# Lecture 5, Properties of Linear, Time-invariant Systems | MIT RES.6.007 Signals and Systems PROFESSOR: Last time,
we talked about the representation of linear
time-invariant systems through the convolution sum in the
discrete-time case and the convolution integral in the
continuous-time case. Now, although the derivation
was relatively straightforward, in fact, the
result is really kind of amazing because what it tells
us is that for linear time-invariant systems, if we
know the response of the system to a single impulse at
t=0, or in fact, at any other time, then we can
determine from that its response to an arbitrary input
through the use of convolution. Furthermore, what we’ll see as
the course develops is that, in fact, the class of linear
time-invariant systems is a very rich class. There are lots of systems
that have that property. And in addition, there are
lots of very interesting things that you can do with
linear time-invariant systems. In today’s lecture, what I’d
like to begin with is focusing on convolution as an algebraic
operation. And we’ll see that it has a
number of algebraic properties that in turn have important
implications for linear time-invariant systems. Then we’ll turn to a discussion
of what the characterization of linear
time-invariant systems through convolution implies, in terms
of the relationship, of various other system properties to the impulse response. Let me begin by reminding you
of the basic result that we developed last time, which
is the convolution sum in discrete time, as I indicate
here, and the convolution integral in continuous time. And what the convolution sum,
or the convolution integral, tells us is how to relate the
output to the input and to the system impulse response. And the expression looks
basically the same in continuous time and
discrete time. And I remind you also that we
talked about a graphical interpretation, where
essentially, to graphically interpret convolution required,
or was developed, in terms of taking the system
impulse response, flipping it, sliding it past the input, and
positioned appropriately, depending on the value of the
independent variable for which we’re computing the convolution,
and then multiplying and summing in the
discrete-time case, or integrating in the
continuous-time case. Now convolution, as an algebraic
operation, has a number of important
properties. One of the properties of
convolution is that it is what is referred to as commutative. Commutative means that we can
think either of convolving x with h, or h with x, and the
order in which that’s done doesn’t affect the
output result. So summarized here is what the
commutative operation is in discrete time, or in
continuous time. And it says, as I just
indicated, that x[n] convolved with h[n] is equal to h[n] convolved with x[n]. Or the same, of course,
in continuous time. And in fact, in the lecture
last time, we worked an example where we had, in
discrete time, an impulse response, which was an
exponential, and an input, which is a unit step. And in the text, what you’ll
find is the same example worked, except in that case, the
input is the exponential. And the system impulse
response is the step. And that corresponds to the
example in the text, which is example 3.1. And what happens in that example
is that, in fact, what you’ll see is that the same
result occurs in example 3.1 as we generated in
the lecture. And there’s a similar comparison
in continuous time. This example was worked
in lecture. And this example is worked
in the text. In other words, the text works
the example where the system impulse response
is a unit step. And the input is
an exponential. All right, now the commutative
property, as I said, tells us that the order in which we do
convolution doesn’t affect the result of the convolution. The same is true for
continuous time and discrete time. And in fact, for the other
algebraic properties that I’ll talk about, the results are
exactly the same for continuous time and
discrete time. So in fact, what we can do is
drop the independent variable as an argument so that we
suppress any kind of difference between continuous
and discrete time. And suppressing the independent
variable, we then state the commutative property
as I’ve rewritten it here. Just x convolved with h equals
h convolved with x. The same in continuous time
and discrete time. Now, the derivation of the
commutative property is, more or less, some algebra
which you can follow through in the book. It involves some changes
of variables and some things of that sort. What I’d like to focus on with
that and other properties is not the derivation, which you
can see in the text, but rather the interpretation. So we have the commutative
property, and now there are two other important algebraic
properties. One being what is referred to
as the associative property, which tells us that if we have x
convolved with the result of convolving h1 with h2, that’s
exactly the same as x convolved with h1, and that
result convolved with h2. And what this permits is for
us to write, for example, x convolved with h1 convolved with
h2 without any ambiguity because it doesn’t matter from
