Lecture 7 ELE 301: Signals and Systems


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1 Lecture 7 ELE 3: Signals and Systems Prof. Paul Cuff Princeton University Fall 22 Cuff (Lecture 7) ELE 3: Signals and Systems Fall 22 / 22 Introduction to Fourier Transforms Fourier transform as a limit of the Fourier series Inverse Fourier transform: The Fourier integral theorem Example: the rect and sinc functions Cosine and Sine Transforms Symmetry properties Periodic signals and δ functions Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
2 Fourier Series Suppose x(t) is not periodic. We can compute the Fourier series as if x was periodic with period T by using the values of x(t) on the interval t [ T /2, T /2). where f = /T. a k = T /2 x(t)e j2πkft dt, T T /2 x T (t) = a k e j2πkft, k= The two signals x and x T will match on the interval [ T /2, T /2) but x(t) will be periodic. What happens if we let T increase? Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Rect Example For example, assume x(t) = rect(t), and that we are computing the Fourier series over an interval T, f(t) = rect(t) /2 /2 t T The fundamental period for the Fourier series in T, and the fundamental frequency is f = /T. The Fourier series coefficients are where sinc(t) = sin(πt) πt. a k = T sinc (kf ) Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
3 The Sinc Function t Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Rect Example Continued Take a look at the Fourier series coefficients of the rect function (previous slide). We find them by simply evaluating T sinc(f ) at the points f = kf. T = ω =2π T =2 ω = π 8π 4π /2 4π 8π ω = n ω 8π 4π 4π 8π T =4 ω = π/2 ω = n ω 8π 4π /4 4π 8π ω = n ω More densely sampled, same sinc() envelope, decreased amplitude. Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
4 Fourier Transforms Given a continuous time signal x(t), define its Fourier transform as the function of a real f : X (f ) = x(t)e j2πft dt This is similar to the expression for the Fourier series coefficients. Note: Usually X (f ) is written as X (i2πf ) or X (iω). This corresponds to the Laplace transform notation which we encountered when discussing transfer functions H(s). Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 We can interpret this as the result of expanding x(t) as a Fourier series in an interval [ T /2, T /2), and then letting T. The Fourier series for x(t) in the interval [ T /2, T /2): where x T (t) = k= a k e j2πkft a k = T /2 x(t)e j2πkft dt. T T /2 Define the truncated Fourier transform: T 2 X T (f ) = x(t)e j2πft dt T 2 so that a k = T X T (kf ) = T X T ( ) k. T Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
5 The Fourier series is then x T (t) = T X T (kf )e j2πkft k= The limit of the truncated Fourier transform is X (f ) = lim T X T (f ) The Fourier series converges to a Riemann integral: x(t) = lim x T (t) T ( ) k = lim T T X T e j2π k T t T k= = X (f )e j2πft df. Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Continuoustime Fourier Transform Which yields the inversion formula for the Fourier transform, the Fourier integral theorem: X (f ) = x(t) = x(t)e j2πft dt, X (f )e j2πft df. Cuff (Lecture 7) ELE 3: Signals and Systems Fall 22 / 22
6 Comments: There are usually technical conditions which must be satisfied for the integrals to converge forms of smoothness or Dirichlet conditions. The intuition is that Fourier transforms can be viewed as a limit of Fourier series as the period grows to infinity, and the sum becomes an integral. X (f )ej2πft df is called the inverse Fourier transform of X (f ). Notice that it is identical to the Fourier transform except for the sign in the exponent of the complex exponential. If the inverse Fourier transform is integrated with respect to ω rather than f, then a scaling factor of /(2π) is needed. Cuff (Lecture 7) ELE 3: Signals and Systems Fall 22 / 22 Cosine and Sine Transforms Assume x(t) is a possibly complex signal. X (f ) = = = x(t)e j2πft dt x(t) (cos(2πft) j sin(2πft)) dt x(t) cos(ωt)dt j x(t) sin(ωt) dt. Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
7 Fourier Transform Notation For convenience, we will write the Fourier transform of a signal x(t) as F [x(t)] = X (f ) and the inverse Fourier transform of X (f ) as F [X (f )] = x(t). Note that F [F [x(t)]] = x(t) and at points of continuity of x(t). Duality Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Notice that the Fourier transform F and the inverse Fourier transform F are almost the same. Duality Theorem: If x(t) X (f ), then X (t) x( f ). In other words, F [F [x(t)]] = x( t). Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
8 Example of Duality Since rect(t) sinc(f ) then sinc(t) rect( f ) = rect(f ) (Notice that if the function is even then duality is very simple) f (t) F(ω) 2π 2π /2 /2 t ω 2π f (t) 2π 2π F(ω) t /2 /2 ω Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Generalized Fourier Transforms: δ Functions A unit impulse δ(t) is not a signal in the usual sense (it is a generalized function or distribution). However, if we proceed using the sifting property, we get a result that makes sense: F [δ(t)] = δ(t)e j2πft dt = so δ(t) This is a generalized Fourier transform. It behaves in most ways like an ordinary FT. δ(t) t ω Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
9 Shifted δ A shifted delta has the Fourier transform F [δ(t t )] = δ(t t )e j2πft dt = e j2πtf so we have the transform pair δ(t t ) e j2πtf δ(t t ) t t e jωt ω Constant Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Next we would like to find the Fourier transform of a constant signal x(t) =. However, direct evaluation doesn t work: F [] = e j2πft dt = j2πf and this doesn t converge to any obvious value for a particular f. We instead use duality to guess that the answer is a δ function, which we can easily verify. F [δ(f )] = δ(f )e j2πft df =. Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
10 So we have the transform pair δ(f ) 2πδ(ω) t ω This also does what we expect a constant signal in time corresponds to an impulse a zero frequency. Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Sinusoidal Signals If the δ function is shifted in frequency, F [δ(f f )] = δ(f f )e j2πft df = e j2πft so e j2πft δ(f f ) e jωt 2πδ(ω ω ) t ω ω Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
11 Cosine With Euler s relations we can find the Fourier transforms of sines and cosines [ F [cos(2πf t)] = F (e j2πft + e j2πft)] = 2 2 ( F [ e j2πft] + F [e j2πft]) = 2 (δ(f f ) + δ(f + f )). so cos(2πf t) 2 (δ(f f ) + δ(f + f )). cos(ω t) πδ(ω + ω ) πδ(ω ω ) t ω ω ω Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22 Sine Similarly, since sin(f t) = 2j (ej2πft e j2πft ) we can show that sin(f t) j 2 (δ(f + f ) δ(f f )). sin(ω t) jπδ(ω + ω ) ω t ω ω jπδ(ω ω ) The Fourier transform of a sine or cosine at a frequency f only has energy exactly at ±f, which is what we would expect. Cuff (Lecture 7) ELE 3: Signals and Systems Fall / 22
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