Examples of Fourier series — periodic impulse train. • Fourier transforms of periodic functions — relation to. Fourier series. • Conclusions. 2.
Impulse response & Transfer function As the name suggests two functions are blended or folded together. ... Fourier transform of the delta function:.
(e.g. x(t) and X(?)
4.5.2 Properties of the Discrete-Time Fourier Series . . . . . . . 55 time LTI system is the running sum of the impulse response.
2.8 and 2.9 develop and explore the Fourier transform representation of sum of the value at index n and all previous values of the impulse sequence.
In other words the Fourier Transform of an everlasting exponential ej?0t is an impulse in the frequency spectrum at ? = ?0 . Page 11. Electronics 2. 11. An
https://www.cns.nyu.edu/csh/csh06/Handouts/convolution.pdf
Is the sum of two periodic functions periodic? I guess the answer is no if you're a mathematician yes if you're an engineer
Multiplying by a function f (t) by an impulse at time T and integrating extracts the value of f (T). This will be important in modeling sampling later in the
Frequency domain analysis and Fourier transforms are a cornerstone of signal Each of the two sinusoids (at frequencies 350 Hz and 440 Hz) alone ...
Fourier transform is purely imaginary For a general real function the Fourier transform will have both real and imaginary parts We can write f˜(k)=f˜c(k)+if˜ s(k) (18) where f˜ s(k) is the Fourier sine transform and f˜c(k) the Fourier cosine transform One hardly ever uses Fourier sine and cosine transforms
The Fourier Transform Consider the Fourier coefficients Let’s define a function F(m) that incorporates both cosine and sine series coefficients with the sine series distinguished by making it the imaginary component: Let’s now allow f(t) to range from –?to ?so we’ll have to integrate
The Fourier Transform of a sum of two functions () () Faft bgt aF ft bF gt The FT of a sum is the sum of the FT’s Also constants factor out f(t) g(t) t t t F( ) G( ) f(t)+g(t) F( ) + G( ) This property reflects the fact that the Fourier transform is a linear operation
Linearity:The Fourier transform is a linear operation so that the Fourier transform of the sum of two functions is given by the sum of the individual Fourier transforms Therefore Ffa f(x)+bg(x)g=aF(u)+bG(u) (6) whereF(u)andG(u)are the Fourier transforms off(x)and andg(x)andaandbare constants
Linearity Theorem: The Fourier transform is linear; that is given twosignals x1(t) andx2(t) and two complex numbers aandb then ax1(t) +bx2(t)aX1(j!) +bX2(j!): This follows from linearity of integrals: 1 (ax1(t) +bx2(t))e j2 ft dt 1 =ax1(t)e j2 ft dt+b =aX1(f) +bX2(f) j2 ft x2(t)e dt Fall 2011-12 Finite Sums
The Fourier components interfere constructively within the bumps at each integral multiple of L and interfere destructively otherwise III THE FOURIER TRANSFORM To eliminate the periodic structure we need to include even more Fourier components; for example it should be clear that we have to include Fourier functions whose period is longer
Indeed, theimaginarypart of the Fourier transform of a real function is This is a Fourier sine transform. Thus the imaginary part vanishes only if the function has nosine components which happens if and only if the function is even. For an odd function, theFourier transform is purely imaginary.
The Fourier components interfere constructively within the bumps at each integral multiple ofLand interfere destructively otherwise. III. THE FOURIER TRANSFORM To eliminate the periodic structure, we need to include even more Fourier components; for example, it should be clear that we have to include Fourier functions whose period is longer thanL.
Considerthis Fourier transform pair for a smallTand large T, sayT= 1 and = 5. The resulting transform pairs are shown below to a commonhorizontal scale: This signal can be written as e atu(t) +eatu( t). Linearity andtime-reversal yield 1 Much easier than direct integration! Z1 Z1 We discussed duality in a previous lecture.
This is the Fourier transform. It is a continuum general-ization of thecn’s of the Fourier series. The inverse of this comes from writing Eq. (1) as a integral. From Eq. (6), we ?ndd kn= 2?L?n. This leads to where we have used Eq. (7) and takenL? ?in the last step. We say thatff˜(k)is theFourier transformof f(x). The factor of2?is just a convention.