The amount of free, useful, precise information coming from this channel is remarkable and something to be grateful for. It legitimizes TH-cam education.
It is not "free". Most likely, Professor Brunton has these lectures as one of the deliverables of many of his NSF grants. Thus, this is paid by the US taxpayer. :)
The video is very nice. Thank you! Just a small remark: The indexing of f and f hat in the matrix vector multiplication is wrong. Should count up to f_{n-1} not f_{n}.
@@Eigensteve Or conversely, shouldn't you simply make the summation from 0 to n? Since for f_0 to f_n you now have n+1 sample points, and x is an n+1 size vector. By making your summation to j=0:n, it is summing over n+1 points which is the standard notation used in approximation theory.
@@iiillililililillil8759 you can change summation range if you pull out the j = 0 term and add it in front of your sum :) similar to how it is done in series solutions for certain differential equations
I like your insight that this should actually be called the Discrete Fourier SERIES. Thank you for your way of relating the matrix to the computation. Your perspective help me see how the matrix is related to the tensor and quantum mechanics.
Oh my goodness! Stumbled onto video 1 in this playlist this evening. and I can't stop. Steve, you're amazing. I actually finally feel like I understand what a fourier series is and why it works. can't wait to get to the end. This is easily the best set of lecture on this topic i've ever experienced. HUGE thanks!
I have to give you credit for giving the absolute best educational videos I have ever seen. The screen is awesome, the audio is great, you explain thoroughly and clearly, you write clearly, your voice is not annoying and everything makes sense. Thank you mr sir Steve.
Dear Steve I really enjoy your teaching format and also your wonderful explanation. Just one suggestion, It would be great if you could have at least one practical lecture at the end of each series of lectures, e.g for Fourier series transformation lecture designing one lecture which shows a real problem is great and enhance the level of understanding. Stay motivated and Many thanks for your consideration
Mr. Brunton. Thank you for clear, concise, organized presentation of DFT. Appreciative of how much time and effort such a presentation / explanation takes to create and deliver. Appreciative of the format you use and precision in getting explanation correct. Explanation of terms and where terms originate has always been helpful in your presentations. Going through the whole DFT, FFT series again to refresh my thinking on the topics. Thanks again. (Erik Gottlieb)
Excellent video! The video was conceptually very clear and to the point. You are an amazing teacher, Prof Brunton! I loved your control systems videos too!
Hi Professor Brunton, Just wanted to let you know I took your AMATH 301 course at UW in 2012. It really kicked my butt but learned so much. I still use the RK4 for work once in a while. You and Prof. Kutz were both outstanding. Wish you both well!
One of my friends posed me an interpolation problem and I instinctively decided to try a DFT. I used some for loops and got the job done, but I never thought that you could build a matrix using fundamental frequencies. That's clean. Then when it came time to using the algorithm, I realized that it was super slow! Granted, it was an interpolation on some 2D data, but still. My laptop couldn't handle an interpolation over fairly small grids (at 35x35, I was waiting seconds for an answer), which blew my mind. But on further inspection, a for loop (or matrix multiplication) is like O(n^2) but likely all the way to O(n^3) after naive implementation details, so it makes sense. What I'm trying to say is, I can see why you think so highly of the FFT, and I'm super excited to learn how it works, and maybe even implement it myself 🙌🏽. You rock, prof!
Heya! I really enjoy the pacing of your lectures. It's also nice for me to get a quick recap of some signal processing before assembling my own lectures. It is also helping me fill in the gaps of knowledge I have around data science, where my training is in Functional Analysis and Operator Theory. This past fall I dug through the literature for my Tomography class looking for a direct connection between the Fourier transform and the DFT. Mostly this is because in Tomography you talk so much about the Fourier transform proper, that abandoning it for what you called a Discrete Fourier series seemed unnatural. There is indeed a route from the Fourier transform to DFT, where you start by considering Fourier transforms over the Schwartz space, then Fourier transforms over Tempered Distributions. Once you have the Poisson summation formula you can take the Fourier transform of a periodic function, which you view as a regular tempered distribution, and split it up over intervals using its period. The Fourier integral would never converge in the truest sense against a periodic function, but it does converge as a series of tempered distributions in the topology of the dual of the Schwartz space. Hunter and Nachtergaele's textbook Applied Analysis (not to be confused with Lanczos' text of the same name) has much of the required details. They give their book away for free online: www.math.ucdavis.edu/~hunter/book/pdfbook.html
The last time I tried to give a similar lecture I messed up the indexing much more than this, it was a little comforting to see you do it too. It made me wonder if it was worth it to count from 0 always when teaching linear algebra (probably not).
