. Question: Why do we need new forms of parametric curves? Answer: Those parametric curves discussed are not very geometric. Itrodctio Qestio: Why do we eed ew forms of parametric crves? Aswer: Those parametric crves discssed are ot very geometric. Itrodctio Give sch a parametric form, it is difficlt to kow the derlyig geometry

More information

is an ordered list of numbers. Each number in a sequence is a term of a sequence. n-1 term

is an ordered list of numbers. Each number in a sequence is a term of a sequence. n-1 term Mthemticl Ptters. Arithmetic Sequeces. Arithmetic Series. To idetify mthemticl ptters foud sequece. To use formul to fid the th term of sequece. To defie, idetify, d pply rithmetic sequeces. To defie rithmetic

More information

Chapter 7 Infinite Series

Chapter 7 Infinite Series MA Ifiite Series Asst.Prof.Dr.Supree Liswdi Chpter 7 Ifiite Series Sectio 7. Sequece A sequece c be thought of s list of umbers writte i defiite order:,,...,,... 2 The umber is clled the first term, 2

More information

LAWS OF INDICES M.K. HOME TUITION. Mathematics Revision Guides Level: GCSE Higher Tier

LAWS OF INDICES M.K. HOME TUITION. Mathematics Revision Guides Level: GCSE Higher Tier Mthetics Revisio Guides Lws of Idices Pge of 7 Author: Mrk Kudlowski M.K. HOME TUITION Mthetics Revisio Guides Level: GCSE Higher Tier LAWS OF INDICES Versio:. Dte: 0--0 Mthetics Revisio Guides Lws of

More information

{ } { S n } is monotonically decreasing if Sn

{ } { S n } is monotonically decreasing if Sn Sequece A sequece is fuctio whose domi of defiitio is the set of turl umers. Or it c lso e defied s ordered set. Nottio: A ifiite sequece is deoted s { } S or { S : N } or { S, S, S,...} or simply s {

More information

[ 20 ] 1. Inequality exists only between two real numbers (not complex numbers). 2. If a be any real number then one and only one of there hold.

[ 20 ] 1. Inequality exists only between two real numbers (not complex numbers). 2. If a be any real number then one and only one of there hold. [ 0 ]. Iequlity eists oly betwee two rel umbers (ot comple umbers).. If be y rel umber the oe d oly oe of there hold.. If, b 0 the b 0, b 0.. (i) b if b 0 (ii) (iii) (iv) b if b b if either b or b b if

More information

Remarks: (a) The Dirac delta is the function zero on the domain R {0}.

Remarks: (a) The Dirac delta is the function zero on the domain R {0}. Sectio Objective(s): The Dirc s Delt. Mi Properties. Applictios. The Impulse Respose Fuctio. 4.4.. The Dirc Delt. 4.4. Geerlized Sources Defiitio 4.4.. The Dirc delt geerlized fuctio is the limit δ(t)

More information

Linford 1. Kyle Linford. Math 211. Honors Project. Theorems to Analyze: Theorem 2.4 The Limit of a Function Involving a Radical (A4)

Linford 1. Kyle Linford. Math 211. Honors Project. Theorems to Analyze: Theorem 2.4 The Limit of a Function Involving a Radical (A4) Liford 1 Kyle Liford Mth 211 Hoors Project Theorems to Alyze: Theorem 2.4 The Limit of Fuctio Ivolvig Rdicl (A4) Theorem 2.8 The Squeeze Theorem (A5) Theorem 2.9 The Limit of Si(x)/x = 1 (p. 85) Theorem

More information

8.1 Arc Length. What is the length of a curve? How can we approximate it? We could do it following the pattern we ve used before

8.1 Arc Length. What is the length of a curve? How can we approximate it? We could do it following the pattern we ve used before 8.1 Arc Legth Wht is the legth of curve? How c we pproximte it? We could do it followig the ptter we ve used efore Use sequece of icresigly short segmets to pproximte the curve: As the segmets get smller

