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TRANSCRIPT
UNIT IUNIT I
SIGNALSIGNAL
►Signal is a physical quantity that Signal is a physical quantity that varies with respect to time , space or varies with respect to time , space or any other independent variableany other independent variable
Eg x(t)= sin t.Eg x(t)= sin t.►the major classifications of the the major classifications of the
signal are:signal are:
(i) Discrete time signal (i) Discrete time signal
(ii) (ii) Continuous time signal Continuous time signal
Unit Step &Unit ImpulseUnit Step &Unit Impulse
Discrete time Unit impulse is defined asDiscrete time Unit impulse is defined as δ [n]= {0, n≠ 0δ [n]= {0, n≠ 0{1, n=0{1, n=0Unit impulse is also known as unit sample.Unit impulse is also known as unit sample.Discrete time unit step signal is defined by Discrete time unit step signal is defined by U[n]={0,n=0U[n]={0,n=0
{1,n>= 0{1,n>= 0Continuous time unit impulse is defined as Continuous time unit impulse is defined as
δ (t)={1, t=0δ (t)={1, t=0 {0, t ≠ 0{0, t ≠ 0
Continuous time Unit step signal is defined as Continuous time Unit step signal is defined as U(t)={0, t<0U(t)={0, t<0
{1, t≥0{1, t≥0
► Periodic Signal & Aperiodic SignalPeriodic Signal & Aperiodic Signal A signal is said to be periodic ,if it exhibits A signal is said to be periodic ,if it exhibits
periodicity.i.e., X(t +T)=x(t), for all values of t. periodicity.i.e., X(t +T)=x(t), for all values of t. Periodic signal has the property that it is unchanged Periodic signal has the property that it is unchanged by a time shift of T. A signal that does not satisfy the by a time shift of T. A signal that does not satisfy the above periodicity property is called an aperiodic above periodicity property is called an aperiodic signalsignal
► even and odd signal ?even and odd signal ? A discrete time signal is said to be even when, x[-A discrete time signal is said to be even when, x[-
n]=x[n]. The continuous time signal is said to be n]=x[n]. The continuous time signal is said to be even when, x(-t)= x(t) For example,Cosωn is an even when, x(-t)= x(t) For example,Cosωn is an even signal.even signal.
SIGNALSIGNAL
Energy and power signalEnergy and power signal
► A signal is said to be energy signal if it A signal is said to be energy signal if it have finite energy and zero power.have finite energy and zero power.
► A signal is said to be power signal if it A signal is said to be power signal if it have infinite energy and finite power.have infinite energy and finite power.
► If the above two conditions are not If the above two conditions are not satisfied then the signal is said to be satisfied then the signal is said to be neigther energy nor power signal neigther energy nor power signal
Fourier SeriesFourier SeriesThe Fourier series represents a periodic signal in terms The Fourier series represents a periodic signal in terms
of frequency components:of frequency components:
We get the Fourier series coefficients as followsWe get the Fourier series coefficients as follows::
The complex exponential Fourier coefficients are a The complex exponential Fourier coefficients are a
sequence of complex numbers representing the sequence of complex numbers representing the
frequency component frequency component ωω00k.k.
