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Frequency Analysis using Z Transform.pptx
1. 7 IT 01
Digital Signal Processing
Unit III: Frequency Analysis using Z- Transform
Prof. (Dr.) Prashant V. Ingole
Professor and Head,
Dept. of Information Technology,
Prof Ram Meghe Institute of Technology and Research, Badnera.
7 IT 01 Digital Signal Processing (Winter 2021) L25
2. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Course Outline
UNIT III: The Z- Transform
β Z-Transform
β Properties of the Region of Convergence of the z-Transform
β The Inverse Z-Transform
β Z-Transform Properties
Course Outcomes (COs)
β’ Analyze DT LTI Systems using Z transform
3. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Z- Transform
β’ We have studied that Fourier Transform play a key role in
representing and analyzing the discrete time signals and systems.
β’ However Fourier Transform is subjected to many constraints
Signal under analysis x(n) need to be absolutely summable.
β’ So we are in need of a generalized analysis tool
β’ Z Transform is the generalized form of Fourier Transform
β’ Analog Signals ο Laplace Transform
β’ Digital Signalsο Z Transform
β’ Motivation
1. Convergence of all types of Signals
2. Convenience of notations
3. Use of powerful complex variable theory for analysis
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z Transform Definition
β’ z transform of the discrete time signal x(n) is defined as a power
series
β’ πΏ π = π=ββ
β π(π)πβπ where z is the complex variable
β’ The Fourier Transform was earlier defined as
β’ π πππ€ = π=ββ
β π₯(π)πβππ€π
β’ Observe the similarity in these two mathematical tools
β’ By comparison it can be easily found that π§ = πππ€ and
so π§βπ = πβππ€π
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z Transform
β’ The z Transform definition equation
π π§ =
π=ββ
β
π₯(π)π§βπ
is also known as direct z transform.
The inverse procedure to obtain the x(n) from X(z) is called as an Inverse
z transform.
Notations:
β’ The z transform of the signal x(n) is denoted as X(z) = z(x(n))
β’ The relationship between x(n) and X(z) is indicated by
x(n) z X(z)
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Fourier Transform of DT Aperiodic Signals
z Transform is a infinite power series (ππ) with z being the complex
variable .
Some time z Transform is considered as an operator and denoted as z( . )
that is
π§ π₯ π = π=ββ
β π₯ π π§βπ = π(π§)
β’ When z transform is defined from -β to β it is referred to as the
bilateral or two sided z transform
β’ But practical signals are causal so the z transform is defined either from
0 to β and it is known as unilateral or single sided z transform
β’ π(π§) = π=0
β
π₯ π π§βπ ο unilateral or single sided z transform
7. 7 IT 01 Digital Signal Processing (Winter 2021) L25
z Transform
In π(π§) = π=0
β
π₯ π π§βπ
if we replace π§ = πππ€
then the equation reduces
to Fourier transform equation
π πππ€ = π=ββ
β π₯(π)πβππ€π
When we consider π§ = πππ€ it is in fact π§ = 1 β πππ€ i.e. in polar form π β πππ€
That means restricting the value of |z|=1 we get Fourier transform.
More generally we can express the complex variable z in polar form as
z = π β πππ€
So the z transform equation becomes π ππππ€
= π=ββ
β
π₯(π)(ππππ€
)βπ
π ππππ€ = π=ββ
β (π₯(π)πβπ)(πππ€)βπ
This equation can be interpreted as the Fourier transform of the signal
π₯(π)πβπ . Obviously for r = 1, this equation reduces to Fourier Transform of
x(n).
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Polar Representation of Z Transform and Unit Circle
Since z Transform is a function of complex variable z it is convenient to
describe, represent and interpret it using a complex z plane.
In the plane a contour corresponding to |z| = 1 is a circle of unit radius.
This contour is also referred to as unit circle in z plane as shown in
figure. π§π
1 w
π 0 π§π
βπ 0
The z transform evaluated on the unit circle is the Fourier Transform
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Periodicity in z Transform
If we evaluate X(z) at the points on the unit circle in z plane beginning at
z = 1 that is at w=0, through z= j (at w= π/2) to z=-1 (at w= π ) to
z= -j (at w= 3π/2) and finally back to z = 1 at w= 2π
We obtain the Fourier Transform for 0 β€ π€ β€ 2π at that time we discussed
w as a linear frequency variable.
The same frequency in z plane is represented as a angular variable which
vary from w=0 at z = 1 to w= π/2 at z = j to w = π at z= -1 to w=
3π
2
at z = -j
to w = 2π at z= 1, thus completing a period around the unit circle.
With this interpretation the inherent periodicity of frequency in Fourier
Transform is captured naturally. Since a change of angle of w = 2π radians in
z plane corresponds to traversing the unit circle one and returning to the
exactly same point.
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Region of Convergence in z Transform
In Fourier series the power series representing the Fourier transform does
not converge for all sequences, because the infinite may not always be
finite.
Similarly z transform does not converge for all sequences or all values of z.
For any given sequence the set of values of z for which the z transform
X(z) converges is called as the Region Of Convergence (ROC).
Since z transform is an infinite power series, it exists for those values of z
for which this series converges.
The region of Convergence X(z) is the set of all values of z for which the
X(z) attains the finite value.
Thus anytime we cite z transform we should also specify the Region of
Convergence (ROC)
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z βTransform Evaluation
Ex 1: Find z transform of finite duration signal x(n)={1, 2, 7, 5, 0, 1}
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) =1.π§0 +2*π§β1 + 7π§β2 + 5π§β3 + 0. π§β4 + 1. π§β5
X(z) =1 +2π§β1 + 7π§β2 + 5π§β3 + π§β5
For finite duration causal signals the ROC is entire z plane except z=0
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z βTransform Evaluation
Ex 1: Find z transform of finite duration signal x(n)={1, 2, 7, 5, 0, 1}
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) =1.π§0 +2*π§β1 + 7π§β2 + 5π§β3 + 0. π§β4 + 1. π§β5
X(z) =1 +2π§β1 + 7π§β2 + 5π§β3 + π§β5
For finite duration causal signals the ROC is entire z plane except z=0
Ex 2: Find z transform of finite duration signal x(n)={1, 2, 7, 5, 0, 1}
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) =1.π§+2 +2*π§+1 + 7π§0 + 5π§β1 + 0. π§β2 + 1. π§β3
X(z) =1π§+2
+2π§+1
+ 7 + 5π§β1
+ π§β3
For finite duration non causal signals the ROC is entire z plane except z=0
and z = β
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z Transform Problems
Ex 3: Find z transform of finite duration signal x(n)={0, 0, 1, 2, 5, 7, 0, 1}
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) = π§β2 + 2π§β3 + 5. π§β4 + 7. π§β5 + 0. π§β6 + 1. π§β7
X(z) =π§β2 + 2π§β3 + 5. π§β4 + 7. π§β5 + 1. π§β7
For finite duration causal signals the ROC is entire z plane except z=0
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z Transform Problems
Ex 3: Find z transform of finite duration signal x(n)={0, 0, 1, 2, 5, 7, 0, 1}
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) = π§β2 + 2π§β3 + 5. π§β4 + 7. π§β5 + 0. π§β6 + 1. π§β7
X(z) =π§β2 + 2π§β3 + 5. π§β4 + 7. π§β5 + 1. π§β7
For finite duration causal signals the ROC is entire z plane except z=0
Ex 4: Find z transform of finite duration signal x(n)=πΏ(π)
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) = 1. π§0 = 1
For this finite duration causal signals the ROC is entire z plane
including z = 0 and z = β .