Thanks for the feedback... yeah, I know that when I make mistakes in class, it actually resonates with some of the students. I hope some of that comes through here.
I *finally* understand it. Memorizing it for exams is not good enough for me, i want to *get* it. Now I do, and see all the great applications for it. Filtering out specific frequencies, isolating specific frequencies, or the same with a broad spectrum of frequencies will be extremely easy with it. Either just calculate a few values individually, or just take/throw away a chunk of the resulting vector. Great videos!
It took me 5min and 55sec to discover that you're writing correctly, I was wondering why are you writing the inverse way! Thank you for the great presentation!
Some videos ago I was concerned at the implications of this being called the DFT, as it not repeating would be problematic for me, and from my understanding of others' implementations, it is supposed to repeat, so I was happy to hear you clear up the easy to make mistake that this was an actual transform and not a series. Things make sense again now. It's still weird that its mislabeled though.
A nice way to think about the mathematical sums, which Prof. Brunton doesn't explicitly mention, is that each of the n+1 rows in the matrix as a vector that functions as a basis function, together which span the space of all n+1 element vectors. Hence all you're doing is taking the inner (dot) product of the original signal with each of those n+1 basis functions (the vectors), i.e. projecting the orignal signal against each of those basic functions to see how much of it is along each of those (vector space) directions.
Thank you so much for this series of videos. Just a small suggestion; to be consistent, it seems that the vector should have points from f_0 to f_(n-1)
I think I'm just going to watch all your videos for my machine learning course this semester instead of my professor's lecture which was so painful and frustrating....
Great video. One of the better ones. I wish you explained the exact meaning of the coefficient in the exponent though ... e.g. I never really understood the relationship between sample frequency and number of data points (N). Seems like they will always be the same.
At 10:49 corrected the matrix size to be n but then the vector size became n+1; needs another correction but I'm still watching! Edit: I saw the same catch in the comments below, but I think the solutions given weren't the best: My solution is as follows: n should be kept the same as it is the number of samples, also the summation should go until n-1 to give n points and nxn matrix size, but the summation formula should contain f_{j+1} keeping everything else the same. This way you don't even need the x_{0} data point. Still liked the video a lot...
As far as I understand, when we take the inverse discrete fourier transform, we end up with the function values at x_0, x_1, x_2, ..., x_n, but how would you determine what the values of x_0, x_1, ... ,x_n are? I need to know this for my masters thesis please help me if you can.
one thing i don't really understand is why there is a "j" in the exponential e^{2\pi1k/n}. Aren't e^{2\pi1k/n} sort of like the basis vectors we are projecting onto? Why do we need to raise each of those to the j's?
If I am not wrong we collect the sample of data from x(t) in time domain so the elements of the second vector (red one) are not the signal frequencies and just the amplitude of our signal in time t?
Does Mr. Brunton have a more conceptual video on why that fundamental frequency is defined, why we sample it with harmonics proportional to it etc.? Thanks
At 14:55 shouldn't the last value be wn ^ (n(n-1)) instead of wn ^ ((n-1)^2) Since the value is at the fnth value row wise and jnth value coloumn wise?
Dear Prof. Steve. I think there are n+1 data points (starting from "0" to "n"), but you have calculated the frequencies for (f1,f2, f3, .., fn) total "n" points. I think that one point is missing? Is something wrong?
In DFT, you can tell there's a linear system of equations (whose dimensions are n*inf) that's being solved through inner products, by eliminating all terms except 1 on each equation, since the complex basis vectors are orthogonal to each other. Thats pretty straightforward and intuitive. However, when f is continuous, Fourier treats it the exact same way, which seems wrong, since the e^(iωx) and e^(i(ω+dω)x) vectors arent orthogonal to each other anymore, so even if we use inner product, there will still exist some non-zero 'remainders' on each equation which we cant get rid of. Also, any F.T. of a function f in the [-inf,+inf] domain is problematic, since the inner product of any pair of 2 basis vectors diverges. Do we assume then, that we extend our domain to [-inf,+inf] in such a way that the I.P. remains 0? Unfortunately, noone explains those.