More information

COMM 602: Digital Signal Processing

COMM 602: Digital Signal Processing COMM 60: Digital Sigal Processig Lecture 4 -Properties of LTIS Usig Z-Trasform -Iverse Z-Trasform Properties of LTIS Usig Z-Trasform Properties of LTIS Usig Z-Trasform -ve +ve Properties of LTIS Usig Z-Trasform

More information

Unit 1. Extending the Number System. 2 Jordan School District

Unit 1. Extending the Number System. 2 Jordan School District Uit Etedig the Number System Jord School District Uit Cluster (N.RN. & N.RN.): Etedig Properties of Epoets Cluster : Etedig properties of epoets.. Defie rtiol epoets d eted the properties of iteger epoets

More information

( a n ) converges or diverges.

( a n ) converges or diverges. Chpter Ifiite Series Pge of Sectio E Rtio Test Chpter : Ifiite Series By the ed of this sectio you will be ble to uderstd the proof of the rtio test test series for covergece by pplyig the rtio test pprecite

More information

Fast Fourier Transform 1) Legendre s Interpolation 2) Vandermonde Matrix 3) Roots of Unity 4) Polynomial Evaluation

Fast Fourier Transform 1) Legendre s Interpolation 2) Vandermonde Matrix 3) Roots of Unity 4) Polynomial Evaluation Algorithm Desig d Alsis Victor Admchi CS 5-45 Sprig 4 Lecture 3 J 7, 4 Cregie Mello Uiversit Outlie Fst Fourier Trsform ) Legedre s Iterpoltio ) Vdermode Mtri 3) Roots of Uit 4) Polomil Evlutio Guss (777

More information

DIGITAL SIGNAL PROCESSING LECTURE 5

DIGITAL SIGNAL PROCESSING LECTURE 5 DIGITAL SIGNAL PROCESSING LECTURE 5 Fll K8-5 th Semester Thir Muhmmd tmuhmmd_7@yhoo.com Cotet d Figures re from Discrete-Time Sigl Processig, e by Oppeheim, Shfer, d Buck, 999- Pretice Hll Ic. The -Trsform

More information

z TRANSFORMS z Transform Basics z Transform Basics Transfer Functions Back to the Time Domain Transfer Function and Stability

z TRANSFORMS z Transform Basics z Transform Basics Transfer Functions Back to the Time Domain Transfer Function and Stability TRASFORS Trnsform Bsics Trnsfer Functions Bck to the Time Domin Trnsfer Function nd Stility DSP-G 6. Trnsform Bsics The definition of the trnsform for digitl signl is: -n X x[ n is complex vrile The trnsform

More information

Chapter 10: The Z-Transform Adapted from: Lecture notes from MIT, Binghamton University Dr. Hamid R. Rabiee Fall 2013

Chapter 10: The Z-Transform Adapted from: Lecture notes from MIT, Binghamton University Dr. Hamid R. Rabiee Fall 2013 Sigls & Systems Chpter 0: The Z-Trsform Adpted from: Lecture otes from MIT, Bighmto Uiversity Dr. Hmid R. Rbiee Fll 03 Lecture 5 Chpter 0 Lecture 6 Chpter 0 Outlie Itroductio to the -Trsform Properties

More information

f(t)dt 2δ f(x) f(t)dt 0 and b f(t)dt = 0 gives F (b) = 0. Since F is increasing, this means that

f(t)dt 2δ f(x) f(t)dt 0 and b f(t)dt = 0 gives F (b) = 0. Since F is increasing, this means that Uiversity of Illiois t Ur-Chmpig Fll 6 Mth 444 Group E3 Itegrtio : correctio of the exercises.. ( Assume tht f : [, ] R is cotiuous fuctio such tht f(x for ll x (,, d f(tdt =. Show tht f(x = for ll x [,

More information

POWER SERIES R. E. SHOWALTER

POWER SERIES R. E. SHOWALTER POWER SERIES R. E. SHOWALTER. sequeces We deote by lim = tht the limit of the sequece { } is the umber. By this we me tht for y ε > 0 there is iteger N such tht < ε for ll itegers N. This mkes precise

