Hello, Anwar.
In this lecture every year, you brief ideas how we with the transforms are applied in some fields.
And we need to transform.
Can we use the four stationary and no stationary signals including but not limited?
Only noise removal from this signal trend analyses and a forecasting detection of abrupt discontinuities,
changes or abnormal behaviors at a central and compressing of large amount of to the new image.
A compression stunner called DPG 2000 is fully based on previously.
Also did encryption i.e. either security the data.
Let's give you some examples, examples of only some of them noise removal and trend analyses.
For example, these have the details very noisy, and so we can remove the noisy part and show the train.
This is a come from one of my people.
Also this one from one of another people you see aft and the wheels transform.
We found the trend.
You see, this is the trend.
We can see the answer and decrease trend.
I see.
And.
Also, we can use a wheel to transform for abrupt discontinuities and a bunch changes.
For example, for this time it is not easy for us to see.
The continuity just means that there are some missing data.
But use we will transform.
You see?
We can see clearly.
Yeah.
Missing details.
See.
Also, U.S. automakers see this more.
Is so high at the detailed and.
Coefficient one.
Yeah.
And don't worry, we will learn this step by step here, just to give you a general idea.
Here you can see the same things.
We can't match the sound of no more changes.
You see, years, as you know, is you increase, decrease, but a small increase.
You see these can be regarded as normal changes.
Okay, let's see.
Another example.
Image compression.
This is the original picture.
It is 32.5 kb aft.
We will to transform.
We compress the.
My old home.
I just use the approximation of the original.
Photos.
See, it becomes 15.1 TB.
Is more than a little bit more than half a size.
Okay.
Thank you.
See you in the next lecture.