1. Chapter 4 The Fourier Series and Fourier Transform

2. Consider the CT signal defined by The frequencies `present in the signal’ are the frequency of the component sinusoids The signal x(t) is completely characterized by the set of frequencies, the set of amplitudes, and the set of phases Representation of Signals in Terms of Frequency Components

3. Consider the CT signal given by three frequency componentsThe signal has only three frequency components at 1,4, and 8 rad/sec, amplitudes and phases The shape of the signal x(t) depends on the relative magnitudes of the frequency components, specified in terms of the amplitudes Example: Sum of Sinusoids

4. Example: Sum of Sinusoids –Cont’d

6. Plot of the amplitudes of the sinusoids making up x(t) vs. Example: Amplitude Spectrum

7. Plot of the phases of the sinusoids making up x(t) vs. Example: Phase Spectrum

8. Euler formulaEuler formula: Thus whence Complex Exponential Form real part

9. And, recalling that where, we can also write This signal contains both positive and negative frequencies The negative frequencies stem from writing the cosine in terms of complex exponentials and have no physical meaning Complex Exponential Form – Cont’d

10. By defining it is also Complex Exponential Form – Cont’d complex exponential form complex exponential form of the signal x(t)

11. amplitude spectrumThe amplitude spectrum of x(t) is defined as the plot of the magnitudes versus phase spectrumThe phase spectrum of x(t) is defined as the plot of the angles versus line spectraThis results in line spectra which are defined for both positive and negative frequencies Notice: for Line Spectra

12. Example: Line Spectra

13. Let x(t) be a CT periodic signal with period T, i.e., rectangular pulse trainExample: the rectangular pulse train Fourier Series Representation of Periodic Signals

14. Then, x(t) can be expressed as fundamental frequency where is the fundamental frequency (rad/sec) of the signal and The Fourier Series is called the constant or dc component of x(t)

15. The frequencies present in x(t) are integer multiples of the fundamental frequency Notice that, if the dc term is added to and we set, the Fourier series is a special case of the above equation where all the frequencies are integer multiples of The Fourier Series – Cont’d

16. A periodic signal x(t), has a Fourier series if it satisfies the following conditions: absolutely integrable 1.x(t) is absolutely integrable over any period, namely finite number of maxima and minima 2.x(t) has only a finite number of maxima and minima over any period finite number of discontinuities 3.x(t) has only a finite number of discontinuities over any period Dirichlet Conditions

17. From figure, whence Clearly x(t) satisfies the Dirichlet conditions and thus has a Fourier series representation Example: The Rectangular Pulse Train

18. Example: The Rectangular Pulse Train – Cont’d

19. By using Euler’s formula, we can rewrite as trigonometric Fourier seriesThis expression is called the trigonometric Fourier series of x(t) Trigonometric Fourier Series dc component k-th harmonic

20. The expression can be rewritten as Example: Trigonometric Fourier Series of the Rectangular Pulse Train

21. Given an odd positive integer N, define the N-th partial sum of the previous series Fourier’s theoremAccording to Fourier’s theorem, it should be Gibbs Phenomenon

22. Gibbs Phenomenon – Cont’d

23. overshoot overshoot: about 9 % of the signal magnitude (present even if )

24. Let x(t) be a periodic signal with period T average powerThe average power P of the signal is defined as Expressing the signal as it is also Parseval’s Theorem

25. We have seen that periodic signals can be represented with the Fourier series aperiodic signalsCan aperiodic signals be analyzed in terms of frequency components? Yes, and the Fourier transform provides the tool for this analysis spectra of aperiodic signalsThe major difference w.r.t. the line spectra of periodic signals is that the spectra of aperiodic signals are defined for all real values of the frequency variable not just for a discrete set of values Fourier Transform

26. Frequency Content of the Rectangular Pulse

27. Since is periodic with period T, we can write Frequency Content of the Rectangular Pulse – Cont’d where

28. What happens to the frequency components of as ? For Frequency Content of the Rectangular Pulse – Cont’d

29. plots of vs. for

30. It can be easily shown that where Frequency Content of the Rectangular Pulse – Cont’d

31. The Fourier transform of the rectangular pulse x(t) is defined to be the limit of as, i.e., Fourier Transform of the Rectangular Pulse

32. The Fourier transform of the rectangular pulse x(t) can be expressed in terms of x(t) as follows: Fourier Transform of the Rectangular Pulse – Cont’d whence

33. Now, by definition and, since inverse Fourier transformThe inverse Fourier transform of is Fourier Transform of the Rectangular Pulse – Cont’d