More information

FIR Filters. Lecture #7 Chapter 5. BME 310 Biomedical Computing - J.Schesser

FIR Filters. Lecture #7 Chapter 5. BME 310 Biomedical Computing - J.Schesser FIR Filters Lecture #7 Chapter 5 8 What Is this Course All About? To Gai a Appreciatio of the Various Types of Sigals ad Systems To Aalyze The Various Types of Systems To Lear the Skills ad Tools eeded

More information

Advanced Algorithmic Problem Solving Le 6 Math and Search

Advanced Algorithmic Problem Solving Le 6 Math and Search Advced Algorithmic Prolem Solvig Le Mth d Serch Fredrik Heitz Dept of Computer d Iformtio Sciece Liköpig Uiversity Outlie Arithmetic (l. d.) Solvig lier equtio systems (l. d.) Chiese remider theorem (l.5

More information

CHAPTER 1 INTRODUCTION NUMBER SYSTEMS AND CONVERSION

CHAPTER 1 INTRODUCTION NUMBER SYSTEMS AND CONVERSION Fudmetls of Logic Desig Chp. CHAPTE /9 INTODUCTION NUMBE SYSTEMS AND CONVESION This chpter i the book icludes: Objectives Study Guide. Digitl Systems d Switchig Circuits. Number Systems d Coversio. Biry

More information

Chapter System of Equations

Chapter System of Equations hpter 4.5 System of Equtios After redig th chpter, you should be ble to:. setup simulteous lier equtios i mtrix form d vice-vers,. uderstd the cocept of the iverse of mtrix, 3. kow the differece betwee

More information

PROGRESSIONS AND SERIES

PROGRESSIONS AND SERIES PROGRESSIONS AND SERIES A sequece is lso clled progressio. We ow study three importt types of sequeces: () The Arithmetic Progressio, () The Geometric Progressio, () The Hrmoic Progressio. Arithmetic Progressio.

More information

B. Examples 1. Finite Sums finite sums are an example of Riemann Sums in which each subinterval has the same length and the same x i

B. Examples 1. Finite Sums finite sums are an example of Riemann Sums in which each subinterval has the same length and the same x i Mth 06 Clculus Sec. 5.: The Defiite Itegrl I. Riem Sums A. Def : Give y=f(x):. Let f e defied o closed itervl[,].. Prtitio [,] ito suitervls[x (i-),x i ] of legth Δx i = x i -x (i-). Let P deote the prtitio

More information

Section 6.3: Geometric Sequences

Section 6.3: Geometric Sequences 40 Chpter 6 Sectio 6.: Geometric Sequeces My jobs offer ul cost-of-livig icrese to keep slries cosistet with ifltio. Suppose, for exmple, recet college grdute fids positio s sles mger erig ul slry of $6,000.

More information

The Definite Integral

The Definite Integral The Defiite Itegrl A Riem sum R S (f) is pproximtio to the re uder fuctio f. The true re uder the fuctio is obtied by tkig the it of better d better pproximtios to the re uder f. Here is the forml defiitio,

More information

Approximate Integration

Approximate Integration Study Sheet (7.7) Approimte Itegrtio I this sectio, we will ler: How to fid pproimte vlues of defiite itegrls. There re two situtios i which it is impossile to fid the ect vlue of defiite itegrl. Situtio:

More information

,... are the terms of the sequence. If the domain consists of the first n positive integers only, the sequence is a finite sequence.