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z Transform Problems
Ex 5: Find z transform of finite duration signal x(n)=πΏ(π β π)
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) = 1. π§βπ
= π§βπ
(for k > 0)
For this finite duration causal signals, the ROC is entire z plane
except z=0 .
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z Transform Problems
Ex 5: Find z transform of finite duration signal x(n)=πΏ(π β π)
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) = 1. π§βπ = π§βπ (for k > 0)
For this finite duration causal signals, the ROC is entire z plane
except z=0 .
Ex 6: Find z transform of finite duration signal x(n)=πΏ(π + π)
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
So X(z) = 1. π§+π = π§+π (for k > 0)
For this finite duration causal signals, the ROC is entire z plane
except z = β .
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Finite Signals and ROC
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Convergence of z Transform
If π§ = πππ€ then ROC is unit circle (r = 1)
So | z | = 1
But let us suppose π§ = π. πππ€
if we replace it in the definition of the z transform
The z transform equation becomes π ππππ€ = π=ββ
β π₯(π)(ππππ€)βπ
π ππππ€ = π=ββ
β (π₯(π)πβπ)(πππ€)βπ
Uniform convergence of the Fourier transform requires that the sequence be
absolutely summable. So applying this condition to the above equation we
get the condition as:
π=ββ
β
π₯ π πβπ
< β
For absolute convergence of the z transform.
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Convergence of z Transform
This condition indicates that because of the multiplication of the
sequence by the real exponential sequence πβπ, it is possible for the z
transform to converge even if the Fourier Transform does not converge.
Ex: Find Fourier Transform of signal x(n) = u(n) ο Answer is FT does not exist
Reason is signal u(n) is not absolutely summable.
Ex: Find z transform of signal x(n) = u(n)
Solution: x(n) = u(n) is not absolutely summable so its FT cannot be found.
However while finding z transform as per the definition we find FT of
π₯ π πβπ
. An this multiplication is absolutely summable for r > 1.
This means z transform of the unit step exists with ROC |z| > 1 i.e. ROC is
exterior of the unit circle in z Plane
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Convergence of z Transform
Convergence of the power series in definition of z transform depend only on |z|.
Since |X(z)| < β if π=0
β
|π₯ π |. |π§|βπ < β
Thus if some value of π§ = π§1 is in the ROC, then all values of z defined by a circle
with radius z = |π§1|will also be in the ROC.
In ROC of X(z), |X(z)| < β
But as per the definition of z transform
|π π§ | = | π=ββ
β
π₯ π πβπ
. πβππ€π
|
|π π§ | < π=ββ
β |π₯ π πβπ|. |πβππ€π|
|π π§ | < π=ββ
β
|π₯ π πβπ
|
Hence |π π§ | is finite if sequence π₯ π πβπ
is absolutely summable.
The problem of finding ROC for finding X(z) is equivalent to determining the range
of values for r for which π π . πβπ
is absolutely summable.
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Convergence of z Transform
Corrolary
|π π§ | < π=ββ
β1
π₯ π πβπ
+ π=0
β
π₯ π πβπ
|π π§ | < π=1
β
π₯ βπ ππ
+ π=0
β
π₯ π /ππ
1) Non Causal component, 2) Causal component
If X(z) converges in some region of the complex z plane, both the above
summations must be finite in that region.
If the first sum converges, there must exist values of r small enough such
that the product sequence π₯ βπ ππ
in range 1 β€ π β€ β is absolutely
summable. Therefore the ROC of the first term π§π
consists of all points in a circle of radius π < π1 π1 π§π
where π1 < β.
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Convergence of z Transform
Corrolary
|π π§ | < π=ββ
β1
π₯ π πβπ
+ π=0
β
π₯ π πβπ
|π π§ | < π=1
β
π₯ βπ ππ + π=0
β
π₯ π /ππ
1) Non Causal component, 2) Causal component
If the second sum converges, there must exist values of r large enough
such that the product sequence π₯ π /ππ
in range 0 β€ π β€ β is
absolutely summable. Hence ROC of the second term in above
equation π2 π§π
consists of all points outside a circle of radius π > π2 π§π
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Convergence of z Transform
Since the convergence of X(z) requires that both sums in the above
equation be finite, it follows that the ROC of X(z) is generally specified as
the annual region in the z plane π2 β€ π β€ π1, which is the common
region where both sums are finite i.e. π2 < π1 is a ring.
π1 π§π
π2
π§π
Annular region in z plane which is a
ROC for the bilateral z transform.
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z Transform Problems
Ex: Determine z Transform of the signal π₯ π =
1
2
π
π’(π)
Solution : In this example the signal x(n) is a infinite power series
So x(n)={1, Β½,
1
2
2
,
1
2
3
,
1
2
4
,
1
2
5
, β¦β¦}
The z transform is given by π π§ = π=0
β
π₯ π . π§βπ
as the signal is causal
Applying this formula to our power series function we get z transform of x(n)
π π§ =
π=0
β
(1/2)π
. π§βπ
π π§ =
π=0
β
(
1
2
. π§β1
)π
Considering A= (
1
2
. π§β1
) we can write above equation as X(z) = 1+A+π΄2
+π΄3
+π΄4
+
β¦.
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z Transform Problems
Ex: Determine z Transform of the signal π₯ π =
1
2
π
π’(π)
Continued : Considering A= (
1
2
. π§β1) ; then X(z) = 1+A+π΄2+π΄3+π΄4+ β¦.