the associative property how we group the terms together. The third important property is
what is referred to as the distributive property, namely
the fact that convolution distributes over addition. And what I mean by that is what
I’ve indicated here on the slide: that if I think of x
convolved with the sum of h1 and h2, that’s identical to
first convolving x with h1, also convolving x with h2, and
then adding the two together. And that result will be the
same as this result. So convolution is commutative,
associative, and it distributes over addition. Three very important algebraic
properties. And by the way, there are other
algebraic operations that have that same property. For example, multiplication
of numbers is likewise commutative, associative,
and distributive. Now let’s look at what these
three properties imply specifically for linear
time-invariant systems. And as we’ll see, the
implications are both very interesting and very
important. Let’s begin with the commutative
property. And consider, in particular,
a system with an impulse response h. And I represent that by simply
writing the h inside the box. An input x and an output,
then, which is x * h. Now, since this operation is
commutative, I can write instead of x * h, I
can write h * x. And that would correspond to a
system with impulse response x, and input h, and
output then h * x. So the commutative property
tells us that for a linear time-invariant system, the
system output is independent of which function we call the
input and which function we call the impulse response. Kind of amazing actually. We can interchange the role of
input and impulse response. And from an output point of
view, the output or the system doesn’t care. Now furthermore, if we combine
the commutative property with the associative property,
we get another very interesting result. Namely that if we have two
linear time-invariant systems in cascade, the overall system
is independent of the order in which they’re cascaded. And in fact, in either case, the
cascade can be collapsed into a single system. To see this, let’s first
consider the cascade of two systems, one with impulse
response h1, the other with impulse response h2. And the output of the first
system is then x * h1. And then that is the input
to the second system. And so the output of that is
that result convolved with h2. So this is the result of
cascading the two. And now we can use the
associative property to rewrite this as x * (h1
* h2), where we group these two terms together. And so using the associative
property, we now can collapse that into a single system with
an input, which is x, and impulse response, which
is h1 * h2. And the output is then x
convolved with the result of those two convolved. Next, we can apply the
commutative property. And the commutative property
says we could write this impulse response that way, or
we could write it this way. And since convolution is
commutative, the resulting output will be exactly
the same. And so these resulting outputs
will be exactly the same. And now, once again we can use
the associative property to group these two terms
together. And x * h2 corresponds to
putting x first through the system h2 and then that output
through the system h1. And so finally applying the
associative property again, as I just outlined, we can expand
that system back into two systems in cascade with h2
first and h1 second, OK, well that involves a certain
amount of algebraic manipulation. And that is not the algebraic manipulation that is important. It’s the result that
it’s important. And what the result says, to
reiterate, is if I have two linear time-invariant systems in
cascade, I can cascade them in any order, and the
result is the same. Now you might think, well gee,
maybe that actually applies to systems in general, whether
you put them this way or that way. But in fact, as we talked
about last time, and I illustrated with an example, in
general, if the systems are not linear and time-invariant,
then the order in which they’re cascaded is important
to the interpretation of the overall system. For example, if one system took
the square root and the other system doubled the input,
taking the square root and then doubling gives us a
different answer than doubling first and then taking
the square root. And of course, one can construct
much more elaborate examples than that. So it’s a property very
particular to linear time-invariant systems. And also one that we will
exploit many, many times as we go through this material. The final property related to an
interconnection of systems that I want to just indicate
develops out of the distributive property. And what it applies to is
an interpretation of the interconnection of systems
in parallel. Recall that the parallel
combination of systems corresponds, as I indicate here,
to a system in which we simultaneously feed the input
into h1 and h2, these representing the impulse
responses. And then, the outputs
are summed to form the overall output. And using the fact that
convolution distributes over addition, we can rewrite
this as x * (h1 + h2). And when we do that then, we
can recognize this as the output of a system with input x
and impulse response, which is the sum of these two
