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PROFESSOR: In the last lecture,
I introduced and
illustrated the kinds of signals
and systems that we'll
be dealing with throughout
this course.
In today's lecture I'd like to
be a little more specific, and
in particular, talk about some
of the basic signals, both
continuous-time and
discrete-time that will form
important building blocks as
the course progresses.
Let's begin with one signal, the
continuous-time sinusoidal
signal, which perhaps
you're already
somewhat familiar with.
Mathematically, the
continuous-time sinusoidal
signal is expressed as
I've indicated here.
There are three parameters,
A, omega_0 and phi.
The parameter A is referred
to as the amplitude, the
parameter omega 0 as the
frequency, and the parameter
phi as the phase.
And graphically, the
continuous-time sinusoidal
signal has the form
shown here.
Now, the sinusoidal signal
has a number of important
properties that we'll find it
convenient to exploit as the
course goes along, one of which
is the fact that the
sinusoidal signal is what is
referred to as periodic.
What I mean by periodic is that
under an appropriate time
shift, which I indicate here as
T_0, the signal replicates
or repeats itself.
Or said another way, if we shift
the time origin by an
appropriate amount T_0, the
smallest value T_0 being
referred to as the period,
then x(t) is
equal to itself, shifted.
And we can demonstrate it
mathematically by simply
substituting into the
mathematical expression for
the sinusoidal signal t
+ T_0, in place of t.
When we carry out the expansion
we then have, for
the argument of the sinusoid,
omega_0 t + omega_0 T_0 + phi.
Now, one of the things that
we know about sinusoidal
functions is that if you change
the argument by any
integer multiple of 2 pi,
then the function
has the same value.
And so we can exploit that
here, in particular with
omega_0 T_0 and integer
multiple of 2 pi.
Then the right-hand side of this
equation is equal to the
left-hand side of
the equation.
So with omega_0 T_0 equal to 2
pi times an integer, or T_0
equal to 2 pi times an
integer divided by
omega_0, the signal repeats.
The period is defined as the
smallest value of T_0.
And so the period is 2 pi
divided by omega_0.
And going back to
our sinusoidal
signal, we can see that--
and I've indicated here, then,
the period as 2 pi / omega_0.
And that's the value under
which the signal repeats.
Now in addition, a useful
property of the sinusoidal
signal is the fact that a time
shift of a sinusoid is
equivalent to a phase change.
And we can demonstrate that
again mathematically, in
particular if we put the
sinusoidal signal
under a time shift--
I've indicated the time
shift that I'm
talking about by t_0--
and expand this out, then we see
that that is equivalent to
a change in phase.
And an important thing to
recognize about this statement
is that not only is a time
shift generating a phase
change, but, in fact, if we
inserted a phase change, there
is always a value of t_0 which
would correspond to an
equivalent time shift.
Said another way, if we take
omega_0 t_0 and think of that
as our change in phase, for any
change in phase, we can
solve this equation for a time
shift, or conversely for any
value of time shift,
that represents
an appropriate phase.
So a time shift corresponds to
a phase change, and a phase
change, likewise, corresponds
to time shift.
And so for example, if we look
at the general sinusoidal
signal that we saw previously,
in effect, changing the phase
corresponds to moving
this signal in time
one way or the other.
For example, if we look at the
sinusoidal signal with a phase
equal to 0 that corresponds
to locating the time
origin at this peak.
And I've indicated that on
the following graph.
So here we have illustrated a
sinusoid with 0 phase, or a
cosine with 0 phase,
corresponding to taking our
general picture and
shifting it.
Shifting it appropriately
as I've indicated here.
This, of course, still has the
property that it's a periodic
function, since we simply
displaced it in time.
And by looking at the graph,
what we see is that it has
another very important property,
a property referred
to as even.
And that's a property that we'll
find useful, in general,
to refer to in relation
to signals.
A signal is said to be even if,
when we reflect it about
the origin, it looks
exactly the same.
So it's symmetric about
the origin.
And looking at this
sinusoid, that, in
fact, has that property.
And mathematically, the
statement that it's even is
equivalent to the statement that
if we replace the time
argument by its negative,
the function
itself doesn't change.
Now this corresponded to a
phase shift of 0 in our
original cosine expression.