The infinite power series can be represented by IGSS as π(π§) =
1
1βπ΄
Putting value of A in the above equation we get π(π§) =
1
1β
1
2
.π§β1
As per IGSS formula A < 1 so
1
2
. π§β1 < 1 that means z >
1
2
This means the ROC is exterior of the circle with radius
1
2
π§π
ROC consists of all points in the region that is 1
2 π§π
Outside the circle with radius
1
2
as shown in figure. ROC
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z Transform Problems
Ex: Determine z Transform of the signal π₯ π = πΌπ
π’(π)
Solution : In this example the signal x(n) is a infinite power series
So x(n)={1, , πΌ 2
, πΌ 3
, πΌ 4
, πΌ 5
, β¦β¦}
The z transform is given by π π§ = π=0
β
π₯ π . π§βπ
as the signal is causal
Applying this formula to our power series function we get z transform of x(n)
π π§ =
π=0
β
(πΌ)π. π§βπ
π π§ =
π=0
β
(πΌ. π§β1
)π
Considering A= (πΌ. π§β1) we can write above equation X(z) = 1+A+π΄2+π΄3+π΄4+ β¦.
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z Transform Problems
Ex: Determine z Transform of the signal π₯ π = πΌ π
π’(π)
Continued : Considering A= (πΌ. π§β1
) ; then X(z) = 1+A+π΄2
+π΄3
+π΄4
+ β¦.
The infinite power series can be represented by IGSS as π(π§) =
1
1βπ΄
Putting value of A in the above equation we get πΏ(π) =
π
πβπΆπβπ
As per IGSS formula A < 1 so πΌ. π§β1 < 1 that means z > πΌ
This means the ROC is exterior of the circle with radius πΌ
ROC consists of all points in the region that is outside the circle with radius
π§π
πΌ π§π
ROC
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z Transform Problems
Thus we have a transform pair
π₯ π = πΌππ’ π β π§ β π(π§) =
1
1βπΌπ§β1 with ROC |z|> πΌ
Thus ROC is exterior of the circle in z plane with radius πΌ
If we set πΌ=1 then the input signal reduces to π₯ π = π’ π and
its z transform reduces to π π§ =
1
1βπ§β1 =
π§
π§β1
with ROC |z| > 1
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z Transform Problems
Ex: Determine z Transform of the signal π₯ π = βπΌππ’(βπ β 1)
Solution : From the definition of z transform we have
π π§ = π=ββ
β π₯ π . π§βπ
as the signal is anti-causal
π π§ =
π=ββ
β1
π₯(π). π§βπ +
π=0
β
π₯(π). π§βπ =
π=ββ
β1
π₯(π). π§βπ
π π§ =
π=ββ
β1
(βπΌπ)π§βπ = β
π=ββ
β1
(πΌπ)π§βπ
π π§ = β
π=ββ
β1
(πΌπ)π§βπ = β
π=1
β
(πΌβπ)π§π
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z Transform Problems
π π§ = β
π=ββ
β1
(πΌπ
)π§βπ
= β
π=1
β
(πΌβπ
)π§π
π π§ = β
π=1
β
(πΌβ1
π§)π
Let A= πΌβ1π§, then as per IGSS formula we have π π§ = β
π΄
1βπ΄
= β
πΌβ1π§
1βπΌβ1π§
π π§ =
1
1 β πΌπ§β1
With A < 1 that is πΌβ1
π§ < 1 that means z < πΌ
This means the ROC is interior of the circle with radius πΌ π§π
ROC consists of all points in the region that is inside πΌ π§π
the circle with radius πΌ.
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z Transform Problems
So we found that signal π₯ π = πΌπ
π’ π causal signal
and signal π₯ π = βπΌππ’(βπ β 1) an anti-causal signal has a same z
transform that is
π π§ =
1
1 β πΌπ§β1
However these signals differ in ROC and it can be easily found that
for π₯ π = πΌππ’ π (causal signal)
the ROC is exterior of the circle in z plane with radius πΌ
For π₯ π = βπΌππ’(βπ β 1) (an anti-causal signal) π§π
the ROC is interior of the circle in z plane with radius πΌ πΌ
π§π
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z Transform Problems
Ex: Det. z Transform of the signal π₯ π = πΌππ’ π + π½ππ’(βπ β 1)
Solution : This is a Bilateral or double sided z transform problem
z transform is given by π π§ = π=ββ
β π₯ π . π§βπ So in this case
π π§ =
π=ββ
β1
π½ππ§βπ +
π=0
β
πΌππ§βπ
π π§ =
π=0
β
πΌππ§βπ +
π=ββ
β1
π½ππ§βπ
π π§ =
π=0
β
πΌππ§βπ +
π=1
β
π½βππ§π
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z Transform Problems
Ex: Det. z Transform of the signal π₯ π = πΌπ
π’ π + π½π
π’(βπ β 1)
Solution :
π π§ =
π=0
β
(πΌπ§β1
)π
+
π=1
β
(π½β1
π§)π
Applying IGSS formula , For first power series we get
1
1βπΌπ§β1 with
|πΌπ§β1
|<1 that is |z| > πΌ that is ROC is exterior of the circle with radius πΌ
Similarly by applying IGSS for second power series we get
π½β1π§
1βπ½β1π§
with
|π½β1π§|<1 that is |z| < π½ that is ROC is interior of the circle with radius Ξ².
In determining the X(z), if | π½ | < | πΌ | ROC in z domain does not coincide
with each other so they do not converge simultaneously
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z Transform Problems
Ex: Det. z Transform of the signal π₯ π = πΌπ
π’ π + π½π
π’(βπ β 1)
Solution :
π π§ =
π=0
β
(πΌπ§β1
)π
+
π=1
β
(π½β1
π§)π
But if | π½ | > | πΌ | ROC of ring shape in z domain coincide with each other
so they converge simultaneously
π π§ =
1
1βπΌπ§β1 +
π½β1π§
1βπ½β1π§
=
1
1βπΌπ§β1 -
1
1βπ½π§β1
And ROC is |πΌ|< |z| <|π½|
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Infinite Signals and ROC
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Properties of z Transform
The z transform is a very powerful tool and this is exhibited by itβs powerful
set of properties.
Note : when we combine several z transforms, the ROC of the overall
transform is at least the intersection of their ROCs.
Properties:
1. Linearity
2. Time Shifting
3. Scaling in z domain
4. Time Reversal
5. Differentiation in z domain
6. Convolution of two sequences
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Linearity Property
1. Linearity:
If π₯1 π β π§ β π1(π§) and π₯2 π β π§ β π2(π§)
Then π₯ π = π1π₯1 π + π2π₯2 π β π§ β π π§ = π1π1 π§ + π2π2 π§
Proof: π§ π₯ π = π π§ = π=ββ
β
π₯(π)π§βπ
For π₯ π = π1π₯1 π + π2π₯2 π
π π§ = π=ββ
β
(π1π₯1 π + π2π₯2(π))π§βπ
= π=ββ
β
π1π₯1 π π§βπ + π2π₯2(π)π§βπ
= π1 π=ββ
β
π₯1 π π§βπ
+ π2 π=ββ
β
π₯2(π)π§βπ
Thus
π1 π§ π2 π§
π π§ = π1π1 π§ + π2π2 π§
Thus the signal can be expressed first in composite form and then its
z transform can be found.
