Mathematical Description of Discrete-Time Signals. 9/10/16 M. J. Roberts - All Rights Reserved 1

Size: px
Start display at page:

Download "Mathematical Description of Discrete-Time Signals. 9/10/16 M. J. Roberts - All Rights Reserved 1"

Transcription

1 Mathematical Descriptio of Discrete-Time Sigals 9/10/16 M. J. Roberts - All Rights Reserved 1

2 Samplig ad Discrete Time Samplig is the acquisitio of the values of a cotiuous-time sigal at discrete poits i time. x t discrete-time sigal. x = x T s ( ) is a cotiuous-time sigal, x ( ) where T s is the time betwee samples is a Samplig Uiform Samplig x() t x x t [ ] () x[ ] ω s or f s 9/10/16 M. J. Roberts - All Rights Reserved 2

3 Samplig ad Discrete Time

4 Siusoids Ulike a cotiuous-time siusoid, a discrete-time siusoid is ot ecessarily periodic. If it is periodic, its period must be a iteger. If a siusoid has the form g [ ] = Acos( 2π F 0 +θ ) the [ ] to be F 0 must be a ratio of itegers (a ratioal umber) for g periodic. If F 0 is ratioal i the form q / N 0 ( q ad N 0 itegers)i which all commo factors i q ad N 0 have already bee cacelled, the the fudametal period of the siusoid is N 0, ot N 0 / q (uless q = 1). Therefore, the geeral form of a periodic siusoid with fudametal period N 0 is g ( ). [ ] = Acos 2πq / N 0 +θ 9/10/16 M. J. Roberts - All Rights Reserved 4

5 Siusoids Periodic Periodic Periodic Aperiodic 9/10/16 M. J. Roberts - All Rights Reserved 5

6 Siusoids A Aperiodic Siusoid 9/10/16 M. J. Roberts - All Rights Reserved 6

7 Siusoids Two siusoids whose aalytical expressios look differet, [ ] = Acos( 2π F 01 +θ ) ad g 2 g 1 may actually be the same. If F 02 = F 01 + m, where m is a iteger [ ] = Acos( 2π F 02 +θ ) the (because is discrete time ad therefore a iteger), Acos( 2π F 02 +θ ) = Acos 2π ( F 01 + m) +θ Acos 2π F 02 +θ ( ) +θ Iteger Multiple of 2π ( ) = Acos 2π F π m (Example o ext slide) = Acos( 2π F 01 +θ ) 9/10/16 M. J. Roberts - All Rights Reserved 7

8 Siusoids g [] = cos 1 1 ( ) g [] = cos 2 1 ( ) /10/16 M. J. Roberts - All Rights Reserved 8

9 Siusoids x[] = cos(2 F) Dashed lie is x(t) = cos(2 Ft) x[] 1 F = 0 x[] 1 F = 0.25 x[] 1 F = 0.5 x[] 1 F = ,t 4,t 4,t 4,t x[] 1 F = 1 x[] 1 F = 1.25 x[] 1 F = 1.5 x[] 1 F = ,t 4,t 4,t 4,t /10/16 M. J. Roberts - All Rights Reserved 9

10 Expoetials The form of the expoetial is [ ] = Az x!# "# $ or x Preferred [ ] = Ae β where z = e β 0 < z < 1 Real z -1 < z < 0 Re(g[]) Complex z z < 1 Im(g[]) z > 1 z < -1 Re(g[]) z > 1 Im(g[]) 9/10/16 M. J. Roberts - All Rights Reserved 10

11 The Uit Impulse Fuctio δ[] 1 [ ] = 1, = 0 δ 0, 0 The discrete-time uit impulse (also kow as the Kroecker delta fuctio ) is a fuctio i the ordiary sese (i cotrast with the cotiuous-time uit impulse). It has a samplig property, [ ] Aδ [ 0 ]x = Ax 0 = but o scalig property. That is, δ [ ] [ ] = δ [ a] for ay o-zero, fiite iteger a. 9/10/16 M. J. Roberts - All Rights Reserved 11

12 The Uit Sequece Fuctio [ ] = 1, 0 u 0, < 0 u[] /10/16 M. J. Roberts - All Rights Reserved 12

13 The Sigum Fuctio 1, > 0 sg = 0, = 0 1, < 0 = 2u δ 1 sg[] /10/16 M. J. Roberts - All Rights Reserved 13

14 The Uit Ramp Fuctio ramp [ ] =, 0 0, < 0 = u ramp[] m= [ ] = u[ m 1] /10/16 M. J. Roberts - All Rights Reserved 14

15 The Periodic Impulse Fuctio δ N m= [ ] = δ mn [ ]... []! N -N N 2N... 9/10/16 M. J. Roberts - All Rights Reserved 15

16 Scalig ad Shiftig Fuctios [ ] be graphically defied by the graph below ad [ ] = 0, > 15 Let g g 9/10/16 M. J. Roberts - All Rights Reserved 16

17 Scalig ad Shiftig Fuctios Time shiftig + 0, 0 a iteger 9/10/16 M. J. Roberts - All Rights Reserved 17

18 Scalig ad Shiftig Fuctios Time compressio K K a iteger > 1 9/10/16 M. J. Roberts - All Rights Reserved 18

19 Scalig ad Shiftig Fuctios Time expasio / K, K > 1 For all such that / K is a iteger, g[ / K ] is defied. For all such that / K is ot a iteger, g[ / K ] is ot defied. 9/10/16 M. J. Roberts - All Rights Reserved 19

20 Scalig ad Shiftig Fuctios There is a form of time expasio that is useful. Let [ ] = x [ / m ], / m a iteger y 0, otherwise All values of y are defied. This type of time expasio is actually used i some digital sigal processig operatios.

