Time and frequency domains

[@electric_sand]

Clear voice, clear images, clearly explained. Thank you.

[@bulbulroyagarwal9647]

The search ends.. Finally an Excellent explanation of the concept with total clarity. Thanks a lot!

[@mikexcohen1]

And now my search for the best KZbin comment has ended! We may both go in peace ;)

[@MeistroJB]

I sure hope so. Shouldn't take me that many more decades.... Omg! seven minutes in, it's true! Can't thank you enough.

[@userhdza2248]

i can confirm that i was looking for so long to know the use of spectrum untill setteled here

[@OmniTraders]

What will this help exactly

[@tidytelz]

It is actually the best explanation I have seen so far.

[@denisjoly4300]

Thanks for the clarity of your explanations! I have to agree with other comments : you give the best lectures on signal analysis I've seen so far!

[@mikexcohen1]

awww, thanks!

[@Yalsha]

You are one of the best in the KZbin to explaining frequency. thank you for your effort

[@mikexcohen1]

You rock, Yasser!

[@a.b3203]

Mike Cohen and Mark Newman. The saving graces of any learner. Good stuff mister 👍🏻👍🏻.

[@donharrold1375]

If only my mathematics tutors had been so clear and concise....

[@mikexcohen1]

Well, I'm glad you're here now :)

[@gregoryacacia8087]

Thank you so much that was so clear !! We have hours of courses in university and still understand nothing, but here with a 10 min videos everything is cristal clear !! Wonderful job

[@mikexcohen1]

Glad it helped!

[@jsmithtraveller]

Thank you. I hope that conveys how much I appreciate you tutorials.

[@mikexcohen1]

Thanks! I'm glad you've found them useful.

[@mvelisompukuzela9034]

You really made this easy, thank you. I was struggling with understanding these two domains but now the light is there.

[@mikexcohen1]

Thank you kindly, Mveliso. I'm glad you found it useful.

[@sourabhkay]

Give this guy a medal!

[@mikexcohen1]

Thank you, kind internet stranger.

[@dakynshew4163]

you r one of the top teacher ive met,now frequency domain is sooo clear ..iv searrch for many youtuber to make me understand it n i found none but only u sir.......people should watch your videos to clear thei concept......you r the first youtuber where i memories the channel name.......keep it up sir

[@mikexcohen1]

Happy to help!

[@razor1887]

I have been searching for a month. I can finally get an intuitive idea. Really appreciated!

[@johnrogers1251]

In under ten minutes, along with clear pictures and verbal descriptions, you have removed the mystery (to me) of understanding the how/why/what-is-it-useful-for of Fourier transforms. I also appreciated that on the last slide, you explained the three things a student must be familiar with to do Fourier analysis (sine wave, complex numbers, dot product), and showed how the three things are combined to reach the end goal of Fourier coefficients. Thank you, and I look forward to watching your videos as I self-educate!

[@mikexcohen1]

Awesome, thanks John :) I hope you find the rest of my videos just as useful!

[@johnrogers1251]

@@mikexcohen1 I bought your course on Udemy, so I will give an assesment on the usefulness/understandability throughout the course. Overall, my goal in taking the course is to gain a better appreciation for signal processing.

[@knightx9405]

i gotta tell you for doing this video that, you are the definition of "Inner peace", at this moment :)

[@mikexcohen1]

If you can't fall asleep at night, try playing this video :P

[@TravisTerrell]

This is so clearly explained! Thank you!

[@joseph13058]

The best explaination I've seen of this so far.

[@mikexcohen1]

Thanks :)

[@hishamtariq7054]

Thank you very much for providing a concise and informative explanation.

[@roymccormick5328]

Thank you very much for this extremely clear and helpful series of over 17 videos explaining the Fourier Transform from basic concepts. so super cool 😎

[@mikexcohen1]

Glad it was helpful!

[@ДеянЦонев-ы7в]

Sir you are born to be a teacher ! I have follow your courses in Udemy and they are wonderfull too!

[@mikexcohen1]

Thank you kindly, user-hg1mn3qo8x.

[@DavidClendenen]

Finally, a clear explanation. Thank you!

[@mikexcohen1]

Glad it was helpful!

[@jurikang6731]

I finally understand why FT is used!! Was really lost in my digital image processing course for a while. Thank you for such a spectacular explanation, you're amazing!

[@mikexcohen1]

Awesome :)

[@jasonstarr2036]

Finally, a very clear explanation! Thank you for posting!

[@mikexcohen1]

:)

[@yaminobia7159]

I am doing a math ia on this topic and this video is extremely helpful and easy to understand. Thank you so much

[@mikexcohen1]

Glad it was helpful!

[@tubarekolah1786]

Finally, I can stop my search on this topic bcos I've got it

[@mikexcohen1]

Now you make me blush :D

[@hparvizi]

FINALLY UNDERSTOOD this basic concepts. well done. thank you

[@mikexcohen1]

Awesome ;)

[@bhupiistersingh4097]

Great way to explain both the domains.