impulse responses. So for linear time-invariant
systems in parallel, we can, if we choose, replace that
interconnection by a single system whose impulse response
is simply the sum of those impulse responses. OK, now we have this very
powerful representation for linear time-invariant systems
in terms of convolution. And we’ve seen so far in this
lecture how convolution and the representation through the
impulse response leads to some important implications for
system interconnections. What I’d like to turn to now
are other system properties and see how, for linear
time-invariant systems in particular, other system
properties can be associated with particular properties or
characteristics of the system impulse response. And what we’ll talk about are
a variety of properties. We’ll talk about the issue of
memory, we’ll talk about the issue of invertibility, and
we’ll talk about the issue of causality and also stability. Well, let’s begin with
the issue of memory. And the question now is what are
the implications for the system impulse response for a
linear time-invariant system? Remember that we’re always
imposing that on the system. What are the implications on
the impulse response if the system does or does
not have memory? Well, we can answer
that by looking at the convolution property. And we have here, as a reminder,
the convolution integral, which tells us how
x(tau) and h(t – tau) are combined to give us y(t). And what I’ve illustrated
above is a general kind of example. Here is x(tau). Here is h(t – tau). And to compute the output at
any time t, we would take these two, multiply them
together, and integrate from -infinity to +infinity. So the question then is what
can we say about h(t), the impulse response in order to
guarantee, let’s say, that the output depends only on
the input at time t. Well, it’s pretty much obvious
from looking at the graphs. If we only want the output to
depend on x(tau) at tau=t, then h(t – tau) better be
non-zero only at tau=t. And so the implication then is
that for the system to be memoryless, what we require is
that h(t – tau) be non-zero only at tau=t. So we want the impulse response
to be non-zero at only one point. We want it to contribute
something after we multiply and go through an integral. And in effect, what that says is
the only thing that it can be and meet all those conditions is a scaled impulse. So if the system is to be
memoryless, then that requires that the impulse response
be a scaled impulse. Any other impulse response then,
in essence, requires that the system have memory,
or implies that the system have memory. So for the continuous-time case
then, memoryless would correspond only to the impulse
response being proportional to an impulse. And in the discrete-time case,
a similar statement, in which case, the output is just
proportional to the input, again either in the
continuous-time or in the discrete-time case. All right. Now we can turn our attention
to the issue of system invertibility. And recall that what is meant by
invertibility of a system, or the inverse of a system. The inverse of a system is a
system, which when we cascade it with the one that we’re
inquiring about, the overall cascade is the identity
system. In other words, the output
is equal to the input. So let’s consider a system
with impulse response h, input is x. And let’s say that the impulse
response of the inverse system is h_i, and the output is y. Then, the output of this system
is x * (h * h_i). And we want this to come
out equal to x. And what that requires than is
that this convolution just simply be equal to an impulse,
either in the discrete-time case or in the continuous-time
case. And under those conditions
then, h_i is equal to the inverse of h. Notationally, by the way, it’s
often convenient to write instead of h_i as the impulse
response of the inverse, you’ll find it convenient often
and more typical to write as the inverse, instead
of h_i, h^(-1). And we mean by that the inverse
impulse response. And one has to be careful not
to misinterpret this as the reciprocal of h(t) or h(n). What’s meant in this notation
is the inverse system. Now, if h_i is the inverse of
h, is h the inverse of h_i? Well, it seems like that ought
to be plausible or perhaps make sense. The question, if you believe
that the answer is yes, is how, in fact, do you
verify that? And I’ll leave it to you
to think about it. The answer is yes, that if h_i is the inverse
of h, then h is the inverse of h_i. And the key to showing that is
to exploit the fact that when we take these systems and
cascade them, we can cascade them in either order. All right now let’s turn to
another system property, the property of stability. And again, we can tie that
property directly to issues related, in particular, to the
system impulse response. Now, stability is defined as
we’ve chosen to define it and as I’ve defined it previously,
as bounded-input bounded-output stability. In other words, for every
bounded input is a bounded output. What you can show– and I won’t go through the
algebra here; it’s gone through in the book– is that a necessary and
sufficient condition for a linear time-invariant system