21 Differecig 9/10/16 M. J. Roberts - All Rights Reserved 21

22 Accumulatio [ ] = h[ m] g m= h[] g[] h[] g[] /10/16 M. J. Roberts - All Rights Reserved 22

23 Eve ad Odd Sigals g[ ] = g[ ] g[ ] = g[ ] Eve Fuctio g[] Odd Fuctio g[] g e [ ] = g [ ] + g[ ] 2 g o [ ] = g [ ] g[ ] 2 9/10/16 M. J. Roberts - All Rights Reserved 23

24 Products of Eve ad Odd Fuctios Two Eve Fuctios 9/10/16 M. J. Roberts - All Rights Reserved 24

25 Products of Eve ad Odd Fuctios A Eve Fuctio ad a Odd Fuctio 9/10/16 M. J. Roberts - All Rights Reserved 25

26 Products of Eve ad Odd Fuctios Two Odd Fuctios 9/10/16 M. J. Roberts - All Rights Reserved 26

27 Symmetric Fiite Summatio... Eve Fuctio g[] Sum #1 Sum #2 -N N Sum #1 = Sum # Odd Fuctio Sum #1 -N g[] N Sum #2 Sum #1 = - Sum #2... N [ ] g = g 0 = N N [ ] + 2 g[ ] g = 0 =1 = N [ ] 9/10/16 M. J. Roberts - All Rights Reserved 27 N

28 Periodic Fuctios A periodic fuctio is oe that is ivariat to the chage of variable + mn where N is a period of the fuctio ad m is ay iteger. The miimum positive iteger value of N for which [ ] = g[ + N ] is called the fudametal period N 0. g 9/10/16 M. J. Roberts - All Rights Reserved 28

29 Periodic Fuctios Fid the fudametal period of [ ] = cos( π /18) + si( 10π / 24) [ ] = cos( 2π / 36) + si 2π ( 5 / 24 ) x x N 0 =36 N 0 = LCM( 36,24) = 72 Fid the fudametal period of x ( ) N 0 =24 [ ] = cos( 5π /13) + si( 8π / 39) [ ] = cos( 2π( 5 / 26) ) + si 2π ( 4 / 39 ) x N 0 =26 ( ) = 78 N 0 = LCM 26, 39 ( ) N 0 =39 9/10/16 M. J. Roberts - All Rights Reserved 29

30 Sigal Eergy ad Power The sigal eergy of a sigal x[ ] is E x = = x[ ] 2 9/10/16 M. J. Roberts - All Rights Reserved 30

31 Sigal Eergy ad Power x[] x[ ] = 0, > 31 x[] 2 Σ x[] 2 Sigal Eergy 9/10/16 M. J. Roberts - All Rights Reserved 31

32 Sigal Eergy ad Power Fid the sigal eergy of Usig E x = N 1 r =0 x[ ] = [ ] 2 ( 5 / 3) 2, 0 < 8 0, otherwise x = 5 / 3 = 5 / 3 = = 7 =0 N, r = 1 1 r N, r 1 1 r ( ) 2 ( )4 E x = 1 5 / / =0 ( ) 4 ( ) /10/16 M. J. Roberts - All Rights Reserved 32

33 Sigal Eergy ad Power Some sigals have ifiite sigal eergy. I that case It is usually more coveiet to deal with average sigal power. The average sigal power of a sigal x 1 P x = lim N 2N For a periodic sigal x P x = 1 N N 1 = N x[ ] 2 [ ] is [ ] the average sigal power is = N x[ ] 2 The otatio meas the sum over ay set of = N cosecutive 's exactly N i legth. 9/10/16 M. J. Roberts - All Rights Reserved 33

34 Sigal Eergy ad Power Fid the average sigal power of 1 P x = lim N 2N N 1 = N [ ] = 2sg[ ] 4 x x[ ] 2 1 = lim x 2N N 1 = N 2sg[ ] 4 2 N 1 N 1 N 1 1 P x = lim N 2N 4 [ sg2 ] sg[ ] = N = N =1 δ[ ] = N =2 N =N 1 N = 1 =N 1+N =2 N 1 1 P x = lim { 4 2N 1 N ( ) + 32N 16 ( 1 ) } = 20 2N 9/10/16 M. J. Roberts - All Rights Reserved 34

35 Sigal Eergy ad Power A sigal with fiite sigal eergy is called a eergy sigal. A sigal with ifiite sigal eergy ad fiite average sigal power is called a power sigal. 9/10/16 M. J. Roberts - All Rights Reserved 35

36 Fudametal Period of a Sum of Two Periodic Sigals δ 14 [!] 6δ! [ ] 8 N 0 =14 N 0 =8 " $$ # $$ % N 0 =LCM( 14,8)=56 ( ( )) 2cos( 3π /12) +11cos( 14π /10) = 2cos 2π 1/ 8 Impulses ad Periodic Impulses cos 2π 7 /10 " $$ # $$ % " $$ # $$ % N 0 =10 $$$$$$$ $$$$$$$ % " N 0 =8 # N 0 =LCM( 8,10)= δ [ + 6] = 1368, 12( 0.4) u[ ]δ 3 [ ] = = 18 Equivalece Property Scalig Property 7 = 4 ( ( )) ( ) 0 + ( 0.4) 3 + ( 0.4) 6 = ( ) δ [ 3] = 27( 0.3) 3 δ [ 3] = 0.729δ [ 3] [ ] = 13δ [ ], ( No scalig property for discrete-time impulses) 13δ 3 22, 4 = 3k 22δ 3 [ 4] = 22 δ [ 4 3k] = k= 0, otherwise = 22, 4 / 3 = k 0, otherwise Sice k is a iteger, impulses occur oly where 4 / 3 is a iteger & 22δ 3 [ 4] 22( k = 0) 0( 4 / 3 k) 0( 8 / 3 k) 22( k = 1) 0( 16 / 3 k) & 9/10/16 M. J. Roberts - All Rights Reserved 36