,... are the terms of the sequence. If the domain consists of the first n positive integers only, the sequence is a finite sequence. Chpter 9 & 0 FITZGERALD MAT 50/5 SECTION 9. Sequece Defiitio A ifiite sequece is fuctio whose domi is the set of positive itegers. The fuctio vlues,,, 4,...,,... re the terms of the sequece. If the domi

More information

Vectors. Vectors in Plane ( 2

Vectors. Vectors in Plane ( 2 Vectors Vectors i Ple ( ) The ide bout vector is to represet directiol force Tht mes tht every vector should hve two compoets directio (directiol slope) d mgitude (the legth) I the ple we preset vector

More information

Chapter 5. The Riemann Integral. 5.1 The Riemann integral Partitions and lower and upper integrals. Note: 1.5 lectures

Chapter 5. The Riemann Integral. 5.1 The Riemann integral Partitions and lower and upper integrals. Note: 1.5 lectures Chpter 5 The Riem Itegrl 5.1 The Riem itegrl Note: 1.5 lectures We ow get to the fudmetl cocept of itegrtio. There is ofte cofusio mog studets of clculus betwee itegrl d tiderivtive. The itegrl is (iformlly)

More information

Taylor Polynomials. The Tangent Line. (a, f (a)) and has the same slope as the curve y = f (x) at that point. It is the best

Taylor Polynomials. The Tangent Line. (a, f (a)) and has the same slope as the curve y = f (x) at that point. It is the best Tylor Polyomils Let f () = e d let p() = 1 + + 1 + 1 6 3 Without usig clcultor, evlute f (1) d p(1) Ok, I m still witig With little effort it is possible to evlute p(1) = 1 + 1 + 1 (144) + 6 1 (178) =

More information

sin m a d F m d F m h F dy a dy a D h m h m, D a D a c1cosh c3cos 0

sin m a d F m d F m h F dy a dy a D h m h m, D a D a c1cosh c3cos 0 Q1. The free vibrtio of the plte is give by By ssumig h w w D t, si cos w W x y t B t Substitutig the deflectio ito the goverig equtio yields For the plte give, the mode shpe W hs the form h D W W W si

More information

Notes 17 Sturm-Liouville Theory

Notes 17 Sturm-Liouville Theory ECE 638 Fll 017 Dvid R. Jckso Notes 17 Sturm-Liouville Theory Notes re from D. R. Wilto, Dept. of ECE 1 Secod-Order Lier Differetil Equtios (SOLDE) A SOLDE hs the form d y dy 0 1 p ( x) + p ( x) + p (

More information

Geometric Sequences. Geometric Sequence. Geometric sequences have a common ratio.

Geometric Sequences. Geometric Sequence. Geometric sequences have a common ratio. s A geometric sequece is sequece such tht ech successive term is obtied from the previous term by multiplyig by fixed umber clled commo rtio. Exmples, 6, 8,, 6,..., 0, 0, 0, 80,... Geometric sequeces hve

More information

Presentation for use with the textbook, Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, Divide-and-Conquer

Presentation for use with the textbook, Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, Divide-and-Conquer Presettio for use with the textook, Algorithm Desig d Applictios, y M. T. Goodrich d R. Tmssi, Wiley, 25 Divide-d-Coquer Divide-d-Coquer Divide-d coquer is geerl lgorithm desig prdigm: Divide: divide the

More information

Lecture 4 Recursive Algorithm Analysis. Merge Sort Solving Recurrences The Master Theorem

Lecture 4 Recursive Algorithm Analysis. Merge Sort Solving Recurrences The Master Theorem Lecture 4 Recursive Algorithm Alysis Merge Sort Solvig Recurreces The Mster Theorem Merge Sort MergeSortA, left, right) { if left < right) { mid = floorleft + right) / 2); MergeSortA, left, mid); MergeSortA,

More information

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 0 FURTHER CALCULUS II. Sequeces d series. Rolle s theorem d me vlue theorems 3. Tlor s d Mcluri s theorems 4. L Hopitl

More information

CS 331 Design and Analysis of Algorithms. -- Divide and Conquer. Dr. Daisy Tang

CS 331 Design and Analysis of Algorithms. -- Divide and Conquer. Dr. Daisy Tang CS 33 Desig d Alysis of Algorithms -- Divide d Coquer Dr. Disy Tg Divide-Ad-Coquer Geerl ide: Divide problem ito subproblems of the sme id; solve subproblems usig the sme pproh, d ombie prtil solutios,

