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# Fourier Analysis - PowerPoint PPT Presentation

Fourier Analysis. PSCI 702 November 2, 2005. Even and Odd Functions. Even Functions. Odd Functions. Even and Odd Functions. Kronecker’s Rule. Periodic Functions. Trigonometric System. Trigonometric System of period 2a. Fourier Series.

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### Fourier Analysis

PSCI 702

November 2, 2005

• The basic Idea behind Fourier series is to express a periodic function in terms of trigonometric system using the orthogonality relations.

source: f(x) =|x|, -8 <= x <= 8

n= 5

n= 1

n= 3

y

Successive approximations of f(x)

f(x)

x

• Let the function f be defined on (0,a). The even extension feand odd extension fo of f are the following functions

1 + 2 + 3

1 + 2

1

1

0

-

2

3

1 + 2 + 3 + 4 + 5

1 + 2 + 3 + 4

Square wave: Y = 0 for - < x < 0 and Y=1 for 0 < x < 

Y = 1/2 + 2/pi( sinx + sin3x/3 + sin5x/5 + sin7x/7 … + sin(2m+1)x/(2m+1) + …)

1 2 3 4 5

May do with sum of cosines too.

The inverse equations:

,

Using the formulas above and some properties of exponential function, the Fourier series can also be written as an expansion in terms of complex exponentials as:

,

Complex version of the Fourier expansion

• Let f is a piecewise smooth function defined over R. Since f may not be periodic, we define fL as the peridic extension of f over (-L,L). fL can be expressed as:

Linearity

Nth Derivative

Convolution

Translation: x-shift & -shift

Convolution illustrated

F(w)

f(t)

t

F

1

w

t

-t/2

0

t/2

-6p

-4p

-2p

2p

4p

6p

0

t

t

t

t

t

t

Fourier Transform Pairs

Fourier Transform Pairs

F(w)

f(t)

(p)

(p)

t

w

0

-w0

w0

0

F(w)

w

-6p

-4p

-2p

2p

4p

6p

t

t

t

t

t

t

0

Duality

• Forward/inverse transforms are similar

• Example: rect(t/t)  t sinc(wt / 2)

• Apply duality t sinc(t t/2)  2 p rect(-w/t)

• rect(·) is even t sinc(t t /2)  2 p rect(w/t)

f(t)

1

t

-t/2

0

t/2

• Suppose that the f is piecewise continous function then:

Sine wave

Delta function

Sinc function

Square wave

Exponential

Lorentzian

Gaussian

Gaussian

Find the Fourier transform of

Find the Fourier transform of

f(x) = Π(x /4) – Λ(x /2) + .5Λ(x)

Using the Fourier transforms of Π and Λ

and the linearity and scaling properties,

F(u) = 4sinc(4u) - 2sinc2(2u) + .5sinc2(u)

*

–1 -.5 0 .5 1

–2 1 0 1 2

Find the Fourier transform of

f(x) = Π(x /4) – 0.5((Π(x /3) * Π(x))

-

Using the Fourier transforms of Π and Λ

and the linearity and scaling and convolution properties ,

F(u) = 4sinc(4u) – 1.5sinc(3u)sinc(u)

Motivation: computer applications of the Fourier transform require that all of the definitions and properties of Fourier transforms be translated into analogous statements appropriate to functions represented by a discrete set of sampling points rather than by continuous functions.

Let f(x) be a function.Let {fk = f(xk)} be a set of N function values, k = 0, 1, …, N-1.Let be the separation of the equidistant sampling points.Assumption: N is even.

The discrete Fourier transform is:

The inverse discrete transform is:

Let’s examine more closely the formula of the discrete Fourier transform:

We know that (it’s called n-th root of unity), so the formula above can be rewritten as:

Say we want to perform a 8 point DFT on a discretized version of a continuous input signal

having frequency components 1KHz and 2KHz

Calculation of Ts : Suppose

Period of x(t) = 1/1Khz = 1/1000

8 samples per period => sample time (Ts) = 1/8000 sec

Or sample rate = 8000 samples/s

t = nTs so

n = 0,1,…,7

Therefore :

DC Component

And so on...

Where X(k) = (k*8Khz)/8

Actually evaluating we get the values:

X(0) = 0 + i 0 (dc)

X(2) = 1.414 + i1.414 (2Khz)

X(4) = 0 + i0 (4Khz)

X(6) = 1.414 – i 1.414 (6Khz)

X(1) = 0 – i 4 (1KHz)

X(3) = 0 + i 0 (3Khz)

X(5) = 0 + i 0 (5Khz)

X(7) = 0 + i 4 (7KHz)

• A direct calculation of N-point DFT requires (N-1)2 multiplications and N(N-1) addition.

• The FFT is begun by noting that W0.5N=-1 and splitting the DFT in to two sums

• Now we halve {fk} into two subsequences, according to whether k is even or odd