to be stable in the bounded-input bounded-output
sense is that the impulse response be what is referred
to as absolutely summable. In other words, if you take the
absolute values and sum them over infinite limits,
that’s finite. Or in the continuous-time
case, that the impulse response is absolutely
integrable. In other words, if you take the
absolute values of h(t) and integrate, that’s finite. And under those conditions,
the system is stable. If those conditions are
violated, then for sure, as you’ll see in the text, the
system is unstable. So stability can also
be tied to the system impulse response. Now, the next property that I
want to talk about is the property of causality. And before I do, what I’d like
to do is introduce a peripheral result that
we’ll then use– when we talked about
causality– namely what’s referred to as the
zero input response of a linear system. The basic result, which is a
very interesting and useful one, is that for a
linear system– and in fact, it’s whether it’s
time-invariant or not, that this applies– if you put nothing into it,
you get nothing out of it. So if we have an input x(t)
which is 0 for all t, and if the output of that system is
y(t), if the input is 0 for all time, then the output
likewise is 0 for all time. That’s true for continuous time,
and it’s also true for discrete time. And in fact, to show that
result is pretty much straightforward. We could do it either by using
convolution, which would, of course, be associated with
linearity and time invariance. But in fact, we can show that
property relatively easily by simply using the fact that, for
a linear system, what we know is that if we have an
input x(t) with an output y(t), then if we scale the
input, then the output scales accordingly. Well, we can simply choose, as
the scale factor, a=0. And if we do that, it
says put nothing in, you get nothing out. And what we’ll see is that has
some important implications in terms of causality. It’s important, though, while
we’re on it, to stress that not every system, obviously,
has that property. That if you put nothing in,
you get nothing out. A simple example is, let’s say,
a battery, let’s say not connected to anything. The output is six volts no
matter what the input is. And it of course then doesn’t
have this zero response to a zero input. It’s very particular
to linear systems. All right, well now let’s see
what this means for causality. To remind you, causality says,
in effect, that the system can’t anticipate the input. That’s what, basically,
causality means. When we talked about it
previously, we defined it in a variety of ways, one of which
was the statement that if two inputs are identical up until
some time, then the outputs must be identical up until
the same time. The reason, kind of intuitively,
is that if the system is causal– so it can’t anticipate
the future– it can’t anticipate whether
these two identical inputs are sometime later going to change
from each other or not. So causality, in general, is
simply this statement, either continuous-time or
discrete-time. And now, so let’s look
at what that means for a linear system. For a linear system, what that
corresponds to or could be translated to is a statement
that says that if x(t) is 0, for t less than t_0, then
y(t) must be 0 for t less than t_0 also. And so what that, in effect,
says, is that the system– for a linear system to be
causal, it must have the property sometimes referred to
as initial rest, meaning it doesn’t respond until there’s
some input that happens. That it’s initially
at rest until the input becomes non-zero. Now, why is this true? Why is this a consequence of
causality for linear systems? Well, the reason is we know that
if we put nothing in, we get nothing out. If we have an input that’s 0 for
t less than t_0, and the system can’t anticipate whether
that input is going to change from 0 or not, then the
system must generate an output that’s 0 up until that time,
following the principle that if two inputs are identical up
until some time, the outputs must be identical up until
the same time. So this basic result for linear
systems is essentially a consequence of the statement
that for a linear system, zero in gives us zero out. Now, that tells us
how to interpret causality for linear systems. Now, let’s proceed to linear
time-invariant systems. And in fact, we can carry the
point one step further. In particular, a necessary and
sufficient condition for causality in the case of linear
time-invariant systems is that the impulse response be
0, for t less than 0 in the continuous-time case, or for
n less than 0 in the discrete-time case. So for linear time-invariant
systems, causality is equivalent to the impulse
response being 0 up until t or n equal to 0. Now, to show this essentially
follows by first considering why causality would imply
that this is true. And that follows because of the
straightforward fact that the impulse itself is
0 for t less than 0. And what we saw before is that
for any linear system, causality requires that if the
input is 0 up until some time, the output must be 0 up
until the same time. And so that’s showing the
result in one direction. To show the result in the other