37 Sigal Eergy ad Sigal Power x[ ] = ( 1.3) u[ ] u[ 4] 3 ( ) 2 = ( ) E x = 1.3 E x = = = x =0 [ ] is periodic ad oe period of x[ ] is described by [ ] = ( 1 ), 3 < 6 x P x = 1 N [ ] 2 x, = N x[ ] P x = 1 [ ] = = ( ) u[ ] u[ 4] ( ) 2 9/10/16 M. J. Roberts - All Rights Reserved 37

Review of Discrete-time Signals. ELEC 635 Prof. Siripong Potisuk

Review of Discrete-time Signals. ELEC 635 Prof. Siripong Potisuk Review of Discrete-time Sigals ELEC 635 Prof. Siripog Potisuk 1 Discrete-time Sigals Discrete-time, cotiuous-valued amplitude (sampled-data sigal) Discrete-time, discrete-valued amplitude (digital sigal)

More information

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course Sigal-EE Postal Correspodece Course 1 SAMPLE STUDY MATERIAL Electrical Egieerig EE / EEE Postal Correspodece Course GATE, IES & PSUs Sigal System Sigal-EE Postal Correspodece Course CONTENTS 1. SIGNAL

More information

ELEG 4603/5173L Digital Signal Processing Ch. 1 Discrete-Time Signals and Systems

ELEG 4603/5173L Digital Signal Processing Ch. 1 Discrete-Time Signals and Systems Departmet of Electrical Egieerig Uiversity of Arasas ELEG 4603/5173L Digital Sigal Processig Ch. 1 Discrete-Time Sigals ad Systems Dr. Jigxia Wu wuj@uar.edu OUTLINE 2 Classificatios of discrete-time sigals

More information

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform Sigal Processig i Mechatroics Summer semester, 1 Lecture 3, Covolutio, Fourier Series ad Fourier rasform Dr. Zhu K.P. AIS, UM 1 1. Covolutio Covolutio Descriptio of LI Systems he mai premise is that the

More information

Generalizing the DTFT. The z Transform. Complex Exponential Excitation. The Transfer Function. Systems Described by Difference Equations

Generalizing the DTFT. The z Transform. Complex Exponential Excitation. The Transfer Function. Systems Described by Difference Equations Geeraliig the DTFT The Trasform M. J. Roberts - All Rights Reserved. Edited by Dr. Robert Akl 1 The forward DTFT is defied by X e jω = x e jω i which = Ω is discrete-time radia frequecy, a real variable.

More information

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5 Sigals ad Systems Sigals ad Systems Sigals are variables that carry iformatio Systemstake sigals as iputs ad produce sigals as outputs The course deals with the passage of sigals through systems T-6.4

More information

Analog and Digital Signals. Introduction to Digital Signal Processing. Discrete-time Sinusoids. Analog and Digital Signals

Analog and Digital Signals. Introduction to Digital Signal Processing. Discrete-time Sinusoids. Analog and Digital Signals Itroductio to Digital Sigal Processig Chapter : Itroductio Aalog ad Digital Sigals aalog = cotiuous-time cotiuous amplitude digital = discrete-time discrete amplitude cotiuous amplitude discrete amplitude

More information

Signals & Systems Chapter3

Signals & Systems Chapter3 Sigals & Systems Chapter3 1.2 Discrete-Time (D-T) Sigals Electroic systems do most of the processig of a sigal usig a computer. A computer ca t directly process a C-T sigal but istead eeds a stream of

More information

Lesson 10: Limits and Continuity

Lesson 10: Limits and Continuity www.scimsacademy.com Lesso 10: Limits ad Cotiuity SCIMS Academy 1 Limit of a fuctio The cocept of limit of a fuctio is cetral to all other cocepts i calculus (like cotiuity, derivative, defiite itegrals

More information

6.003: Signal Processing

6.003: Signal Processing 6.003: Sigal Processig Discrete-Time Fourier Series orthogoality of harmoically related DT siusoids DT Fourier series relatios differeces betwee CT ad DT Fourier series properties of DT Fourier series

More information

1.3 Convergence Theorems of Fourier Series. k k k k. N N k 1. With this in mind, we state (without proof) the convergence of Fourier series.

1.3 Convergence Theorems of Fourier Series. k k k k. N N k 1. With this in mind, we state (without proof) the convergence of Fourier series. .3 Covergece Theorems of Fourier Series I this sectio, we preset the covergece of Fourier series. A ifiite sum is, by defiitio, a limit of partial sums, that is, a cos( kx) b si( kx) lim a cos( kx) b si(

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

Finite-length Discrete Transforms. Chapter 5, Sections

Finite-length Discrete Transforms. Chapter 5, Sections Fiite-legth Discrete Trasforms Chapter 5, Sectios 5.2-50 5.0 Dr. Iyad djafar Outlie The Discrete Fourier Trasform (DFT) Matrix Represetatio of DFT Fiite-legth Sequeces Circular Covolutio DFT Symmetry Properties

More information

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense, 3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [

More information

Web Appendix O - Derivations of the Properties of the z Transform

Web Appendix O - Derivations of the Properties of the z Transform M. J. Roberts - 2/18/07 Web Appedix O - Derivatios of the Properties of the z Trasform O.1 Liearity Let z = x + y where ad are costats. The ( z)= ( x + y )z = x z + y z ad the liearity property is O.2

