SlideShare une entreprise Scribd logo
1  sur  17
Télécharger pour lire hors ligne
EC533: Digital Signal Processing
  5          l      l

             Lecture 6
The Z-Transform and its application in
          signal processing
6.1 - Z-Transform and LTI System
                                                                          ∞
 • S t
   System Function of LTI Systems:
          F ti      f LTI S t                               h (n ) = ∑ h (n )z − n
                   x(n )
                                                                     n = −∞
                                  h(n )        y (n )
                                                                          Y (z )
                   X (z )         H (z )        Y (z )         H (z ) =
                                                                          X (z )
• As the LTI system can be characterized by the difference equation, written as




• The diffe ence equation specifies the actual operation that must be performed by
   he difference                               ope ation              pe fo med
the discrete-time system on the input data, in the time domain, in order to generate
the desired output.
  In Z domain
     Z-domain

   If the O/p of the system depends only on the present & past I/p samples but not
on previous outputs, i.e., bk=0’s FIR system, Else, Infinite Impulse Response
                              0s
(IIR)
6.1.1 - LTI System Transfer Function


  If the O/p of the system depends only on the present & past i/p samples but not
on previous outputs i.e., bk=0’s
            outputs, i e     0s     Finite Impulse Response (FIR) system ,
                                    Finite Impulse Response (FIR) system
             H (z ) = ∑ a k z − k
                           N

                        k =0           h(n) = 0, n < 0, h(n) = 0, n >N,
 FIR system is an all zero system and are always stable



 If bk≠0’s, the system is called Infinite Impulse Response (IIR) system
                                                               IIR filter has poles

                  h( n)
                      ),       -∞ ≤ n ≤ ∞
6.1.2 - Properties of LTI Systems Using the Z-
                       Transform
Causal Systems : ROC extends outward from the outermost pole.
                                                      Im



                                                                    R
                                                                    Re



Stable Systems (H(z) is BIBO): ROC includes the unit circle.
                                                               Im
A stable system requires that its Fourier transform is
uniformly convergent.                                           1
                                                                    Re
6.2 - Z-transform and Frequency Response
                                   Estimation
• The frequency response of a system (as digital filter spectrum) can be readily obtained
from its z-transform.

                                                                        H(z)
                                                                        H( )
 as,
 where σ is a transient term & it tends to zero as f
     at steady state,                                   Steady‐state frequency response of 
                                                        a system (DTFT).



  where, A(ω)≡ Amplitude (Magnitude) Response , B(ω) ≡ Angle (Phase) Response




                                       Steady‐state
6.2 - Frequency Response Estimation – cont.

• Phase Delay
The amount of time delay, each frequency component of the signal suffers in
                    delay
going through the system.




• Group Delay
The average time delay the composite signal suffers at each frequency.
6.3 - Inverse Z-Transform

                               where
                        DTFT

             IDTFT




                                 (A contour integral)


where, for a fixed r,
6.3 - The Inverse Z-Transform – cont.
•   There is an inversion integral for the z transform,

                              1
                     x[n ] =       X(z)z n−1dz
                             j2π ∫
                                 C


    but doing it requires integration in the complex plane and
    it is rarely used in engineering practice.


•   There are two other common methods,

        Power series method (long division method)
        Partial-Fraction Expansion
                           p
6.3.1 - Power Series (Long Division) Method
Suppose it is desired to find the inverse z transform of
                   z2
              z3 −
H(z ) =            2
           15 2 17     1
        z − z + z−
         3

           12      36 18
Synthetically dividing the numerator by the
denominator yields the infinite series
d     i t      i ld th i fi it     i
         3 −1 67 −2
     1+ z +          z +L
         4      144
This will always work but the answer is not
in closed form (Disadvantage).
6.3.2 - Partial-Fraction Expansion

•Put H(z-1) in a fractional form with the degree of numerator less than degree of
 denominator.
•Put the denominator in the form of simple poles.
• A l partial fraction expansion.
  Apply   i lf     i         i
• Apply inverse z transform for those simple fractions.

