8 - Exercise 3 Recap + Exam Prep [ID:58268]
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All right, let's start on time this time.

Yeah, today we will just do a quick recap of exercise three.

There's not much to talk about, which is good for you and good for me, I guess.

Those temperatures that we have today.

I don't really want to waste too much time in here.

We will today primarily talk about the 2D FURIA transform.

I think most of you already more or less understand how it works.

I said I can't share my screen.

Give me a second.

That's a very nice PowerPoint to accompany that.

If I can't find it.

I should have sorted it out a bit beforehand.

I hear this.

This isn't the correct one.

Change the...

I hear this.

All right, I hope that the basic concept of the FURIA transform was more or less already covered

in the lecture so that you...

I hope that you understand it, how it works in theory.

In theory, we could also just repeat everything that was mentioned already.

But yeah, it's not really worth it.

So we'll just mention a few sections that might be very relevant for you.

And this is the first thing.

Don't worry about the German slides.

I will explain it in English.

What we have here at the top is a signaling ring.

And we want to transform this green signal to...

Yeah, FURIA transform.

But this comes with a problem.

Let's stop the recording.

If we use the FURIA transform, the FURIA transform assumes that our signal is periodic so that

if we reach the boundaries of the signal that we look at, that it just continues in the same way.

So it just repeats and repeats and repeats without anything to it.

But what if that is not the case?

What if the signal doesn't repeat, for example, because we used the same...

Yeah, the false or wrong sampling rate?

Well, then we can have certain effects that we don't want to see.

In this case, we have an example where it fits pretty well, as you can see.

And here, when we FURIA transform it, we have this one peak that we expect to see,

because the signal at the top is more or less a sine signal or a sine wave without any extras.

So we expect a peak in a FURIA transform, as you can see here at 10 Hz.

But then we also have this low area.

That's far lower than the peak.

And that's normal to have it to some degree, but the strength of the areas besides the peak

also depends on this property, on this leakage property.

Oh, you don't hear me. That's not good.

Should be able to hear me. It's weird.

I do hear you, actually.

Oh, okay. Yeah.

Then... Okay, yeah.

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00:45:53 Min

Aufnahmedatum

2025-07-02

Hochgeladen am

2025-07-03 14:48:45

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en-US

Primarily a discussion of the Fourier Transform

Tags

Introduction Python Computer Vision Machine Learning
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