p
0
tikk dte)t(x
p1
X 0
1p
0n
nikk
0e)n(xp1
X
1p
0k
nikk
0eX)n(x
k
tikk
0eX)t(x
Fourier seriesFourier series
► Fourier series: a complicated waveform analyzed into Fourier series: a complicated waveform analyzed into a number of harmonically related sine and cosine a number of harmonically related sine and cosine functionsfunctions
► A continuous periodic signal x(t) with a period T0 may A continuous periodic signal x(t) with a period T0 may be represented by: be represented by: X(t)=ΣX(t)=Σ∞∞
k=1k=1 ( (AAkk cos cos kkω ω t + Bt + Bkk sin sin kkω ω t)+ t)+ AA00
► Dirichlet conditions Dirichlet conditions must be placed on must be placed on x(t) x(t) for the for the series to be valid: the integral of the magnitude of series to be valid: the integral of the magnitude of x(t) x(t) over a complete period must be finite, and the signal over a complete period must be finite, and the signal can only have a finite number of discontinuities in any can only have a finite number of discontinuities in any finite intervalfinite interval
Trigonometric form for Fourier Trigonometric form for Fourier seriesseries
► If the two fundamental components of a If the two fundamental components of a periodic signal areB1cosω0t and periodic signal areB1cosω0t and C1sinω0t, then their sum is expressed by C1sinω0t, then their sum is expressed by trigonometric identities:trigonometric identities:
►X(t)= X(t)= AA00 + + ΣΣ∞∞k=1 k=1 (( BBk k
22++ AAk k 22))1/21/2 (C (Ckk cos cos kkω ω t- t-
φφkk) or ) or
►X(t)= X(t)= AA00 + + ΣΣ∞∞k=1 k=1 (( BBk k
22++ AAk k 22))1/21/2 (C (Ckk sin k sin kω ω
t+ t+ φφkk))
UNIT IIUNIT II
Fourier TransformFourier Transform
► Viewed periodic functions in terms of frequency components Viewed periodic functions in terms of frequency components (Fourier series) as well as ordinary functions of time(Fourier series) as well as ordinary functions of time
► Viewed LTI systems in terms of what they do to frequency Viewed LTI systems in terms of what they do to frequency components (frequency response)components (frequency response)
► Viewed LTI systems in terms of what they do to time-domain Viewed LTI systems in terms of what they do to time-domain signals (convolution with impulse response)signals (convolution with impulse response)
► View aperiodic functions in terms of frequency components View aperiodic functions in terms of frequency components via Fourier transformvia Fourier transform
► Define (continuous-time) Fourier transform and DTFT Define (continuous-time) Fourier transform and DTFT ► Gain insight into the meaning of Fourier transform through Gain insight into the meaning of Fourier transform through
comparison with Fourier seriescomparison with Fourier series
The Fourier TransformThe Fourier Transform
►A transform takes one function (or A transform takes one function (or signal) and turns it into another signal) and turns it into another function (or signal)function (or signal)
►Continuous Fourier Transform:Continuous Fourier Transform:
dfefHth
dtethfH
ift
ift
2
2
Continuous Time Fourier TransformContinuous Time Fourier TransformWe can extend the formula for continuous-time Fourier We can extend the formula for continuous-time Fourier series coefficients for a periodic signalseries coefficients for a periodic signal
to aperiodic signals as well. The continuous-time to aperiodic signals as well. The continuous-time Fourier series is not defined for aperiodic signals, but Fourier series is not defined for aperiodic signals, but we call the formulawe call the formula
the (continuous time)the (continuous time)
Fourier transformFourier transform..
2/p
2/p
tikp
0
tikk dte)t(x
p1
dte)t(xp1
X 00
dte)t(x)(X ti
Inverse TransformsInverse TransformsIf we have the full sequence of Fourier coefficients for a If we have the full sequence of Fourier coefficients for a
periodic signal, we can reconstruct it by multiplying the periodic signal, we can reconstruct it by multiplying the
complex sinusoids of frequency complex sinusoids of frequency ωω00k by the weights Xk by the weights Xkk and and
summing:summing:
We can perform a similar reconstruction for aperiodic We can perform a similar reconstruction for aperiodic
signalssignals
These are called the These are called the inverse transformsinverse transforms..
1p
0k
nikk
0eX)n(x
k
tikk
0eX)t(x
de)(X21
)t(x ti
de)(X21
)n(x ni
Fourier Transform of Impulse FunctionsFourier Transform of Impulse FunctionsFind the Fourier transform of the Dirac delta function:Find the Fourier transform of the Dirac delta function:
Find the DTFT of the Kronecker delta function:Find the DTFT of the Kronecker delta function:
The delta functions contain all frequencies at equal The delta functions contain all frequencies at equal
amplitudes.amplitudes.
Roughly speaking, that’s why the system response to an Roughly speaking, that’s why the system response to an
impulse input is important: it tests the system at all impulse input is important: it tests the system at all
frequencies.frequencies.