@@samarendra109 I had to pause the video to look in the comments to see if he was writing backwards, It was driving me crazy, small obsessive compulsive attack XD
He is writing on a piece of glass and he flips the video after. He is a lefty, which you can see in his early unflipped videos. His part is also the other way.
I'm thinking the same thing. If it is mirrored then Professor is left-handed and writing to the right, then nothing is wrong here. If not then it is very hard to do. So I think it is mirrored. Very good video, clear explanation, 4k image quality really helps me to focus. Thank you very much, professor!
I believe it's the same technique as professor Matt Anderson uses on his physics videos. He explains this method on the video: "Learning Glass - What is he writing on?" link: th-cam.com/video/CWHMtSNKxYA/w-d-xo.html
3:44 - It's a really interesting idea to perform the car diagnosis like this! But what stage goes after the FFT one, is it a neural network or something else?
Hi, great video. Question: you say you multiply the vector with the matrix, but to make dimesions match, shouldn't you multiply the matrix with the vector ?
I'm not quite sure I understand your question. If you are asking why the DFT/FFT has multiple "mirror" copies, this is because the DFT/FFT is complex-valued, and so there is redundancy in going from "n" real valued data points to "n" complex valued Fourier coefficients.
This is excellent, thank you very much. A question - does it matter if the spacing between your independent variable samples isn't even/periodic? If it does, how do you approach that scenario?
In any kind of complex maths explanation, I value preciseness the most. This guy has a good visualization but should have been prepared better if he is interested to make the video helpful.
How did you write it??? I mean it seems you are standing behind a clean glass, that means you must have to write everything from right to left,sort of a mirror image of a normal writing............that's so cool, I really wanna know if that's how you did it??? (Also yeah I'm supposed to concentrating on DFT instead of the mirror image writing, but that's me,I can't help it...)
It's called a "lightboard." They are writing normally on glass, and recording the person writing. You have a choice: Capture the work in a mirror, and video the mirror, so everything looks normal writing, OR, record as they write through the glass, and then put the video into Microsoft Movie Maker and "FLIP HORIZONTAL." The glass is low-iron glass, so no reflections, there are LEDs at the top and the bottom of the glass. The light gets trapped in the glass, and, as they write on the glass, the marker ink makes a path whereby the light can escape. Also, black backdrops behind the writer and the camera. Easy once you know the trick behind the magic.
@@JohnVKaravitis OOOHHHH Thanks brother, I thought he must have trained his brain to write in reverse, which would have been pretty impressive, but this was cool too , thanks
When i try to discretize f_hat from the continuous Fourier transform, I can't figure out how dx disappears. Shouldn't some delta x be part of the f_hat function?
How would an efficient DFT look, if I have a series of n-coefficients λ0, λ1, λ2, λ3, ..., λn which are prime numbers (2, 3, 5, 7, ..., P(n)) times a factor (f0, f1, f2, f3, ..., fn). And each factor is a positive integer, including zero?
Is there a difference between the Discrete Fourier Transform and the Discrete-Time Fourier Series? They seem the same thing, including their formulas. Even at the beginning of the video you said the DFT should be called a FS.
sorry, I just could not concentrate on the DTFT while trying to figure out how the hell youre doing the writing, right hand, from right to left, is there a mirror involved (i cant see how) or did you really learn to write the other way ?!, thanks!
Some of your lectures are very good but you need to be more specific please when teaching. Its fine when talking to people already familiar with the concepts but understand that you are teaching to new learners
The amount of free, useful, precise information coming from this channel is remarkable and something to be grateful for. It legitimizes TH-cam education.
It is not "free". Most likely, Professor Brunton has these lectures as one of the deliverables of many of his NSF grants. Thus, this is paid by the US taxpayer. :)
Can't say this for many videos, but my mind is now blown. 🤯
Finally after years the DFT makes sense.
Awesome!
Steve, you really are the best professor on the planet period ....thank you so much for all these incredible high quality lectures.
I’m glad I have this guy as my uncle
The video is very nice. Thank you!
Just a small remark:
The indexing of f and f hat in the matrix vector multiplication is wrong. Should count up to f_{n-1} not f_{n}.
Good catch, you are definitely right!