                                           Note
                 If N: order of numerator, & M: order of denominator.
                 then,

                 If N=M     Divide

                 If 1N<M 
                 If 1N<M     Make direct P.F.
                             Make direct P F

                 If N>M     Make long division then P.F.
Example 1:

find,       a) Transfer Function   b) Impulse Response




                                        -1     -1/3      7/3




                                   *3
                                    3


        3         3                 3
Example 2:
 Consider the discrete system,

a)
 )      Determine the poles & zeros.
        D            h     l
b)      Plot (locate) them on the z-plane.
c)      Discuss the stability.
d)      Find the impulse response.
e)      Find the first 4 samples of h(n)

     Solution:

     a) zeros when N(z)=0                                       b)
         z1=2        z2= - 0.5
         poles when D(z)=0                                                    xo x          o
                                                                                      0.3   2
         p1=0.3     p2= - 0.6

                                                                      ‐ 0.6   ‐ 0.5

     c) Since all poles lies inside the unit circle, therefore the system is stable.
Example 2 – cont.
d) As discussed before, use partial fraction expansion and the table of transformation to
    get the inverse z-transform
c) Divide N(z) by D(z) using long division,
 )           () y ()        g    g         ,




                                      1

                                          1 2 3

                                                    ‐ 0.24
                                           ‐ 0.28

                                      ‐ 1.8
Example 3:
 Consider the system described by the following difference equation,


Find,
a) The transfer function.
b) Th steady state frequency response.
      The t d t t f
c) The O/p of the system when a sine wave of frequency 50 Hz & amplitude of 10 is
      applied at its input, the sampling frequency is 1 KHz.

   Solution:
               a)
Example 3– cont.
 b)
Example 3– cont.
c)   x(n) = A sin(ω0 nT + θ )
                                1
          = 10 sin((2π .50.n.      ) + θ ) = 10 sin(0.1πn)
                              1000
     Since the I/p is sinusoidal, the O/p should be sinusoidal,
     y (n) = Ay sin(0.1πn + θ y )
     where, A = A A(ω )
              y    x    0

               θ y = θ x + θ (ω0 )
                                                1
     Ay = 10 ×
                 1 − (0.5 cos(ω0T )) 2 + (0.5 sin(ω0T )) 2
                              10
          =                                             = 18.3
            1 − (0.5 cos(0.1π )) 2 + (0.5 sin(0.1π )) 2
                     ⎛ 0.5 sin(0.1π ) ⎞
     θ y = θ x − tan ⎜     −1
                                          ⎟ = 1780 24′
                     ⎜ 1 − 0.5 cos(0.1π ) ⎟
                     ⎝                    ⎠
6.4 Relationships between System
           Representations
              p


Express H(z) in
   1/z cross                      H(z)                 Take inverse z-
 multiply and                                             transform
 take inverse     Take z-transform            Take
                   solve for Y/X         z-transform
          Difference
                                                         h(n)
           Equation        Substitute
                            z=ejwT
                                         Take inverse
                                             DTFT

      Take DTFT solve            H(ejwT)
                                                   Take Fourier
          for Y/X
                                                    transform

Contenu connexe

Tendances

Dot convention in coupled circuits
Dot convention in coupled circuitsDot convention in coupled circuits
Dot convention in coupled circuitsmrunalinithanaraj
 
Initial and final condition for circuit
Initial and final condition for circuitInitial and final condition for circuit
Initial and final condition for circuitvishalgohel12195
 
Region of Convergence (ROC) in the z-plane
Region of Convergence (ROC) in the z-planeRegion of Convergence (ROC) in the z-plane
Region of Convergence (ROC) in the z-planevenkatasuman1983
 
discrete time signals and systems
 discrete time signals and systems  discrete time signals and systems
discrete time signals and systems Zlatan Ahmadovic
 
DSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and Systems
DSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and SystemsDSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and Systems
DSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and SystemsAmr E. Mohamed
 
inverse z-transform ppt
inverse z-transform pptinverse z-transform ppt
inverse z-transform pptmihir jain
 
Tuning of pid controller
Tuning of pid controllerTuning of pid controller
Tuning of pid controllerSubhankar Sau
 
Discreate time system and z transform
Discreate time system and z transformDiscreate time system and z transform
Discreate time system and z transformVIKAS KUMAR MANJHI
 
STate Space Analysis
STate Space AnalysisSTate Space Analysis
STate Space AnalysisHussain K
 
Vector calculus
Vector calculusVector calculus
Vector calculusKumar
 
Chapter 4 time domain analysis
Chapter 4 time domain analysisChapter 4 time domain analysis
Chapter 4 time domain analysisBin Biny Bino
 