direction, namely to show that if, in fact, the
impulse response satisfies that condition, then the
system is causal. While I won’t work through it
in detail, it essentially boils down to recognizing that
in the convolution sum, or in the convolution integral, if,
in fact, that condition is satisfied on the impulse
response, then the upper limit on the sum, in the
discrete-time case, changes to n. And in the continuous-time
case, changes to t. And that, in effect, says that
values of the input only from -infinity up to time n are
used in computing y[n]. And a similar kind
of result for the continuous-time case y(t). OK, so we’ve seen how the
impulse response, or rather how certain system properties
in the linear time-invariant case can, be converted into
various requirements on the impulse response of a linear
time-invariant system, the impulse response being a
complete characterization. Let’s look at some particular
examples just to kind of cement the ideas further. And let’s begin with a system
that you’ve seen previously, which is an accumulator. An accumulator, as you recall,
has an output y[n], which is the accumulated value of the
input from -infinity up to n. Now, you’ve seen in the impulse
in a previous lecture, or rather in the video course
manual for a previous lecture, that an accumulator is a linear
time-invariant system. And in fact, its impulse
response is the accumulated values of an impulse. Namely, the impulse response
is equal to a step. So what we want to answer is,
knowing what that impulse response is, what some
properties are of the accumulator. And let’s first ask
about memory. Well, we recognize that the
impulse response is not simply an impulse. In fact, it’s a step. And so this implies what? Well, it implies that the
system has memory. Second, the impulse response
is 0 for n less than 0. That implies that the
system is causal. And finally, if we look at the
sum of the absolute values of the impulse response
from -infinity to +infinity, this is a step. If we accumulate those values
over infinite limits, then that in fact comes out
to be infinite. And so what that implies, then,
is that the accumulator is not stable in the
bounded-input bounded-output sense. Now I want to turn to
some other systems. But while we’re on the
accumulator, I just want to draw your attention to the fact,
which will kind of come up in a variety of ways again
later, that we can rewrite the equation for an accumulator,
the difference equation, by recognizing that we could, in
fact, write the output as the accumulated values up to
time n – 1 and then add on the last value. And in fact, if we do that, this
corresponds to y[n-1]. And so we could rewrite this
difference equation as y[n]=y[n-1] + x[n]. So the output is the
previously-computed output plus the input. Expressed that way, what that
corresponds to is what is called a recursive difference
equation. And different equations will
be a topic of considerable emphasis in the next lecture. Now, does an accumulator
have an inverse? Well, the answer is,
in fact, yes. And let’s look at what the
inverse of the accumulator is. The impulse response of the
accumulator is a step. To inquire about its inverse,
we inquire about whether there’s a system, which when
we cascade the accumulator with that system, which I’m
calling its inverse, we get an impulse out. Well, let’s see. The impulse response of the
accumulator is a step. We want to put the step
into something and get out an impulse. And in fact, what you recall
from the lecture in which we introduced steps and impulses,
the impulse is, in fact, the first difference of
the units step. So we have a difference equation
that describes for us how the impulse is related
to the step. And so if this system does this,
the output will be that, an impulse. And so if we think of x_2[n] as the input and y_2[n] as the output, then the
difference equation for the inverse system is what
I’ve indicated here. And if we want to look at the
impulse response of that, we can then inquire as to what
the response is with an impulse in. And what develops in a
straightforward way then is delta[n], which is our impulse
input, minus delta[n-1] is equal to the impulse
response of the inverse system. So I’ll write that
as h^(-1)[n] (h-inverse of n). Now, we have then that the
accumulator has an inverse. And this is the inverse. And you can examine issues
of memory, stability, causality, et cetera. What you’ll find is that the
system has memory, the inverse accumulator. It’s stable, and it’s causal. And it’s interesting to note, by
the way, that although the accumulator was an unstable
system, the inverse of the accumulator is a
stable system. In general, if the system is
stable, its inverse does not have to be stable
or vice versa. And the same thing
with causality. OK now, there are a number of
other examples, which, of course, we could discuss. And let me just quickly point
to one example, which is a difference equation, as
I’ve indicated here. And as we’ll talk about in
more detail in our next lecture, where we’ll get
involved in a fairly detailed discussion of linear
constant-coefficient difference and differential
