Hello.
And wine.
In this lecture, we will glance over briefly some basic concepts for your transform FTE.
As you know, there are two great theories or tools in the field of signal processing.
One is fully a transform.
Another is really to transform.
From the development history of these two theories, wavelet analyses was developed, developed based
on the advantages of the transform.
So it is very necessary to touch some basic concepts of free address for.
And this course is a practical course.
We were only taught some basic concepts of the theory rather than digging into the mathematics behind.
Okay.
Let's see what's free address for.
A fully address phone.
Can transform a signal.
This is a signal into.
Sign.
Assign a voice at different frequencies.
In this mathematical equation.
C f omega is the frequency.
T is the signal e power i omega t is the sum of a series of signs and a cool signs.
From this increasing we can see the big disadvantages of fully it transform is that it has only frequency
resolution.
And no time resolution.
Yes, only the f omega.
The frequency.
We are able to determine all the frequencies present.
But we don't know when the present.
Fully transform works well in Tunisia's stationary small state.
While it doesn't work well, these continuous now stationary and bursty data.
And in the short time the address for and as TFT.
Eight.
Cut the signal of interest into several person by foot window.
Yeah.
On this diagram?
Yeah.
This window and a theme analyzes the past separately.
And by moving the window.
Yeah.
That's when it.
And also from the equation we can see.
Yeah, there's a time this tool is a time localization factor thing or omega the frequency.
Yeah.
The difference from the traditional player transform you see we add a window is W and T minus Paul this
is the window.
So now it is able to get more information about are aware of the different the frequency companies.
But the problems are how we can define this window, especially the sites or sites.
So the strategy was knowing to have poor localization improve time in a frequency.
This is why we are talking about the we willing to transform.
Thank you.
See you in the next lecture about valid analyses of transform.