# Frequency-Domain Analysis: the Discrete Fourier Series and the Fourier Transform

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1 Frequency-Domain Analysis: the Discrete Fourier Series and the Fourier Transform John Chiverton School of Information Technology Mae Fah Luang University 1st Semester 2009/ 2552

2 Outline Overview Lecture Contents Introduction to Frequency-Domain Analysis Discrete Fourier Series Spectra of Periodic Digital Signals Magnitude and Phase of Line Spectra Other Types of Signals The Fourier Transform Aperiodic Digital Sequences

3 Lecture Contents Overview Lecture Contents Introduction to Frequency-Domain Analysis Discrete Fourier Series Spectra of Periodic Digital Signals Magnitude and Phase of Line Spectra Other Types of Signals The Fourier Transform Aperiodic Digital Sequences

4 Outline Overview Lecture Contents Introduction to Frequency-Domain Analysis Discrete Fourier Series Spectra of Periodic Digital Signals Magnitude and Phase of Line Spectra Other Types of Signals The Fourier Transform Aperiodic Digital Sequences

5 Frequency-Domain Analysis Point 1: Sinusoidal and exponential signals occur everywhere. Most signals can be decomposed into component frequencies; Response of a LTI processor to each frequency component can only alter the amplitude and phase, not the frequency; The output can then be found with the Principle of Superposition. Point 2: If an input signal has a frequency spectrum and the LTI processor has a frequency response then the output signal spectrum is found by multiplication; Simpler than time-domain convolution. Point 3: Frequency response is a typical requirement for DSP algorithms Such as low-pass, bandstop or bandpass filters.

6 Continuous-time Fourier Analysis A signal can be decomposed into sinusoidal components; Even functions are composed of only cosine functions; Odd functions are composed of only sine functions; A finite number of frequency components can be used to approximate a signal; Frequency components of a periodic signal are harmonically related with discrete spectral lines, line spectrum, described by Fourier series; Fourier series can be expressed in exponential form; Aperiodic (non-repetitive) signals are decomposed into non-harmonically related sinusoids. Resulting spectrum is continuous, described by Fourier Transform; The inverse Fourier Transform reverses the process; The frequency response of an LTI system specifies how each sinusoidal or exponential component is modified in amplitude and phase; Multiplication of the frequency response along with the input signal spectrum gives the output signal spectrum.

7 Discrete-time Fourier Analysis Parallel set of Fourier techniques applicable to digital signals Two representations: A discrete-time Fourier Series, applicable to periodic digital signals A discrete-time Fourier Transform, applicable to aperiodic digital signals and LTI processors

8 Outline Overview Lecture Contents Introduction to Frequency-Domain Analysis Discrete Fourier Series Spectra of Periodic Digital Signals Magnitude and Phase of Line Spectra Other Types of Signals The Fourier Transform Aperiodic Digital Sequences

9 Spectra of Periodic Digital Signals Periodic digital signal x[n] can be represented by Fourier Series Line spectrum coefficients can be found using the analysis equation: a[k] = 1 N N 1 n=0 ( ) j2πkn x[n] exp N where a[k] is the k spectral component or harmonic and N is the number of sample values in each period of the signal. Regeneration of the original signal x[n] can be found using the synthesis equation: x[n] = N 1 k=0 ( j2πkn a[k] exp N ).

10 Finding Line Spectra with a computer Remember Euler s identity: real part imaginary part ( ) { (}} ){{ (}} ){ j2πkn 2πkn 2πkn exp = cos j sin N N N }{{}}{{} Re( ) Im( ) ( ( j2πkn Re exp ( Im exp )) ( ) 2πkn = cos N N ( )) ( ) j2πkn 2πkn = j sin N N The real Re(a[k]) and imaginary Im(a[k]) components can be calculated individually

11 Finding Line Spectra with a computer a[k] = 1 N 1 X j2πkn x[n] exp N N n=0 Two loops: A loop over n A loop over k Pseudo-code: 1. Given x[n] and N: «= 1 N 1 X N n=0 x[n] cos 2. Create a[k] size N and set all elements to zero a[k] = 0 3. For k=0 to N-1 % outer loop «««2πkn 2πkn j sin N N 3.1 For n=0 to N-1 % inner loop Re(a[k]) = a[k] + x[n] cos(2πkn/n) % real part Im(a[k]) = a[k] x[n] sin(2πkn/n) % imaginary part 3.2 End For 3.3 Let Re(a[k]) = Re(a[k])/N and Let Im(a[k]) = Im(a[k])/N 4. End For