Dss
Dss Dss
Dss nil65
 
Digital Signal Processing Tutorial:Chapt 2 z transform
Digital Signal Processing Tutorial:Chapt 2 z transformDigital Signal Processing Tutorial:Chapt 2 z transform
Digital Signal Processing Tutorial:Chapt 2 z transformChandrashekhar Padole
 
Z trasnform & Inverse Z-transform in matlab
Z trasnform & Inverse Z-transform in matlabZ trasnform & Inverse Z-transform in matlab
Z trasnform & Inverse Z-transform in matlabHasnain Yaseen
 
Fourier analysis of signals and systems
Fourier analysis of signals and systemsFourier analysis of signals and systems
Fourier analysis of signals and systemsBabul Islam
 

Tendances (20)

Z Transform
Z TransformZ Transform
Z Transform
 
Dot convention in coupled circuits
Dot convention in coupled circuitsDot convention in coupled circuits
Dot convention in coupled circuits
 
Initial and final condition for circuit
Initial and final condition for circuitInitial and final condition for circuit
Initial and final condition for circuit
 
Region of Convergence (ROC) in the z-plane
Region of Convergence (ROC) in the z-planeRegion of Convergence (ROC) in the z-plane
Region of Convergence (ROC) in the z-plane
 
discrete time signals and systems
 discrete time signals and systems  discrete time signals and systems
discrete time signals and systems
 
DSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and Systems
DSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and SystemsDSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and Systems
DSP_2018_FOEHU - Lec 03 - Discrete-Time Signals and Systems
 
inverse z-transform ppt
inverse z-transform pptinverse z-transform ppt
inverse z-transform ppt
 
Tuning of pid controller
Tuning of pid controllerTuning of pid controller
Tuning of pid controller
 
Discreate time system and z transform
Discreate time system and z transformDiscreate time system and z transform
Discreate time system and z transform
 
STate Space Analysis
STate Space AnalysisSTate Space Analysis
STate Space Analysis
 
Bode plot
Bode plotBode plot
Bode plot
 
Oscillators
OscillatorsOscillators
Oscillators
 
Vector calculus
Vector calculusVector calculus
Vector calculus
 
Chapter 4 time domain analysis
Chapter 4 time domain analysisChapter 4 time domain analysis
Chapter 4 time domain analysis
 
Unit step function
Unit step functionUnit step function
Unit step function
 
Dss
Dss Dss
Dss
 
Bode Plots
Bode Plots Bode Plots
Bode Plots
 
Digital Signal Processing Tutorial:Chapt 2 z transform
Digital Signal Processing Tutorial:Chapt 2 z transformDigital Signal Processing Tutorial:Chapt 2 z transform
Digital Signal Processing Tutorial:Chapt 2 z transform
 
Z trasnform & Inverse Z-transform in matlab
Z trasnform & Inverse Z-transform in matlabZ trasnform & Inverse Z-transform in matlab
Z trasnform & Inverse Z-transform in matlab
 
Fourier analysis of signals and systems
Fourier analysis of signals and systemsFourier analysis of signals and systems
Fourier analysis of signals and systems
 

En vedette

Dsp U Lec05 The Z Transform
Dsp U   Lec05 The Z TransformDsp U   Lec05 The Z Transform
Dsp U Lec05 The Z Transformtaha25
 
Dsp U Lec04 Discrete Time Signals & Systems
Dsp U   Lec04 Discrete Time Signals & SystemsDsp U   Lec04 Discrete Time Signals & Systems
Dsp U Lec04 Discrete Time Signals & Systemstaha25
 
Dsp U Lec07 Realization Of Discrete Time Systems
Dsp U   Lec07 Realization Of Discrete Time SystemsDsp U   Lec07 Realization Of Discrete Time Systems
Dsp U Lec07 Realization Of Discrete Time Systemstaha25
 
Dsp U Lec01 Real Time Dsp Systems
Dsp U   Lec01 Real Time Dsp SystemsDsp U   Lec01 Real Time Dsp Systems
Dsp U Lec01 Real Time Dsp Systemstaha25
 
Dsp U Lec10 DFT And FFT
Dsp U   Lec10  DFT And  FFTDsp U   Lec10  DFT And  FFT
Dsp U Lec10 DFT And FFTtaha25
 