More information

Solution of EECS 315 Final Examination F09

Solution of EECS 315 Final Examination F09 Solutio of EECS 315 Fial Examiatio F9 1. Fid the umerical value of δ ( t + 4ramp( tdt. δ ( t + 4ramp( tdt. Fid the umerical sigal eergy of x E x = x[ ] = δ 3 = 11 = ( = ramp( ( 4 = ramp( 8 = 8 [ ] = (

More information

Unit 6: Sequences and Series

Unit 6: Sequences and Series AMHS Hoors Algebra 2 - Uit 6 Uit 6: Sequeces ad Series 26 Sequeces Defiitio: A sequece is a ordered list of umbers ad is formally defied as a fuctio whose domai is the set of positive itegers. It is commo

More information

Chapter 6 Infinite Series

Chapter 6 Infinite Series Chapter 6 Ifiite Series I the previous chapter we cosidered itegrals which were improper i the sese that the iterval of itegratio was ubouded. I this chapter we are goig to discuss a topic which is somewhat

More information

The Z-Transform. (t-t 0 ) Figure 1: Simplified graph of an impulse function. For an impulse, it can be shown that (1)

The Z-Transform. (t-t 0 ) Figure 1: Simplified graph of an impulse function. For an impulse, it can be shown that (1) The Z-Trasform Sampled Data The geeralied fuctio (t) (also kow as the impulse fuctio) is useful i the defiitio ad aalysis of sampled-data sigals. Figure below shows a simplified graph of a impulse. (t-t

More information

EEO 401 Digital Signal Processing Prof. Mark Fowler

EEO 401 Digital Signal Processing Prof. Mark Fowler EEO 40 Digital Sigal Processig Prof. Mark Fowler Note Set #3 Covolutio & Impulse Respose Review Readig Assigmet: Sect. 2.3 of Proakis & Maolakis / Covolutio for LTI D-T systems We are tryig to fid y(t)

More information

M2.The Z-Transform and its Properties

M2.The Z-Transform and its Properties M2.The Z-Trasform ad its Properties Readig Material: Page 94-126 of chapter 3 3/22/2011 I. Discrete-Time Sigals ad Systems 1 What did we talk about i MM1? MM1 - Discrete-Time Sigal ad System 3/22/2011

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

1. Nature of Impulse Response - Pole on Real Axis. z y(n) = r n. z r

1. Nature of Impulse Response - Pole on Real Axis. z y(n) = r n. z r . Nature of Impulse Respose - Pole o Real Axis Causal system trasfer fuctio: Hz) = z yz) = z r z z r y) = r r > : the respose grows mootoically > r > : y decays to zero mootoically r > : oscillatory, decayig

More information

x c the remainder is Pc ().

x c the remainder is Pc (). Algebra, Polyomial ad Ratioal Fuctios Page 1 K.Paulk Notes Chapter 3, Sectio 3.1 to 3.4 Summary Sectio Theorem Notes 3.1 Zeros of a Fuctio Set the fuctio to zero ad solve for x. The fuctio is zero at these

More information

(Figure 2.9), we observe x. and we write. (b) as x, x 1. and we write. We say that the line y 0 is a horizontal asymptote of the graph of f.

(Figure 2.9), we observe x. and we write. (b) as x, x 1. and we write. We say that the line y 0 is a horizontal asymptote of the graph of f. The symbol for ifiity ( ) does ot represet a real umber. We use to describe the behavior of a fuctio whe the values i its domai or rage outgrow all fiite bouds. For eample, whe we say the limit of f as

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j The -Trasform 7. Itroductio Geeralie the complex siusoidal represetatio offered by DTFT to a represetatio of complex expoetial sigals. Obtai more geeral characteristics for discrete-time LTI systems. 7.

More information

Chapter 2 Systems and Signals

Chapter 2 Systems and Signals Chapter 2 Systems ad Sigals 1 Itroductio Discrete-Time Sigals: Sequeces Discrete-Time Systems Properties of Liear Time-Ivariat Systems Liear Costat-Coefficiet Differece Equatios Frequecy-Domai Represetatio

More information

Vibratory Motion. Prof. Zheng-yi Feng NCHU SWC. National CHung Hsing University, Department of Soil and Water Conservation

Vibratory Motion. Prof. Zheng-yi Feng NCHU SWC. National CHung Hsing University, Department of Soil and Water Conservation Vibratory Motio Prof. Zheg-yi Feg NCHU SWC 1 Types of vibratory motio Periodic motio Noperiodic motio See Fig. A1, p.58 Harmoic motio Periodic motio Trasiet motio impact Trasiet motio earthquake A powerful

More information

y[ n] = sin(2" # 3 # n) 50

y[ n] = sin(2 # 3 # n) 50 Period of a Discrete Siusoid y[ ] si( ) 5 T5 samples y[ ] y[ + 5] si() si() [ ] si( 3 ) 5 y[ ] y[ + T] T?? samples [iteger] 5/3 iteger y irratioal frequecy ysi(pisqrt()/5) - - TextEd si( t) T sec cotiuous

More information

Sampling and Discrete Time. Discrete-Time Signal Description. Sinusoids. Sampling and Discrete Time. Sinusoids An Aperiodic Sinusoid.

Sampling and Discrete Time. Discrete-Time Signal Description. Sinusoids. Sampling and Discrete Time. Sinusoids An Aperiodic Sinusoid. Sampling and Discrete Time Discrete-Time Signal Description Sampling is the acquisition of the values of a continuous-time signal at discrete points in time. x t discrete-time signal. ( ) is a continuous-time

More information

Signal Processing. Lecture 02: Discrete Time Signals and Systems. Ahmet Taha Koru, Ph. D. Yildiz Technical University.