If instead, we had chosen a
phase shift of, let's say,
-pi/2, then instead of a
cosinusoidal signal, what we
would regenerate is a sinusoid
with the appropriate phase.
Or, said another way, if we take
our original cosine and
substitute in for the phase
-pi/2, then of course we have
this mathematical expression.
Using just straightforward
trigonometric identities, we
can express that alternately
as sin(omega_0*t).
The frequency and amplitude,
of course, haven't changed.
And that, you can convince
yourself, also is equivalent
to shifting the cosine by an
amount in time that I've
indicated here, namely a
quarter of a period.
So illustrated below is the
graph now, when we have a
phase of -pi/2 in our cosine,
which is a sinusoidal signal.
Of course, it's still
periodic.
It's periodic with a period of
2 pi / omega_0 again, because
all that we've done by
introducing a phase change is
introduced the time shift.
Now, when we look at the
sinusoid in comparison with
the cosine, namely with this
particular choice of phase,
this has a different symmetry,
and that symmetry
is referred to odd.
What odd symmetry means,
graphically, is that when we
flip the signal about the time
origin, we also multiply it by
a minus sign.
So that's, in effect,
anti-symmetric.
It's not the mirror image,
but it's the mirror
image flipped over.
And we'll find many occasions,
not only to refer to signals
more general than sinusoidal
signals, as even in some cases
and odd in other cases.
And in general, mathematically,
an odd signal
is one which satisfies the
algebraic expression, x(t).
When you replace t by its
negative, is equal to -x(-t).
So replacing the argument by its
negative corresponds to an
algebraic sign reversal.
OK.
So this is the class of
continuous-time sinusoids.
We'll have a little more
to say about it later.
But I'd now like to turn to
discrete-time sinusoids.
What we'll see is that
discrete-time sinusoids are
very much like continuous-time
ones, but also with some very
important differences.
And we want to focus, not only
on the similarities, but also
on the differences.
Well, let's begin with the
mathematical expression.
A discrete-time sinusoidal
signal, mathematically, is as
I've indicated here, A
cos(omega_0 n + phi).
And just as in the
continuous-time case, the
parameter A is what we'll refer
to as the amplitude,
omega_0 as the frequency,
and phi as the phase.
And I've illustrated here
several discrete-time
sinusoidal signals.
And they kind of look similar.
In fact, if you track what
you might think of as the
envelope, it looks very much
like what a continuous-time
sinusoid might look like.
But keep in mind that the
independent variable, in this
case, is an integer variable.
And so the sequence only takes
on values at integer values of
the argument.
And we'll see that has a very
important implication, and
we'll see that shortly.
Now, one of the issues that
we addressed in the
continuous-time case
was periodicity.
And I want to return to that
shortly, because that is one
of the areas where there is
an important distinction.
Let's first, though, examine the
statement similar to the
one that we examined for
continuous time, namely the
relationship between a time
shift and a phase change.
Now, in continuous time, of
course, we saw that a time
shift corresponds to a phase
change, and vice versa.
Let's first look at the
relationship between shifting
time and generating
a change in phase.
In particular for discrete time,
if I implement a time
shift that generates
a phase change--
and we can see that easily
by simply inserting a
time shift, n + n_0.
And if we expand out this
argument, we have omega_0 n +
omega_0 n_0.
And so I've done that on the
right-hand side of the
equation here.
And the omega_0 n_0, then,
simply corresponds
to a change in phase.
So clearly, a shift in time
generates a change in phase.
And for example, if we take a
particular sinusoidal signal,
let's say we take the cosine
signal at a particular
frequency, and with a phase
equal to 0, a sequence that we
might generate is one that
I've illustrated here.
So what I'm illustrating
here is the cosine
signal with 0 phase.
And it has a particular behavior
to it, which will
depend somewhat on
the frequency.
If I now take this same sequence
and shift it so that
the time origin is shifted a
quarter of a period away, then
you can convince yourself--
and it's straightforward to
work out-- that that time
shift corresponds to a
phase shift of pi/2.
So in that case, with the cosine
with a phase of -pi/2,
that will correspond to the
expression that I have here.
We could alternately write
that, using again a
trigonometric identity,
as a sine function.
And that, I've stated, is
equivalent to a time shift.
Namely, this shift of pi/2 is
equal to a certain time shift,
and the time shift for this
particular example is a
quarter of a period.
So here, we have the sinusoid.