34. Fourier transformGiven a signal x(t), its Fourier transform is defined as A signal x(t) is said to have a Fourier transform in the ordinary sense if the above integral converges The Fourier Transform in the General Case

35. The integral does converge if well-behaved 1.the signal x(t) is “well-behaved” absolutely integrable 2.and x(t) is absolutely integrable, namely, well behavedNote: well behaved means that the signal has a finite number of discontinuities, maxima, and minima within any finite time interval The Fourier Transform in the General Case – Cont’d

36. Consider the signal Clearly x(t) does not satisfy the first requirement since Fourier transform in the ordinary senseTherefore, the constant signal does not have a Fourier transform in the ordinary sense Fourier transform in a generalized senseLater on, we’ll see that it has however a Fourier transform in a generalized sense Example: The DC or Constant Signal

37. Consider the signal Its Fourier transform is given by Example: The Exponential Signal

38. If, does not exist If, and does not exist either in the ordinary sense If, it is Example: The Exponential Signal – Cont’d amplitude spectrum phase spectrum

39. Example: Amplitude and Phase Spectra of the Exponential Signal

40. Consider Since in general is a complex function, by using Euler’s formula Rectangular Form of the Fourier Transform

41. can be expressed in a polar form as where Polar Form of the Fourier Transform

42. If x(t) is real-valued, it is Moreover whence Fourier Transform of Real-Valued Signals Hermitian symmetry

43. Even signalEven signal: Odd signal:Odd signal: Fourier Transforms of Signals with Even or Odd Symmetry

44. Consider the even signal It is Example: Fourier Transform of the Rectangular Pulse

45. Example: Fourier Transform of the Rectangular Pulse – Cont’d

46. amplitude spectrum phase spectrum

47. bandlimited B bandwidth of the signalA signal x(t) is said to be bandlimited if its Fourier transform is zero for all where B is some positive number, called the bandwidth of the signal It turns out that any bandlimited signal must have an infinite duration in time, i.e., bandlimited signals cannot be time limited Bandlimited Signals

48. infinite bandwidthinfinite spectrumIf a signal x(t) is not bandlimited, it is said to have infinite bandwidth or an infinite spectrum Time-limited signals cannot be bandlimited and thus all time-limited signals have infinite bandwidth However, for any well-behaved signal x(t) it can be proven that whence it can be assumed that Bandlimited Signals – Cont’d B being a convenient large number

49. inverse Fourier transformGiven a signal x(t) with Fourier transform, x(t) can be recomputed from by applying the inverse Fourier transform given by Transform pairTransform pair Inverse Fourier Transform

50. Properties of the Fourier Transform Linearity:Linearity: Left or Right Shift in Time:Left or Right Shift in Time: Time Scaling:Time Scaling:

51. Properties of the Fourier Transform Time Reversal:Time Reversal: Multiplication by a Power of t:Multiplication by a Power of t: Multiplication by a Complex Exponential:Multiplication by a Complex Exponential:

52. Properties of the Fourier Transform Multiplication by a Sinusoid (Modulation):Multiplication by a Sinusoid (Modulation): Differentiation in the Time Domain:Differentiation in the Time Domain:

53. Properties of the Fourier Transform Integration in the Time Domain:Integration in the Time Domain: Convolution in the Time Domain:Convolution in the Time Domain: Multiplication in the Time Domain:Multiplication in the Time Domain:

54. Properties of the Fourier Transform Parseval’s Theorem:Parseval’s Theorem: Duality:Duality: if

55. Properties of the Fourier Transform - Summary

56. Example: Linearity

57. Example: Time Shift

58. Example: Time Scaling time compression frequency expansion time expansion frequency compression

59. Example: Multiplication in Time

60. Example: Multiplication in Time – Cont’d

61. Example: Multiplication by a Sinusoid sinusoidal burst

62. Example: Multiplication by a Sinusoid – Cont’d

63. Example: Integration in the Time Domain

64. Example: Integration in the Time Domain – Cont’d The Fourier transform of x(t) can be easily found to be Now, by using the integration property, it is

65. Example: Integration in the Time Domain – Cont’d

66. Fourier transform of Applying the duality property Generalized Fourier Transform generalized Fourier transform generalized Fourier transform of the constant signal

67. Generalized Fourier Transform of Sinusoidal Signals

68. Fourier Transform of Periodic Signals Let x(t) be a periodic signal with period T; as such, it can be represented with its Fourier transform Since, it is

69. Since using the integration property, it is Fourier Transform of the Unit-Step Function

70. Common Fourier Transform Pairs