[@carlosvillarreal1933]

Hi, I use wavelets and Hilbert transform methods to analyze sea wave data for my Ph.D. in oceanography. Your videos and your book are really helpful. Thanks for your work

[@mikexcohen1]

Thank you, Carlos. I'm glad you're finding these useful.

[@bugraaksu1252]

Wonderful explanation, brief, clear and simple. Lot of thankss..

[@mikexcohen1]

Awesome, I'm glad you found it useful.

[@imaginer04]

A nice explanation with most clear concept.

[@sukursukur3617]

Can we say that: fourier transform is a crosscorellation of a time dependent function with sine or cosine function for different frequencies.

[@mikexcohen1]

Hmm, I would use that description for a wavelet analysis. The term "cross-correlation" means to repeatedly shift one signal relative to the other. The Fourier transform is better thought of as the correlation (not cross-correlation) between the signal and a set of sine waves. The correlation has two normalization factors that the Fourier transform doesn't have, but otherwise it's a good analogy.

[@wasilwestside]

Hi Mike, I hope you are well. Absolutely beautiful way to explain the process, very impressive. Keep up the good work

[@mikexcohen1]

Thank you kindly, Wasil.

[@Ranjit4uy2k]

Nice way presented the Noise, which i struggled before to understand. One Question... 1. In nose induced signal, time domain max amplitude goes to ~5. But frequency domain is 1. Could you clarify plz?

[@mikexcohen1]

I'm not sure which graph you're referring to, but the time domain signal is a combination of all frequencies. Noise is a good example of the advantage of the frequency domain, because noise amplitudes might be smaller in the frequency domain than in the time domain.

[@Ranjit4uy2k]

@@mikexcohen1 This Clarified my query. Awesome explanation dude. Now I understand the FFT.

[@isaganicomia4958]

Thank you, this is the explanation im looking for quite sometime.

[@salmanjamil1248]

Wow! Excellent explanation!

[@mikexcohen1]

Glad you liked it :)

[@Ayoub.Naderei]

the way you teach and also your contents are amazing :)))))

[@mikexcohen1]

Thank you! 😃

[@lohithh9253]

Wow..... What an explanation that is. Clear. Thanks a lot.

[@bonjour5651]

Thank you so much!! I was trying to find a source to understand the difference clearly. You are awesome! I appreciate.

[@mikexcohen1]

No, *you* are awesome!

[@muhamadariefhidayat1914]

thank for the clear explanation. i wonder how to interpret frequency domain in 2D. like image. each row in image can be interpreted like your explaination. but as we know that image contain many rows. how we can visualize frequency domain of many rows. in addition images have columns too. thank you

[@kmsrog]

hi, is there a difference in sound? i saw ifi ad from their dongle, instead of frequency domain they manipulate the time domain. sorry i don't understand any of your explanation(i am on health sector). thank you if you ever read and answer this.

[@ComputerScienceLessons]

EXCELLENT!!!

[@ginmarx6104]

thank you ! easy to understand and visually striking !

[@MGYTRAVELLER]

Thank you for the clear explanation!!!

[@unknownworld177]

Thank you, you are great on this subjects. keep educating us

[@mikexcohen1]

Will do! Thanks!

[@aminechniouel1418]

why do you count the picks and not the periodes of the signals ? thank you for the videos

[@mikexcohen1]

You can also do that. I just counted the peaks to illustrate the concept.

[@arshadhussain734]

Crystal clear explanation! Thank you

[@Christian-rf5zv]

thank you for such a great and informative video!

[@saraghorbani70]

It was so beneficial to me, and the explains were so clear, but you pointed out why the amplitude is half of the distance between throughs and the peaks. Could you please explain that?

[@mikexcohen1]

Thank you, Sara, that's nice to hear. The answer to your question is in my playlist NEW-ANTS#2, don't remember offhand which video exactly.

[@christianleyva3166]

Excatly what I needed, thank you.

[@barbiliandan]

Great video :-) thanks. Something is not clear for me. In the third picture (time domain) if I count how many "big" peaks there are i will observe tgere are only 2 large peaks until 1sec, therefore I could not understand why in the frequency domain for the third picture the we still have some information when freq = 5Hz. Thanks!

[@mikexcohen1]

Great question :) The two frequencies actually cause some interference patterns, which causes the other features you see, like some peaks seem "lower" while others are "higher." It's another example of how signals get mixed in the time domain yet are easily separated in the frequency domain.

[@husseinalsajer4381]

nice ! please , if I want to create image from sampled signal ( sine wave for example ), how can get this please the image like white line and black line

[@praveenlb1]

Very useful video,easy to understand.

[@mikexcohen1]

Glad it was helpful!