1edte)t(dte)t(x)(X 0ititi
1ee)n(e)n(x)(X 0i
n
ni
n
ni
Laplace TransformLaplace Transform► Lapalce transform is a generalization of the Fourier transform in Lapalce transform is a generalization of the Fourier transform in
the sense that it allows “complex frequency” whereas Fourier the sense that it allows “complex frequency” whereas Fourier analysis can only handle “real frequency”. Like Fourier transform, analysis can only handle “real frequency”. Like Fourier transform, Lapalce transform allows us to analyze a “linear circuit” problem, Lapalce transform allows us to analyze a “linear circuit” problem, no matter how complicated the circuit is, in the frequency domain no matter how complicated the circuit is, in the frequency domain in stead of in he time domain.in stead of in he time domain.
► Mathematically, it produces the benefit of converting a set of Mathematically, it produces the benefit of converting a set of differential equations into a corresponding set of algebraic differential equations into a corresponding set of algebraic equations, which are much easier to solve. Physically, it produces equations, which are much easier to solve. Physically, it produces more insight of the circuit and allows us to know the bandwidth, more insight of the circuit and allows us to know the bandwidth, phase, and transfer characteristics important for circuit analysis phase, and transfer characteristics important for circuit analysis and design.and design.
► Most importantly, Laplace transform lifts the limit of Fourier Most importantly, Laplace transform lifts the limit of Fourier analysis to allow us to find both the steady-state and “transient” analysis to allow us to find both the steady-state and “transient” responses of a linear circuit. Using Fourier transform, one can responses of a linear circuit. Using Fourier transform, one can only deal with he steady state behavior (i.e. circuit response only deal with he steady state behavior (i.e. circuit response under indefinite sinusoidal excitation). under indefinite sinusoidal excitation).
► Using Laplace transform, one can find the response under any Using Laplace transform, one can find the response under any types of excitation (e.g. switching on and off at any given time(s), types of excitation (e.g. switching on and off at any given time(s), sinusoidal, impulse, square wave excitations, etcsinusoidal, impulse, square wave excitations, etc..
Laplace TransformLaplace Transform
Application of Laplace Application of Laplace Transform to Circuit AnalysisTransform to Circuit Analysis
system
►• A system is an operation that transforms input signal x into output signal y.
LTI Digital Systems
►Linear Time Invariant• Linearity/Superposition:►If a system has an input that can be
expressed as a sum of signals, then the response of the system can be expressed as a sum of the individual responses to the respective systems.
►LTI
Time-Invariance &Causality
► If you delay the input, response is just a delayed version of original response.
►X(n-k) y(n-k)
►Causality could also be loosely defined by “there is no output signal as long as there is no input signal” or “output at current time does not depend on future values of the input”.
Convolution
►The input and output signals for LTI systems have special relationship in terms of convolution sum and integrals.
►Y(t)=x(t)*h(t) Y[n]=x[n]*h[n]
UNIT IIIUNIT III
Sampling theory► The theory of taking discrete sample values (grid of
color pixels) from functions defined over continuous domains (incident radiance defined over the film plane) and then using those samples to reconstruct new functions that are similar to the original (reconstruction).
► Sampler: selects sample points on the image plane► Filter: blends multiple samples together
Sampling theory
►For band limited function, we can just increase the sampling rate
►• However, few of interesting functions in computer graphics are band limited, in particular, functions with discontinuities.
►• It is because the discontinuity always falls between two samples and the samples provides no information of the discontinuity.