More information

MTH 146 Class 16 Notes

MTH 146 Class 16 Notes MTH 46 Clss 6 Notes 0.4- Cotiued Motivtio: We ow cosider the rc legth of polr curve. Suppose we wish to fid the legth of polr curve curve i terms of prmetric equtios s: r f where b. We c view the cos si

More information

M3P14 EXAMPLE SHEET 1 SOLUTIONS

M3P14 EXAMPLE SHEET 1 SOLUTIONS M3P14 EXAMPLE SHEET 1 SOLUTIONS 1. Show tht for, b, d itegers, we hve (d, db) = d(, b). Sice (, b) divides both d b, d(, b) divides both d d db, d hece divides (d, db). O the other hd, there exist m d

More information

INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS)

INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS) Mthemtics Revisio Guides Itegrtig Trig, Log d Ep Fuctios Pge of MK HOME TUITION Mthemtics Revisio Guides Level: AS / A Level AQA : C Edecel: C OCR: C OCR MEI: C INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS)

More information

THEORY OF EQUATIONS SYNOPSIS. Polyomil Fuctio: If,, re rel d is positive iteger, the f)x) = + x + x +.. + x is clled polyomil fuctio.. Degree of the Polyomil: The highest power of x for which the coefficiet

More information

A General Construction Method of Simultaneous Confounding in

A General Construction Method of Simultaneous Confounding in hk Uiv J Sci : - Jly A Geerl Costrctio ethod of Simlteos Cofodig i - Fctoril Eerimet A Jlil ertmet of Sttistics iosttistics d formtics Uiversity of hk hk gldesh E-mil: mjlil@ivdhked Received o Acceted

More information

Module 4. Signal Representation and Baseband Processing. Version 2 ECE IIT, Kharagpur

Module 4. Signal Representation and Baseband Processing. Version 2 ECE IIT, Kharagpur Module 4 Sigl Represettio d Bsed Processig Versio ECE IIT, Khrgpur Lesso 5 Orthogolity Versio ECE IIT, Khrgpur Ater redig this lesso, you will ler out Bsic cocept o orthogolity d orthoormlity; Strum -

More information

Westchester Community College Elementary Algebra Study Guide for the ACCUPLACER

Westchester Community College Elementary Algebra Study Guide for the ACCUPLACER Westchester Commuity College Elemetry Alger Study Guide for the ACCUPLACER Courtesy of Aims Commuity College The followig smple questios re similr to the formt d cotet of questios o the Accuplcer Elemetry

More information

Particle in a Box. and the state function is. In this case, the Hermitian operator. The b.c. restrict us to 0 x a. x A sin for 0 x a, and 0 otherwise

Particle in a Box. and the state function is. In this case, the Hermitian operator. The b.c. restrict us to 0 x a. x A sin for 0 x a, and 0 otherwise Prticle i Box We must hve me where = 1,,3 Solvig for E, π h E = = where = 1,,3, m 8m d the stte fuctio is x A si for 0 x, d 0 otherwise x ˆ d KE V. m dx I this cse, the Hermiti opertor 0iside the box The

More information

ALGEBRA. Set of Equations. have no solution 1 b1. Dependent system has infinitely many solutions

ALGEBRA. Set of Equations. have no solution 1 b1. Dependent system has infinitely many solutions Qudrtic Equtios ALGEBRA Remider theorem: If f() is divided b( ), the remider is f(). Fctor theorem: If ( ) is fctor of f(), the f() = 0. Ivolutio d Evlutio ( + b) = + b + b ( b) = + b b ( + b) 3 = 3 +

More information

FOURIER SERIES PART I: DEFINITIONS AND EXAMPLES. To a 2π-periodic function f(x) we will associate a trigonometric series. a n cos(nx) + b n sin(nx),