Signal Processing. Lecture 02: Discrete Time Signals and Systems. Ahmet Taha Koru, Ph. D. Yildiz Technical University. Sigal Processig Lecture 02: Discrete Time Sigals ad Systems Ahmet Taha Koru, Ph. D. Yildiz Techical Uiversity 2017-2018 Fall ATK (YTU) Sigal Processig 2017-2018 Fall 1 / 51 Discrete Time Sigals Discrete

More information

(, ) (, ) (, ) ( ) ( )

(, ) (, ) (, ) ( ) ( ) PROBLEM ANSWER X Y x, x, rect, () X Y, otherwise D Fourier trasform is defied as ad i D case it ca be defied as We ca write give fuctio from Eq. () as It follows usig Eq. (3) it ( ) ( ) F f t e dt () i(

More information

ADVANCED DIGITAL SIGNAL PROCESSING

ADVANCED DIGITAL SIGNAL PROCESSING ADVANCED DIGITAL SIGNAL PROCESSING PROF. S. C. CHAN (email : sccha@eee.hku.hk, Rm. CYC-702) DISCRETE-TIME SIGNALS AND SYSTEMS MULTI-DIMENSIONAL SIGNALS AND SYSTEMS RANDOM PROCESSES AND APPLICATIONS ADAPTIVE

More information

Discrete-Time Signals and Systems. Discrete-Time Signals and Systems. Signal Symmetry. Elementary Discrete-Time Signals.

Discrete-Time Signals and Systems. Discrete-Time Signals and Systems. Signal Symmetry. Elementary Discrete-Time Signals. Discrete-ime Sigals ad Systems Discrete-ime Sigals ad Systems Dr. Deepa Kudur Uiversity of oroto Referece: Sectios. -.5 of Joh G. Proakis ad Dimitris G. Maolakis, Digital Sigal Processig: Priciples, Algorithms,

More information

Appendix: The Laplace Transform

Appendix: The Laplace Transform Appedix: The Laplace Trasform The Laplace trasform is a powerful method that ca be used to solve differetial equatio, ad other mathematical problems. Its stregth lies i the fact that it allows the trasformatio

More information

ECE 301: Signals and Systems Homework Assignment #4

ECE 301: Signals and Systems Homework Assignment #4 ECE 301: Sigals ad Systems Homework Assigmet #4 Due o October 28, 2015 Professor: Aly El Gamal TA: Xiaglu Mao 1 Aly El Gamal ECE 301: Sigals ad Systems Homework Assigmet #4 Problem 1 Problem 1 Let x[]

More information

Riemann Sums y = f (x)

Riemann Sums y = f (x) Riema Sums Recall that we have previously discussed the area problem I its simplest form we ca state it this way: The Area Problem Let f be a cotiuous, o-egative fuctio o the closed iterval [a, b] Fid

More information

Some sufficient conditions of a given. series with rational terms converging to an irrational number or a transcdental number

Some sufficient conditions of a given. series with rational terms converging to an irrational number or a transcdental number Some sufficiet coditios of a give arxiv:0807.376v2 [math.nt] 8 Jul 2008 series with ratioal terms covergig to a irratioal umber or a trascdetal umber Yu Gao,Jiig Gao Shaghai Putuo college, Shaghai Jiaotog

More information

Essential Question How can you recognize an arithmetic sequence from its graph?

Essential Question How can you recognize an arithmetic sequence from its graph? . Aalyzig Arithmetic Sequeces ad Series COMMON CORE Learig Stadards HSF-IF.A.3 HSF-BF.A. HSF-LE.A. Essetial Questio How ca you recogize a arithmetic sequece from its graph? I a arithmetic sequece, the

More information

Chapter 7: The z-transform. Chih-Wei Liu

Chapter 7: The z-transform. Chih-Wei Liu Chapter 7: The -Trasform Chih-Wei Liu Outlie Itroductio The -Trasform Properties of the Regio of Covergece Properties of the -Trasform Iversio of the -Trasform The Trasfer Fuctio Causality ad Stability

More information

Classification of DT signals

Classification of DT signals Comlex exoetial A discrete time sigal may be comlex valued I digital commuicatios comlex sigals arise aturally A comlex sigal may be rereseted i two forms: jarg { z( ) } { } z ( ) = Re { z ( )} + jim {

More information

is also known as the general term of the sequence

is also known as the general term of the sequence Lesso : Sequeces ad Series Outlie Objectives: I ca determie whether a sequece has a patter. I ca determie whether a sequece ca be geeralized to fid a formula for the geeral term i the sequece. I ca determie

More information

1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable

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

Lecture 3. Digital Signal Processing. Chapter 3. z-transforms. Mikael Swartling Nedelko Grbic Bengt Mandersson. rev. 2016

Lecture 3. Digital Signal Processing. Chapter 3. z-transforms. Mikael Swartling Nedelko Grbic Bengt Mandersson. rev. 2016 Lecture 3 Digital Sigal Processig Chapter 3 z-trasforms Mikael Swartlig Nedelko Grbic Begt Madersso rev. 06 Departmet of Electrical ad Iformatio Techology Lud Uiversity z-trasforms We defie the z-trasform

More information

MAT1026 Calculus II Basic Convergence Tests for Series

MAT1026 Calculus II Basic Convergence Tests for Series MAT026 Calculus II Basic Covergece Tests for Series Egi MERMUT 202.03.08 Dokuz Eylül Uiversity Faculty of Sciece Departmet of Mathematics İzmir/TURKEY Cotets Mootoe Covergece Theorem 2 2 Series of Real