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Problems on Linearity Property
Ex: Determine the z Transform and ROC of the Signal
x(n) =(ππ + πβπ)π’(π)
Solution : Z Transform is given by π π§ = π=ββ
β
π₯ π . π§βπ
This signal can be represented as π₯1 = πππ’ π and π₯2 = πβππ’ π
So we have π₯ π = π₯1 π + π₯2(π)
For π₯1 = πππ’ π by applying z transform π1 π§ =
1
1βππ§β1 with
|ππ§β1|<1 or |z| > a
Similarly for π₯2 = πβπ
π’ π by applying z transform π1 π§ =
1
1βπβ1π§β1 with
|πβ1π§β1|<1 or |z| < πβ1
So X π§ =
1
1βππ§β1 β
1
1βπβ1π§β1
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Problems on Linearity Property
Ex: Determine the z Transform and ROC of the Signal
x(n) =(ππ + πβπ)π’(π)
Solution :
So X π§ =
1
1βππ§β1 β
1
1βπβ1π§β1
=
π§
π§βπ
β
π§
π§βπβ1
With ROC as intersection of ROCs of these two regions that are
|z|> a and |z| < 1/a
So ROC is the annual region between circle with radius 1/a and circle with
Radius a
40. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
EX: Determine the z transform of signal π₯(π) = (cos π€0π)u(n)
Solution : Given signal is π₯(π) = (cos π€0π)u(n)
This signal can be decomposed by using Eularβs Identity as
cos π€0π =
πππ€0π + πβππ€0π
2
So π₯(π) = (
πππ€0π+πβππ€0π
2
)u(n)
Thus the decomposed signal is represented as
π₯ π =
πππ€0π
2
u n +
πβππ€0π
2
u n
π π§ =
1
2
[π§(πππ€0π)+π§(πβππ€0π)]
We can write π₯1(π) = πππ€0ππ’(π) and π₯2(π) = πβππ€0ππ’(π)
41. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
πΏ π =
π
π
[π(πππππ)+π(πβππππ)]
We can write π₯1 = πππ€0ππ’(π) and π₯2 = πβππ€0ππ’(π)
Let us say πΌ = πππ€0 so πΌ = πππ€0 = 1
π1 π§ = π§{ πππ€0π π’ π } = π=0
β
πππ€0π π§βπ
π1 π§ = π=0
β
(πππ€0π§β1)π =
1
1β(πππ€0π§β1)
where |πππ€0π§β1| < 1
As πππ€0 = 1 , ROC is |z|> 1
Now let us say πΌ = πβππ€0 so πΌ = πβππ€0 = 1
π2 π§ = π§{ πβππ€0π π’ π } = π=0
β
πβππ€0π π§βπ
π2 π§ = π=0
β
(πβππ€0π§β1
)π
=
1
1β(πβππ€0π§β1)
where |πβππ€0π§β1
| < 1
As πππ€0 = 1 , ROC is |z|> 1
42. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
πΏ π =
π
π
[π(πππππ)+π(πππππ)]
π π§ =
1
2
{
1
1β(πππ€0π§β1)
+
1
1β(πβππ€0π§β1)
}
Where ROC in both cases is |z|> 1
π π§ =
1
2
{
1β(πβππ€0π§β1+1β(πππ€0π§β1)
1βπππ€0π§β1 β(1βπβππ€0π§β1)
}
π π§ =
1
2
{
2βπβππ€0π§β1βπππ€0π§β1
1βπππ€0π§β1 β(1βπβππ€0π§β1)
}
Solving Numerator = 2 β πβππ€0π§β1 β πππ€0π§β1
= 2 β
2(πππ€0+πβππ€0) π§β1
2
= 2 β 2 πππ π€0π§β1
43. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
π π§ =
1
2
{
2βπβππ€0π§β1βπππ€0π§β1
1βπππ€0π§β1 β(1βπβππ€0π§β1)
}
Solving Denominator = (1 β πππ€0π§β1 β πβππ€0π§β1 + π§β2)
= (1 β 2π§β1 πππ€0+πβππ€0
2
+ π§β2)
= 1 β 2π§β1 cos π€π + π§β2
So
π(π§) =
2(1 β πππ π€0π§β1)
2(1 β 2π§β1 cos π€π + π§β2)
Ans is πΏ(π) =
πβππππππβπ
πβππβπ πππ ππ+πβπ with ROC is |z|>1
44. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
EX: Determine the z transform of signal π₯(π) = (sin π€0π)u(n)
Solution : Given signal is π₯(π) = (π ππ π€0π)u(n)
This signal can be decomposed by using Eularβs Identity as
sin π€0π =
πππ€0π β πβππ€0π
2π
So π₯(π) = (
πππ€0πβπβππ€0π
2π
)u(n)
Thus the decomposed signal is represented as
π₯ π =
πππ€0π
2π
u n β
πβππ€0π
2π
u n
π π§ =
1
2π
[π§(πππ€0π)-π§(πβππ€0π)]
We can write π₯1 = πππ€0π
π’(π) and π₯2 = πβππ€0π
π’(π)
45. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
πΏ π =
π
ππ
[π(πππππ)-π(πβππππ)]
We can write π₯1 = πππ€0ππ’(π) and π₯2 = πβππ€0ππ’(π)
Let us say πΌ = πππ€0 so πΌ = πππ€0 = 1
π1 π§ = π§{ πππ€0π π’ π } = π=0
β
πππ€0π π§βπ
π1 π§ = π=0
β
(πππ€0π§β1
)π
=
1
1β(πππ€0π§β1)
where |πππ€0π§β1
| < 1
As πππ€0 = 1 , ROC is |z|> 1
Now let us say πΌ = πβππ€0 so πΌ = πβππ€0 = 1
π1 π§ = π§{ πβππ€0π π’ π } = π=0
β
πβππ€0π π§βπ
π1 π§ = π=0
β
(πβππ€0π§β1)π =
1
1β(πβππ€0π§β1)
where |πβππ€0π§β1| < 1
As πππ€0 = 1 , ROC is |z|> 1
46. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
πΏ π =
π
ππ
[π(πππππ)-π(πβππππ)]
π π§ =
1
2π
{
1
1β(πππ€0π§β1)
β
1
1β(πβππ€0π§β1)
}
Where ROC in both cases is |z|> 1
π π§ =
1
2π
{
1βπβππ€0π§β1β1+πππ€0π§β1)
1βπππ€0π§β1 β(1βπβππ€0π§β1)
}
π π§ =
1
2π
{
βπβππ€0π§β1+πππ€0π§β1
1βπππ€0π§β1 β(1βπβππ€0π§β1)
}
Solving Numerator = πβππ€0π§β1 β πππ€0π§β1
=
(πππ€0βπβππ€0) π§β1
2π
= sin π€0 π§β1
47. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
π π§ =
1
2
{
2βπβππ€0π§β1βπππ€0π§β1
1βπππ€0π§β1 β(1βπβππ€0π§β1)
}
Solving Denominator = (1 β πππ€0π§β1 β πβππ€0π§β1 + π§β2)
= (1 β 2π§β1 πππ€0+πβππ€0
2
+ π§β2)
= 1 β 2π§β1 cos π€π + π§β2
So
π(π§) =
π ππ π€0π§β1
2(1 β 2π§β1 cos π€π + π§β2)
Ans is πΏ(π) =
πππ πππβπ
πβππβπ πππ ππ+πβπ with ROC |z|>1
48. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
EX: Determine the z transform and ROC of the signal
π₯ π = β
1
3
π
u n + β
1
2
π
u(βn β 1)
Solution : Given signal is π₯(π) so X(z) =z(x(n))
Let π₯1(π) = β
1
3
π
π’(π) and π₯2(π) = β
1
2
π
π’(βπ β 1)
So π₯ π = π₯1 π + π₯2(π)
For z transform of power series in n, π₯ π = πΌππ’ π
by using IGSSS formula
we have π π§ =
1
1βπΌπ§β1 with ROC |z|>πΌ
so π1 π§ = π§ π₯1 π = z( β
1
3
π
π’(π)) =
1
1+
1
3
π§β1
with ROC |z|>
1
3
49. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Linearity Property
Similarly
π2 π§ = π§ π₯2 π = z( β
1
2
π
π’(βπ β 1)) =
1
1+
1
2
π§β1
with ROC |z|<
1
2
So
π π§ =
1
1+
1
3
π§β1
+
1
1+
1
2
π§β1
with ROC is |z|> 1/3 and |z|<1/2
50. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Properties of z Transform
The z transform is a very powerful tool and this is exhibited by itβs powerful
set of properties.