FOURIER SERIES PART I: DEFINITIONS AND EXAMPLES. To a 2π-periodic function f(x) we will associate a trigonometric series. a n cos(nx) + b n sin(nx), FOURIER SERIES PART I: DEFINITIONS AND EXAMPLES To -periodic fuctio f() we will ssocite trigoometric series + cos() + b si(), or i terms of the epoetil e i, series of the form c e i. Z For most of the

More information

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution EEL5: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we begi our mathematical treatmet of discrete-time s. As show i Figure, a discrete-time operates or trasforms some iput sequece x [

More information

ALGEBRA II CHAPTER 7 NOTES. Name

ALGEBRA II CHAPTER 7 NOTES. Name ALGEBRA II CHAPTER 7 NOTES Ne Algebr II 7. th Roots d Rtiol Expoets Tody I evlutig th roots of rel ubers usig both rdicl d rtiol expoet ottio. I successful tody whe I c evlute th roots. It is iportt for

More information

SUTCLIFFE S NOTES: CALCULUS 2 SWOKOWSKI S CHAPTER 11

SUTCLIFFE S NOTES: CALCULUS 2 SWOKOWSKI S CHAPTER 11 UTCLIFFE NOTE: CALCULU WOKOWKI CHAPTER Ifiite eries Coverget or Diverget eries Cosider the sequece If we form the ifiite sum 0, 00, 000, 0 00 000, we hve wht is clled ifiite series We wt to fid the sum

More information

General properties of definite integrals

General properties of definite integrals Roerto s Notes o Itegrl Clculus Chpter 4: Defiite itegrls d the FTC Sectio Geerl properties of defiite itegrls Wht you eed to kow lredy: Wht defiite Riem itegrl is. Wht you c ler here: Some key properties

More information

Application of Digital Filters

Application of Digital Filters Applicatio of Digital Filters Geerally some filterig of a time series take place as a reslt of the iability of the recordig system to respod to high freqecies. I may cases systems are desiged specifically

More information

Laws of Integral Indices

Laws of Integral Indices A Lws of Itegrl Idices A. Positive Itegrl Idices I, is clled the se, is clled the idex lso clled the expoet. mes times.... Exmple Simplify 5 6 c Solutio 8 5 6 c 6 Exmple Simplify Solutio The results i

More information

Recurrece reltio & Recursio 9 Chpter 3 Recurrece reltio & Recursio Recurrece reltio : A recurrece is equtio or iequlity tht describes fuctio i term of itself with its smller iputs. T T The problem of size

More information

MATRIX ALGEBRA, Systems Linear Equations

MATRIX ALGEBRA, Systems Linear Equations MATRIX ALGEBRA, Systes Lier Equtios Now we chge to the LINEAR ALGEBRA perspective o vectors d trices to reforulte systes of lier equtios. If you fid the discussio i ters of geerl d gets lost i geerlity,

More information

Mathacle. PSet Stats, Concepts In Statistics Level Number Name: Date:

Mathacle. PSet Stats, Concepts In Statistics Level Number Name: Date: APPENDEX I. THE RAW ALGEBRA IN STATISTICS A I-1. THE INEQUALITY Exmple A I-1.1. Solve ech iequlity. Write the solutio i the itervl ottio..) 2 p - 6 p -8.) 2x- 3 < 5 Solutio:.). - 4 p -8 p³ 2 or pî[2, +

More information

Statistics for Financial Engineering Session 1: Linear Algebra Review March 18 th, 2006

Statistics for Financial Engineering Session 1: Linear Algebra Review March 18 th, 2006 Sttistics for Ficil Egieerig Sessio : Lier Algebr Review rch 8 th, 6 Topics Itroductio to trices trix opertios Determits d Crmer s rule Eigevlues d Eigevectors Quiz The cotet of Sessio my be fmilir to

More information

FIR Filter Design: Part II

FIR Filter Design: Part II EEL335: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we cosider how we might go about desigig FIR filters with arbitrary frequecy resposes, through compositio of multiple sigle-peak

