Unit – 3

Fourier and Z-Transforms

The Fourier integral of f(x) is-

………. (1)

Which can be written as-

…………… (2)

Because is an even function of . Also since is an odd function of then we have-

……… (3)

Multiply (3) by ‘i’ and add to (2)-

We get-

Which is the complex form of the Fourier integral.

Example 1:

Using complex form, find the Fourier series of the function

f(x) = sinx =

Solution:

We calculate the coefficients

=

=

Hence the Fourier series of the function in complex form is

We can transform the series and write it in the real form by renaming as

n=2k-1,n=

=

Example 2:

Using complex form find the Fourier series of the function f(x) = x2, defined on te interval [-1,1]

Solution:

Here the half-period is L=1.Therefore, the co-efficient c0 is,

For n

Integrating by parts twice, we obtain

=

=

= .

= .

If f, then

Proof .We first claim that

Let By the multiplication formula we get

Since is a good kernel, n the first integral goes to f(0) as tends to 0.Since the

Second integral clearly converges to as our claim is proved .

In general , let F(y)=f(y+x) so that

Example:1

Find the fourier integral representation of the function

Solution:

The graph of the function is shown in the below figure satisfies the hypothesis of

Theorem -1 . Hence from Eqn,(5) and (6), we have

Substituting these coefficients in Eqn.(4) we obtain

This is the Fourier integral representation of the given function.

Example:2

Find the Fourier integral representation of the function

Solution:

The graph of the given function is shown in the below figure . Clearly, the given function f(x) is an even function. We represent f(x) by the fourier cosine integral . We obtain

And thus ,

The following notations are used to find the Fourier sine and cosine integrals.

If the function f(x) is even

B(w)= 0

If the function f(x) is odd

Example:1

Find the Fourier cosine integral of , where x>0, k>0 hence show that

Solution:

The Fourier cosine integral of f(x) is given by:

Example:2

Find the half –range sine series of the function

Solution: xx

Where

In mathematics, a Fourier transform (FT) is a mathematical transform that decomposes a function (often a function of time, or a signal) into its constituent frequencies, such as the expression of a musical chord in terms of the volumes and frequencies of its constituent notes. The term Fourier transform refers to both the frequency domain representation and the mathematical operation that associates the frequency domain representation to a function of time.

The Fourier transform of a function of time is a complex-valued function of frequency, whose magnitude (absolute value) represents the amount of that frequency present in the original function, and whose argument is the phase offset of the basic sinusoid in that frequency. The Fourier transform is not limited to functions of time, but the domain of the original function is commonly referred to as the time domain. There is also an inverse Fourier transform that mathematically synthesizes the original function from its frequency domain representation, as proven by the Fourier inversion theorem

The function-

Is called the Fourier transform of the function f(x).

The function f(x) given below is called inverse Fourier transform of F(s)-

Properties of Fourier transform-

Property-1: Linear property- If F(s) and G(s) are two Fourier transforms of f(x) and g(x) respectively, then-

Where a and b are the constants.

Property-2: Change of scale- If F(s) is a Fourier transform of f(x), then

Property-3: Shifting property- If F(s) is a Fourier transform of f(x), then

Property-4: Modulation- If F(s) is a Fourier transform of f(x), then

Example: Find the Fourier transform of-

Hence evaluate

Sol. As we know that the Fourier transform of f(x) will be-

So that-

For s = 0, we get- F(s) = 2

Hence by the inverse formula, we get-

Putting x = 0, we get

So-

Example: Find the Fourier transform of

Sol. As we know that the Fourier transform of f(x) will be-

So that-

Now put

So that-

Then-

The function is known as the Fourier sine transform of f(x) in 0<x<∞

And the function f(x) s called the inverse Fourier sine transform of .

And-

Then

The function is known as the Fourier cosine transform of f(x) in 0<x<∞

And the function f(x) s called the inverse Fourier cosine transform of .

Finite Fourier sine and cosine transforms-

The finite Fourier sine transform of f(x), in 0<x<c, is given by-

Where n is an integer.

And the function f(x) is called the inverse finite Fourier sine transform of which is defined by-

The finite Fourier cosine transform of f(x), in 0<x<c, is given by-

Where n is an integer.

And the function f(x) is called the inverse finite Fourier cosine transform of which is defined by-

Example-1: Find the Fourier sine transform of

Sol. Here x being positive in the interval (0, ∞)

Fourier sine transform of will be-

Example-2: Find the Fourier cosine transform of-

Sol. We know that the Fourier cosine transform of f(x)-

=

=

=

Example-3: Find the Fourier sine transform of

Sol. Let

Then the Fourier sine transform will be-

Now suppose,

Differentiate both sides with respect to x, we get-

……. (1)

On integrating (1), we get-

In mathematics and signal processing, the Z-transform converts a discrete-time signal, which is a sequence of real or complex numbers, into a complex frequency-domain representation. It can be considered as a discrete-time equivalent of the Laplace transform. This similarity is explored in the theory of time-scale calculus.

