Hi, everyone.
In this lecture, I will introduce you briefly some basic concepts.
We eat and we will need to transform.
Once a week we bleat isa.
We like oscillation.
It's a brief oscillation or small alternation.
Leat means a small.
Is amplitude begins at zero.
The increase is increases and decreases the vein return to zero y or more times.
And here are some examples of the we release.
The first is Mexican and we will eat you see from zero thing increase decrease return to zero.
The second one.
This one is more complete.
You see is.
From zero.
They increase, decrease, increase again.
Decrease.
Yeah.
Like if this thing return to zero.
This is a complex.
Someone will lead.
You mean complex?
You see, it's quite a complex and so forth.
Okay.
What's really stressful in the signal process context, it is a method to decompose an input signal
of interest into a set of atoms, waveforms and i.e. that we release it provides a way to analyze the
signal by examining the coefficients or wins of these people is that we have leads.
Transform all wavelet analyses is probably the most recent solution to overcome the shortcomings of
the free entrance transform.
There are two basic factors for a family to transform.
The first one is skill, or at least.
It is as stretched or compressed.
We've linked shift.
Our location is a position.
Position of the late.
We really do transform uses in their products to compare their similarities.
Yeah.
These really the similarities between the signal and.
The wavelet and varia skills and the position using different skills.
I.e. the seeped and compressed and stretched portion of it.
Yeah.
It's a sample of the diagram.
They obtained the wavelet coefficient.
There are relations between the scale and the frequencies.
Yuri Yeah.
This is the principle.
Yeah.
Small skill, a.
Means compressed, really compressed.
I see this white smoke compressed.
We really.
Means rapidly changing details.
Corresponds to high frequency.
Long skills.
This one line skill.
A factory means stretched.
We really?
It means slowly changing course features.
Which corresponds to low frequency.
Okay.
Thank you.
See you in the next lecture.