Dsp U Lec02 Data Converters
Dsp U   Lec02 Data ConvertersDsp U   Lec02 Data Converters
Dsp U Lec02 Data Converterstaha25
 
Dsp U Lec03 Analogue To Digital Converters
Dsp U   Lec03 Analogue To Digital ConvertersDsp U   Lec03 Analogue To Digital Converters
Dsp U Lec03 Analogue To Digital Converterstaha25
 
Z TRANSFORM PROPERTIES AND INVERSE Z TRANSFORM
Z TRANSFORM PROPERTIES AND INVERSE Z TRANSFORMZ TRANSFORM PROPERTIES AND INVERSE Z TRANSFORM
Z TRANSFORM PROPERTIES AND INVERSE Z TRANSFORMTowfeeq Umar
 
Dsp U Lec09 Iir Filter Design
Dsp U   Lec09 Iir Filter DesignDsp U   Lec09 Iir Filter Design
Dsp U Lec09 Iir Filter Designtaha25
 
Z transforms and their applications
Z transforms and their applicationsZ transforms and their applications
Z transforms and their applicationsRam Kumar K R
 
Applications of Z transform
Applications of Z transformApplications of Z transform
Applications of Z transformAakankshaR
 
3F3 – Digital Signal Processing (DSP) - Part1
3F3 – Digital Signal Processing (DSP) - Part13F3 – Digital Signal Processing (DSP) - Part1
3F3 – Digital Signal Processing (DSP) - Part1op205
 
Digital Implementation of Costas Loop with Carrier Recovery
Digital Implementation of Costas Loop with Carrier RecoveryDigital Implementation of Costas Loop with Carrier Recovery
Digital Implementation of Costas Loop with Carrier RecoveryIJERD Editor
 
Neural nets: How regular expressions brought about deep learning
Neural nets: How regular expressions brought about deep learningNeural nets: How regular expressions brought about deep learning
Neural nets: How regular expressions brought about deep learningMatthew
 
Documents.mx polytronics
Documents.mx polytronicsDocuments.mx polytronics
Documents.mx polytronicsPOWER EEE
 

En vedette (20)

Dsp U Lec05 The Z Transform
Dsp U   Lec05 The Z TransformDsp U   Lec05 The Z Transform
Dsp U Lec05 The Z Transform
 
z transforms
z transformsz transforms
z transforms
 
Dsp U Lec04 Discrete Time Signals & Systems
Dsp U   Lec04 Discrete Time Signals & SystemsDsp U   Lec04 Discrete Time Signals & Systems
Dsp U Lec04 Discrete Time Signals & Systems
 
Dsp U Lec07 Realization Of Discrete Time Systems
Dsp U   Lec07 Realization Of Discrete Time SystemsDsp U   Lec07 Realization Of Discrete Time Systems
Dsp U Lec07 Realization Of Discrete Time Systems
 
Dsp U Lec01 Real Time Dsp Systems
Dsp U   Lec01 Real Time Dsp SystemsDsp U   Lec01 Real Time Dsp Systems
Dsp U Lec01 Real Time Dsp Systems
 
Dsp U Lec10 DFT And FFT
Dsp U   Lec10  DFT And  FFTDsp U   Lec10  DFT And  FFT
Dsp U Lec10 DFT And FFT
 
Dsp U Lec02 Data Converters
Dsp U   Lec02 Data ConvertersDsp U   Lec02 Data Converters
Dsp U Lec02 Data Converters
 
Dsp U Lec03 Analogue To Digital Converters
Dsp U   Lec03 Analogue To Digital ConvertersDsp U   Lec03 Analogue To Digital Converters
Dsp U Lec03 Analogue To Digital Converters
 
Z TRANSFORM PROPERTIES AND INVERSE Z TRANSFORM
Z TRANSFORM PROPERTIES AND INVERSE Z TRANSFORMZ TRANSFORM PROPERTIES AND INVERSE Z TRANSFORM
Z TRANSFORM PROPERTIES AND INVERSE Z TRANSFORM
 
Dsp U Lec09 Iir Filter Design
Dsp U   Lec09 Iir Filter DesignDsp U   Lec09 Iir Filter Design
Dsp U Lec09 Iir Filter Design
 