[@nasrink2086]

Very great explanation. How we can convert from the time domain to the frequency domain in MATLAB? I used the following code to convert data from time domain to frequency, but the plots in the frequency domain are totally different from what I see in this video and I can not get information from them. This is the code: %% Compute the Fast Fourier Transform FFT of the refrigerator dt=.001; n=length(ref(9906:31449)); fhat=fft(ref(9906:31449),n); % Compute the Fast Fourier Transform PSD=fhat.*conj(fhat)/n; %Power spectrum (power per frerquency) freq=1/(dt*n)*(0:n); %Create x-axis of frequencies in Hz L=1:floor(n/2); %Only plot the first half of freqs figure; plot(freq(L),PSD(L)) title('FFT')

[@Mulkek]

Thanks, and it's so easy & simple!

[@shoebshaikh1790]

This is pure gold ❤

[@mikexcohen1]

❤

[@lolo-cz3yk]

Search ends

[@abbasbookwala]

AT 3:50 when you say its difficult, yet possible to figure out the frequency components from the time graph, can you help how you would figure that out?

[@mikexcohen1]

Well, you'd have to look at the time series data and count the number of peaks (or troughs) within a 1-second window. It's not very precise and can be impossible if there's too much noise.

[@abbasbookwala]

@@mikexcohen1 Thank you so much for your response

[@Ranjit4uy2k]

One More Question-- I am in a way to convert a random road load data to PSD graph for FEA simulation. Could you help me understand the physics involved to simplify the data in frequency domain. Also need to understand the role of Gaussian or PDF in the algorithm!

[@jaivalani4609]

Thanks Mike this clearly explained . Can it happen Noise Amplitude starts dominating sinosodial waves meaning SNR

[@mikexcohen1]

Cleaning noise from a signal can be trivial, difficult, or impossible, depending on the nature of the signal and the noise. So there isn't one specific strategy that always works. But if the signal and noise have different spectral signatures, then filtering (e.g., FIR filters) is usually pretty successful.

[@frinikarayanidis4]

Fantastic resources, thanks Mike!

[@mikexcohen1]

Thanks Frini!

[@xavihernandez6477]

Mr., where r u all these time?

[@mikexcohen1]

Don't worry, I'm still around :) working on new courses, books, research, etc. And trying to enjoy the weather now and then!

[@ckguleria7]

finally I know what these frequency graphs tell...

[@mikexcohen1]

Nice ;)

[@salihaamoura232]

شكرا جزيلا لك thank you very much :)

[@tsehayenegash8394]

I want the code

[@ErikaBeatrizDelgadoZhagui]

Thank for such as great explanation!!

[@mikexcohen1]

Glad it was helpful!

[@MrMec09]

Thank you, it help me here!

[@yssjc1414]

Very well explained!

[@AriaBreath]

Thanks so much for this fantastic explanation :)

[@mikexcohen1]

Glad it was helpful!

[@KingBaa-d6x]

Amazing. Thanks

[@Pateriyadivya]

searched a lot with the physical revelance of frequency domain and the search ended here. Thanks

[@mikexcohen1]

Awesome :)

[@thomasbayes2154]

Thank you, I finally understood

[@mikexcohen1]

Awesome.

[@mechanicalbaba2484]

Thats what I was finding, thanks

[@yasithsam9664]

Great Explanation :)

[@mikexcohen1]

Glad you liked it!

[@hannav7125]

thanks Mike

[@friendshipgreat5290]

Thanks bro for this awesome vedio

[@mikexcohen1]

You got it, bro.

[@jasoncui2620]

I'm your big fan

[@mikexcohen1]

:D

[@zeynepbetulkaya3645]

thank you

[@Rushikesh21]

cleared all my doubts sir:)

[@mikexcohen1]

That's good. Doubts make you age faster, so I'm happy I can help you stay young ;)

[@Rushikesh21]

@@mikexcohen1 😅

[@ahmedalwaheshi8334]

Finaly i understand it thhhaaaaaannnnkkkkkk uuuuu ssssooooooo muchhhhhhhhhh u r a hero

[@mikexcohen1]

Yoooouuuu'rreee weeeeellllllcccooommmeee!!!

[@roymoran1151]

Thank you.

[@mikexcohen1]

You're welcome!

[@mehmetsensoy96]

thank you for that's awesome video

[@nwars3961]

Amazing, thank you ;)

[@irethoronar34]

Cristal Clear

[@mikexcohen1]

Noice.

[@mertpurtas8913]

That was purly ı was looking for .

[@mikexcohen1]

Nice :)

[@SteveGergetz]

That was excellent

[@mikexcohen1]

Thanks Steve.

[@adhil8918]

Thanks br0😁

[@rabishrestha804]

Thanks wow

[@aditikumari3677]

Thank u so much it was really helpful 😇

[@3almne]

In the second example, the amplitude must be 5 not 1

[@mikexcohen1]

Why must that be the case?

[@ahmednor5806]

💐💐💐💐

[@8ZER08]

i love you

[@mikexcohen1]

I love you too, 808.

[@ahmednor5806]

🙏🙏🌹🌹

[@h-salah]

THANK YOU

[@abdulmalikadeola]

Thank you.