-
There. So now what I'm going to show you
is this. I'm going to sample this over and
-
over again. Okay so in other words suppose
I have my detector send out a read a
-
signal. I read a signal this. I keep
reading 30 second slices of time. Okay and
-
presumably I keep getting different noise
every time, right, cause white noise. So
-
what I'm going to do is ask what happens
to these if I go through and I start
-
sampling over and over again. Okay. I can
do anything I want. I am master of my
-
code. I can make noise a million. And by
the way, that might just be one
-
realization. I run it again. Okay, so
there's a little bit of a spike there,
-
fine, we'll go 30. I want to try to clean
that up. As far as you can see in that
-
blue there is no evidence of anything
right remember you got to launch a missile
-
or do something cool like that or save
your dog. So you got to figure this out,
-
don't want any dead dogs in this class,
cuz I'll be held accountable, probably.
-
Alright, here's what we're going to do,
we're going to do a little loop, for J
-
equals 1-30. So what I going to do in this
loop is the following. I'm going to take
-
my. Deal here. Let's, let's actually this
just needs to be defined once so let's
-
bring this outside Lou so I have some
noise which is 30. Yeah let me take away
-
these sub-plotting routines for a moment.
Okay. Here's what I want to do. In this
-
loop we've gone this loop 30 times and
each time we do it I'm going to generate a
-
new signal. Sorry I have the same signal,
but I'm going to generate a new signal
-
because I'm going to add noise to it. So
it's the same signal that I keep reading,
-
right? So in a radar problem if there's a
plane out there, I keep picking up that
-
signal from the plane but it's buried, but
it's buried inside noise which keeps
-
changing in time. Okay so every time I do
this I have this issue of t that stays the
-
same but now the noise changes every time.
So every time I do this I get different
-
noise on it, different noise on it. So
each one of'em just looks like just what
-
we had before nothing there. So what I'm
going to do is I'm going to just keep
-
doing this. And I'm going to ask if I do
this and I go through this loop I want to
-
actually just calculate the average of
what my spectrum looks like. I'm going to
-
add if I take 30 measurements. I'm going
to add them together and divide by 30.
-
What happens to the noise? What I know is,
is white noise, when it's uncorrelated.
-
All that noise should have mean. Sweet.
Everything just going to just fair
-
anybody. One. Tough crowd. Tough crowd.
Alright. I'm going to show ya a magic
-
trick, right? Okay, all right laptops
usually don't come out of your sleeves
-
[LAUGH] iPhones can. but. Okay. One
believer in the class. Let's see how many
-
of the others I can make believe in this.
Alright, so here we go. . What's that?
-
Inverse. Inverse voice transform. So let's
go through this and say, okay, here's what
-
I'm going to do. I've got to keep track of
all this, right? So what I'm going to do
-
is the following. I'm going to make a
vector, called average, And I'm going to
-
start off with just a bunch of 0s in it.
And what I'm going to do with this vector
-
is after I've done computing the signal
I'll say well my average is what it was
-
before plus this UTN. We'll just keep
stacking the information in it. And at the
-
very end, after done this 30 times, we go
through this loop 30 times, keep stacking
-
my average information there. At the end
I'm just going to take this signal, and
-
divide by the total number of times I did
this, right. So I could say, alright well,
-
how about we do this? Average is equal to
absolute value of. So first I'm going to
-
shift it. I took 30 slices so just divide
by 30 we'll make this a little more
-
general in a minute actually let's do the
following. so we took 30 realizations is
-
a, I don't like to make variable names
that long. Cuz I misspelled them that
-
long. so what I'm going to do is I'm going
to take, I'm going to basically, remember
-
av is basically I've added it all
together, by the way you don't add the
-
absolute value, okay? So I'm like you take
the, , absolute value of each, an add them
-
together you take the raw signal,
otherwise you've, the minus and plus's
-
that are there don't cancel, it's all
positive, and it all adds up and it'll
-
look like no it's worse if you take more
data, right? It should always be better
-
you sample 30 times, you got 30 things now
to make a decision with, right? Okay, so I
-
add them all together. I'm going to ask
that you shift it, take steps of five,
-
divided by the realization and I want to
plot that, okay? So, let's go ahead and
-
plot it. All right, so here's why I don't
have a plot in here first. The original
-
signal. The absolute value at oop. So this
is my perfect signal, we crop that in red.
-
So remember what I did is I, before I
polluted this thing with noise, I had
-
this, I know what the answer's supposed to
be, right, and I've added noise to it. So
-
this is what the answer's supposed to be.
That red line is going to come out, okay?