12 Finding Line Spectra Simple Example - manually Let N = 2 and x[0] = 7, x[1] = 1. Find a[k]. To start find the real part of a[k]... To start Let n = 0 and k = 0, then Re(a[0]) = Re(a[0]) + x[0] cos(2πkn/2) = cos(2π0 0/2) = 7 Increment n, so n = 1, then Re(a[0]) = Re(a[0]) + x[1] cos(2πkn/2) = cos(2π0 1/2) = 8 Divide by N: Re(a[0]) = Re(a[0])/N = 4 If we increment n any more then it will be larger than N 1 so Let n = 0 and increment k so that k = 1, then Re(a[1]) = Re(a[1]) + x[0] cos(2πkn/2) = cos(2π1 0/2) = 7 Increment n, so n = 1, then Re(a[1]) = Re(a[1]) + x[1] cos(2πkn/2) = cos(2π1 1/2) = 6 Divide by N: Re(a[0]) = Re(a[0])/N = 3 So the real part of a[k] is given by Re(a[0]) = 4 and Re(a[1]) = 3.

13 Finding Line Spectra Simple Example - manually Finding the imaginary part of a[k]... To start Let n = 0 and k = 0, then Im(a[0]) = Im(a[0]) x[0] sin(2πkn/2) = sin(2π0 0/2) = 0 Increment n, so n = 1, then Im(a[0]) = Im(a[0]) x[1] sin(2πkn/2) = sin(2π0 1/2) = 0 Divide by N: Im(a[0]) = Im(a[0])/N = 0 If we increment n any more then it will be larger than N 1 so Let n = 0 and increment k so that k = 1, then Im(a[1]) = Im(a[1]) x[0] sin(2πkn/2) = sin(2π1 0/2) = 0 Increment n, so n = 1, then Im(a[1]) = Im(a[1]) x[1] sin(2πkn/2) = sin(2π1 1/2) = 0 Divide by N: Im(a[0]) = Im(a[0])/N = 0 So the imaginary part of a[k] is given by Im(a[0]) = 0 and Im(a[1]) = 0.

14 Finding Line Spectra Another Example - but by computer 0 This time let N = 7 so that x = = ( ) T or C A x[0] = +2, x[1] = +1,..., x[6] = +1. A plot of this periodic signal is given by

15 Finding Line Spectra Another Example - but by computer 0 This time let N = 7 so that x = = ( ) T or C A x[0] = +2, x[1] = +1,..., x[6] = +1. A plot of this periodic signal is given by

16 Finding Line Spectra Another Example - but by computer The line spectra in this case are found by a computer program using the algorithm described earlier. Notice (for the real coefficients) the mirror image, where a[1] = a[6] and a[2] = a[5] etc.

17 Finding Line Spectra Another Example - but by computer The line spectra in this case are found by a computer program using the algorithm described earlier. Notice (for the imaginary coefficients) a similar effect, except for a change of sign, e.g. a[1] = a[6].

18 Finding Line Spectra Another Example - but by computer The line spectra in this case are found by a computer program using the algorithm described earlier. The line spectra are also periodic. The line spectra have the same periodicity as the original signal.

19 Finding Line Spectra Another Example - but by computer The line spectra in this case are found by a computer program using the algorithm described earlier. The line spectra are also periodic. The line spectra have the same periodicity as the original signal.

20 Magnitude and Phase of Line Spectra The real and imaginary components are often quoted in terms of magnitude and phase, or more commonly magnitude only. The magnitude can be calculated with: Mag(a[k]) = p Re(a[k]) 2 + Im(a[k]) 2 and the phase with: «Im(a[k]) φ(a[k]) = tan 1. Re(a[k]) The magnitude indicates the relative strength of the signal at different frequencies; The phase indicates the phase angle of the signal at different frequencies; The strength of the signal at different frequencies is often the more important information for a description of a system or a signal.

