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25.1 Fourier Series
In your MATH 215 course, you have at this point discussed orthogonal bases
of vector spaces and dimension. We will now use this concept in this
class.
Recall that the inner product of two functions defined on an interval
is defined as
and an orthogonal set of functions are a set of functions such that
.
A function such that
is said to be square integrable and the vector space of all squareintegrable functions on the interval
is given the name
. Square integrability will be a property which is used throughout electrical and
computer engineering and will be used in ECE 207 Signals and Systems in 2B.
We will now look at a number of bases of squareintegrable functions.
Given any interval of width
, the set of functions
forms a basis for .
This basis is called the Fourier series or Fourier basis of squareintegrable functions defined on the interval .
Recall also from MATH 215 that we may use projections to map an arbitrary function
onto one of the orthogonal basis vectors using
and we may therefore write that
where the ratios in the parentheses are constants are and called Fourier coefficients.
Historical Note
Joseph Fourier published a paper with Fourier series in
1807 before the concept of vector spaces was formalized by
Giuseppe Peano in the late 1800s.
Well, the origin is obvious from the name: It was the French mathematical physicist JeanBaptist Joseph Fourier (17681830) who introduced these series. As a person, the man was rather weird, and if you are interested
I'll tell you some stories about his life in class. What he did for mathematics nd physics, however, history will never
forget. In fact, Fourier belongs to the select company of work whose
work is so fundamental that their names are used as adjectives!
To understand the impact of Fourier's ideas, picture this scene: The year is 1807 and
a paper submitted by Fourier to the French Academy reaches the hands of three of the
greatest mathematicians of the time (for refereeing). Now these were no average
guys: Lagrange, Laplace, and Legendre. And the paper makes a significant impression on
them, although they point out that Fourier had failed to prove his main result; without such a proof, Fourier's
claim was simply outrageous. One may believe that jealousy was involved, but that is not the
case for this is what Fourier claimed:
 Take a function defined on .
 The function need not be nice: it can have an arbitrary but finite
number of discontinuities.
 Extend the function periodically over the real line; that is, take the function on
and repeat it forever to the left and to the right.
 Then
and
for
.
 Moreover, this series converges to for each point where the function
is continuous and the series converges to at
any point where the function is discontinuous.
Why is this outrageous? Suppose that the function has a jump discontinuity
at some point . Therefore, does not exist, and yet
all the functions in the series are infinitely differentiable everywhere; so how can one hope to represent
a function with no derivative at a point by a series of other functions that have derivatives at all points.

25.2 Fourier Series on the Interval [π, π]
We will begin with a very simple interval: .
In this case, the width of the interval is and therefore
the basis functions are
.
These functions are orthogonal on this interval, for example,
.
On the other hand, we must also calculate:
for each of these functions. In the case of the function 1, it is easy:
.
Calculating the others may appear initially to be more difficult; however, look at the squares of each of these functions, as
is shown in Figure 1.
Figure 1. The squares of on the
interval .
In each case, the region below the function occupies half the area of the rectangle of width
and height 1. Consequently, it must be true that
for any integer .
Consequently, we get the traditional definition of a Fourier series from linear algebra:
.
For clarity, for a given function we will define
and
and thus we may write the Fourier series as
.
Approximating Functions
A Fourier series has an infinite number of terms; however, engineers cannot practically deal with an infinite series.
Fortunately, for almost all functions which engineers will ever encounter, we may approximate a function
with a finite Fourier series
where is some sufficiently large integer.
Example 1
Consider the function given by
.
First, this function is odd and therefore
and for all
values of . Therefore for all .
Determining the other coefficients requires us to solve
.
This requires one integration by parts, that is, where
in this case, and . Therefore
.
The second integral is zero and the first term simplifies to
.
We note that , , , and
thus , and thus we have
.
Therefore, our Fourier series is
.
To see how this series approximates the function , let us consider the
partial sums:
,
,
, and
and these are shown in Figure 2.
Figure 2. The first four Fourier series approximations of the function .