Z transform
Z transformZ transform
Z transform
 
Z transforms and their applications
Z transforms and their applicationsZ transforms and their applications
Z transforms and their applications
 
Applications of Z transform
Applications of Z transformApplications of Z transform
Applications of Z transform
 
3F3 – Digital Signal Processing (DSP) - Part1
3F3 – Digital Signal Processing (DSP) - Part13F3 – Digital Signal Processing (DSP) - Part1
3F3 – Digital Signal Processing (DSP) - Part1
 
Digital Implementation of Costas Loop with Carrier Recovery
Digital Implementation of Costas Loop with Carrier RecoveryDigital Implementation of Costas Loop with Carrier Recovery
Digital Implementation of Costas Loop with Carrier Recovery
 
Dsp iit workshop
Dsp iit workshopDsp iit workshop
Dsp iit workshop
 
Neural nets: How regular expressions brought about deep learning
Neural nets: How regular expressions brought about deep learningNeural nets: How regular expressions brought about deep learning
Neural nets: How regular expressions brought about deep learning
 
One sided z transform
One sided z transformOne sided z transform
One sided z transform
 
Chapter5 system analysis
Chapter5 system analysisChapter5 system analysis
Chapter5 system analysis
 
Documents.mx polytronics
Documents.mx polytronicsDocuments.mx polytronics
Documents.mx polytronics
 

Similaire à Dsp U Lec06 The Z Transform And Its Application

Z-transform and Its Inverse.ppt
Z-transform and Its Inverse.pptZ-transform and Its Inverse.ppt
Z-transform and Its Inverse.pptNahi20
 
A Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR FiltersA Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
 
An Intuitive Approach to Fourier Optics
An Intuitive Approach to Fourier OpticsAn Intuitive Approach to Fourier Optics
An Intuitive Approach to Fourier Opticsjose0055
 
Generating Chebychev Chaotic Sequence
Generating Chebychev Chaotic SequenceGenerating Chebychev Chaotic Sequence
Generating Chebychev Chaotic SequenceCheng-An Yang
 
Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片
Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片
Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片Chyi-Tsong Chen
 
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
 
Dsp U Lec08 Fir Filter Design
Dsp U   Lec08 Fir Filter DesignDsp U   Lec08 Fir Filter Design
Dsp U Lec08 Fir Filter Designtaha25
 
ADSP (17 Nov)week8.ppt
ADSP (17 Nov)week8.pptADSP (17 Nov)week8.ppt
ADSP (17 Nov)week8.pptAhmadZafar60
 
EC8553 Discrete time signal processing
EC8553 Discrete time signal processing EC8553 Discrete time signal processing
EC8553 Discrete time signal processing ssuser2797e4
 
ch6_digital_filters.pptx
ch6_digital_filters.pptxch6_digital_filters.pptx
ch6_digital_filters.pptxKadiriIbrahim2
 

Similaire à Dsp U Lec06 The Z Transform And Its Application (20)

Z-transform and Its Inverse.ppt
Z-transform and Its Inverse.pptZ-transform and Its Inverse.ppt
Z-transform and Its Inverse.ppt
 
A Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR FiltersA Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR Filters
 
Final
FinalFinal
Final
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
 
Rdnd2008
Rdnd2008Rdnd2008
Rdnd2008
 
Adc
AdcAdc
Adc
 
An Intuitive Approach to Fourier Optics
An Intuitive Approach to Fourier OpticsAn Intuitive Approach to Fourier Optics
An Intuitive Approach to Fourier Optics
 
Dsp 2marks
Dsp 2marksDsp 2marks
Dsp 2marks
 
Generating Chebychev Chaotic Sequence
Generating Chebychev Chaotic SequenceGenerating Chebychev Chaotic Sequence
Generating Chebychev Chaotic Sequence
 
Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片
Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片
Intelligent Process Control Using Neural Fuzzy Techniques ~陳奇中教授演講投影片
 
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
 
ADSP (17 Nov).ppt
ADSP (17 Nov).pptADSP (17 Nov).ppt
ADSP (17 Nov).ppt
 
Dsp U Lec08 Fir Filter Design
Dsp U   Lec08 Fir Filter DesignDsp U   Lec08 Fir Filter Design
Dsp U Lec08 Fir Filter Design
 