Sampling theory
Aliasing
ZZ-transforms-transforms
►For discrete-time systems, For discrete-time systems, zz-transforms -transforms play the same role of Laplace transforms play the same role of Laplace transforms do in continuous-time systemsdo in continuous-time systems
►As with the Laplace transform, we As with the Laplace transform, we compute forward and inverse compute forward and inverse zz-transforms -transforms by use of transforms pairs and propertiesby use of transforms pairs and properties
n
nznhzH ][
Bilateral Forward z-transform
R
n dzzzHj
nh 1 ][ 2
1][
Bilateral Inverse z-transform
Region of ConvergenceRegion of Convergence
► Region of the Region of the complex complex zz-plane for -plane for which forward which forward zz--transform transform convergesconverges
Im{z}
Re{z}Entire plane
Im{z}
Re{z}Complement of a disk
Im{z}
Re{z}Disk
Im{z}
Re{z}
Intersection of a disk and complement of a disk
► Four possibilities Four possibilities ((zz=0 is a special =0 is a special case and may or case and may or may not be may not be included)included)
ZZ-transform Pairs-transform Pairs
►hh[[nn] = ] = [[nn]]
Region of convergence: Region of convergence: entire entire zz-plane-plane
►hh[[nn] = ] = [[n-1n-1]]
Region of convergence: Region of convergence: entire entire zz-plane-plane
hh[[nn-1] -1] zz-1 -1 HH[[zz]]
1 ][0
0
n
n
n
n znznzH
11
1
1 1][
zznznzHn
n
n
n
1 if 1
1
][
00
z
a
za
z
aza
znuazH
n
n
n
nn
n
nn
►hh[[nn] = ] = aan n uu[[nn]]
Region of Region of convergence: |convergence: |zz| > || > |aa| which is the | which is the complement of a complement of a diskdisk
azza
nuaZ
n
for 1
11
StabilityStability
►Rule #1: For a causal sequence, poles are Rule #1: For a causal sequence, poles are inside the unit circle (applies to z-transform inside the unit circle (applies to z-transform functions that are ratios of two polynomials)functions that are ratios of two polynomials)
►Rule #2: More generally, unit circle is Rule #2: More generally, unit circle is included in region of convergence. (In included in region of convergence. (In continuous-time, the imaginary axis would be continuous-time, the imaginary axis would be in the region of convergence of the Laplace in the region of convergence of the Laplace transform.)transform.)
This is stable if |This is stable if |aa| < 1 by rule #1.| < 1 by rule #1. It is stable if |It is stable if |zz| > || > |aa| and || and |aa| < 1 by rule #2.| < 1 by rule #2.
Inverse Inverse zz-transform-transform
►Yuk! Using the definition requires a contour Yuk! Using the definition requires a contour integration in the complex integration in the complex zz-plane.-plane.
►Fortunately, we tend to be interested in Fortunately, we tend to be interested in only a few basic signals (pulse, step, etc.)only a few basic signals (pulse, step, etc.) Virtually all of the signals we’ll see can be built Virtually all of the signals we’ll see can be built
up from these basic signals. up from these basic signals. For these common signals, the For these common signals, the zz-transform pairs -transform pairs
have been tabulated (see Lathi, Table 5.1)have been tabulated (see Lathi, Table 5.1)
dzzzFj
nf njc
jc
1
2
1
ExampleExample
► Ratio of polynomial z-Ratio of polynomial z-domain functionsdomain functions
►Divide through by the Divide through by the highest power of zhighest power of z
► Factor denominator Factor denominator into first-order factorsinto first-order factors
► Use partial fraction Use partial fraction decomposition to get decomposition to get first-order termsfirst-order terms
21
23
12][
2
2
zz
zzzX
21
21
21
23
1
21][
zz
zzzX
11
21
121
1
21][
zz
zzzX
12
1
10 1
21
1][
z
A
z
ABzX
Example (con’t)Example (con’t)
►Find Find BB00 by by polynomial polynomial divisiondivision
►Express in terms Express in terms of of BB00
►Solve for Solve for AA11 and and AA22
15
23
2121
2
3
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1
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12
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z
z
z
zzA
z
zzA
Example (con’t)Example (con’t)
►Express Express XX[[zz]] in terms of in terms of BB00, , AA11, and , and AA22
►Use table to obtain inverse Use table to obtain inverse zz-transform-transform
►With the unilateral With the unilateral zz-transform, or the -transform, or the bilateral bilateral zz-transform with region of -transform with region of convergence, the inverse convergence, the inverse zz-transform is -transform is uniqueunique
11 1
8
21
1
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zzzX
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1 9 2
ZZ-transform Properties-transform Properties
►LinearityLinearity
►Right shift (delay)Right shift (delay)
zFazFanfanfa 22112211
zFzmnumnf m
m
n
nmm znfzzFznumnf1
ZZ-transform Properties-transform Properties
zFzF
zrfzmf
zrfmf
zmnfmf
zmnfmf
mnfmfZnfnfZ
mnfmfnfnf
r
rm
m
m r
mr
m n
n
n
n
m
m
m
21
21
21
21
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2121
► Convolution definitionConvolution definition
► Take Take zz-transform-transform
► ZZ-transform definition-transform definition
► Interchange Interchange summationsummation
► SubstituteSubstitute r r = = nn - - mm
► ZZ-transform definition-transform definition
UNIT IVUNIT IV
IntroductionIntroduction
► Impulse responseImpulse response hh[n] can fully characterize a LTI [n] can fully characterize a LTI system, and we can have the output of LTI system, and we can have the output of LTI system assystem as
► The z-transform of impulse response is called The z-transform of impulse response is called transfer or system functiontransfer or system function HH((zz).).