Note : when we combine several z transforms, the ROC of the overall
transform is at least the intersection of their ROCs.
Properties:
1. Linearity
2. Time Shifting
3. Scaling in z domain
4. Time Reversal
5. Differentiation in z domain
6. Convolution of two sequences
51. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Time Shifting Property
π) π»πππ πΊπππππππ π·πππππππ
If π₯ π π§ π π§ Then π₯ π β π π§ π§βππ π§
Proof:
π π§ = π§ π₯ π β π =
π=0
β
π₯(π β π)π§βπ
Let m= n-k so above equation becomes
π π§ =
π=0
β
π₯(π)π§β(π+π) =
π=0
β
π₯ π π§βπ. π§βπ
πΏ π = πβπ
π=π
β
π π πβπ
= πβπ
. πΏ(π) Hence Proved
So this property states that shifting sequence in time corresponds to
multiplication Z transform by π§βπ in z domain.
52. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Time Shifting Property
Ex : Find out z transform of x(n) =u(n-1) using time shifting property.
Solution : Given signal is shifted form of unit sample sequence by 1.
So π π§ =
1
1βπ§β1 =
π§
π§β1
The given signal is shifted by 1 sample so k=1 in this case so
π π§ = π§β1
π§
π§ β 1
=
1
π§ β 1
53. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Time Shifting Property
Ex : Find out z transform of x(n) =u(n-1) using time shifting property.
Solution : Given signal is shifted form of unit sample sequence by 1.
So π π§ =
1
1βπ§β1 =
π§
π§β1
with ROC |z|>1
The given signal is shifted by 1 sample so k=1 in this case so
π π§ = π§β1
π§
π§ β 1
=
1
π§ β 1
With ROC as |z|>1 that is ROC is exterior of the circle with radius 1
in z plane.
54. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Time Shifting Property
Ex : Find z transform of finite duration signal x(n)={1, 2, 7, 5, 0, 1} and
then find z transform of x(n-3) and x(n+2) using time shifting property .
Solution : The z transform equation is π π§ = π=0
β
π₯ π . π§βπ
1) So X(z) =1.π§0
+2*π§β1
+ 7π§β2
+ 5π§β3
+ 0. π§β4
+ 1. π§β5
X(z) =1 +2π§β1 + 7π§β2 + 5π§β3 + π§β5
For finite duration causal signals the ROC is entire z plane except z=0
2) For x(n-3)={0, 0 , 0, 1, 2, 7, 5, 0, 1} here k=3
π1(z) =π§β3 π π§ = π§β3(1 +2π§β1 + 7π§β2 + 5π§β3 + π§β5)
π1(z) = π§β3 +2π§β4 + 7π§β5 + 5π§β6 + π§β8) :ROC entire z plane except z=0
3) For x(n+2)={1, 2, 7, 5, 0, 1} here k=-2
π2(z) =π§+2 π π§ = π§+2(1 +2π§β1 + 7π§β2 + 5π§β3 + π§β5)
π2(z) =π§+2 π π§ = π§+2 +2π§+1 + 7π§0 + 5π§β1 + π§β3) :ROC entire z plane except z=0 and z = β
55. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Properties of z Transform
The z transform is a very powerful tool and this is exhibited by itβs powerful
set of properties.
Note : when we combine several z transforms, the ROC of the overall
transform is at least the intersection of their ROCs.
Properties:
1. Linearity
2. Time Shifting
3. Scaling in z domain
4. Time Reversal
5. Differentiation in z domain
6. Convolution of two sequences
56. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Scaling in z-Domain Property
3) Scaling in Z domain :
If π₯ π π§ π π§ with ROC π1 < π§ < π2
Then πππ₯ π π§ π πβ1π§ with ROC |π|π1 < π§ < |π|π2
Where a may be real or complex.
Proof : Let us apply the definition of z transform
π π§ = π§ πππ₯ π = π=0
β
π₯ π . ππ. π§βπ
π π§ =
π=0
β
π₯ π . (πβ1
π§)βπ
So this is a form of πβ1π§ transform that is X(πβ1π§)
Now putting this value in z in ROC equation get π1 < πβ1π§ < π2 and by
cross multiplication we get |π|π1 < π§ < |π|π2
57. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on scaling in z domain Property
EX: Determine z transform of signal using scaling in z domain
π₯(π) = (ππcos π€0π)u(n)
Solution: By knowing the z transform of (cos π€0π)u(n) it becomes very easy
to find z transform of given signal using the scaling in z domain property.