Analysis of continuous time LTI systems can be done using z-transforms. It is a powerful mathematical tool to convert differential equations into algebraic equations.

The bilateral (two sided) z-transform of a discrete time signal x(n) is given as

Z.T[x(n)] = X(Z) =

The unilateral (one sided) z-transform of a discrete time signal x(n) is given as

Z.T[x(n) = X(Z) =

Z-transform may exist for some signals for which Discrete Time Fourier Transform (DTFT) does not exist.

The Z-transform can be defined as either a one-sided or two-sided transform.

Bilateral Z-transform

The bilateral or two-sided Z-transform of a discrete-time signal x(n) is the formal power series X(z) defined as

X (z) = Z =

Where n is an integer and z is, in general, a complex number:

Where A is the magnitude of z,j is the imaginary unit, and is the complex argument (also referred to as angle or phase) in radians.

Unilateral Z-transform

Alternatively, in cases where x[n] is defined only for the single-sided or unilateral Z-transform is defined as

X (z) = Z =

In signal processing, this definition can be used to evaluate the Z-transform of the unit impulse response of a discrete-time causal system.

An important example of the unilateral Z-transform is the probability-generating function, where the component x[n] is the probability that a discrete random variable takes the value n, and the function X(z) is usually written as X(s) in terms of s = x-1. The properties of Z-transforms (below) have useful interpretations in the context of probability theory.

Z-transform properties:

Z-Transform has following properties:

1. Linearity Property

If x(n)X(Z)

And

y(n)Y(Z)

Then linearity property states that

2. Time Shifting Property

Then the time shifting property states that

Multiplication by Exponential Sequence Property

If x(n)

Then multiplication by an exponential sequency property is

3. Time Reversal Property

If x(n)

Then time reversal property states that

Differentiation in Z-Domain OR Multiplication by n Property

If

Then multiplication by n or differentiation in z-domain property states that,

4. Convolution Property

If

And

Then convolution property states that

5. Correlation Property

If

And

Then co-relation property states that

Region of Convergence (ROC) of Z-Transform

The range of variation of z for which z-transform converges is called region of convergence of z-transform.

Properties of ROC of Z-Transforms

- ROC of z-transform is indicated with circle in z-plane.
- ROC does not contain any poles.
- If x(n) is a finite duration causal sequence or right sided sequence, then the ROC is entire z-plane except at z = 0.
- If x(n) is a finite duration anti-causal sequence or left sided sequence, then the ROC is entire z-plane except at z = ∞.
- If x(n) is a infinite duration causal sequence, ROC is exterior of the circle with radius a. i.e. |z| > a.
- If x(n) is a infinite duration anti-causal sequence, ROC is interior of the circle with radius a. i.e. |z| < a.
- If x(n) is a finite duration two sided sequence, then the ROC is entire z-plane except at z = 0 & z = ∞.

Some standard formulae-

1.

2.

3.

4.

5.

6.

Example-1: Find Z-transform of the following functions-

(i)

(ii)

Sol.(i)

(ii)

Example-2: Find Z-transform of the following functions-

(i)

(ii)

Sol. (i) As we know that-

So that-

(ii) we know that-

So that-

Sequence | z-transformation |

1 | |

Example 1:

Find the z-transformation of the following left-sided sequence

Solution:

=

= 1-

=

If

Example 2:

Solution:

Long division method to obtain

2

Now x(z) can be written as,

X(z) = 2-

Working procedure to solve linear difference equation by ZT-

1. Take the ZT of both sides of the difference equations .

2. Transpose all terms without U(z) to the right

3. Divide by the coefficients of U(z)

4. Express this function in terms of the Z-transform of known function and take the invers ZT of both sides.

This will give as a function of n, is the required solution.

Example 1:

Solve the differential equation by z-transformation method.

Solution:

Given,

Let y(z) be the z-transform of

Taking z-transforms of both sides of eq(1) we get,

Ie.

Using the given condition ,it reduces to

(z+1)y(z) =

Ie.

Y(z) =

Or Y(Z) =

On taking inverse Z-transforms, we obtain

Example 2:

Solve using z-transforms

Solution:

Consider,

Taking z-transforms on both sides, we get

=

or

Now,

Using inverse z-transform we obtain,

Example-3:

Solve the following by using Z-transform

Sol. If then

And

Now taking the Z-transform of both sides, we get

z[

It becomes-

So that,

Now-

On inversion, we get-

Reference Books

1. Erwin Kreyszig, “Advanced Engineering Mathematics”, Wiley India,10th Edition.

2. M.D. Greenberg, “Advanced Engineering Mathematics”, Pearson Education, 2 nd Edition.

3. Peter. V and O‟Neil, “Advanced Engineering Mathematics”, Cengage Learning,7th Edition.

4. S.L. Ross, “Differential Equations”, Wiley India, 3rd Edition.

5. S. C. Chapra and R. P. Canale, “Numerical Methods for Engineers”, McGraw-Hill, 7th Edition.

6. J. W. Brown and R. V. Churchill, “Complex Variables and Applications”, McGraw-Hill Inc, 8th Edition.