-
Now, let's look at this compared to the
average, which is I added everything up,
-
divided by the total number of
realizations, and I'm going to plot that
-
in black. Okay, and by the way, let me
plot a single realization. If, if we go
-
through this loop, we went through this
loop from J equals one to realizations.
-
The last time we went through the loop we
had this thing called UTN. So that was
-
just one example of realization. Let's
also plot that. So, KS versus the absolute
-
value of, and I, FFT shift of ETN, there
and we'll plot that in yellow. I want it
-
to be sort of subtle. Okay, how about
cyan? Okay, I want it to be sort of not a
-
strong, dominant color. Or else, it'll
take up the whole picture. Okay, so first
-
of all, so let's talk about this picture.
The psy N is one measurement. My last one.
-
I took 30, that's what the psy an is. The
red is the exact answer and the black.
-
That's averaged over 30, okay? Actually,
that's not so bad look at that kind of
-
get's my peak, right? Let's see how well
we can do. So right, so un fact, what,
-
here's what I'm going to do. We're going
to start looking at realizations. What
-
happe ns if I have one realization, were I
get the blue? If I have two, how well do I
-
do? You know and then we'll start stepping
it up to see how well does this converge.
-
What's the answer if I have a thousand
reali, you know, a million, okay? What's
-
the answer of a thousa, infinity. Sorry,
that's the answer I was looking for. My
-
daughters their nine and six. And their
trying to understand this thing called
-
infinity. They've heard of it like, my
youngest daughter, six, is like what
-
happens if you, and she always makes up
these toy problems. Like, you know ten
-
billion 35, is that bigger than infinity?
. Now, all you got to go figure what
-
happens though if you multiply that plus
twenty . The older one king of gets at me,
-
be a little pissed and the younger one,
she's, she, she's there, gettin in concept
-
infinity, and if you go to infinity. That
red line will be black. The black line
-
will go red to the red line. And you had a
question. No. Yes. Okay. Yes. Why don't
-
you need to FFT shift to average? Oh, we
did already. I did. You did it when you
-
add it . Yeah. I . So now, so now let's
look at this thing here. And, in fact, you
-
know what? This is going to be pretty
sweet. We're going to make a little movie.
-
Ready? You're clearly not. But anyway,
okay. . Alright, let's, let's try to do
-
this. Let's put this inside the loop. And
I will plot it inside the loop. So when J
-
is one, we're going to plot this, hold.
And so then we're going to do this pause
-
for half a second. And so when it goes
through the first time, you'll see. In
-
fact, what we're going to do, is take out
the cyan line, that's the individual run.
-
And all we're going to plot is.
Essentially the average verses the what
-
were suppose to get ready? Okay let's see.
J, thanks. Gary, any, anybody else, I
-
think that, hopefully is it. And now,
we're going to go also through and do this
-
a hundred times. Oh yeah, it's not a good
idea either, right? Hold on, hold on. Oh,
-
so I, I just, I should call this something
else. How about that? I faked my code out.
-
Thank you. I have tw o, okay? Are we
ready? I think we're ready to push the go
-
button. It will, hold on, here it, I got
pull this in so you can see it, oh come
-
on, come on, no, okay, now I got to kill
it, okay, there it is okay, fine, it's
-
going through, see that, see what's
happening. Dang. I know what's it look
-
like its doing to you. Okay it's looking
pretty good to me. Is it looking good to
-
you? I don't know what realization we're
on. And what you can see, as we take more
-
and more realizations, right? We take more
and more averages, we keep sampling this
-
thing out. The noise cancels itself out.
And you basically get the black line is
-
just folding right on to the red but it's
suppose to. There's a real signal in
-
there, okay? Now, if there was no signal
in there it would all just go to zero.
-
Okay, so this is another way to take your
data subtract the noise out. You know
-
something about noise make use of it. If
there's anything in the problem that
-
you're not making use of you're probably
not doing the problem right, okay? Every
-
piece of information you have should be
used. Here we're using information now
-
about the noise. Okay, after a hundred,
this is what you get. So, this can be like
-
your dog problem. Your dog problem is the
following, right? You're going to first of
-
all, I haven't told you what the center
frequency of your dog problem is. Here I
-
kind of knew. You know, I embedded, I have
the answer, right? It's on my computer. I
-
said make a little signal right there in
the middle of your domain. I don't do that
-
with the dog problem. I make it up, and I
don't tell you where it is. So you've got
-
to figure out where I should even filter,
right? All right, so in this process you
-
notice if I sample a lot, I clearly have a
spike right there. So even if I didn't
-
know where to filter I'd say Oh, maybe I
should filter right around here. There
-
seems to be something there that's not
just noise. We go that?