For x n = (cos π€0π)u(n) the z transform is
πΏ(π) =
πβππππππβπ
πβππβπ πππ ππ+πβπ with ROC is |z|>1
By knowing this and using scaling in z domain property z transform of the
given signal can be found by replacing z by πβππ in above equation
X(z) =
1 β cosw0(aβ1
z)β1
1 β 2(aβ1z)β1cos wo + (aβ1z)β2
58. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on scaling in z domain Property
X(z) =
1 β ππ§β1cosw0
1 β 2ππ§β1 cos wo + π2π§β2
For finding the ROC replace z by aβ1z
For ROC |z| > 1 we will have
ROC as |aβ1z|>1
That is |z| > a
59. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on scaling in z domain Property
EX: Determine z transform of signal using scaling in z domain
π₯(π) = (ππsin π€0π)u(n)
Solution:By knowing the z transform of (π ππ π€0π)u(n) it becomes very easy
to find z transform of given signal using the scaling in z domain property.
For x n = (π ππ π€0π)u(n) the z transform is
X(z) =
π§β1sinw0
1 β 2π§β1 cos wo + π§β2
By knowing this and using scaling in z domain property z transform of the
given signal can be found by replacing z by πβππ in above equation
X(z) =
(aβ1z)β1sinw0
1 β 2(aβ1z)β1cos wo + (aβ1z)β2
60. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on scaling in z domain Property
EX: Determine z transform of signal using scaling in z domain
π₯(π) = (ππsin π€0π)u(n)
X(z) =
azβ1sinw0
1 β 2ππ§β1 cos wo + π2π§β2
For finding the ROC replace z by aβ1
z
For ROC |z| > 1 we will have
ROC as |aβ1z|>1
That is |z| > a
61. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Properties of z Transform
The z transform is a very powerful tool and this is exhibited by itβs powerful
set of properties.
Note : when we combine several z transforms, the ROC of the overall
transform is at least the intersection of their ROCs.
Properties:
1. Linearity
2. Time Shifting
3. Scaling in z domain
4. Time Reversal
5. Differentiation in z domain
6. Convolution of two sequences
62. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Time Reversal Property
4) Time Reversal Property :
If π₯ π π§ π π§ with ROC π1 < π§ < π2
Then x βπ π§ π π§β1
with ROC
1
π2
< π§ <
1
π1
Where a may be real or complex.
Proof : Let us apply the definition of z transform
π π§ = π§ π₯ βπ = π=ββ
β π₯ βπ . π§βπ
Now let l=-n then
π§ π₯ βπ =
π=ββ
β
π₯ π . (π§β1
)βπ
= π(π§β1
)
ROC is π1 < π§β1 < π2 that is π1 <
1
π§
< π2 that means
1
π2
< π§ <
1
π1
63. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Time Reversal Property
Ex: Determine z transform of π₯(π) = π’(βπ)
Solution: z transform is defined as
π π§ =
π=0
β
π₯ π . (π§)βπ
We know the transform pair
π§ π’ π = π(π§) =
1
1βπ§β1 with ROC |z| >1
By using the time reversal property
π§ π’ βπ = π(π§β1
) =
1
1β(π§β1)β1 with ROC |z| < 1
π§ π’ βπ =
1
1βπ§
with ROC |z| < 1
64. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Properties of z Transform
The z transform is a very powerful tool and this is exhibited by itβs powerful
set of properties.
Note : when we combine several z transforms, the ROC of the overall
transform is at least the intersection of their ROCs.
Properties:
1. Linearity
2. Time Shifting
3. Scaling in z domain
4. Time Reversal
5. Differentiation in z domain
6. Convolution of two sequences
65. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Differentiation in z Domain Property
5) Differentiation in z Domain Property :
If π₯ π) π§ π π§
Then n x π π§ β π§
ππ(π§)
ππ§
with same ROC
Proof : For signal π₯ π π§ π π§
Using the definition of z transform
π π§ = π§ π₯ π = π=0
β
π₯ π . π§βπ
Taking differentiation of both sides
ππ π§
ππ§
=
π=0
β
π₯ π .
π
ππ§
π§βπ
ππ π§
ππ§
=
π=0
β
π₯ π . (βπ. π§βπβ1)
66. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Differentiation in z Domain Property
ππ π§
ππ§
=
π=0
β
π₯ π . (βπ. π§βπβ1
)
ππ π§
ππ§
= β
π=0
β
ππ₯ π . (π§β1
) π§βπ
ππ π§
ππ§
= βπ§β1
π=0
β
ππ₯ π . π§βπ
ππ π§
ππ§
= βπ§β1
π§ ππ₯ π
βπ§
ππ π§
ππ§
= π§ ππ₯ π
βπ§
ππ π§
ππ§
π§ ππ₯ π
With both X π§ πππ
ππ π§
ππ§
has same ROC
67. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Diff. in z Domain Property
EX : Determine the z transform of the given signal π₯ π = πππ
π’(π)
Solution : In this case we can write π₯ π = π [πππ’ π ]
let π₯1 π = πππ’(π)
So π₯ π = ππ₯1(π)
π1 π§ =
1
1βππ§β1 with ROC |z| > a
By using differentiation in z domain property z transform of ππ₯(π) is
βπ§
ππ π§
ππ§
so in this case π§ π₯ π = βπ§
π
ππ§
(
1
1βππ§β1)
Using differentiation formula
π
ππ§
π’
π£
=
π’.ππ£βπ£.ππ’
π£2
We get X π§ =
ππ§β1
(1βππ§β1)2 with ROC |z| > a
68. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Problems on Diff. in z Domain Property
EX : Determine the z transform of the given signal π₯ π = πππ
π’(π)
So : In this case we can write π₯ π = π [πππ’ π ]let π₯1 π = ππ
π’(π) , So π₯ π = ππ₯1(π)
π1 π§ =
1
1βππ§β1 with ROC |z| > a
By using differentiation in z domain property z transform of ππ₯(π) is
βπ§
ππ π§
ππ§
so in this case π§ π₯ π = βπ§
π
ππ§
(
1
1βππ§β1)
Using differentiation formula
π
ππ§
π’
π£
=
π’.ππ£βπ£.ππ’
π£2
We get X π§ =
ππ§β1
(1βππ§β1)2 with ROC |z| > a
If we consider a=1 we get a ramp signal π₯ π = ππ’(π)
So its z transform is X π§ =
π§β1
(1βπ§β1)2 with ROC |z| > 1
69. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Properties of z Transform
The z transform is a very powerful tool and this is exhibited by itβs powerful
set of properties.
Note : when we combine several z transforms, the ROC of the overall
transform is at least the intersection of their ROCs.
Properties:
1. Linearity
2. Time Shifting
3. Scaling in z domain
4. Time Reversal
5. Differentiation in z domain
6. Convolution of two sequences
70. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Convolution Property
6) Convolution of Two Sequences Property
If π₯1 π π§ π1 π§ and If π₯2 π π§ π2 π§
Then for π₯ π = π₯1 π β π₯2 π π§ π π§ = π1 π§ . π2 π§
ROC of X(z) is at least an intersection of ROCs for π1 π§ and π2 π§ .