► Frequency responseFrequency response at is valid at is valid if ROC includes and if ROC includes and
nhnxny
.zHzXzY
1
z
j zHeH
,1z
jjj eHeXeY
5.1 Frequency Response of LIT 5.1 Frequency Response of LIT SystemSystem
► Consider and Consider and , then , then magnitudemagnitude
phasephase
► We will model and analyze LTI systems based on We will model and analyze LTI systems based on the magnitude and phase responses. the magnitude and phase responses.
)()()( jeXjjj eeXeX )()()(
jeHjjj eeHeH
)()()( jjj eHeXeY
)()()( jjj eHeXeY
System FunctionSystem Function
►General form of LCCDEGeneral form of LCCDE
►Compute the z-transformCompute the z-transform
knxbknyaM
kk
N
kk
00
zXzbzYza kM
kk
N
k
kk
00
)(
N
k
kk
kM
kk
za
zb
zX
zYzH
0
0
System Function: Pole/zero System Function: Pole/zero FactorizationFactorization
►Stability requirement can be verified.Stability requirement can be verified.
►Choice of ROC determines causality.Choice of ROC determines causality.
►Location of zeros and poles Location of zeros and poles
determines the frequency response determines the frequency response
and phaseand phase
N
kk
M
kk
zd
zc
a
bzH
1
1
1
1
0
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.,...,,:poles 21 Nddd
Second-order SystemSecond-order System
► Suppose the system function of a LTI system isSuppose the system function of a LTI system is
► To find the difference equation that is satisfied To find the difference equation that is satisfied by the input and out of this systemby the input and out of this system
► Can we know the impulse response? Can we know the impulse response?
.)
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1)(21
1(
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11
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System Function: StabilitySystem Function: Stability
►Stability of LTI system:Stability of LTI system:
►This condition is identical to the condition This condition is identical to the condition that that
The stability condition is equivalent to the The stability condition is equivalent to the condition that the ROC of condition that the ROC of HH((zz) includes the unit ) includes the unit circle.circle.
n
nh ][
.1 when][
zznhn
n
System Function: CausalitySystem Function: Causality
► If the system is causal, it follows that If the system is causal, it follows that hh[[nn] must be ] must be a right-sided sequence. The ROC of a right-sided sequence. The ROC of HH((zz) must be ) must be outside the outside the outermostoutermost pole. pole.
► If the system is anti-causal, it follows that If the system is anti-causal, it follows that hh[[nn] must ] must be a left-sided sequence. The ROC of be a left-sided sequence. The ROC of HH((zz) must be ) must be inside the inside the innermostinnermost pole. pole.
1a
Im
Re 1a
Im
Re ba
Im
Re
Right-sided(causal)
Left-sided(anti-causal)
Two-sided(non-causal)
Determining the ROCDetermining the ROC
►Consider the LTI systemConsider the LTI system
►The system function is obtained asThe system function is obtained as
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5][ nxnynyny
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1(
1
25
1
1)(
11
21
zz
zzzH
System Function: Inverse System Function: Inverse SystemsSystems
► is an inverse system for , ifis an inverse system for , if
► The ROCs of must overlap.The ROCs of must overlap.