equations, this falls into that category. And under the imposition of
what’s referred to as initial rest, which corresponds to the
response being 0 up until the time that the input becomes
non-zero, the impulse response is a^n times u[n]. And something that you’ll be
asked to think about in the video course manual is whether
that system has memory, whether it’s causal, and
whether it’s stable. And likewise, for a linear
constant coefficient differential equation, the
specific one that I’ve indicated here, under the
assumption of initial rest, the impulse response is
e^(-2t) times u(t). And in the video course manual
again, you’ll be asked to examine whether the system has
memory, whether it’s causal, and whether it’s stable. OK well, as I’ve indicated, in
the next lecture we’ll return to a much more detailed
discussion of linear constant-coefficient
differential and difference equations. Now, what I’d like to finally do
in this lecture is use the notion of convolution in a much
different way to help us with a problem that I
alluded to earlier. In particular, the issue of how
to deal with some of the mathematical difficulties
associated with impulses and steps. Now, let me begin by
illustrating kind of what the problem is and an example of the
kind of paradox that you sort of run into when dealing
with impulse functions and step functions. Let’s consider, first of all,
a system, which is the identity system. And so the output is, of course,
equal to the input. And again, we can talk about
that either in continuous time or in discrete time. Well, we know that the function
that you convolve with a signal to retain the
signal is an impulse. And so that means that the
impulse response of an identity system is an impulse. Makes logical sense. Furthermore, if I take two
identity systems and cascade them, I put in an input, get
the same thing out of the first system. That goes into the
second system. Get the same thing out
of the second. In other words, if I have two
identity systems in cascade, the cascade, likewise, is
an identity system. In other words, this
overall system is also an identity system. And the implication there is
that the impulse response of this is an impulse. The impulse response of
this is an impulse. And the convolution of those
two is also an impulse. So for continuous time, we
require, then, that an impulse convolved with itself
is an impulse. And the same thing for
discrete time. Now, in discrete time, we don’t
have any particular problem with that. If you think about convolving
these together, it’s a straightforward mathematical
operation since the impulse in discrete time is very
nicely defined. However, in continuous time, we
have to be somewhat careful about the definition of the
impulse because it was the derivative of a step. A step has a discontinuity. You can’t really differentiate
at a discontinuity. And the way that we dealt with
that was to expand out the discontinuity so that it had
some finite time region in which it happened. When we did that, we ended up
with a definition for the impulse, which was the
limiting form of this function, which is a rectangle
of width Delta, and height 1 / Delta, and an area equal to 1. Now, if we think of convolving
this signal with itself, the impulse being the limiting
form of this, then the convolution of this with itself
is a triangle of width 2 Delta, height 1 /
Delta, and area 1. In other words, this triangular
function is this approximation delta_Delta(t)
convolved with delta_Delta(t). And since the limit of this
would correspond to the impulse response of the identity
system convolved with itself, the implication is that
not only should the top function, this one, correspond
in its limiting form to an impulse, but also this should
correspond in its limiting form to an impulse. So one could wonder well,
what is an impulse? Is it this one in the limit? Or is it this one
in the limit? Now, beyond that– so kind of
what this suggests is that in the limiting form, you kind of
run into a contradiction unless you don’t try to
distinguish between this rectangle and the triangle. Things get even worse when you
think about what happens when you put an impulse into
a differentiator. And a differentiator is a very
commonly occurring system. In particular, suppose we had a
system for which the output was the derivative
of the input. So if we put in x(t), we
got out dx(t) / dt. If I put in an impulse, or if
I talked about the impulse response, what is that? And of course, the problem is
that if you think that the impulse itself is very badly
behaved, then what about its derivative, which is not only
infinitely big, but there’s a positive-going one, and a
negative-going one, and the difference between there
has some area. And you end up in a
lot of difficulty. Well, the way around this,
formally, is through a set of mathematics referred to as
generalized functions. We won’t be quite that formal. But I’d like to, at least,
suggest what the essence of that formality is. And it really helps us in
interpreting the impulses in steps and functions
of that type. And what it is is an operational
definition of steps, impulses, and their
derivatives in the following sense. Usually when we talk about a