More information

Lesson-2 PROGRESSIONS AND SERIES

Lesson-2 PROGRESSIONS AND SERIES Lesso- PROGRESSIONS AND SERIES Arithmetic Progressio: A sequece of terms is sid to be i rithmetic progressio (A.P) whe the differece betwee y term d its preceedig term is fixed costt. This costt is clled

More information

THE NATIONAL UNIVERSITY OF IRELAND, CORK COLÁISTE NA hollscoile, CORCAIGH UNIVERSITY COLLEGE, CORK SUMMER EXAMINATION 2005 FIRST ENGINEERING

THE NATIONAL UNIVERSITY OF IRELAND, CORK COLÁISTE NA hollscoile, CORCAIGH UNIVERSITY COLLEGE, CORK SUMMER EXAMINATION 2005 FIRST ENGINEERING OLLSCOIL NA héireann, CORCAIGH THE NATIONAL UNIVERSITY OF IRELAND, CORK COLÁISTE NA hollscoile, CORCAIGH UNIVERSITY COLLEGE, CORK SUMMER EXAMINATION 2005 FIRST ENGINEERING MATHEMATICS MA008 Clculus d Lier

More information

Introduction to Computational Molecular Biology. Suffix Trees

Introduction to Computational Molecular Biology. Suffix Trees 18.417 Itroductio to Computtiol Moleculr Biology Lecture 11: October 14, 004 Scribe: Athich Muthitchroe Lecturer: Ross Lippert Editor: Toy Scelfo Suffix Trees This is oe of the most complicted dt structures

More information

Formal Languages The Pumping Lemma for CFLs

Formal Languages The Pumping Lemma for CFLs Forl Lguges The Pupig Le for CFLs Review: pupig le for regulr lguges Tke ifiite cotext-free lguge Geertes ifiite uer of differet strigs Exple: 3 I derivtio of log strig, vriles re repeted derivtio: 4 Derivtio

More information

Homework 2 solutions

Homework 2 solutions Sectio 2.1: Ex 1,3,6,11; AP 1 Sectio 2.2: Ex 3,4,9,12,14 Homework 2 solutios 1. Determie i ech uctio hs uique ixed poit o the speciied itervl. gx = 1 x 2 /4 o [0,1]. g x = -x/2, so g is cotiuous d decresig

More information

Limit of a function:

Limit of a function: - Limit of fuctio: We sy tht f ( ) eists d is equl with (rel) umer L if f( ) gets s close s we wt to L if is close eough to (This defiitio c e geerlized for L y syig tht f( ) ecomes s lrge (or s lrge egtive

More information

Infinite Series Sequences: terms nth term Listing Terms of a Sequence 2 n recursively defined n+1 Pattern Recognition for Sequences Ex:

Infinite Series Sequences: terms nth term Listing Terms of a Sequence 2 n recursively defined n+1 Pattern Recognition for Sequences Ex: Ifiite Series Sequeces: A sequece i defied s fuctio whose domi is the set of positive itegers. Usully it s esier to deote sequece i subscript form rther th fuctio ottio.,, 3, re the terms of the sequece

More information

Riemann Integral Oct 31, such that

Riemann Integral Oct 31, such that Riem Itegrl Ot 31, 2007 Itegrtio of Step Futios A prtitio P of [, ] is olletio {x k } k=0 suh tht = x 0 < x 1 < < x 1 < x =. More suitly, prtitio is fiite suset of [, ] otiig d. It is helpful to thik of

More information

n 2 + 3n + 1 4n = n2 + 3n + 1 n n 2 = n + 1

n 2 + 3n + 1 4n = n2 + 3n + 1 n n 2 = n + 1 Ifiite Series Some Tests for Divergece d Covergece Divergece Test: If lim u or if the limit does ot exist, the series diverget. + 3 + 4 + 3 EXAMPLE: Show tht the series diverges. = u = + 3 + 4 + 3 + 3