► Useful for canceling the effects of another systemUseful for canceling the effects of another system
► See the discussion in Sec.5.2.2 regarding ROCSee the discussion in Sec.5.2.2 regarding ROC
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All-pass SystemAll-pass System
►A system of the form (or cascade of A system of the form (or cascade of these)these)
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All-pass System: General FormAll-pass System: General Form
► In general, all pass systems have formIn general, all pass systems have form
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real poles complex poles
All-Pass System ExampleAll-Pass System Example
0.8
0.5
z-planeUnit circle
4
3
3
4 2
Re
Im
1 and 2 cr MM
zeros. and poles 42 has system pass-all This rc MMNM
jj erre 1conjugate & reciprocal :zero:pole
Minimum-Phase SystemMinimum-Phase System
► Minimum-phase system:Minimum-phase system: all zeros and all poles are all zeros and all poles are inside the unit circle.inside the unit circle.
► The name The name minimum-phaseminimum-phase comes from a property of comes from a property of the phase response (minimum phase-lag/group-delay).the phase response (minimum phase-lag/group-delay).
► Minimum-phase systems have some special properties.Minimum-phase systems have some special properties.
► When we design a filter, we may have multiple choices When we design a filter, we may have multiple choices to satisfy the certain requirements. Usually, we prefer to satisfy the certain requirements. Usually, we prefer the minimum phase which is unique.the minimum phase which is unique.
► All systems can be represented as a minimum-phase All systems can be represented as a minimum-phase system and an all-pass system.system and an all-pass system.
UNIT VUNIT V
ExampleExample
►Block diagram representation ofBlock diagram representation of nxb2nya1nyany 021
Block Diagram Block Diagram RepresentationRepresentation
►LTI systems with rational system LTI systems with rational system function can be represented as function can be represented as constant-coefficient difference constant-coefficient difference equationequation
►The implementation of difference The implementation of difference equations requires delayed values equations requires delayed values of theof the inputinput outputoutput intermediate results intermediate results
►The requirement of delayed The requirement of delayed elements implies need for storageelements implies need for storage
►We also need means of We also need means of additionaddition multiplicationmultiplication
Direct Form IDirect Form I
►General form of difference equationGeneral form of difference equation
►Alternative equivalent formAlternative equivalent form
M
0kk
N
0kk knxbknya
M
0kk
N
1kk knxbknyany
Direct Form IDirect Form I
►Transfer function can be written asTransfer function can be written as
►Direct Form I RepresentsDirect Form I Represents
N
1k
kk
M
0k
kk
za1
zbzH
zVza1
1zVzHzY
zXzbzXzHzV
zbza1
1zHzHzH
N
1k
kk
2
M
0k
kk1
M
0k
kkN
1k
kk
12
nvknyany
knxbnv
N
1kk
M
0kk
Alternative RepresentationAlternative Representation
►Replace order of cascade LTI systemsReplace order of cascade LTI systems
zWzbzWzHzY
zXza1
1zXzHzW
za1
1zbzHzHzH
M
0k
kk1
N
1k
kk
2
N
1k
kk
M
0k
kk21
M
0kk
N
1kk
knwbny
nxknwanw
Alternative Block DiagramAlternative Block Diagram
►We can change the order of the We can change the order of the cascade systemscascade systems
M
0kk
N
1kk
knwbny
nxknwanw
Direct Form IIDirect Form II
► No need to store the same No need to store the same data twice in previous systemdata twice in previous system
► So we can collapse the delay So we can collapse the delay elements into one chainelements into one chain
► This is called Direct Form II or This is called Direct Form II or the Canonical Formthe Canonical Form
► Theoretically no difference Theoretically no difference between Direct Form I and IIbetween Direct Form I and II
► Implementation wise Implementation wise Less memory in Direct IILess memory in Direct II Difference when using Difference when using
finite-precision arithmeticfinite-precision arithmetic
Signal Flow Graph Signal Flow Graph RepresentationRepresentation
►Similar to block diagram representationSimilar to block diagram representation Notational differencesNotational differences