function, we talk about what the value of the function is
at any instant of time. And of course, the trouble
with an impulse is it’s infinitely big, in zero width,
and has some area, et cetera. What we can turn to is what is
referred to as an operational definition where the operational
definition is related not to what the impulse
is, but to what the impulse does under the operation
of convolution. So what is an impulse? An impulse is something,
which under convolution, retains the function. And that then can serve as a
definition of the impulse. Well, let’s see where
that gets us. Suppose that we now want to talk
about the derivative of the impulse. Well, what we ask about is
what it is operationally. And so if we have a system,
which is a differentiator, and we inquire about its impulse
response, which let’s say we define notationally as u_1(t). What’s important about this
function u_1(t) is not what it is at each value of time
but what it does under convolution. What does it do under
convolution? Well, the output of the
differentiator is the convolution of the input with
the impulse response. And so what u_1(t) does under
convolution is to differentiate. And that is the operational
definition. And now, of course, we can
think of extending that. Not only would we want to think
about differentiating an impulse, but we would also
want to think about differentiating the derivative
of an impulse. We’ll define that as
a function u_2(t). u_2(t)– because we have this impulse
response convolved with this one is u_1(t) * u_1(t). And what is u_2(t)
operationally? It is the operation such that
when you convolve that with x(t), what you get is the
second derivative. OK now, we can carry this
further and, in fact, talk about the result of convolving
u_1(t) with itself more times. In fact, if we think of the
convulution of u_1(t) with itself k times, then
logically we would define that as u_k(t). Again, we would interpret
that operationally. And the operational definition
is through convolution, where this corresponds to u_k(t) being
the impulse response of k differentiators in cascade. So what is the operational
definition? Well, it’s simply that x(t)
* u_k(t) is the k derivative of x(t). And this now gives us a set
of what are referred to as singularity functions. Very badly behaved
mathematically in a sense, but as we’ve seen, reasonably well
defined under an operational definition. With k=0, incidentally, that’s
the same as what we have referred to previously
as the impulse. So with k 0, that’s
just delta(t). Now to be complete, we can also
go the other way and talk about the impulse response of
a string of integrators instead of a string of
differentiators. Of course, the impulse
response of a single integrator is a unit step. Two integrators together
is the integral of a unit step, et cetera. And that, likewise, corresponds
to a set of what are called singularity
functions. In particular, if I take a
string of m integrators in cascade, then the impulse
response of that is denoted as u sub minus m of t. And for example, with a single
integrator, u sub minus 1 of t corresponds to our unit step as
we talked about previously. u sub minus 2 of t corresponds
to a unit ramp, et cetera. And there is, in fact, a reason
for choosing negative values of the argument when
going in one direction near integration as compared with
positive values of the argument when going in the
other direction, namely differentiation. In particular, we know that with
u sub minus m of t, the operational definition is the
mth running integral. And likewise, u_k(t)– so with a positive
sub script– has an operational definition,
which is the derivative. So it’s the kth derivative
of x(t). And partly as a consequence of
that, if we take u_k(t) and convolve it with u_l(t), the
result is the singularity function with the subscript,
which is the sum of k and l. And that holds whether this
is positive values of the subscript or negative values
of the subscript. So just to summarize this last
discussion, we’ve used an operational definition to talk
about derivatives of impulses and integrals of impulses. This led to a set of singularity
functions– what I’ve called singularity
functions– of which the impulse and the
step are two examples. But using an operational
definition through convolution allows us to define, at least in
an operational sense, these functions that otherwise
are very badly behaved. OK now, in this lecture and
previous lectures, for the most part, our discussion
has been about linear time-invariant systems in
fairly general terms. And we’ve seen a variety of
properties, representation through convolution, and
properties as they can be associated with the
impulse response. In the next lecture, we’ll turn
our attention to a very important subclass of those
systems, namely systems that are describable by linear
constant-coefficient difference equations in the
discrete-time case, and linear constant-coefficient
differential equations in the continuous-time case. Those classes, while not forming
all of the class of linear time-invariant
systems, are a very important sub class. And we’ll focus in on those
specifically next time. Thank you.