21 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) The real and imaginary components contain separate information about the frequency content of the signal. The sine function is not present in the real part. Real Component Imaginary Component

22 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) The magnitude provides a convenient overview of the frequency content of the signal. Magnitude Phase

23 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) + cos(2πn/32) The cosine part of the signal is present in the real part. Real Component Imaginary Component

24 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) + cos(2πn/32) The cosine frequency is not present in the phase as it has zero phase. The sine part is present as it has ±π/2 phase. Magnitude Phase

25 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) + cos(2πn/32) +0.1 sin(2πn/4) Real Component Imaginary Component

26 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) + cos(2πn/32) +0.1 sin(2πn/4) Magnitude Phase

27 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) + cos(2πn/32) +0.1 sin(2πn/4) cos(2πn/16) Real Component Imaginary Component

28 Magnitude and Phase of Line Spectra Example Original Signal x[n] = sin(2πn/64) + cos(2πn/32) +0.1 sin(2πn/4) cos(2πn/16) Magnitude Phase

29 Magnitude and Phase of Signals with Discontinuities Original Signal x[n] = sin(2πn/26) This signal is not periodic as the sine function is analyzed over 2 2 periods. The end 5 of the signal does not join up with the beginning, resulting in a discontinuity. The discontinuity has many frequency components with different phases. Magnitude Phase

30 Magnitude and Phase of Impulse Function Original Signal x[n] = δ[0] The line spectra of a (periodic) impulse function is composed of all frequencies This illustrates the usefulness of an impulse function in characterizing a system s frequency response Zero phase because the function is even, i.e. x[n] = x[ n], frequency response composed cosine functions only. Magnitude Phase

31 Magnitude and Phase of Impulse Function Original Signal x[n] = δ[1] The line spectra of a (periodic) impulse function is composed of all frequencies This illustrates the usefulness of an impulse function in characterizing a system s frequency response Phase components present when odd function, i.e. x[n] = x[ n], composed of sine functions only. Magnitude Phase

32 Useful Properties of Discrete Fourier Series Parseval s theorem Equates the total power of a signal in the time and frequency domains: Example Impulse function, δ[0] = 1 N 1 1 X (x[n]) 2 = N n=0 N 1 X (Mag(a[k])) 2 k=0 and which are equal. N 1 1 X (x[n]) 2 = 1 N N n=0 N 1 X (Mag(a[k])) 2 = N k=0 «2 1 = 1 N N

33 Other Example Useful Properties of Discrete Fourier Series x[n] a[k] symbolizes a[k] is the discrete Fourier Series of x[n]. Linearity: If x 1 [n] a 1 [k] and x 2 [n] a 2 [k] then w 1 x 1 [n] + w 2 x 2 [n] w 1 a 1 [k] + w 2 a 2 [k] Time-shifting (invariance): If x[n] a[k] then x[n n 0 ] a[k] exp( j2πkn 0 /N), i.e. The shift is just a phase shift and does not affect the magnitude.

34 Outline Overview Lecture Contents Introduction to Frequency-Domain Analysis Discrete Fourier Series Spectra of Periodic Digital Signals Magnitude and Phase of Line Spectra Other Types of Signals The Fourier Transform Aperiodic Digital Sequences

35 Aperiodic Digital Sequences Most signals do not endlessly repeat (i.e. not periodic); Most signals are therefore known as aperiodic. Different analysis and synthesis equations are necessary for aperiodic sequences, known as the Fourier Transform for aperiodic digital sequences X(Ω) = F(x[n]) = n= x[n] exp( jωn) and the inverse Fourier Transform for aperiodic digital sequences x[n] = F 1 (X(Ω)) = 1 X(Ω) exp(jωn)dω. 2π Note: X(Ω) is a continuous function. It is also periodic which is a result of the ambiguities in discretely sampled signals. 2π

36 Fourier Transform for Aperiodic Digital Sequences Comparing the Fourier Transform: X(Ω) = n= with the Fourier Series analysis equations: a[k] = 1 N N 1 n=0 x[n] exp( jωn), (1) ( j2πkn x[n] exp N ). (2) We can see that Ω = 2πk N. n has also been taken to ± and because of this the Fourier Transform is no longer divided by N (otherwise X(Ω) would be zero) so that X(Ω) can in some way be equated with N a[k].

37 Fourier Transform Boxcar Example The Fourier Transform of the impulse function δ[0]: x[n] = δ[0] X(Ω) = n= δ[0] exp( jωn) = exp( jω 0) = 1. In other words, the Fourier Transform of an impulse function consists of all frequencies. Similar to the Fourier Series representation of a periodic impulse function, calculated earlier.

38 Fourier Transform Example j 0.2 if 2 n 2, If x[n] = 0 otherwise. X(Ω) = 0.2 (2 cos(ω2) + 2 cos(ω1) + 1). F Then X 2X X(Ω) = x[n] exp( jωn) = 0.2 exp( jωn) n= n= 2 =0.2 (exp(jω2) + exp(jω1) + exp( jω0) + exp( jω1) + exp( jω2)) =0.2 (cos(ω2) + j sin(ω2) + cos(ω1) + j sin(ω1) cos(ω1) j sin(ω1) + cos(ω2) j sin(ω2)) =0.2 (2 cos(ω2) + 2 cos(ω1) + 1).

