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Thanks sir, do you have some code example on this?

great

Great video. You showed everything. I suspect some may need to know that the amplitude comes from the square root of the sum of the squares of x and y. They may also need to know that the ArcTan (y/x) gives the angle...

THERE ARE A LOT OF THIS TYPE OF UNUSEFUL VIDEOS

I wish my professor even explained 10% of this as effectively

SI no vas a explicar y demosrar nada. PARA q haces videos. PARA ganarvisitas. ?????

AT LEAST USE SUBTITLE'S. DONT DOIT LIKE A SCHOOLKID

I THINK IS UNUSEFUL TO TRY TO UNDERSTAND THIS EGIP

DO IT IN PDF

NOLIKE FOR UGLY KIDDING GRAPHICS

REDOIT BUT WELL USING BIG IMAGES

WHY NO EXAMPLE????????? BECAUSE IT DOESN WORK

CAN YOU EXPLAIN SOMETHING ABOUT THE ALGOFFT????????

Refresh long a go known thank you.

It's really helpful for me, thanks!

What is the formula of u[n] in 3:26 such the a variable's value determes the value of x[n] such a way in each case,i don't understand. And why in 4:36 when you aply the z transform formula on to x[n] you write Sum n-oo to +oo a^n*z^-n instead of Sum -oo to +oo a^n*u[n]*z^-n suposed the x[n] is a^n*u[n] not only a^n ? I don't understand this neither.

you sck bro

thanks sir

This is a good overview of FFT. It would be nice to explain how the DFT convolution sum is derived. Also, the de-interlacing of the inputs was glossed over (not explained clearly) but only the reversed binary notation was mentioned (this is just an after-the-fact observation of How, not an explanation of Why). Readers who dive deeper into the splitting of a larger N-point FFT into two smaller N/2-point FFT’s, or understand the relationships between the twiddle factors (and their periodic nature) would understand and retain better the FFT technique (and be able to conquer any arbitrary size of N-point FFT (N being a power of 2, of course).

your the best sir . thanks it really helpedfor my exam :)

Excellent !

There are unknown way to visualize subspace, or vector spaces. You can stretching the width of the x axis, for example, in the right line of a 3d stereo image, and also get depth, as shown below. L R |____| |______| TIP: To get the 3d depth, close one eye and focus on either left or right line, and then open it. This because the z axis uses x to get depth. Which means that you can get double depth to the image.... 4d depth??? :O p.s You're good teacher!

PFE @6:50 is wrong. The residues are 6/5 and 4/5.

@ 3:50 the direction of the vector is wrong... it should be in the opposite direction

@ 1:11 the product in pole-zero form should start from k=1.

great video. short, straight to the point

plz.can you support my (what is the conditions of the linearity in phase for FIR filters. Enhance your answer with formulas)

Beautiful explanation! Loved it.

Why the link to all signal processing has die

Could you please make a video on Fast Iterative shrinkage threshold Algorithm and denoise an audio signal using it.

Consider an at rest linear system described by y"+25y=2sint+5cos 5t The response of this system will be Decaying oscillations in time. Oscillatory in time. Growing oscillations in time: None of the above.

15:33 ♡♡

Hello, I need help clarifying two concepts. First, 4:31 makes perfect sense to me. We're just using the definition of Euler's Identity; if we wanted to re-expand back to regular Acos(x) + Aisin(x) notation, and our function didn't have an imaginary component, the second term would go to zero. I.e., I can represent any sinusoid with Euler - even if it doesn't have an imaginary component. This is nice because we can break up our sinusoid into time dependent & independent components. This makes perfect sense. Then, at 4:57 we seem to transition into something completely different. I understand the math for both, but I don't understand why I would use 4:57 over 4:31? What was wrong with just using Euler's Identity like 4:31? For example, at 7:59 I could have just as well used Euler's Identity like done at 4:31 instead of using this cos definition. Could you please help me connect these two ideas? Thank you, sir.

Thanks a lot for this awesome content!

This video is so underrated, it's literally the most straight forward explanation of this topic I've seen

Brilliant thank you you

4:35-5:40 In my calculation of DFT X[2]=-1, not 0. Is the picture of DFM 7:36 right?

I know some of these words

the best i have watched so far..

will it be online?I could attend online as I am based out of India

i just want to say that i love you man

this is great :) Thanks!!

Awesome channel! Really helping me through advanced DSP!

Not sure why you left out the recursive relation between the odd and even functions and the DFT. I was so confused where the speed gain was from.

Came here because I’m reading the LoRA LLM paper. Thank you for the clear summary!

after we get the optimized w, how to get the optimized b?

On advantage of the DTFT is its ability to provide greater frequency resolution with a single dominant frequency than the DFT for a given N. One application could be trying to use a dsp to accurately estimate the frequency of a guitar string for tuning to the proper pitch. We wouldn't want our tuner to be limited to only 1/T. Also, the small battery powered DSP cannot do an very large FFT for more resolution. Can you derive or simulate the resolution enhancement limits with the W(e(-jw) convolution?

One advantage of the DTFT is you get a continuous frequency domain. With a single dominating frequency, the peak frequency can be resolved with higher resolution than with the DFT with frequency samples of 1/T. Can you calculate or show a simulation of the limiting resolution of DTFT over the DFT based on your convolution of W(e^jw) factor? Give the same N.

WHat happen to the website

Congratulations Professor Van Veen for your ability and beautful presentation! Mary Christmans nas Happy New Year! Gos bless you! Jacareí -Sao Paulo-Brasil