Previously we had the cosine.
The cosine was exactly the same
sequence, but with the
origin located here.
And in fact, that's exactly the
way we drew this graph.
Namely, we just simply took the
same values and changed
the time origin.
Now, looking at this sequence,
which is the sinusoidal
sequence, the phase of -pi/2,
that has a certain symmetry.
And in fact, what we see is that
it has an odd symmetry,
just as in the continuous-time
case.
Namely, if we take that
sequence, flip it about the
axis, and flip it over in sign,
that we get the same
sequence back again.
Whereas with 0 phase
corresponding to the cosine
that I showed previously, that
has an even symmetry.
Namely, if I flip it about the
time origin and don't do a
sign reversal, then the sequence
is maintained.
So here, we have an odd
symmetry, expressed
mathematically as
I've indicated.
Namely, replacing the
independent variable by its
negative attaches a negative
sign to the whole sequence.
Whereas in the previous case,
what we have is 0 phase and an
even symmetry.
And that's expressed
mathematically as x[n]
= x[-n].
Now, one of the things I've
said so far about
discrete-time sinusoids is that
a time shift corresponds
to a phase change.
And we can then ask whether the
reverse statement is also
true, and we knew that the
reverse statement was true in
continuous time.
Specifically, is it true that
a phase change always
corresponds to a time shift?
Now, we know that that is
true, namely, that this
statement works both ways
in continuous time.
Does it in discrete time?
Well, the answer, somewhat
interestingly or surprisingly
until you sit down and think
about it, is no.
It is not necessarily true in
discrete time that any phase
change can be interpreted
as a simple time
shift of the sequence.
And let me just indicate
what the problem is.
If we look at the relationship
between the left side and the
right side of this equation,
expanding this out as we did
previously, we have that omega_0
n + omega_0 n_0 must
correspond to omega_0 n + phi.
And so omega_0 n_0
must correspond
to the phase change.
Now, what you can see pretty
clearly is that depending on
the relationship between phi
and omega_0, n_0 may or may
not come out to be an integer.
Now, in continuous time, the
amount of time shift did not
have to be an integer amount.
In discrete time, when we talk
about a time shift, the amount
of time shift-- obviously,
because of the nature of
discrete time signals--
must be an integer.
So the phase changes related
to time shifts must satisfy
this particular relationship.
Namely, that omega_0 n_0, where
n_0 is an integer, is
equal to the change in phase.
OK.
Now, that's one distinction
between continuous time and
discrete time.
Let's now focus on another
one, namely the issue of
periodicity.
And what we'll see is that
again, whereas in continuous
time, all continuous-time
sinusoids are periodic, in the
discrete-time case that is
not necessarily true.
To explore that a little more
carefully, let's look at the
expression, again, for a general
sinusoidal signal with
an arbitrary amplitude,
frequency, and phase.
And for this to be periodic,
what we require is that there
be some value, N, under which,
when we shift the sequence by
that amount, we get the same
sequence back again.
And the smallest-value
N is what we've
defined as the period.
Now, when we try that on a
sinusoid, we of course
substitute in for n, n + N.
And when we expand out the
argument here, we'll get the
argument that I have on the
right-hand side.
And in order for this to repeat,
in other words, in
order for us to discard this
term, omega_0 N, where N is
the period, must be an integer
multiple of 2 pi.
And in that case, it's periodic
as long as omega_0 N,
N being the period, is 2
pi times an integer.
Just simply dividing this out,
we have N, the period, is 2 pi
m / omega_0.
Well, you could say, OK
what's the big deal?
Whatever N happens to come out
to be when we do that little
bit of algebra, that's
the period.
But in fact, N, or 2 pi m /
omega_0, may not ever come out
to be an integer.
Or it may not come out
to be the one that
you thought it might.
For example, let's look at some
particular sinusoidal signals.
Let's see.
We have the first one
here, which is a
sinusoid, as I've shown.
And it has a frequency, what
I've referred to as the
frequency, omega_0
= 2 pi / 12.
And what we'd like to look at
is 2 pi / omega_0, then find
an integer to multiply
that by in order
to get another integer.
Let's just try that here.
If we look at 2 pi / omega_0, 2
pi / omega_0, for this case,
is equal to 12.
Well, that's fine.
12 is an integer.
So what that says is that this
sinusoidal signal is periodic.
And in fact, it's periodic
with a period of 12.
Let's look at the next one.
The next one, we would have
2 pi / omega_0 again.
And that's equal to 31/4.
So what that says is that
the period is 31/4.
But wait a minute.
31/4 isn't an integer.
We have to multiply
that by an integer
to get another integer.
Well, we'd multiply that by 4,
so (2 pi / omega_0) times 4 is
31, 31 is an integer.
And so what that says is this is
periodic, not with a period
of 2 pi / omega_0, but with a
period of (2 pi / omega_0)
times 4, namely with
a period of 31.
Finally, let's take the example
where omega_0 is equal
to 1/6, as I've shown here.
That actually looks, if you
track it with your eye, like
it's periodic.
2 pi / omega_0, in that case,
is equal to 12 pi.
Well, what integer can I
multiply 12 pi by and get
another integer?
The answer is none, because pi
is an irrational number.
So in fact, what that says is
that if you look at this
sinusoidal signal, it's not
periodic at all, even though
you might fool yourself into
thinking it is simply because
the envelope looks periodic.
Namely, the continuous-time
equivalent of this is
periodic, the discrete-time
sequence is not.
OK.
Well, we've seen, then, some
important distinctions between
continuous-time sinusoidal
signals and discrete-time
sinusoidal signals.
The first one is the fact that
in the continuous-time case, a
time shift and phase change
are always equivalent.
Whereas in the discrete-time
case, in effect, it works one
way but not the other way.
We've also seen that for a
continuous-time signal, the
continuous-time signal is always
periodic, whereas the
discrete-time signal
is not necessarily.
In particular, for the
continuous-time case, if we
have a general expression for
the sinusoidal signal that
I've indicated here,
that's periodic for
any choice of omega_0.
Whereas in the discrete-time
case, it's periodic only if 2
pi / omega_0 can be multiplied
by an integer
to get another integer.
Now, another important and,
as it turns out, useful
distinction between the
continuous-time and
discrete-time case is the fact
that in the discrete-time
case, as we vary what I've
called the frequency omega_0,
we only see distinct
signals as omega_0
varies over a 2 pi interval.
And if we let omega_0 vary
outside the range of, let's
say, -pi to pi, or 0 to 2 pi,
we'll see the same sequences
all over again, even though at
first glance, the mathematical
expression might
look different.
So in the discrete-time case,
this class of signals is
identical for values of omega_0
separated by 2 pi,
whereas in the continuous-time
case, that is not true.
In particular, if I consider
these sinusoidal
continuous-time signals, as I
vary omega_0, what will happen
is that I will always see
different sinusoidal signals.
Namely, these won't be equal.
And in effect, we can justify
that statement algebraically.
And I won't take the time
to do it carefully.
But let's look, first of all,
at the discrete-time case.
And the statement that I'm
making is that if I have two
discrete-time sinusoidal signals
at two different
frequencies, and if these
frequencies are separated by
an integer multiple of 2 pi--
namely if omega_2 is equal to
omega_1 + 2 pi times
an integer m--
when I substitute this into this
expression, because of
the fact that n is also an
integer, I'll have m * n as an
integer multiple of 2 pi.
And that term, of course, will
disappear because of the
periodicity of the sinusoid,
and these two
sequences will be equal.
On the other hand in the
continuous-time case, since t
is not restricted to be an
integer variable, for
different values of omega_1 and
omega_2, these sinusoidal
signals will always
be different.
OK.
Now, many of the issues that
I've raised so far, in
relation to sinusoidal signals,
are elaborated on in
more detail in the text.
And of course, you'll have an
opportunity to exercise some
of this as you work through
the video course manual.
Let me stress that sinusoidal
signals will play an extremely
important role for us as
building blocks for general
signals and descriptions of
systems, and leads to the
whole concept Fourier analysis,
which is very
heavily exploited throughout
the course.
What I'd now like to turn to is
another class of important
building blocks.
And in fact, we'll see that
under certain conditions,
these relate strongly to
sinusoidal signals, namely the
class of real and complex
exponentials.
Let me begin, first of all, with
the real exponential, and
in particular, in the
continuous-time case.
A real continuous-time
exponential is mathematically
expressed, as I indicate here,
x(t) = C e ^ (a t), where for
the real exponential, C and
a are real numbers.
And that's what we mean by
the real exponential.
Shortly, we'll also consider
complex exponentials, where
these numbers can then
become complex.
So this is an exponential
function.
And for example, if the
parameter a is positive, that
means that we have a growing
exponential function.
If the parameter a is negative,
then that means that
we have a decaying exponential
function.
Now, somewhat as an aside, it's
kind of interesting to
note that for exponentials, a
time shift corresponds to a
scale change, which is somewhat
different than what
happens with sinusoids.
In the sinusoidal case, we saw
that a time shift corresponded
to a phase change.
With the real exponential, a
time shift, as it turns out,
corresponds to simply
changing the scale.
There's nothing particularly
crucial or
exciting about that.
And in fact, perhaps stressing
it is a little misleading.
For general functions, of
course, about all that you can
say about what happens when you
implement a time shift is
that it implements
a time shift.
OK.
So here's the real
exponential.
Just C e ^ (a t).
Let's look at the real
exponential, now, in the
discrete-time case.
And in the discrete-time case,
we have several alternate ways
of expressing it.
We can express the real
exponential in the form C e ^
(beta n), or as we'll find more
convenient, in part for a
reason at I'll indicate shortly,
we can rewrite this
as C alpha ^ n, where of course,
alpha = e ^ beta.
More typically in the
discrete-time case, we'll
express the exponential
as C alpha ^ n.
So for example, this becomes,
essentially, a geometric
series or progression
as n continues for
certain values of alpha.
Here for example, we have for
alpha greater than 0, first of
all on the top, the case where
the magnitude of alpha is
greater than 1, so that the
sequence is exponentially or
geometrically growing.
On the bottom, again with alpha
positive, but now with
its magnitude less than 1, we
have a geometric progression
that is exponentially or
geometrically decaying.
OK.
So this, in both of these
cases, is with alpha
greater than 0.
Now the function that we're
talking about is alpha ^ n.
And of course, what you can
see is that if alpha is
negative instead of positive,
then when n is even, that
minus sign is going
to disappear.
When n is odd, there will
be a minus sign.
And so for alpha negative, the
sequence is going to alternate
positive and negative values.
So for example, here we have
alpha negative, with its
magnitude less than 1.
And you can see that, again,
its envelope decays
geometrically, and the values
alternate in sign.
And here we have the magnitude
of alpha greater than 1, with
alpha negative.
Again, they alternate in sign,
and of course it's growing
geometrically.
Now, if you think about alpha
positive and go back to the
expression that I have at the
top, namely C alpha ^ n.
With alpha positive, you can
see a straightforward
relationship between
alpha and beta.
Namely, beta is the natural
logarithm of alpha.
Something to think
about is what
happens if alpha is negative?
Which is, of course, a very
important and useful class of
real discrete-time exponentials
also.
Well, it turns out that with
alpha negative, if you try to
express it as C e ^ (beta n),
then beta comes out to be an
imaginary number.
And that is one, but not the
only reason why, in the
discrete-time case, it's often
most convenient to phrase real
exponentials in the form alpha ^
n, rather than e ^ (beta n).
In other words, to express them
in this form rather than
in this form.
Those are real exponentials,
continuous-time and
discrete-time.
Now let's look at the
continuous-time complex
exponential.
And what I mean by a complex
exponential, again, is an
exponential of the
form C e ^ (a t).
But in this case, we allow the
parameters C and a to be
complex numbers.
And let's just track this
through algebraically.
If C and a are complex numbers,
let's write C in
polar form, so it has a
magnitude and an angle.
Let's write a in rectangular
form, so it has a real part
and an imaginary part.
And when we substitute these
two in here, combine some
things together--
well actually, I haven't
combined yet.
I have this for the amplitude
factor, and this for the
exponential factor.
I can now pull out of this the
term corresponding to e ^ (r
t), and combine the imaginary
parts together.
And I come down to the
expression that I have here.
So following this further, an
exponential of this form, e ^
(j omega) or e ^ (j phi), using
Euler's relation, can be
expressed as the sum of a cosine
plus j times a sine.
And so that corresponds
to this factor.
And then there is this
time-varying amplitude factor
on top of it.
Finally putting those together,
we end up with the
expression that I show
on the bottom.
And what this corresponds to are
two sinusoidal signals, 90
degrees out of phase, as
indicated by the fact that
there's a cosine and a sine.