39 Periodicity of Fourier Transform Also note that the Fourier Transform of an aperiodic signal is periodic. The periodicity is every 2π periods, a result of the sampling in the digitisation process.

40 Frequency Response of LTI Systems An LTI system has an input x[n] and an output y[n]: Input, x[n] Linear Time Invariant System Output, y[n] Recall (see lecture 2) that an LTI system has an impulse response, h[n]: Input, x[n] h[n], Linear Time Invariant System Output, y[n] which describes the response of the system when an impulse function is given as the input. The impulse response is useful as it can be used to calculate the output signal for a given input signal: y[n] = x[n] h[n] where is convolution NOT multiplication.

41 Frequency Response of LTI Systems An LTI system can also be described in the frequency domain: Input, X( ) Ω H( ), Linear Time Invariant System Ω Output, Y( ) Ω where The input frequency domain signal is X(Ω) = F(x[n]), The output frequency domain signal is Y (Ω) = F(y[n]) The LTI system is described by H(Ω) = F(h[n]) which is known as the frequency response of the system and is the Fourier Transform of the impulse response. In the frequency domain, the output can be calculated more easily: Y (Ω) = X(Ω) H(Ω), where multiplication IS used here. In other words, convolution is performed by multiplication in the frequency domain.

42 Frequency Response of LTI Systems The frequency domain convolution (multiplication) equation: can be re-arranged so that: Y (Ω) = X(Ω) H(Ω), H(Ω) = Y (Ω) X(Ω) so if we want to find the frequency response of a system then we can find it via this equation or via the Fourier transform of the time domain representation h[n].

43 Frequency Response of LTI Systems Recall the general form of LTI difference equations (see lecture 2): NX a[m]y[n m] = m=0 MX b[m]x[n m]. m=0 Using the linearity and time-shifting properties of Fourier transforms we can convert it to an expression using frequency domain terms: NX a[m] exp( jkω)y (Ω) = m=0 MX b[m] exp( jkω)x(ω). m=0 Therefore the frequency response of a system can also be described by H(Ω) = MP b[m] exp( jmω) NP. a[m] exp( jmω) m=0 m=0 This equation can be used to directly find the frequency response of a system even if only the coefficients a[m] and b[m] are known.

44 Frequency Response Example Q. A moving average filter has y[n] = 1 (x[n] + x[n 1] + x[n 2]). Find the 3 frequency response of this filter. A. We can find the frequency response by using the coefficients: There is only 1 output coefficient, a[0] = 1. There are 3 input coefficients, b[0] = b[1] = b[2] = 1 3 Therefore 1 MP exp( jmω) 3 m=0 H(Ω) = = 1 (1 + exp( jω) + exp( j2ω)) exp( j0ω) 3 = 1 (1 + cos(ω) j sin(ω) + cos(2ω) j sin(2ω)) 3 = 1 (1 + cos(ω) + cos(2ω) j(sin(ω) + sin(2ω))) 3

45 Frequency Response Example cont d. Magnitude: Mag(H(Ω)) = r 1 3 ((1 + cos(ω) + cos(2ω))2 + (sin(ω) + sin(2ω)) 2 ) Phase: «φ(h(ω)) = tan 1 (sin(ω) + sin(2ω)) (1 + cos(ω) + cos(2ω)) The magnitude can be simplified using: 2 sin(ω) sin(2ω) = cos(ω) cos(3ω) 2 cos(ω) cos(2ω) = cos(ω) + cos(3ω) sin 2 (Ω) = 1 cos(2ω) 2 Resulting in: sin 2 (2Ω) = 1 cos(4ω) 2 cos 2 (Ω) = 1+cos(2Ω) 2 cos 2 (2Ω) = 1+cos(4Ω) 2 q Mag(H(Ω)) = 1 (3 + 2(2 cos(ω) + cos(2ω))) 3

46 Frequency Response Example cont d. Moving Average Filter (k=3) Frequency Response Magnitude Phase Mag(H(Ω)) q = (3 + 2(2 cos(ω) + cos(2ω))) 1 3 φ(h(ω)) = tan 1 (sin(ω)+sin(2ω)) (1+cos(Ω)+cos(2Ω))

47 Lecture Summary This lecture has covered... Introduction to frequency domain analysis Discrete Fourier Series Spectra of Periodic Digital Signals Magnitude and Phase of Line Spectra The Fourier Transform for aperiodic digital sequences

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