More information

Notes 8 Singularities

Notes 8 Singularities ECE 6382 Fall 27 David R. Jackso Notes 8 Sigularities Notes are from D. R. Wilto, Dept. of ECE Sigularity A poit s is a sigularity of the fuctio f () if the fuctio is ot aalytic at s. (The fuctio does

More information

Exponential Functions and Taylor Series

Exponential Functions and Taylor Series MATH 4530: Aalysis Oe Expoetial Fuctios ad Taylor Series James K. Peterso Departmet of Biological Scieces ad Departmet of Mathematical Scieces Clemso Uiversity March 29, 2017 MATH 4530: Aalysis Oe Outlie

More information

Once we have a sequence of numbers, the next thing to do is to sum them up. Given a sequence (a n ) n=1

Once we have a sequence of numbers, the next thing to do is to sum them up. Given a sequence (a n ) n=1 . Ifiite Series Oce we have a sequece of umbers, the ext thig to do is to sum them up. Give a sequece a be a sequece: ca we give a sesible meaig to the followig expressio? a = a a a a While summig ifiitely

More information

Frequency Response of FIR Filters

Frequency Response of FIR Filters EEL335: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we itroduce the idea of the frequecy respose of LTI systems, ad focus specifically o the frequecy respose of FIR filters.. Steady-state

More information

Complex Numbers Solutions

Complex Numbers Solutions Complex Numbers Solutios Joseph Zoller February 7, 06 Solutios. (009 AIME I Problem ) There is a complex umber with imagiary part 64 ad a positive iteger such that Fid. [Solutio: 697] 4i + + 4i. 4i 4i

More information

ENGI Series Page 6-01

ENGI Series Page 6-01 ENGI 3425 6 Series Page 6-01 6. Series Cotets: 6.01 Sequeces; geeral term, limits, covergece 6.02 Series; summatio otatio, covergece, divergece test 6.03 Stadard Series; telescopig series, geometric series,

More information

f(w) w z =R z a 0 a n a nz n Liouville s theorem, we see that Q is constant, which implies that P is constant, which is a contradiction.

f(w) w z =R z a 0 a n a nz n Liouville s theorem, we see that Q is constant, which implies that P is constant, which is a contradiction. Theorem 3.6.4. [Liouville s Theorem] Every bouded etire fuctio is costat. Proof. Let f be a etire fuctio. Suppose that there is M R such that M for ay z C. The for ay z C ad R > 0 f (z) f(w) 2πi (w z)

More information

Unit 4: Polynomial and Rational Functions

Unit 4: Polynomial and Rational Functions 48 Uit 4: Polyomial ad Ratioal Fuctios Polyomial Fuctios A polyomial fuctio y px ( ) is a fuctio of the form p( x) ax + a x + a x +... + ax + ax+ a 1 1 1 0 where a, a 1,..., a, a1, a0are real costats ad

More information

We are mainly going to be concerned with power series in x, such as. (x)} converges - that is, lims N n

We are mainly going to be concerned with power series in x, such as. (x)} converges - that is, lims N n Review of Power Series, Power Series Solutios A power series i x - a is a ifiite series of the form c (x a) =c +c (x a)+(x a) +... We also call this a power series cetered at a. Ex. (x+) is cetered at

More information

Polynomial Functions and Their Graphs

Polynomial Functions and Their Graphs Polyomial Fuctios ad Their Graphs I this sectio we begi the study of fuctios defied by polyomial expressios. Polyomial ad ratioal fuctios are the most commo fuctios used to model data, ad are used extesively

More information

Polynomial Functions. New Section 1 Page 1. A Polynomial function of degree n is written is the form:

Polynomial Functions. New Section 1 Page 1. A Polynomial function of degree n is written is the form: New Sectio 1 Page 1 A Polyomial fuctio of degree is writte is the form: 1 P x a x a x a x a x a x a 1 1 0 where is a o-egative iteger expoet ad a 0 ca oly take o values where a, a, a,..., a, a. 0 1 1.

More information

Areas and Distances. We can easily find areas of certain geometric figures using well-known formulas:

Areas and Distances. We can easily find areas of certain geometric figures using well-known formulas: Areas ad Distaces We ca easily fid areas of certai geometric figures usig well-kow formulas: However, it is t easy to fid the area of a regio with curved sides: METHOD: To evaluate the area of the regio

More information

RADICAL EXPRESSION. If a and x are real numbers and n is a positive integer, then x is an. n th root theorems: Example 1 Simplify

RADICAL EXPRESSION. If a and x are real numbers and n is a positive integer, then x is an. n th root theorems: Example 1 Simplify Example 1 Simplify 1.2A Radical Operatios a) 4 2 b) 16 1 2 c) 16 d) 2 e) 8 1 f) 8 What is the relatioship betwee a, b, c? What is the relatioship betwee d, e, f? If x = a, the x = = th root theorems: RADICAL

More information

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3 MATH 337 Sequeces Dr. Neal, WKU Let X be a metric space with distace fuctio d. We shall defie the geeral cocept of sequece ad limit i a metric space, the apply the results i particular to some special

More information

Introduction to Digital Signal Processing

Introduction to Digital Signal Processing Fakultät Iformatik Istitut für Systemarchitektur Professur Recheretze Itroductio to Digital Sigal Processig Walteegus Dargie Walteegus Dargie TU Dresde Chair of Computer Networks I 45 Miutes Refereces

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

Discrete-Time Signals and Systems. Signals and Systems. Digital Signals. Discrete-Time Signals. Operations on Sequences: Basic Operations

Discrete-Time Signals and Systems. Signals and Systems. Digital Signals. Discrete-Time Signals. Operations on Sequences: Basic Operations -6.3 Digital Sigal Processig ad Filterig..8 Discrete-ime Sigals ad Systems ime-domai Represetatios of Discrete-ime Sigals ad Systems ime-domai represetatio of a discrete-time sigal as a sequece of umbers

More information

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece,, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet as

More information

Math 2784 (or 2794W) University of Connecticut

Math 2784 (or 2794W) University of Connecticut ORDERS OF GROWTH PAT SMITH Math 2784 (or 2794W) Uiversity of Coecticut Date: Mar. 2, 22. ORDERS OF GROWTH. Itroductio Gaiig a ituitive feel for the relative growth of fuctios is importat if you really

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak

More information

s = and t = with C ij = A i B j F. (i) Note that cs = M and so ca i µ(a i ) I E (cs) = = c a i µ(a i ) = ci E (s). (ii) Note that s + t = M and so

s = and t = with C ij = A i B j F. (i) Note that cs = M and so ca i µ(a i ) I E (cs) = = c a i µ(a i ) = ci E (s). (ii) Note that s + t = M and so 3 From the otes we see that the parts of Theorem 4. that cocer us are: Let s ad t be two simple o-egative F-measurable fuctios o X, F, µ ad E, F F. The i I E cs ci E s for all c R, ii I E s + t I E s +

More information

Chapter 7 z-transform

Chapter 7 z-transform Chapter 7 -Trasform Itroductio Trasform Uilateral Trasform Properties Uilateral Trasform Iversio of Uilateral Trasform Determiig the Frequecy Respose from Poles ad Zeros Itroductio Role i Discrete-Time

More information

Solving equations (incl. radical equations) involving these skills, but ultimately solvable by factoring/quadratic formula (no complex roots)

Solving equations (incl. radical equations) involving these skills, but ultimately solvable by factoring/quadratic formula (no complex roots) Evet A: Fuctios ad Algebraic Maipulatio Factorig Square of a sum: ( a + b) = a + ab + b Square of a differece: ( a b) = a ab + b Differece of squares: a b = ( a b )(a + b ) Differece of cubes: a 3 b 3

More information

Definition of z-transform.

Definition of z-transform. - Trasforms Frequecy domai represetatios of discretetime sigals ad LTI discrete-time systems are made possible with the use of DTFT. However ot all discrete-time sigals e.g. uit step sequece are guarateed

More information

Chapter 2 The Monte Carlo Method

Chapter 2 The Monte Carlo Method Chapter 2 The Mote Carlo Method The Mote Carlo Method stads for a broad class of computatioal algorithms that rely o radom sampligs. It is ofte used i physical ad mathematical problems ad is most useful

More information

Ma 530 Introduction to Power Series

Ma 530 Introduction to Power Series Ma 530 Itroductio to Power Series Please ote that there is material o power series at Visual Calculus. Some of this material was used as part of the presetatio of the topics that follow. What is a Power

More information

MAT 271 Project: Partial Fractions for certain rational functions

MAT 271 Project: Partial Fractions for certain rational functions MAT 7 Project: Partial Fractios for certai ratioal fuctios Prerequisite kowledge: partial fractios from MAT 7, a very good commad of factorig ad complex umbers from Precalculus. To complete this project,

More information

SEQUENCE AND SERIES NCERT

SEQUENCE AND SERIES NCERT 9. Overview By a sequece, we mea a arragemet of umbers i a defiite order accordig to some rule. We deote the terms of a sequece by a, a,..., etc., the subscript deotes the positio of the term. I view of

More information

Lecture Chapter 6: Convergence of Random Sequences

Lecture Chapter 6: Convergence of Random Sequences ECE5: Aalysis of Radom Sigals Fall 6 Lecture Chapter 6: Covergece of Radom Sequeces Dr Salim El Rouayheb Scribe: Abhay Ashutosh Doel, Qibo Zhag, Peiwe Tia, Pegzhe Wag, Lu Liu Radom sequece Defiitio A ifiite

More information

Infinite Series. Definition. An infinite series is an expression of the form. Where the numbers u k are called the terms of the series.

Infinite Series. Definition. An infinite series is an expression of the form. Where the numbers u k are called the terms of the series. Ifiite Series Defiitio. A ifiite series is a expressio of the form uk = u + u + u + + u + () 2 3 k Where the umbers u k are called the terms of the series. Such a expressio is meat to be the result of

More information

Exponential Moving Average Pieter P

Exponential Moving Average Pieter P Expoetial Movig Average Pieter P Differece equatio The Differece equatio of a expoetial movig average lter is very simple: y[] x[] + (1 )y[ 1] I this equatio, y[] is the curret output, y[ 1] is the previous

More information

Sigma notation. 2.1 Introduction

Sigma notation. 2.1 Introduction Sigma otatio. Itroductio We use sigma otatio to idicate the summatio process whe we have several (or ifiitely may) terms to add up. You may have see sigma otatio i earlier courses. It is used to idicate

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

Ma 530 Infinite Series I

Ma 530 Infinite Series I Ma 50 Ifiite Series I Please ote that i additio to the material below this lecture icorporated material from the Visual Calculus web site. The material o sequeces is at Visual Sequeces. (To use this li

More information

Chapter 8. DFT : The Discrete Fourier Transform

Chapter 8. DFT : The Discrete Fourier Transform Chapter 8 DFT : The Discrete Fourier Trasform Roots of Uity Defiitio: A th root of uity is a complex umber x such that x The th roots of uity are: ω, ω,, ω - where ω e π /. Proof: (ω ) (e π / ) (e π )

More information

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function.

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function. MATH 532 Measurable Fuctios Dr. Neal, WKU Throughout, let ( X, F, µ) be a measure space ad let (!, F, P ) deote the special case of a probability space. We shall ow begi to study real-valued fuctios defied

More information

ECE 308 Discrete-Time Signals and Systems

ECE 308 Discrete-Time Signals and Systems ECE 38-5 ECE 38 Discrete-Time Sigals ad Systems Z. Aliyazicioglu Electrical ad Computer Egieerig Departmet Cal Poly Pomoa ECE 38-5 1 Additio, Multiplicatio, ad Scalig of Sequeces Amplitude Scalig: (A Costat

More information

} is said to be a Cauchy sequence provided the following condition is true.

} is said to be a Cauchy sequence provided the following condition is true. Math 4200, Fial Exam Review I. Itroductio to Proofs 1. Prove the Pythagorea theorem. 2. Show that 43 is a irratioal umber. II. Itroductio to Logic 1. Costruct a truth table for the statemet ( p ad ~ r

More information

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018 CSE 353 Discrete Computatioal Structures Sprig 08 Sequeces, Mathematical Iductio, ad Recursio (Chapter 5, Epp) Note: some course slides adopted from publisher-provided material Overview May mathematical

More information

Solutions to Math 347 Practice Problems for the final

Solutions to Math 347 Practice Problems for the final Solutios to Math 347 Practice Problems for the fial 1) True or False: a) There exist itegers x,y such that 50x + 76y = 6. True: the gcd of 50 ad 76 is, ad 6 is a multiple of. b) The ifiimum of a set is

More information

Morphological Image Processing

Morphological Image Processing Morphological Image Processig Biary dilatio ad erosio Set-theoretic iterpretatio Opeig, closig, morphological edge detectors Hit-miss filter Morphological filters for gray-level images Cascadig dilatios

More information

Sequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

Sequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece 1, 1, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet

More information

Objective Mathematics

Objective Mathematics . If sum of '' terms of a sequece is give by S Tr ( )( ), the 4 5 67 r (d) 4 9 r is equal to : T. Let a, b, c be distict o-zero real umbers such that a, b, c are i harmoic progressio ad a, b, c are i arithmetic

More information

1 Introduction. 1.1 Notation and Terminology

1 Introduction. 1.1 Notation and Terminology 1 Itroductio You have already leared some cocepts of calculus such as limit of a sequece, limit, cotiuity, derivative, ad itegral of a fuctio etc. Real Aalysis studies them more rigorously usig a laguage

More information

Fall 2013 MTH431/531 Real analysis Section Notes

Fall 2013 MTH431/531 Real analysis Section Notes Fall 013 MTH431/531 Real aalysis Sectio 8.1-8. Notes Yi Su 013.11.1 1. Defiitio of uiform covergece. We look at a sequece of fuctios f (x) ad study the coverget property. Notice we have two parameters

More information

from definition we note that for sequences which are zero for n < 0, X[z] involves only negative powers of z.

from definition we note that for sequences which are zero for n < 0, X[z] involves only negative powers of z. We ote that for the past four examples we have expressed the -trasform both as a ratio of polyomials i ad as a ratio of polyomials i -. The questio is how does oe kow which oe to use? [] X ] from defiitio

More information

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

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

More information

Ma 4121: Introduction to Lebesgue Integration Solutions to Homework Assignment 5

Ma 4121: Introduction to Lebesgue Integration Solutions to Homework Assignment 5 Ma 42: Itroductio to Lebesgue Itegratio Solutios to Homework Assigmet 5 Prof. Wickerhauser Due Thursday, April th, 23 Please retur your solutios to the istructor by the ed of class o the due date. You

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 301 Sigals & Systems Prof. Mark Fowler Note Set #8 D-T Covolutio: The Tool for Fidig the Zero-State Respose Readig Assigmet: Sectio 2.1-2.2 of Kame ad Heck 1/14 Course Flow Diagram The arrows here

More information

Exponential Functions and Taylor Series

Exponential Functions and Taylor Series Expoetial Fuctios ad Taylor Series James K. Peterso Departmet of Biological Scieces ad Departmet of Mathematical Scieces Clemso Uiversity March 29, 207 Outlie Revistig the Expoetial Fuctio Taylor Series

More information

Basic Sets. Functions. MTH299 - Examples. Example 1. Let S = {1, {2, 3}, 4}. Indicate whether each statement is true or false. (a) S = 4. (e) 2 S.

Basic Sets. Functions. MTH299 - Examples. Example 1. Let S = {1, {2, 3}, 4}. Indicate whether each statement is true or false. (a) S = 4. (e) 2 S. Basic Sets Example 1. Let S = {1, {2, 3}, 4}. Idicate whether each statemet is true or false. (a) S = 4 (b) {1} S (c) {2, 3} S (d) {1, 4} S (e) 2 S. (f) S = {1, 4, {2, 3}} (g) S Example 2. Compute the

More information

Find a formula for the exponential function whose graph is given , 1 2,16 1, 6

Find a formula for the exponential function whose graph is given , 1 2,16 1, 6 Math 4 Activity (Due by EOC Apr. ) Graph the followig epoetial fuctios by modifyig the graph of f. Fid the rage of each fuctio.. g. g. g 4. g. g 6. g Fid a formula for the epoetial fuctio whose graph is

More information

NAME: ALGEBRA 350 BLOCK 7. Simplifying Radicals Packet PART 1: ROOTS

NAME: ALGEBRA 350 BLOCK 7. Simplifying Radicals Packet PART 1: ROOTS NAME: ALGEBRA 50 BLOCK 7 DATE: Simplifyig Radicals Packet PART 1: ROOTS READ: A square root of a umber b is a solutio of the equatio x = b. Every positive umber b has two square roots, deoted b ad b or

More information