Proof : Convolution of π₯1 π πππ π₯2 π is defined as
π¦ π =
π=ββ
β
π₯1 π . π₯2(π β π)
Z transform of x(n) is π π§ = π=ββ
β
π₯ π . π§βπ
π π§ =
π=ββ
β
π₯1 π .
π=ββ
β
π₯2(π β π) . π§βπ
71. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Convolution Property
π π§ =
π=ββ
β
π₯1 π .
π=ββ
β
π₯2(π β π) . π§βπ
Interchanging the order of summation and using the property of shifting
in time domain we have z transform of x(n-k) as π§βπ
π π§ . So we can
write above equation as
π π§ =
π=ββ
β
π₯1 π . π§βππ2(π§)
π π§ = π1 π§ π2(π§)
72. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Convolution Property
Compute convolution of signals π₯1(π)={1, 2, 1} and π₯1(π)={1 : 0 β€ n β€5
{ 0 : Elsewhere
Solution: π₯1(π)={1, 2, 1} and π₯1(π)={1, 1, 1, 1, 1, 1}
Z transform is given by π π§ = π=ββ
β
π₯ π . π§βπ
So π1 π§ = 1 + 2π§β1
+ π§β2
and
π2 π§ = 1 + π§β1 + π§β2 + π§β3 + π§β4 + π§β5
Using convolution property
π₯ π = π₯1 π β π₯2 π π‘βππ π π§ = π1 π§ . π2 π§
π π§ =(1 + 2π§β1 + π§β2).(1 + π§β1 + π§β2 + π§β3 + π§β4 + π§β5)
π π§ = (1 + 3π§β1
+ 4π§β2
+ 4π§β3
+ 4π§β4
+ 4π§β5
+ 3π§β6
+ π§β7
).
The convolved signal can be obtained by taking inverse z transform as
x(n)={1, 3, 4, 4, 4, 4, 3, 1}
73. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Convolution Property
Compute convolution of signals π₯1(π)={1, -2, 1} and π₯1(π)={1 : 0 β€ n β€5
{ 0 : Elsewhere
Solution: π₯1(π)={1, -2, 1} and π₯2(π)={1, 1, 1, 1, 1, 1}
Z transform is given by π π§ = π=ββ
β
π₯ π . π§βπ
So π1 π§ = 1 β 2π§β1
+ π§β2
and
π2 π§ = 1 + π§β1 + π§β2 + π§β3 + π§β4 + π§β5
Using convolution property
π₯ π = π₯1 π β π₯2 π π‘βππ π π§ = π1 π§ . π2 π§
π π§ =(1 β 2π§β1 + π§β2).(1 + π§β1 + π§β2 + π§β3 + π§β4 + π§β5)
π π§ = (1 β π§β1
β π§β6
+ π§β7
).
The convolved signal can be obtained by taking inverse z transform as
x(n)={1, -1, 0, 0, 0, 0, -1, 1}
74. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Summary of z Transform Pairs
75. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Forward z transform equation is
z x n = X z =
π=ββ
β
π₯ π . π§βπ
This is used for frequency analysis. When we need the signal back in time
domain we need to take Inverse z Transform.
π₯ π = π§β1
π π§ .
By definition of Inverse z Transform
π₯ π‘ =
1
2π π
π§πβ1π(π§)ππ§
To find Inverse z Transform there are three methods :
1) Power Series Method
2) Partial Fraction Method
3) Residues of Contour Integral method
76. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
1) Power Series Method :
This method requires division of polynomials in z.
In this we represent π π§ =
π(π§)
π(π§)
,
By directly performing this division we obtain.
π π§ = π0 + π1π§β1
+ π2π§β2
+ π3π§β3
+ π4π§β4
+ β― .
This form is very suitable for identifying fixed duration signals and
infinite signals by comparing it with definition of z transform
π π =
π=ββ
β
π₯ π . π§βπ
π π§ = π₯(0) + π₯(1)π§β1 + π₯(2)π§β2 + π₯(3)π§β3 + π₯(4)π§β4 for causal signal
So by comparing above equation with z transform definition we can
directly find out the time domain sequence.
77. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Ex: Determine Inverse Z Transform (IZT) of
π π§ =
1
1βππ§β1 with ROC |z| > a
Solution: Given is π π§ =
1
1βππ§β1
Simplifying given equation π π§ =
π§
π§βπ
So π π§ = 1 + ππ§β1 + π2π§β2 + π3π§β3+. .
For given ROC |z| > a means ROC is
Exterior of the circle with radius a
and the sequence is causal.
This sequence can be written as
π π§ = π=0
β
(ππ§β1)βπ
78. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Ex: Determine Inverse Z Transform (IZT) of
π π§ =
1
1βππ§β1 with ROC |z| > a
Solution: Given is π π§ =
1
1βππ§β1
Simplifying given equation π π§ =
π§
π§βπ
So π π§ = 1 + ππ§β1 + π2π§β2 + π3π§β3+. .
π π§ = π=0
β
(ππ§β1
)βπ
π π§ = π=0
β
πππ§βπ
So this is z transform of signal π₯ π = πππ’(π) for n β₯ 0 or
as usual, for all n.
79. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Ex: Determine Inverse Z Transform (IZT) of
π π§ =
1
1βππ§β1 with ROC |z| < a
Solution: Given is π π§ =
1
1βππ§β1
Simplifying given equation π π§ =
π§
π§βπ
If ROC is given as the |z| < a
Then write π π§ =
π§
βπ+π§
and perform
Division
So π π§ = βπβ1
π§1
β πβ2
π§2
+ πβ3
π§3
+. .
For given ROC |z| < a means ROC is
interior of the circle with radius a
and the sequence is anticausal.
This sequence can be written as
π π§ = π=0
β
(πβ1
π§)βπ
80. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Ex: Determine Inverse Z Transform (IZT) of
π π§ =
1
1βππ§β1 with ROC |z| < a
Solution: Given is π π§ =
1
1βππ§β1
Simplifying given equation π π§ =
π§
βπ+π
So π π§ = π π§ = βπβ1π§1 β πβ2π§2 + πβ3π§3+. .
π π§ = β
π=1
β
π1π§β1 βπ = β
π=1
β
πβππ§π
π π§ = π=β1
ββ
πππ§βπ
So this is z transform of signal π₯ π = βππ
π’(βπ β 1)
81. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Ex: Determine Inverse Z Transform (IZT) of
π π§ =
1
1β2π§β1+π§β2 with
1) ROC |z| > 1
2) ROC |z| < 0.75
Solution: Given is π π§ =
1
1β2π§β1+π§β2
For Case 1) Perform division as π π§ =
1
1β2π§β1+π§β2
For Case 2) Perform division as π π§ =
1
π§β2β2π§β1+1
82. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Forward z transform equation is
z x n = X z =
π=ββ
β
π₯ π . π§βπ
This is used for frequency analysis. When we need the signal back in time
domain we need to take Inverse z Transform.