As you will note, with each new term added to the series, the approximation becomes better (though more wavy). Figure 3 shows
an animation of how more and more terms being added to the Fourier series results in a better and better approximation.
Figure 3. An animation showing the first fiftyone Fourier series approximations of the function .
Note that Figure 3 is not a continuous movie. Each successive frame draws the Fourier series approximation with one additional term. Because the magnitude of each additional term grows successively smaller, it
appears as if it is a continuous movie.
You will notice that at any one point, it appears to converge; however, there is a jump in the approximations near
the end points. We will revisit this again.
Example 2
Next, consider the function given by
.
First, this function is odd and therefore for
all values of . Determining the other coefficients requires us
to solve
and, using two integration by parts,
.
Thus, we have the Fourier series approximation
.
The first five approximations are
,
,
,
, and
and these are shown in Figure 4.
Figure 4. The first five Fourier series approximations of the function .
You will notice that the first approximation is a constant function: it is the constant function which has the same integral
of the function on the interval .
As you will note, with each new term added to the series, the approximation becomes better. Figure 5 shows
an animation of how more and more terms being added to the Fourier series results in a better and better approximation.
Figure 5. An animation showing the first fiftyone Fourier series approximations of the function .
You will notice that, unlike the previous example, it appears to converge everywhere without an overshoot at either end. This will, again be explained
later.
Example 3
Next, consider the function given by
. This is the unit
step function shown in Figure 6.
Figure 6. The unit step function on the interval .
.
The cosine terms, however, are all zero:
.
The sine terms are more interesting:
.
Thus, if is odd, the coefficient is ; while if
is even, the coefficient is . Therefore, we may write the Fourier series as
.
The first five terms different approximations are
,
,
,
, and
and these are shown in Figure 7.
Figure 7. The first five different Fourier series approximations of the unit step function.
In this case, again, you may notice the jumps at , , and .
The first twentysix different approximations are shown in Figure 8.
Figure 8. An animation showing the first fiftyone Fourier series approximations of the unit step function.
Example 4
Consider the function
given by
or
.
The function is shown in Figure 9.
Figure 9. The function defined on .
We can calculate the coefficients using integration:
and
for .
The first approximation is and the next is
, followed by
. Figure 10 shows
various approximations. Figure 11 is an animation showing the first hundred approximations.
Figure 10. The approximations
through
, though because
, the approximations
and
are unchanged.
Figure 11. An animation showing
through
to demonstrate how they converge to the target function.
Note that Figure 11 is not a continuous movie. Each successive frame draws the Fourier series approximation with one additional term. Because the magnitude of each additional term grows successively smaller, it
appears as if it is a continuous movie.
It becomes apparent in Figure 11 how the sequence of functions does appear to converge everywhere and,
in addition, each approximation appears to be very close to 0.5 at the points
.
Example 5
Consider the modification of the function shown in Figure 12 which
adds a spike of width 0.1 around the point .
Figure 12. The function in Figure 9 with a spike added around
.
Figure 13 shows the approximations
while Figure 14 shows
the first 101 approximations.
Figure 13. The first three Fourier approximations of the function shown
in Figure 12.
Figure 14. An animation showing
through
to demonstrate how they converge to the target function shown in Figure 12.
25.3 Periodic Extensions and Power Signals
In all of these cases, the function has been approximated on the interval
and we have only plotted the solution on that
interval. There is no reason not to plot the solutions over a larger interval and
for Examples 1 through 4, these are shown in Figures 15 through 18.
Figure 15. The plot of the approximation .
Figure 16. The plot of the approximation .
Figure 17. The plot of the approximation .
In each case, the result is an approximation of the function defined on
but repeated periodically. Given any function defined on
, this repetition of the function outside the interval is called
the periodic extension of the function.
The power of such a periodic function is defined as
,
or, for the periodic extension of a function defined on an interval
as
.
For example, the power of the functions in Examples 1 through 4 are
,
,
, and
. This will be used in the course ECE 207 Signals and Systems the
response of a system based on periodic input will be covered in significantly more detail.