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Hello everybody.

So we are starting to experience the mid-semester breakdown in attendance rates,

but I'm happy that still a lot of the people that are listed in the course that will go to the exam probably are here.

So for those of you that are here, good news is it's better to be actually physically present in the lecture

and kind of doze off and sleep than to not go.

So your mean grade, if you are just here and sleep, is 0.6 better.

So always have that in mind.

It's not my recommendation to sleep. That's not what you should infer from that information,

but it's good to be physically present in the lectures.

Today I would like to conclude the topic on biomedical signal analysis filtering,

and therefore we will talk today about frequency filters, our filters in the frequency domain.

We will talk about specific filter concepts like the adaptive filter concept and the Wiener filter.

And I hope that after the lecture today everybody will understand how you can build the optimal filter,

which the Wiener filter promises to do.

Last week we talked about a few things, and as two weeks ago I would like to do a Tweet back with you.

So it's just two questions. Let's not do it with the smartphones for the reasons I gave you.

So we'll do a hand-based answering again.

So we have a question with gaps, derivative-based operator, blah blah blah, the DC component, blah blah blah,

low frequencies and blah blah blah, high frequencies, the phase response is blah blah blah.

So how would you fill the gaps? You see the answers, ABCD.

So the answers translate for those of you that have not been present last two weeks ago.

The ABCD translates to either one finger for A, two fingers for B, three fingers for C or four fingers for D.

So everybody prepare your fingers when you have the answer,

and everybody is asked to give the answer at the same time when I say now.

So think about the possible answers.

So everybody prepare your fingers, one, two, three or four fingers.

And I will say three, two, one now, and everybody put your hand up.

I don't see lots of prepared fingers, so prepare your fingers.

Three, two, one, up.

Wow, that's good. Lots of two-finger answers, so Bs, and that's correct.

So we talked about the derivative-based operator, the filter in the time domain, it's a very simple concept.

It basically eliminates the DC component, so you get rid of it.

And that's good, especially when you think about noise processes, for example.

It amplifies the DC component, it attenuates the DC component, it amplifies high frequencies, and the phase response is linear.

So that's the correct answer here.

Again, another answer to give.

So what is the correct formula here?

The input-output relation of the first order derivative operator in the time domain.

What is it?

Answer A, B, C, D.

Everybody has understood the question and is ready to give an answer.

So three, two, one, now.

Okay, so when the lecturer also gives the answer, then it's super simple, you can just switch.

So answer C is correct.

It's a very simple time domain filter that you can use when you have operations where you want to use derivative-based operations to do certain things,

and those things we talked about last week in the lecture.

So that's where we are currently, or what we have talked about currently.

We have talked about basic filter concepts, the S and the set domain representations of these filters.

There's a bracket missing.