So there's a real part and
an imaginary part, with
sinusoidal components 90 degrees
out of phase, and a
time-varying amplitude factor,
which is a real exponential.
So it's a sinusoid multiplied by
a real exponential in both
the real part and the
imaginary part.
And let's just see what one of
those terms might look like.
What I've indicated at the top
is a sinusoidal signal with a
time-varying exponential
envelope, or an envelope which
is a real exponential, and in
particular which is growing,
namely with r greater than 0.
And on the bottom, I've
indicated the same thing with
r less than 0.
And this kind of sinusoidal
signal, by the way, is
typically referred to as
a damped sinusoid.
So with r negative, what we have
in the real and imaginary
parts are damped sinusoids.
And the sinusoidal components of
that are 90 degrees out of
phase, in the real part and
in the imaginary part.
OK.
Now, in the discrete-time case,
we have more or less the
same kind of outcome.
In particular we'll make
reference to our complex
exponentials in the
discrete-time case.
The expression for the complex
exponential looks very much
like the expression for the real
exponential, except that
now we have complex factors.
So C and alpha are
complex numbers.
And again, if we track through
the algebra, and get to a
point where we have a real
exponential multiplied by a
factor which is a purely
imaginary exponential, apply
Euler's relationship to this, we
then finally come down to a
sequence, which has a real
exponential amplitude
multiplying one sinusoid
in the real part.
And in the imaginary part,
exactly the same kind of
exponential multiplying a
sinusoid that's 90 degrees out
of phase from that.
And so if we look at what one
of these factors might look
like, it's what we would expect
given the analogy with
the continuous-time case.
Namely, it's a sinusoidal
sequence with a real
exponential envelope.
In the case where alpha
is positive, then
it's a growing envelope.
In the case where alpha
is negative--
I'm sorry--
where the magnitude of alpha
is greater than 1, it's a
growing exponential envelope.
Where the magnitude of alpha
is less than 1, it's a
decaying exponential envelope.
And so I've illustrated
that here.
Here we have the magnitude
of alpha greater than 1.
And here we have the magnitude
of alpha less than 1.
In both cases, sinusoidal
sequences underneath the
envelope, and then an envelope
that is dictated by what the
magnitude of alpha is.
OK.
Now, in the discrete-time case,
then, we have results
similar to the continuous-time
case.
Namely, components in a real and
imaginary part that have a
real exponential factor
times a sinusoid.
Of course, if the magnitude of
alpha is equal to 1, then this
factor disappears,
or is equal to 1.
And this factor is equal to 1.
And so we have sinusoids
in both the real
and imaginary parts.
Now, one can ask whether,
in general, the complex
exponential with the magnitude
of alpha equal to 1 is
periodic or not periodic.
And the clue to that can be
inferred by examining this
expression.
In particular, in the
discrete-time case with the
magnitude of alpha equal to 1,
we have pure sinusoids in the
real part and the
imaginary part.
And in fact, in a
continuous-time case with r
equal to 0, we have sinusoids
in the real part and the
imaginary part.
In a continuous-time case when
we have a pure complex
exponential, so that the terms
aren't exponentially growing
or decaying, those
exponentials are always periodic.
Because, of course, the real
and imaginary sinusoidal
components are periodic.
In the discrete-time case, we
know that the sinusoids may or
may not be periodic, depending
on the value of omega_0.
And so in fact, in the
discrete-time case, the
exponential e ^ (j omega_0 n),
that I've indicated here, may
or may not be periodic depending
on what the value of
omega_0 is.
OK.
Now, to summarize, in this
lecture I've introduced and
discussed a number of important
basic signals.
In particular, sinusoids and
real and complex exponentials.
One of the important outcomes of
the discussion, emphasized
further in the text, is that
there are some very important
similarities between them.
But there are also some very
important differences.
And these differences will
surface when we exploit
sinusoids and complex
exponentials as basic building
blocks for more general
continuous-time and
discrete-time signals.
In the next lecture, what I'll
discuss are some other very
important building blocks,
namely, what are referred to
as step signals and
impulse signals.
And those, together with the
sinusoidal signals and
exponentials as we've talked
about today, will really form
the cornerstone for,
essentially, all of the signal
and system analysis that we'll
be dealing with for the
remainder of course.
Thank you.
