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Transcript
4/30/2017
582735450
1/9
Basis Functions
Q: So let’s be specific. You say a continuous analog signal can
be expressed as a discrete sequence of values an , given some
set of known functions  n t  . What are these functions  n t  ,
and what are the values an ? Please give examples!
A: The functions  n t  are known as the basis functions of
signal expansion:
v t    an  n t 
n
There are essentially an infinite number
ψ n t 
of basis functions to choose from, but
here are some of our favorites!
1. Polynomial
Consider basis functions of the form:
 n t   t n
n 0
Resulting in a polynomial of variable t:
v t   a0  a1 t  a2 t  a3 t 
2
3

  an t n
n 0
This signal expansion is of course know as the Taylor Series
expansion.
Jim Stiles
The Univ. of Kansas
Dept. of EECS
4/30/2017
582735450
2/9
2. Fourier
Among the most popular basis is this one:
 j  2 n  t
e  T 

 n t   
0


0  t T
t  0,t T
So therefore:
v t  

a
n 
n
e
 2 n 
j
t
 T 
for 0  t T
The astute among you will recognize this signal
expansion as the Fourier Series!
Q: Yes, just why is Fourier analysis so
prevalent?
A: The basis functions of Fourier Analysis are the eigenfunctions of linear time-invariant systems (like linear
circuits)!!
Jim Stiles
The Univ. of Kansas
Dept. of EECS
4/30/2017
582735450
3/9
Q: OK Poindexter, all eigen stuff this
might be interesting if you’re a
mathematician, but is it at all useful
to us electrical engineers?
A: It is unfathomably useful to us
electrical engineers!
Say a linear, time-invariant circuit is excited (only) by a
sinusoidal source (e.g., vs t   cos ot ). Since the source
function is the eigen function of the circuit, we will find that
at every point in the circuit, both the current and voltage will
have the same functional form.
That is, every current and voltage in the circuit will
likewise be a perfect sinusoid with frequency o !!
Of course, the magnitude of the sinusoidal
oscillation will be different at different
points within the circuit, as will the relative
phase. But we know that every current and
voltage in the circuit can be precisely
expressed as a function of this form:
A cos ot   
Q: Isn’t this pretty obvious?
Jim Stiles
The Univ. of Kansas
Dept. of EECS
4/30/2017
582735450
4/9
A: Why should it be? Say our source function was instead a
square wave, or triangle wave, or a sawtooth wave. We would
find that (generally speaking) nowhere in the circuit would we
find another current or voltage that was a perfect square
wave (etc.)!
In fact, we would find that not
only are the current and voltage
functions within the circuit
different than the source
function (e.g. a sawtooth) they
are (generally speaking) all
different from each other.
We find then that a linear circuit will (generally
speaking) distort any source function—unless that
function is the eigen function (i.e., an sinusoidal
function).
Thus, using an eigen function as circuit source greatly
simplifies our linear circuit analysis problem. All we need to
accomplish this is to determine the magnitude A and relative
phase  of the resulting (and otherwise identical) sinusoidal
function!
3. Sinc Function
As popular as the Fourier basis function is, an even more
popular set of basis functions is the sinc basis function.
A sinc function is defined as:
Jim Stiles
The Univ. of Kansas
Dept. of EECS
4/30/2017
sinc t 
582735450
5/9
sin t 
t
The set of basis functions derived from this are:
t  n  sin  t  n   

 t  n  
  
 n t   sinc 
Q: Is this function likewise an eigen function of linear timeinvariant systems?
A: Nope. Sinusoids are the ONLY eigen function of linear
time-invariant systems!
Q: So why then are these basis functions so popular?
A: Determining the sequence of values an for this signal
expansion, i.e.,
Jim Stiles
t  n 
v t    an sinc 
  
n
The Univ. of Kansas
Dept. of EECS
4/30/2017
582735450
6/9
is extremely easy (at least when compared to other basis
functions)!!!
4. Wavelet Basis Functions
The past twenty years has shown the rise of a new
kind of signal basis function, known as the wavelet
basis function (watch for future math Nobel Prize
 winners).
There are many wavelet basis functions (e.g., Daubechies), but
they all are a bit of a hybrid between the sinc and Fourier
basis functions.
Q: Why have they become so popular?
Jim Stiles
The Univ. of Kansas
Dept. of EECS
4/30/2017
582735450
7/9
A: Ultimately, we would like to accurately represent our
signal with a discrete sequence of values an that is as short
as possible.
Theoretically, we require an infinite number of values an to
ensure that our representation has no error:

v t    an  n t 
(bummer!)
n
However, we find that typically we can represent our function
with very good accuracy using a finite number of values an :
N
v t    an  n t 
n
For Fourier basis functions, the number of required values an
defines our signal bandwidth B.
For sinc basis functions, the number of required values an
defines our signal timewidth T.
We find that many useful signals exist over a wide timewidth
T, and a wide bandwidth B—only not necessarily at the same
time.
The classic example is music, where
all different notes occur within a
song, only not all at the same time.
Jim Stiles
The Univ. of Kansas
Dept. of EECS
4/30/2017
582735450
8/9
Wavelet basis functions allow a signal to be more localized
simultaneously in time and frequency. The result often leads
to a fewer number of values an (i.e., fewer than Fourier or
sinc) required to accurately describe the signal.
 This is the “basis” of JPEG and MPEG compression
algorithms!
We simply retain only the values of an deemed large enough
to be “significant”.
Original
Jim Stiles
The Univ. of Kansas
Dept. of EECS
4/30/2017
582735450
9/9
Moderate
compression
Significant
compression
http://watermarking.unige.ch/Checkmark/attacks/examples_wavelet.h
tm
Jim Stiles
The Univ. of Kansas
Dept. of EECS