ADSP (17 Nov)week8.ppt
ADSP (17 Nov)week8.pptADSP (17 Nov)week8.ppt
ADSP (17 Nov)week8.ppt
 
Signal Processing Homework Help
Signal Processing Homework HelpSignal Processing Homework Help
Signal Processing Homework Help
 
Chapter 5
Chapter 5Chapter 5
Chapter 5
 
EC8553 Discrete time signal processing
EC8553 Discrete time signal processing EC8553 Discrete time signal processing
EC8553 Discrete time signal processing
 
ch6_digital_filters.pptx
ch6_digital_filters.pptxch6_digital_filters.pptx
ch6_digital_filters.pptx
 
Signals and Systems Assignment Help
Signals and Systems Assignment HelpSignals and Systems Assignment Help
Signals and Systems Assignment Help
 
Online Signals and Systems Assignment Help
Online Signals and Systems Assignment HelpOnline Signals and Systems Assignment Help
Online Signals and Systems Assignment Help
 

Dernier

Breaking Down the Flutterwave Scandal What You Need to Know.pdf
Breaking Down the Flutterwave Scandal What You Need to Know.pdfBreaking Down the Flutterwave Scandal What You Need to Know.pdf
Breaking Down the Flutterwave Scandal What You Need to Know.pdfUK Journal
 
AI mind or machine power point presentation
AI mind or machine power point presentationAI mind or machine power point presentation
AI mind or machine power point presentationyogeshlabana357357
 
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...marcuskenyatta275
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxFIDO Alliance
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераMark Opanasiuk
 
WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024Lorenzo Miniero
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptxFIDO Alliance
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...FIDO Alliance
 
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?Paolo Missier
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...ScyllaDB
 
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FIDO Alliance
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingScyllaDB
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...FIDO Alliance
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptxHarnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptxFIDO Alliance
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfSrushith Repakula
 
Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Hiroshi SHIBATA
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGDSC PJATK
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideStefan Dietze
 

Dernier (20)

Breaking Down the Flutterwave Scandal What You Need to Know.pdf
Breaking Down the Flutterwave Scandal What You Need to Know.pdfBreaking Down the Flutterwave Scandal What You Need to Know.pdf
Breaking Down the Flutterwave Scandal What You Need to Know.pdf
 
AI mind or machine power point presentation
AI mind or machine power point presentationAI mind or machine power point presentation
AI mind or machine power point presentation
 
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptx
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджера
 
WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
 
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptxHarnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdf
 
Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 Warsaw
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
 