π₯ π = π§β1(π π§ )
To find Inverse z Transform there are three methods :
1) Power Series Method
2) Partial Fraction Method
3) Residues of Contour Integral method
83. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
2) Partial Fraction Method:
Basic of this method is if the pole zero form of the X(z) is available only
then this method is useful. If pole zero form of the X(z) is available then
by partial fraction method we get the equation in the form of terms
π§
π§βπ
for which we know the x(n).
For this we must know the equivalent pairs
Partial Fraction Term Signal Converges absolutely if | z | > a
π§
π§βπ
ππ
n β₯ 0
π§2
(π§βπ)2 (π + 1) ππ n β₯ 0
π§3
(π§βπ)3
1
2
(π + 1)(π + 2) ππ
n β₯ 0
84. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
2) Partial Fraction Method:
Partial Fraction Term Signal Converges absolutely if | z | < a
π§
π§βπ
βππ n < 0
π§2
(π§βπ)2 β(π + 1) ππ
n < 0
π§3
(π§βπ)3 β
1
2
(π + 1)(π + 2) ππ n < 0
85. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
EX : 2) Partial Fraction Method:
Partial Fraction Term Signal Converges absolutely if | z | < a
π§
π§βπ
βππ n < 0
π§2
(π§βπ)2 β(π + 1) ππ
n < 0
π§3
(π§βπ)3 β
1
2
(π + 1)(π + 2) ππ n < 0
General form of the X(z) after partial fraction is
π π§ = πΆ0 +
πΆ1π§
π§ β π1
+
πΆ2π§
π§ β π2
+
πΆ3π§
π§ β π3
86. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Ex: Determine IZT using partial fraction method for given function
π π§ =
3π§
2π§2β5π§+2
ROC | z | >1
Solution : For the given Z T equation π π§ =
3π§
2π§2β5π§+2
Performing the Partial fraction
π π§ =
3π§
(π§β2)(2π§β1)
π1=2 and π2 =
1
2
General form of the X(z) after partial fraction is
π π§ = πΆ0 +
πΆ1π§
π§βπ1
+
πΆ2π§
π§βπ2
πΆ0=π π§ |π§=0 = 0
π π§ =
πΆ1π§
π§βπ1
+
πΆ2π§
π§βπ2
88. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
π π§ =
3π§
(π§β2)(2π§β1)
π π§ =
π§
π§β2
β
2π§
2(π§β
1
2
)
π π§ =
π§
π§ β 2
β
π§
π§ β
1
2
=
1
1 β 2π§β1
β
1
1 β
1
2
π§β1
So by comparing with the standard form we have the time domain
equation as
x π‘ = 2ππ’ π β
1
2
π
π’ π
x π‘ = (2π β
1
2
π
)π’ π
89. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Forward z transform equation is
z x n = X z =
π=ββ
β
π₯ π . π§βπ
This is used for frequency analysis. When we need the signal back in time
domain we need to take Inverse z Transform.
π₯ π = π§β1(π π§ )
To find Inverse z Transform there are three methods :
1) Power Series Method
2) Partial Fraction Method
3) Residues of Contour Integral method
90. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
3)Residues of Contour Integral method
This method actually use the definition of the inverse z transform
This is most complicated method out of these three methods for IZT
By definition of Inverse z Transform
π₯ π‘ =
1
2π π
π§πβ1π(π§)ππ§
Direct evaluation of this contour integral is generally difficult, so
residues theorem is used.
According to this theorem we find coefficients of given X(z) at poles
This gives us x(n)
π π§=π =
ππβ1
ππ§πβ1
(
π§ β π π
π β 1 !
πΊ(π§))|π§=1
91. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
π π§=π =
ππβ1
ππ§πβ1
(
π§ β π π
π β 1 !
πΊ(π§))|π§=1
Ex: Determine Inverse z Transform of π π§ =
1
π§β1 (π§β2)
Solution : By using definition find G(z)
πΊ(π§) = π§πβ1π(π§)
πΊ(π§) =
π§πβ1
π§β1 (π§β2)
In this case if n=0 then π§β1
in the numerator will become simple pole
πΊ(π§) =
1
π§ π§β1 (π§β2)
for |z| >1
92. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Case : n=0
πΊ(π§) =
1
π§ π§β1 (π§β2)
Using Residue Theorem
π₯ 0 = πΊπ π§=0 + πΊπ π§=1 + πΊπ π§=2
πΊπ π§=0 = π§ β 0 πΊ(π§) for pole at z=0
πΊπ π§=0 = π§πΊ π§ =
π§
π§ π§β1 (π§β2)
=
1
π§β1 (π§β2)
|π§=0
πΊπ π§=0 =
1
π§ β 1 (π§ β 2)
|π§=0 =
1
2
πΊπ π§=1 = π§ β 1 πΊ(π§) for pole at z = 1
πΊπ π§=1 = π§ β 1 πΊ π§ =
π§ β 1
π§ π§ β 1 π§ β 2
=
1
π§ π§ β 2
|π§=1 =
1
(1)(β1)
= β1
93. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Case I: n=0
πΊπ π§=2 = π§ β 2 πΊ(π§) for pole at z=2
πΊπ π§=2 = π§ β 2 πΊ π§ =
π§ β 2
π§ π§ β 1 π§ β 2
=
1
π§ π§ β 1
|π§=2 =
1
(2)(1)
=
1
2
π₯ 0 = πΊπ π§=0 + πΊπ π§=1 + πΊπ π§=2 = 1/2 β 1 + 1/2 =0
Case II :n > 0
π₯ 0 = πΊπ π§=1 + πΊπ π§=2
Here πΊ π§ =
π§πβ1
π§β1 π§β2
πΊπ π§=1 = π§ β 1 πΊ(π§) for pole at z = 1
πΊπ π§=1 = π§ β 1 πΊ π§ =
π§ β 1 π§πβ1
π§ β 1 π§ β 2
=
π§πβ1
π§ β 2
|π§=1 =
(1)πβ1
(β1)
= β1
94. 7 IT 01 Digital Signal Processing (Winter 2021) L25
Inverse z Transform
Case I: n >0
πΊπ π§=2 = π§ β 2 πΊ π§ =
π§ β 2 π§πβ1
π§ β 1 π§ β 2
=
π§πβ1
π§ β 1
|π§=2 =
(2)πβ1
(1)
= β1
πΊπ π§=1 = π§ β 1 πΊ(π§) for pole at z = 1
π₯ π = β1 + 2 πβ1
π₯ π = β(1 + 2 πβ1)
So complete signal is
0 for n=0
x(n)=
-(1-(2)πβ1
) for n >0
95. THANK YOU !
7 IT 01 Digital Signal Processing (Winter 2021) L25