►A network of directed branches connected at nodesA network of directed branches connected at nodes
►Example representation of a difference equationExample representation of a difference equation
ExampleExample
►Representation of Direct Form II with Representation of Direct Form II with signal flow graphssignal flow graphs
nwny
1nwnw
nwbnwbnw
nwnw
nxnawnw
3
24
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12
41
1nwbnwbny
nx1nawnw
1110
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Determination of System Determination of System Function from Flow GraphFunction from Flow Graph
nwnwny
1nwnw
nxnwnw
nwnw
nxnwnw
42
34
23
12
41
zWzWzY
zzWzW
zXzWzW
zWzW
zXzWzW
42
134
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12
41
zWzWzY z11zzX
zW
z11zzX
zW
42
1
1
4
1
1
2
nu1nunh
z1z
zXzY
zH
1n1n
1
1
Basic Structures for IIR Basic Structures for IIR Systems: Direct Form ISystems: Direct Form I
Basic Structures for IIR Basic Structures for IIR Systems: Direct Form IISystems: Direct Form II
Basic Structures for IIR Basic Structures for IIR Systems: Cascade FormSystems: Cascade Form
►General form for cascade implementationGeneral form for cascade implementation
► More practical form in 2More practical form in 2ndnd order systems order systems
21
21
N
1k
1k
1k
N
1k
1k
M
1k
1k
1k
M
1k
1k
zd1zd1zc1
zg1zg1zf1AzH
1M
1k2
k21
k1
2k2
1k1k0
zaza1zbzbb
zH
ExampleExample
► Cascade of Direct Form I subsectionsCascade of Direct Form I subsections
► Cascade of Direct Form II subsectionsCascade of Direct Form II subsections
1
1
1
1
11
11
21
21
z25.01z1
z5.01z1
z25.01z5.01z1z1
z125.0z75.01zz21
zH
Basic Structures for IIR Basic Structures for IIR Systems: Parallel FormSystems: Parallel Form
► Represent system function using partial fraction expansionRepresent system function using partial fraction expansion
► Or by pairingthe real polesOr by pairingthe real poles
P PP N
1k
N
1k1
k1
k
1kk
1k
kN
0k
kk zd1zd1
ze1Bzc1
AzCzH
SP N
1k2
k21
k1
1k1k0
N
0k
kk zaza1
zeezCzH
ExampleExample►Partial Fraction ExpansionPartial Fraction Expansion
►Combine poles to getCombine poles to get
1121
21
z25.0125
z5.0118
8z125.0z75.01
zz21zH
21
1
z125.0z75.01z87
8zH
Transposed FormsTransposed Forms
►Linear signal flow graph property:Linear signal flow graph property: Transposing doesn’t change the input-output relationTransposing doesn’t change the input-output relation
►Transposing:Transposing: Reverse directions of all branchesReverse directions of all branches Interchange input and output nodesInterchange input and output nodes
►Example:Example:
Reverse directions of branches and interchange input Reverse directions of branches and interchange input and outputand output
1az1
1zH
ExampleExample
Transpose
►Both have the same system function Both have the same system function or difference equationor difference equation
2nxb1nxbnxb2nya1nyany 21021
Basic Structures for FIR Systems: Direct Basic Structures for FIR Systems: Direct FormForm
►Special cases of IIR direct form structuresSpecial cases of IIR direct form structures
► Transpose of direct form I gives direct form II Transpose of direct form I gives direct form II ► Both forms are equal for FIR systemsBoth forms are equal for FIR systems
►Tapped delay lineTapped delay line
Basic Structures for FIR Basic Structures for FIR Systems: Cascade FormSystems: Cascade Form
►Obtained by factoring the polynomial Obtained by factoring the polynomial system functionsystem function
M
0n
M
1k
2k2
1k1k0
nS
zbzbbznhzH
Structures for Linear-Phase Structures for Linear-Phase FIR SystemsFIR Systems
► Causal FIR system with generalized linear phase are Causal FIR system with generalized linear phase are
symmetricsymmetric::
► Symmetry means we can half the number of Symmetry means we can half the number of multiplicationsmultiplications
► Example: For even M and type I or type III systemsExample: For even M and type I or type III systems::
IV)or II (type M0,1,...,n nhnMh
III)or I (type M0,1,...,n nhnMh
2/Mnx2/MhkMnxknxkh
kMnxkMh2/Mnx2/Mhknxkh
knxkh2/Mnx2/Mhknxkhknxkhny
12/M
0k
12/M
0k
12/M
0k
M
12/Mk
12/M
0k
M
0k
Structures for Linear-Phase Structures for Linear-Phase FIR SystemsFIR Systems
►Structure for even MStructure for even M
►Structure for odd MStructure for odd M