More information

Lesson 4 Linear Algebra

Lesson 4 Linear Algebra Lesso Lier Algebr A fmily of vectors is lierly idepedet if oe of them c be writte s lier combitio of fiitely my other vectors i the collectio. Cosider m lierly idepedet equtios i ukows:, +, +... +, +,

More information

Lecture 2: Matrix Algebra

Lecture 2: Matrix Algebra Lecture 2: Mtrix lgebr Geerl. mtrix, for our purpose, is rectgulr rry of objects or elemets. We will tke these elemets s beig rel umbers d idicte elemet by its row d colum positio. mtrix is the ordered

More information

UNIT V: Z-TRANSFORMS AND DIFFERENCE EQUATIONS. Dr. V. Valliammal Department of Applied Mathematics Sri Venkateswara College of Engineering

UNIT V: Z-TRANSFORMS AND DIFFERENCE EQUATIONS. Dr. V. Valliammal Department of Applied Mathematics Sri Venkateswara College of Engineering UNIT V: -TRANSFORMS AND DIFFERENCE EQUATIONS D. V. Vllimml Deptmet of Applied Mthemtics Si Vektesw College of Egieeig TOPICS:. -Tsfoms Elemet popeties.. Ivese -Tsfom usig ptil fctios d esidues. Covolutio

More information

Logarithmic Scales: the most common example of these are ph, sound and earthquake intensity.

Logarithmic Scales: the most common example of these are ph, sound and earthquake intensity. Numercy Itroductio to Logrithms Logrithms re commoly credited to Scottish mthemtici med Joh Npier who costructed tle of vlues tht llowed multiplictios to e performed y dditio of the vlues from the tle.

More information

DETERMINANT. = 0. The expression a 1. is called a determinant of the second order, and is denoted by : y + c 1

DETERMINANT. = 0. The expression a 1. is called a determinant of the second order, and is denoted by : y + c 1 NOD6 (\Dt\04\Kot\J-Advced\SMP\Mths\Uit#0\NG\Prt-\0.Determits\0.Theory.p65. INTRODUCTION : If the equtios x + b 0, x + b 0 re stisfied by the sme vlue of x, the b b 0. The expressio b b is clled determit

More information

Similar idea to multiplication in N, C. Divide and conquer approach provides unexpected improvements. Naïve matrix multiplication

Similar idea to multiplication in N, C. Divide and conquer approach provides unexpected improvements. Naïve matrix multiplication Next. Covered bsics of simple desig techique (Divided-coquer) Ch. of the text.. Next, Strsse s lgorithm. Lter: more desig d coquer lgorithms: MergeSort. Solvig recurreces d the Mster Theorem. Similr ide

More information

moment = m! x, where x is the length of the moment arm.

moment = m! x, where x is the length of the moment arm. th 1206 Clculus Sec. 6.7: omets d Ceters of ss I. Fiite sses A. Oe Dimesiol Cses 1. Itroductio Recll the differece etwee ss d Weight.. ss is the mout of "stuff" (mtter) tht mkes up oject.. Weight is mesure

More information

10.5 Power Series. In this section, we are going to start talking about power series. A power series is a series of the form

10.5 Power Series. In this section, we are going to start talking about power series. A power series is a series of the form 0.5 Power Series I the lst three sectios, we ve spet most of tht time tlkig bout how to determie if series is coverget or ot. Now it is time to strt lookig t some specific kids of series d we will evetully

More information

PRODUCT OF DISTRIBUTIONS AND ZETA REGULARIZATION OF DIVERGENT INTEGRALS

PRODUCT OF DISTRIBUTIONS AND ZETA REGULARIZATION OF DIVERGENT INTEGRALS PRODUCT OF DISTRIBUTIONS AND ZETA REGULARIZATION OF DIVERGENT INTEGRALS s AND FOURIER TRANSFORMS Jose Jvier Grci Moret Grdte stdet of Physics t the UPV/EHU (Uiversity of Bsqe cotry) I Solid Stte Physics

More information