Dsp U Lec06 The Z Transform And Its Application

  • 1. EC533: Digital Signal Processing 5 l l Lecture 6 The Z-Transform and its application in signal processing
  • 2. 6.1 - Z-Transform and LTI System ∞ • S t System Function of LTI Systems: F ti f LTI S t h (n ) = ∑ h (n )z − n x(n ) n = −∞ h(n ) y (n ) Y (z ) X (z ) H (z ) Y (z ) H (z ) = X (z ) • As the LTI system can be characterized by the difference equation, written as • The diffe ence equation specifies the actual operation that must be performed by he difference ope ation pe fo med the discrete-time system on the input data, in the time domain, in order to generate the desired output. In Z domain Z-domain If the O/p of the system depends only on the present & past I/p samples but not on previous outputs, i.e., bk=0’s FIR system, Else, Infinite Impulse Response 0s (IIR)
  • 3. 6.1.1 - LTI System Transfer Function If the O/p of the system depends only on the present & past i/p samples but not on previous outputs i.e., bk=0’s outputs, i e 0s Finite Impulse Response (FIR) system , Finite Impulse Response (FIR) system H (z ) = ∑ a k z − k N k =0 h(n) = 0, n < 0, h(n) = 0, n >N, FIR system is an all zero system and are always stable If bk≠0’s, the system is called Infinite Impulse Response (IIR) system IIR filter has poles h( n) ), -∞ ≤ n ≤ ∞
  • 4. 6.1.2 - Properties of LTI Systems Using the Z- Transform Causal Systems : ROC extends outward from the outermost pole. Im R Re Stable Systems (H(z) is BIBO): ROC includes the unit circle. Im A stable system requires that its Fourier transform is uniformly convergent. 1 Re
  • 5. 6.2 - Z-transform and Frequency Response Estimation • The frequency response of a system (as digital filter spectrum) can be readily obtained from its z-transform. H(z) H( ) as, where σ is a transient term & it tends to zero as f at steady state, Steady‐state frequency response of  a system (DTFT). where, A(ω)≡ Amplitude (Magnitude) Response , B(ω) ≡ Angle (Phase) Response Steady‐state
  • 6. 6.2 - Frequency Response Estimation – cont. • Phase Delay The amount of time delay, each frequency component of the signal suffers in delay going through the system. • Group Delay The average time delay the composite signal suffers at each frequency.
  • 7. 6.3 - Inverse Z-Transform where DTFT IDTFT (A contour integral) where, for a fixed r,
  • 8. 6.3 - The Inverse Z-Transform – cont. • There is an inversion integral for the z transform, 1 x[n ] = X(z)z n−1dz j2π ∫ C but doing it requires integration in the complex plane and it is rarely used in engineering practice. • There are two other common methods, Power series method (long division method) Partial-Fraction Expansion p
  • 9. 6.3.1 - Power Series (Long Division) Method Suppose it is desired to find the inverse z transform of z2 z3 − H(z ) = 2 15 2 17 1 z − z + z− 3 12 36 18 Synthetically dividing the numerator by the denominator yields the infinite series d i t i ld th i fi it i 3 −1 67 −2 1+ z + z +L 4 144 This will always work but the answer is not in closed form (Disadvantage).
  • 10. 6.3.2 - Partial-Fraction Expansion •Put H(z-1) in a fractional form with the degree of numerator less than degree of denominator. •Put the denominator in the form of simple poles. • A l partial fraction expansion. Apply i lf i i • Apply inverse z transform for those simple fractions. Note If N: order of numerator, & M: order of denominator. then, If N=M  Divide If 1N<M  If 1N<M Make direct P.F. Make direct P F If N>M  Make long division then P.F.
  • 11. Example 1: find, a) Transfer Function b) Impulse Response -1 -1/3 7/3 *3 3 3 3 3
  • 12. Example 2: Consider the discrete system, a) ) Determine the poles & zeros. D h l b) Plot (locate) them on the z-plane. c) Discuss the stability. d) Find the impulse response. e) Find the first 4 samples of h(n) Solution: a) zeros when N(z)=0 b) z1=2 z2= - 0.5 poles when D(z)=0 xo x o 0.3 2 p1=0.3 p2= - 0.6 ‐ 0.6 ‐ 0.5 c) Since all poles lies inside the unit circle, therefore the system is stable.
  • 13. Example 2 – cont. d) As discussed before, use partial fraction expansion and the table of transformation to get the inverse z-transform c) Divide N(z) by D(z) using long division, ) () y () g g , 1 1 2 3 ‐ 0.24 ‐ 0.28 ‐ 1.8
  • 14. Example 3: Consider the system described by the following difference equation, Find, a) The transfer function. b) Th steady state frequency response. The t d t t f c) The O/p of the system when a sine wave of frequency 50 Hz & amplitude of 10 is applied at its input, the sampling frequency is 1 KHz. Solution: a)
  • 16. Example 3– cont. c) x(n) = A sin(ω0 nT + θ ) 1 = 10 sin((2π .50.n. ) + θ ) = 10 sin(0.1πn) 1000 Since the I/p is sinusoidal, the O/p should be sinusoidal, y (n) = Ay sin(0.1πn + θ y ) where, A = A A(ω ) y x 0 θ y = θ x + θ (ω0 ) 1 Ay = 10 × 1 − (0.5 cos(ω0T )) 2 + (0.5 sin(ω0T )) 2 10 = = 18.3 1 − (0.5 cos(0.1π )) 2 + (0.5 sin(0.1π )) 2 ⎛ 0.5 sin(0.1π ) ⎞ θ y = θ x − tan ⎜ −1 ⎟ = 1780 24′ ⎜ 1 − 0.5 cos(0.1π ) ⎟ ⎝ ⎠
  • 17. 6.4 Relationships between System Representations p Express H(z) in 1/z cross H(z) Take inverse z- multiply and transform take inverse Take z-transform Take solve for Y/X z-transform Difference h(n) Equation Substitute z=ejwT Take inverse DTFT Take DTFT solve H(ejwT) Take Fourier for Y/X transform