convolution, fourier series, and the fourier transform
DESCRIPTION
Convolution, Fourier Series, and the Fourier Transform. Convolution. A mathematical operator which computes the “amount of overlap” between two functions. Can be thought of as a general moving average Discrete domain: Continuous domain:. Discrete domain. Basic steps - PowerPoint PPT PresentationTRANSCRIPT
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Convolution, Fourier Series, and the Fourier Transform
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Convolution
A mathematical operator which computes the “amount of overlap” between two functions. Can be thought of as a general moving average
Discrete domain:
Continuous domain:
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Discrete domain
Basic steps1. Flip (reverse) one of the digital functions.
2. Shift it along the time axis by one sample.
3. Multiply the corresponding values of the two digital functions.
4. Summate the products from step 3 to get one point of the digital convolution.
5. Repeat steps 1-4 to obtain the digital convolution at all times that the functions overlap.
Example
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Continuous domain example
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Continuous domain example
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LTI (Linear Time-Invariant) Systems
Convolution can describe the effect of an LTI system on a signal
Assume we have an LTI system H, and its impulse response h[n]
Then if the input signal is x[n], the output signal is y[n] = x[n] * h[n]
Hx[n] y[n] = x[n]*h[n]
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Mathematics of Waves
Periodic phenomena occur everywhere– vision, sound, electronic communication, etc.
In order to manipulate physical world, we need mathematical tools
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Fourier Series
Any reasonable function can be expressed as a (infinite) linear combination of sines and cosines
F(t) = a0 + a1cos (ωt) + b1sin(ωt) + a2cos (2ωt) + b2sin(2ωt) + …
=
F(t) is a periodic function with
))sin()cos((0
nnn tnbtna
T
2
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Reasonable?
F(t) is a periodic function with
must satisfy certain other conditions– finite number of discontinuities within T– finite average within T– finite number of minima and maxima
T
2
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Calculate Coefficients
T
k dttktfT
a0
)cos()(2
T
dttf
a
T
0
0
)(
T
k dttktfT
b0
)sin()(2
))sin()cos(()(0
nnn tnbtnatF
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Example
F(t) = square wave, with T=1.0s ( )0.1
2
0
1
0 1 2 3 4 5 6 7
Series1
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Example
ttF
sin4
)(
0
1
0 1 2 3 4 5 6 7
Series1
Series2
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Example
ttttF
5sin5
43sin
3
4sin
4)(
0
1
0 1 2 3 4 5 6 7
Series1
Series3
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Example
What if – T = .01s;
– T = .05s;
– T = 50s;
ttttF
5sin5
43sin
3
4sin
4)(
ttttF
5sin5
43sin
3
4sin
4)(
ttttF
5sin5
43sin
3
4sin
4)(
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Time
Frequency
Time vs. Frequency Domain
0
1
0 1 2 3 4 5 6 7
Series1
Series3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1w 3w 5w
ttttF
5sin5
43sin
3
4sin
4)(
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Why Frequency Domain?
Allows efficient representation of a good approximation to the original function
Makes filtering easy
Note that convolution in the time domain is equivalent to multiplication in the frequency domain (and vice versa)
– When you are faced with a difficult convolution (or multiplication), you can switch domains and do the complement operation
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Fourier Family
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Discrete Fourier Transform
Rarely have closed form equation for F(t)
Must sample at periodic intervals– generates a series of numeric values– apply transform to a time window of values– N = number of sample points– x[ ] contains sample points
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DFT Notation
ck[i] and sk [i] are the cosine and sine waves, each N points in length– ck is cosine wave for amplitude in ReX[k]
– sk is sine wave for amplitude in ImX[k]
k refers to the frequency of the wave– usually between 0 and N/2
)/2sin(][ Nkiisk
)/2cos(][ Nkiick
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Complex exponentials
Alternatively, we can use complex exponentials
Euler’s formula: eiw = cos(w) + i*sin(w)
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Calculate DFT
Separate sinusoids
Complex exponentials
)/2cos(][][Re1
0
NkiixkXN
i
)/2sin(][][Im1
0
NkiixkXN
i
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Calculate Inverse DFT
Coefficients first need to be normalized
With two special cases
2/
][Re][Re
N
kXkX
N
XX
]0[Re]0[Re
2/
][Im][Im
N
kXkX
N
NXNX
]2/[Re]2/[Re
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Calculate Inverse DFT
Corresponding points within the basis function contribute to each input value– Equation uses the normalized coefficients
Result is exactly equal to original data (within rounding error), ie IDFT(DFT(x[n])) = x[n]
)/2(sin][Im)/2(cos][Re][2/
0
2/
0
NkikXNkikXixN
k
N
k
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DCT (Discrete Cosine Transform)
DCT is very similar to DFT– Sine wave + phase shift equals cosine wave– N coefficients of cosine basis functions– Advantage: Result is purely real– Most common is “Type II”– Used in JPEG and MPEG
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FFT (Fast Fourier Transform)
FFT is an efficient algorithm for computing the DFT– Naïve method for DFT requires O(N2) operations– FFT uses divide and conquer to break up problem into many 2-
point DFT’s (which are easy to compute)– 2-point DFT: X[0] = x[0] + x[1]
X[1] = x[0] – x[1]– log2N stages, O(N) operations per stage => O(N log N) total
operations– Ideally, want N to be a power of 2 or close to a power of 2
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4-point FFT “butterfly” diagram
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Fourier Transform
Definitions:
Can be difficult to compute =>
Often rely upon table of transforms
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Delta function
Definition:
Often, the result of the Fourier Transform needs to be expressed in terms of the delta function
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Fourier Transform pairs
There is a duality in all transform pairs