@@Eigensteve Or conversely, shouldn't you simply make the summation from 0 to n? Since for f_0 to f_n you now have n+1 sample points, and x is an n+1 size vector. By making your summation to j=0:n, it is summing over n+1 points which is the standard notation used in approximation theory.
@@iiillililililillil8759 you can change summation range if you pull out the j = 0 term and add it in front of your sum :) similar to how it is done in series solutions for certain differential equations
Thanks for great lecture.
However, I think the last element of vectors must be F_n-1 instead of F_n.
I like your insight that this should actually be called the Discrete Fourier SERIES.
Thank you for your way of relating the matrix to the computation.
Your perspective help me see how the matrix is related to the tensor and quantum mechanics.
Oh my goodness! Stumbled onto video 1 in this playlist this evening. and I can't stop. Steve, you're amazing. I actually finally feel like I understand what a fourier series is and why it works. can't wait to get to the end. This is easily the best set of lecture on this topic i've ever experienced. HUGE thanks!
Also, are you writing on a window? ......backwards?!
I have to give you credit for giving the absolute best educational videos I have ever seen. The screen is awesome, the audio is great, you explain thoroughly and clearly, you write clearly, your voice is not annoying and everything makes sense. Thank you mr sir Steve.
Dear Steve
I really enjoy your teaching format and also your wonderful explanation. Just one suggestion, It would be great if you could have at least one practical lecture at the end of each series of lectures, e.g for Fourier series transformation lecture designing one lecture which shows a real problem is great and enhance the level of understanding. Stay motivated and Many thanks for your consideration
Great suggestion. Let me think about how to do that.
When he said "thank you" in the end I wanted to take a huge mirror and send it right back at him
Mr. Brunton. Thank you for clear, concise, organized presentation of DFT. Appreciative of how much time and effort such a presentation / explanation takes to create and deliver. Appreciative of the format you use and precision in getting explanation correct. Explanation of terms and where terms originate has always been helpful in your presentations. Going through the whole DFT, FFT series again to refresh my thinking on the topics. Thanks again. (Erik Gottlieb)
Excellent video! The video was conceptually very clear and to the point. You are an amazing teacher, Prof Brunton! I loved your control systems videos too!
Thanks Steve for contributing on humanity. cheers!
This is by far the best explanation I’ve ever seen. Thank you Steve, I hope to find reason to buy your book soon.
Hi Professor Brunton,
Just wanted to let you know I took your AMATH 301 course at UW in 2012. It really kicked my butt but learned so much. I still use the RK4 for work once in a while. You and Prof. Kutz were both outstanding. Wish you both well!
That is so nice to hear! Really glad it has been useful since then... that must have been my first class too!
This is very concise and organized and easy to understand. Thank you for posting it.
One of my friends posed me an interpolation problem and I instinctively decided to try a DFT. I used some for loops and got the job done, but I never thought that you could build a matrix using fundamental frequencies. That's clean. Then when it came time to using the algorithm, I realized that it was super slow! Granted, it was an interpolation on some 2D data, but still. My laptop couldn't handle an interpolation over fairly small grids (at 35x35, I was waiting seconds for an answer), which blew my mind. But on further inspection, a for loop (or matrix multiplication) is like O(n^2) but likely all the way to O(n^3) after naive implementation details, so it makes sense. What I'm trying to say is, I can see why you think so highly of the FFT, and I'm super excited to learn how it works, and maybe even implement it myself 🙌🏽. You rock, prof!
Heya! I really enjoy the pacing of your lectures. It's also nice for me to get a quick recap of some signal processing before assembling my own lectures. It is also helping me fill in the gaps of knowledge I have around data science, where my training is in Functional Analysis and Operator Theory.
This past fall I dug through the literature for my Tomography class looking for a direct connection between the Fourier transform and the DFT. Mostly this is because in Tomography you talk so much about the Fourier transform proper, that abandoning it for what you called a Discrete Fourier series seemed unnatural.
There is indeed a route from the Fourier transform to DFT, where you start by considering Fourier transforms over the Schwartz space, then Fourier transforms over Tempered Distributions. Once you have the Poisson summation formula you can take the Fourier transform of a periodic function, which you view as a regular tempered distribution, and split it up over intervals using its period.
The Fourier integral would never converge in the truest sense against a periodic function, but it does converge as a series of tempered distributions in the topology of the dual of the Schwartz space. Hunter and Nachtergaele's textbook Applied Analysis (not to be confused with Lanczos' text of the same name) has much of the required details. They give their book away for free online: www.math.ucdavis.edu/~hunter/book/pdfbook.html
The last time I tried to give a similar lecture I messed up the indexing much more than this, it was a little comforting to see you do it too. It made me wonder if it was worth it to count from 0 always when teaching linear algebra (probably not).
Thanks for the feedback... yeah, I know that when I make mistakes in class, it actually resonates with some of the students. I hope some of that comes through here.
Hey Steve, your videos are great. I love the format and the clarity of the exposition, keep up the good work.
Thanks!
I agree, it's both clear and enjoyable, you sir are a life savior. Merci !
I *finally* understand it. Memorizing it for exams is not good enough for me, i want to *get* it. Now I do, and see all the great applications for it.
Filtering out specific frequencies, isolating specific frequencies, or the same with a broad spectrum of frequencies will be extremely easy with it. Either just calculate a few values individually, or just take/throw away a chunk of the resulting vector. Great videos!
yo how tf u writing like that
It took me 5min and 55sec to discover that you're writing correctly, I was wondering why are you writing the inverse way! Thank you for the great presentation!
Thank you. This video really helped me. Thank you for keeping this open and free for everyone.
Please never stop uploading useful content like this, nice teaching method!
Big appreciate Prof. Steven Brunton.
Some videos ago I was concerned at the implications of this being called the DFT, as it not repeating would be problematic for me, and from my understanding of others' implementations, it is supposed to repeat, so I was happy to hear you clear up the easy to make mistake that this was an actual transform and not a series. Things make sense again now. It's still weird that its mislabeled though.
At 0:30 you already solved the question that brought me here. Thank you!
A nice way to think about the mathematical sums, which Prof. Brunton doesn't explicitly mention, is that each of the n+1 rows in the matrix as a vector that functions as a basis function, together which span the space of all n+1 element vectors. Hence all you're doing is taking the inner (dot) product of the original signal with each of those n+1 basis functions (the vectors), i.e. projecting the orignal signal against each of those basic functions to see how much of it is along each of those (vector space) directions.
Dear Professor
Thank You so much for your nice explanation!!! 💓
This is one of thewonderful lessons I've got, thank you so much for your enthusiastic!
Damn STEVE...YOU SAVED MY DAY...THANK YOU SO MUCH FOR SUCH A COOL PRESENTATION.
Thank you so much for this series of videos.
Just a small suggestion; to be consistent, it seems that the vector should have points from f_0 to f_(n-1)
I think I'm just going to watch all your videos for my machine learning course this semester instead of my professor's lecture which was so painful and frustrating....
Absolutely fantastic video, sir! Thank you very much!
Great video. One of the better ones. I wish you explained the exact meaning of the coefficient in the exponent though ... e.g. I never really understood the relationship between sample frequency and number of data points (N). Seems like they will always be the same.
the matrix for the Fourier coefficients and the f function samples should also go up to n-1 and . If someone was confused about it.
At 10:49 corrected the matrix size to be n but then the vector size became n+1; needs another correction but I'm still watching! Edit: I saw the same catch in the comments below, but I think the solutions given weren't the best: My solution is as follows: n should be kept the same as it is the number of samples, also the summation should go until n-1 to give n points and nxn matrix size, but the summation formula should contain f_{j+1} keeping everything else the same. This way you don't even need the x_{0} data point. Still liked the video a lot...
I'm just here because I wanted to make an audio visualizer as an add-on for my gui exercise in c++. Guess I underestimated it.
Amazing video. Very clear and well presented
As far as I understand, when we take the inverse discrete fourier transform, we end up with the function values at x_0, x_1, x_2, ..., x_n, but how would you determine what the values of x_0, x_1, ... ,x_n are? I need to know this for my masters thesis please help me if you can.
I got some insights. Thank you.
The FFT next.
There's a minor issue after reindexing, the last index should be n-1 not n. But it's not that important, great video as always!
I think the summation should go from 0 to n because you have n + 1 rows in the pink column vector and n columns in the yellow matrix.
Or there should have been n-1 measurements
More impressive than the math is that Steve is writing mirror-imaged. Leonardo DaVinci would be proud.
one thing i don't really understand is why there is a "j" in the exponential e^{2\pi1k/n}. Aren't e^{2\pi1k/n} sort of like the basis vectors we are projecting onto? Why do we need to raise each of those to the j's?
Great description thanks, FFT was a nice bonus.
Always leaning a lot from your lecture! Appreciate it, sir.
How is something like this recorded? is he writing on transparent glass or mirror? how is the background removed?
One thing i want to point out i suspect the DFT matrix is a symmetric one ..... Is it ?
Yes
COME ON DUDE LETSGO LETS MAKE ME SMART!!!! i have an exam in the morning it's currently 2 AM and I'm cramminggggggggggg
on a side note... how did you write backwards? or was the video flipped?
or did you actually write backwards.....?
Thank you so much for this video. I think that our data vector should be :[f_0, f_1, f_2, . . ., f_{n-1}] instead of [f_0, f_1, f_2, . . ., f_n].
What would be the 2-d version of the DFT system? will the vectors be matrices and the DFT matrix be a 3d tensor?
are you drawing everything mirrored? That's impressive if so
Awesome! I’m curious if it is too much to expand matrix form for a 2D function, i.e. 3D matrix.
This is coming up soon when we look at the DFT/FFT for 2D images.
If I am not wrong we collect the sample of data from x(t) in time domain so the elements of the second vector (red one) are not the signal frequencies and just the amplitude of our signal in time t?
Does Mr. Brunton have a more conceptual video on why that fundamental frequency is defined, why we sample it with harmonics proportional to it etc.? Thanks
great way to explain. huge respect
Insightful… also, how in the world did you write backwards on that glass and make it look so good??
i thought maybe its mirrored
At 14:55 shouldn't the last value be wn ^ (n(n-1)) instead of wn ^ ((n-1)^2) Since the value is at the fnth value row wise and jnth value coloumn wise?
Dear Prof. Steve. I think there are n+1 data points (starting from "0" to "n"), but you have calculated the frequencies for (f1,f2, f3, .., fn) total "n" points. I think that one point is missing? Is something wrong?
In DFT, you can tell there's a linear system of equations (whose dimensions are n*inf) that's being solved through inner products, by eliminating all terms except 1 on each equation, since the complex basis vectors are orthogonal to each other. Thats pretty straightforward and intuitive.
However, when f is continuous, Fourier treats it the exact same way, which seems wrong, since the e^(iωx) and e^(i(ω+dω)x) vectors arent orthogonal to each other anymore, so even if we use inner product, there will still exist some non-zero 'remainders' on each equation which we cant get rid of.
Also, any F.T. of a function f in the [-inf,+inf] domain is problematic, since the inner product of any pair of 2 basis vectors diverges. Do we assume then, that we extend our domain to [-inf,+inf] in such a way that the I.P. remains 0? Unfortunately, noone explains those.
Only one minor thing, if change the index from 1 to 0, f range in the equation is from f0 to fn-1, not fn.
Professor, I have a question. Since I often notice that a lot of fhat are zeros, can we use a different number of basis (preferably less) than n?
Wait wait wait, are you writing all that backwards on a glass pane, so that we see it correctly written?
no, the video is just mirror reversed. (See his hair. It's mirror reversed)
@@samarendra109 I had to pause the video to look in the comments to see if he was writing backwards, It was driving me crazy, small obsessive compulsive attack XD
I've been thinking about how he did that for an hour but still can't get it.
He is writing on a piece of glass and he flips the video after. He is a lefty, which you can see in his early unflipped videos. His part is also the other way.
Its a tech invented by a prof from northwestern university, heard about it while doing a course from northwestern
These videos are amazing! Thanks much!
Hi Professor, just out of curiosity I am asking this. Are you writing backward on the other side of the mirror or what? 🤔 Nevertheless, Greats videos.
I'm thinking the same thing. If it is mirrored then Professor is left-handed and writing to the right, then nothing is wrong here. If not then it is very hard to do.
So I think it is mirrored.
Very good video, clear explanation, 4k image quality really helps me to focus. Thank you very much, professor!
I believe it's the same technique as professor Matt Anderson uses on his physics videos. He explains this method on the video: "Learning Glass - What is he writing on?" link: th-cam.com/video/CWHMtSNKxYA/w-d-xo.html
How you are writing in reverse direction???
3:44 - It's a really interesting idea to perform the car diagnosis like this! But what stage goes after the FFT one, is it a neural network or something else?
Hi, great video. Question: you say you multiply the vector with the matrix, but to make dimesions match, shouldn't you multiply the matrix with the vector ?
so how do I do a complex matrix multiplication on the computer f.e using c++ ? just store sin & cos for every entry or is there a better way ?
Great video! I have one question. Why do we have multiple images of our signal in time domain after performing DFT?
I'm not quite sure I understand your question. If you are asking why the DFT/FFT has multiple "mirror" copies, this is because the DFT/FFT is complex-valued, and so there is redundancy in going from "n" real valued data points to "n" complex valued Fourier coefficients.
@@Eigensteve So that is why after DFT our signal is periodic? Or it is because we have discret spectrum.
@@resu2381 Yeah, the DFT is assuming we have periodic data, so you can't build a DFT model that isn't periodic.
@@Eigensteve Thank you!
made a lot of things clear, thank you
Why does the number of frequencies have to equal the number of samples.
This is excellent, thank you very much. A question - does it matter if the spacing between your independent variable samples isn't even/periodic? If it does, how do you approach that scenario?
In any kind of complex maths explanation, I value preciseness the most. This guy has a good visualization but should have been prepared better if he is interested to make the video helpful.
How did you write it??? I mean it seems you are standing behind a clean glass, that means you must have to write everything from right to left,sort of a mirror image of a normal writing............that's so cool, I really wanna know if that's how you did it??? (Also yeah I'm supposed to concentrating on DFT instead of the mirror image writing, but that's me,I can't help it...)
It's called a "lightboard." They are writing normally on glass, and recording the person writing. You have a choice: Capture the work in a mirror, and video the mirror, so everything looks normal writing, OR, record as they write through the glass, and then put the video into Microsoft Movie Maker and "FLIP HORIZONTAL." The glass is low-iron glass, so no reflections, there are LEDs at the top and the bottom of the glass. The light gets trapped in the glass, and, as they write on the glass, the marker ink makes a path whereby the light can escape. Also, black backdrops behind the writer and the camera. Easy once you know the trick behind the magic.
@@JohnVKaravitis OOOHHHH Thanks brother, I thought he must have trained his brain to write in reverse, which would have been pretty impressive, but this was cool too , thanks
Thank you very much!! This video is amazing!!
When i try to discretize f_hat from the continuous Fourier transform, I can't figure out how dx disappears. Shouldn't some delta x be part of the f_hat function?
great lecture
How would an efficient DFT look, if I have a series of n-coefficients λ0, λ1, λ2, λ3, ..., λn which are prime numbers (2, 3, 5, 7, ..., P(n)) times a factor (f0, f1, f2, f3, ..., fn). And each factor is a positive integer, including zero?
Is there a difference between the Discrete Fourier Transform and the Discrete-Time Fourier Series? They seem the same thing, including their formulas.
Even at the beginning of the video you said the DFT should be called a FS.
Are the vector dimensions correct, shouldn't the coefficients be indexed from 0 to n-1?
if you don't know the fundamental frequency, how do you proceed, ? step-wise ??
How you remove sonic waves from antenna and wirreless media and data, from outside your home?
sorry, I just could not concentrate on the DTFT while trying to figure out how the hell youre doing the writing, right hand, from right to left, is there a mirror involved (i cant see how) or did you really learn to write the other way ?!, thanks!
so brilliant
Half way throught the video I realized you write everything horizontally mirrored
I always wonder, how he writes mirroring the words?
In time 6:40 the light blue-colored equation, the k is undefined. Why don't you write it out?
Thank you so much for this...
Hi Steve, I think the formula should be fhat=summ(j=1 to n) f_j * (w_n)^(i-1)*(j-1) , then we dont need fo and it goes all the way up to fn as well.
Absolutely, this is also a good way to index. I always give myself 50/50 odds of messing up the index, so good to double check!
@@Eigensteve btw i'd like to thank you and Prof. Kutz for posting these videos.. invaluable learning ..
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Some of your lectures are very good but you need to be more specific please when teaching. Its fine when talking to people already familiar with the concepts but understand that you are teaching to new learners
I'm just blown away that he can write all this backwards.
thank you😇
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