## 25 thoughts on “Lecture 5, Properties of Linear, Time-invariant Systems | MIT RES.6.007 Signals and Systems”

1. William Linkmeyer says:

127 views
fuck america

2. Diarmuid says:

opening music is dope

3. Mojomatrix says:

Free Ivy League lectures ? Shit man, I'm all up in this !

4. Tibor Szekeres says:

Time travel still possible!!! Thanks Utube!

5. Juan Pablo Monzon says:

I wish I had profesors like these from MIT when I was in college. Then again I wasn't at MIT.

6. maneymac says:

little bit confusing……… 🙁

7. Yep, this is a fake name. says:

Prof. Oppenheim, RESPECT. Your teaching is an inspiration.

8. Yep, this is a fake name. says:

Haha!

In any case, I find that watching the video again helps a great deal. Good luck!

9. ziqian zhang says:

ummmn. i dont get it yet..

10. The Compiler says:

didn't you watch the previous lectures' videos? 😛

11. Truth B Heard says:

im reading oppenheim's book on signals and systems.

12. Jiangqiao Hu says:

i got totally confused on this course when i was 19 in the second year of my university, and now after several years, when i came back to study it again, everything becomes so easy. human brain does changes in their early 20s.

13. Adil Can Balçık says:

different shirt , same dude 🙂

14. BYAMBAJARGAL Lhagwaa says:

Thank you

15. Ashwin Aashu says:

Alan is perhaps the best signals engineer ever. The way he teaches compels me to know more, understand and reason – with myself and others. I love the MIT courseware , thank you for this wonderful series.

16. Andy says:

In terms of Bounded Input, Bounded Output stability, I read the Wikipedia proof for discrete time systems and it makes perfect sense:

|y| = |h*x| <= sum{ |h(n-k)| |x(k)| } by the triangle inequality. Now pull out the maximum value x can take on and we have that L1 norm must exist.

Another way to see the second step is that we can split the sum into 4 cases when h(n-k) and x(n) are (-, -), …, (+, +). We want all of these to be controllable, so they shouldn't be infinite and the same technique for guaranteeing that is the L1 norm.

17. Arnab Thakuria says:

this man is amazing

18. Khaled Jofanee says:

please guys, i want the reference of this course

19. Subrahmanyam Gorthi says:

As usual. great Lecture by Prof. Oppenheim. I have a question, and I hope someone here will kindly answer that.
Can there be a system which linear but non-causal?
I could not think of any example where a linear system is non-causal. Please shed some light.

20. jitendra singh Bhadoriya says:

I like the arrangement of white board …sometime it never ends

21. gadipudi tushara says:

These videos are so helpful for me.Thank you sir.

22. Deepak Mecheri says:

I was so used to hearing the lectures at 2x speed now, when I play it in normal, it literally feels like the professor is talking in slow-mo

23. Melad Alaqra says:

Great lecture, I would like to share this examples about generalized functions which is pointed by the Prof. in the last minutes of the lecture.
24. Animesh S. says:
25. Pedro Ernias says: