+ All Categories
Home > Documents > Digital Signal Processing

Digital Signal Processing

Date post: 05-Jan-2016
Category:
Upload: aquene
View: 25 times
Download: 0 times
Share this document with a friend
Description:
Digital Signal Processing. Prof. Nizamettin AYDIN naydin @ yildiz .edu.tr http:// www . yildiz .edu.tr/~naydin. Digital Signal Processing. Lecture 14 Z Transforms: Introduction. READING ASSIGNMENTS. This Lecture: Chapter 7, Sects 7-1 through 7-5 Other Reading: Recitation: Ch. 7 - PowerPoint PPT Presentation
30
1 Prof. Nizamettin AYDIN [email protected] http://www.yildiz.edu.tr/~naydin Digital Signal Processing
Transcript
Page 1: Digital  Signal Processing

1

Prof. Nizamettin AYDIN

[email protected]

http://www.yildiz.edu.tr/~naydin

Digital Signal Processing

Page 2: Digital  Signal Processing

2

Lecture 14

Z Transforms: IntroductionZ Transforms: Introduction

Digital Signal Processing

Page 3: Digital  Signal Processing

4

READING ASSIGNMENTS

• This Lecture:– Chapter 7, Sects 7-1 through 7-5

• Other Reading:– Recitation: Ch. 7

• CASCADING SYSTEMS

– Next Lecture: Chapter 7, 7-6 to the end

Page 4: Digital  Signal Processing

5

LECTURE OBJECTIVES

• INTRODUCE the Z-TRANSFORM– Give Mathematical Definition– Show how the H(z) POLYNOMIAL simplifies

analysis• CONVOLUTION is SIMPLIFIED !

• Z-Transform can be applied to– FIR Filter: h[n] --> H(z)– Signals: x[n] --> X(z)

n

nznhzH ][)(

)(][ zHnh )(][ zXnx

Page 5: Digital  Signal Processing

6

THREE DOMAINS

Z-TRANSFORM-DOMAIN

POLYNOMIALS: H(z)

FREQ-DOMAIN

kjM

kk

j ebeH ˆ

0

ˆ )(

TIME-DOMAIN

M

kk knxbny

0

][][

}{ kb

Page 6: Digital  Signal Processing

7

Three main reasons for Z-Transform

• Offers compact and convenient notation for describing digital signals and systems

• Widely used by DSP designers, and in the DSP literature

• Pole-zero description of a processor is a great help in visualizing its stability and frequency response characteristic

Page 7: Digital  Signal Processing

8

TRANSFORM CONCEPT

• Move to a new domain where– OPERATIONS are EASIER & FAMILIAR– Use POLYNOMIALS

• TRANSFORM both ways– x[n] ---> X(z) (into the z domain)– X(z) ---> x[n] (back to the time domain)

)(][ zXnx ][)( nxzX

Page 8: Digital  Signal Processing

9

“TRANSFORM” EXAMPLE

• Equivalent Representations

y[n]x[n]

y[n]x[n]

n

njj enheH ˆˆ ][)(

ˆˆ 1)( jj eeH

]1[][][ nnnh

Page 9: Digital  Signal Processing

10

Z-TRANSFORM IDEA

• POLYNOMIAL REPRESENTATION

y[n]x[n]

y[n]x[n] )(zH

][nh

n

nznhzH ][)(

Page 10: Digital  Signal Processing

11

Z-Transform DEFINITION

• POLYNOMIAL Representation of LTI SYSTEM:

• EXAMPLE:

n

nznhzH ][)(

APPLIES toAny SIGNAL

POLYNOMIAL in z-1

43210 20302)( zzzzzzH42 232 zz

4121 )(2)(32 zz

}2,0,3,0,2{]}[{ nh

Page 11: Digital  Signal Processing

12

Z-Transform EXAMPLE

• ANY SIGNAL has a z-Transform:

n

nznxzX ][)(

4321 24642)( zzzzzX?)( zX

Page 12: Digital  Signal Processing

13

531 321)( zzzzX

EXPONENT GIVESTIME LOCATION

?][ nx

Page 13: Digital  Signal Processing

14

Example

• Find the Z-Transform of the exponentially decaying signal shown in the following figure, expressing is as compact as possible.

x[n] 1

0.8 0.64

0.512

… n

Page 14: Digital  Signal Processing

15

• The Z-Transform of the signal:

8.08.01

1

)512.0()64.0()8.0(1

512.064.08.01

][)(

1

31211

321

0

z

z

z

zzz

zzz

znxzXn

n

Page 15: Digital  Signal Processing

16

Example

• Find and sketch, the signal corresponding to the Z-Transform:

2.1

1)(

z

zX

Page 16: Digital  Signal Processing

17

• Recasting X(z) as a power series in z-1, we obtain:

• Succesive values of x[n], starting at n=0, are therefore:

0, 1, -1.2, 1.44, -1.728, ···

4321

312111

1111

1

728.144.12.1

})2.1()2.1()2.1(1{

)2.11()2.11()2.1(

1)(

zzzz

zzzz

zzz

z

zzX

Page 17: Digital  Signal Processing

18

• x[n] is shown in the following figure:

x[n]

… n

1

1.44

-1.728

-1.2

Page 18: Digital  Signal Processing

19

Z-Transform of FIR Filter

• CALLED the SYSTEM FUNCTIONSYSTEM FUNCTION• h[n] is same as {bk}

FIR DIFFERENCE EQUATION

M

k

M

kk knxkhknxbny

00

][][][][

CONVOLUTION

SYSTEMFUNCTION

M

k

kM

k

kk zkhzbzH

00

][)(

Page 19: Digital  Signal Processing

20

]2[]1[5][6][ nxnxnxny

211 56)( zzzbzH k

Z-Transform of FIR Filter

• Get H(z) DIRECTLY from the {bk}

• Example 7.3 in the book:

}1,5,6{}{ kb

Page 20: Digital  Signal Processing

21

Ex. DELAY SYSTEM

• UNIT DELAY: find h[n] and H(z)

y[n]x[n]

y[n] = x[n-1]x[n] ]1[ n

nznzH ]1[)( 1z

1z

Page 21: Digital  Signal Processing

22

DELAY EXAMPLE

• UNIT DELAY: find y[n] via polynomials– x[n] = {3,1,4,1,5,9,0,0,0,...}

6543210 95430)( zzzzzzzzY

)9543()( 543211 zzzzzzzY

)()( 1 zXzzY

Page 22: Digital  Signal Processing

23

DELAY PROPERTY

Page 23: Digital  Signal Processing

24

GENERAL I/O PROBLEM

• Input is x[n], find y[n] (for FIR, h[n])

• How to combine X(z) and H(z) ?

Page 24: Digital  Signal Processing

25

FIR Filter = CONVOLUTION

M

k

M

kk knxkhknxbny

00

][][][][CONVOLUTION

Page 25: Digital  Signal Processing

26

CONVOLUTION PROPERTY

• PROOF:

MULTIPLYZ-TRANSFORMS

Page 26: Digital  Signal Processing

27

CONVOLUTION EXAMPLE

• MULTIPLY the z-TRANSFORMS:

MULTIPLY H(z)X(z)

Page 27: Digital  Signal Processing

28

CONVOLUTION EXAMPLE

• Finite-Length input x[n]

• FIR Filter (L=4) MULTIPLYZ-TRANSFORMS

y[n] = ?

Page 28: Digital  Signal Processing

29

CASCADE SYSTEMS

• Does the order of S1 & S2 matter?

– NO, LTI SYSTEMS can be rearranged !!!

– Remember: h1[n] * h2[n]

– How to combine H1(z) and H2(z) ?

S1 S2

Page 29: Digital  Signal Processing

30

CASCADE EQUIVALENT

• Multiply the System Functions

x[n] )(1 zH y[n])(2 zH

)()()( 21 zHzHzH

y[n]x[n] )(zH

EQUIVALENTSYSTEM

Page 30: Digital  Signal Processing

31

CASCADE EXAMPLE

y[n]x[n] )(zH

x[n])(1 zH

y[n])(2 zH

w[n]

12 1)( zzH

11 1)( zzH

211 1)1)(1()( zzzzH

]2[][][ nxnxny

]1[][][ nxnxnw ]1[][][ nwnwny


Recommended