Another fantastic video - by far the best explanation I've seen in all the texts/ videos I've used on this subject. You do a great job of covering the important maths concepts with a focus on practical application which is great for us Engineering students!
Thank you very much professor. I watched all of your videos on Signals and Systems and I have to say that your way of teaching is great. You explain everything in such a way for an intuitive understanding of the concepts. Can't be grateful enough. Greetings from Greece!
Thank you very much, sir! This is the best explanation of Fourier Transform I have ever seen! But I have a question. If you want to determine whether there is a specific frequency in f(t), you need to calculate an integral once. But there are countless frequencies, so do you need to calculate countless integral operations to know the result?
Many thanks for the feedback! Yes indeed, one would need to calculate an integral (a product of 2 functions and then sum) for each and every frequency of interest. However, there is only a finite number of frequencies in the case of discrete functions (the discrete Fourier transform is used), so it is feasible. As it can however be slow, this is where the fast Fourier transform (FFT) comes in - it dramatically speeds up the calculation of the discrete Fourier transform. For continuous functions, analytic integration can be done, to give the solution in the form of another function.
Can you explain a little more what you mean? The error arising from missing frequency information, and the degree to which this matters for a signal to still be recovered / "understood"?
That is too cool! I have a mathematics/physics background but I love to get into things like image/signal processing and related fields. How does one make a career studying such topics?
One can make a career as an academic with these topics - applied to research (e.g. medical imaging, AI and deep learning applications), and also used in teaching. Usual pathway to that would be a PhD, postdoc research etc.
Hello sir, thanks a lot for such intuitive explanation. Could you please share the link to the ppts and animations it would be very helpful to make notes.
Many thanks for your feedback! The animations have web links provided in the video, I hope they can be located online? Otherwise for the PDF version, I would normally share that with local students only.
I accidentally hit the dislike button, and got a message that my feedback would be shared with the creator. It was a mistake. I corrected it to the "like" button, but I don't know if that stops the erroneous feedback.
😎 Best Fourier Analysis! Thank you dear professor for this intuitive lecture !!
Thanks so much for the feedback!!
Great, you are the champion🥇 Many thank 🙏
Many thanks for the feedback!
Another fantastic video - by far the best explanation I've seen in all the texts/ videos I've used on this subject. You do a great job of covering the important maths concepts with a focus on practical application which is great for us Engineering students!
Thanks so much for the feedback, means a lot!
Thank you very much professor. I watched all of your videos on Signals and Systems and I have to say that your way of teaching is great. You explain everything in such a way for an intuitive understanding of the concepts. Can't be grateful enough. Greetings from Greece!
Thanks so much Steve, your feedback is really appreciated! Glad you found the video series helpful. Greetings from London!
Thank you sir!!
All my struggling solved in 33.21 min video.
Delighted to hear! Thanks for the feedback!
Thank you very much, sir! This is the best explanation of Fourier Transform I have ever seen! But I have a question. If you want to determine whether there is a specific frequency in f(t), you need to calculate an integral once. But there are countless frequencies, so do you need to calculate countless integral operations to know the result?
Many thanks for the feedback! Yes indeed, one would need to calculate an integral (a product of 2 functions and then sum) for each and every frequency of interest. However, there is only a finite number of frequencies in the case of discrete functions (the discrete Fourier transform is used), so it is feasible. As it can however be slow, this is where the fast Fourier transform (FFT) comes in - it dramatically speeds up the calculation of the discrete Fourier transform. For continuous functions, analytic integration can be done, to give the solution in the form of another function.
Another great explanation. Thank you! Question...is the PDF from the presentation and course material available somehere?
Thanks Julio. Please send me an email, I might be able to send a PDF
It makes me think of what the delta is of missing information like in Fourier analysis such that the receiver capability is resulting in coherence.
Can you explain a little more what you mean? The error arising from missing frequency information, and the degree to which this matters for a signal to still be recovered / "understood"?
That is too cool! I have a mathematics/physics background but I love to get into things like image/signal processing and related fields. How does one make a career studying such topics?
One can make a career as an academic with these topics - applied to research (e.g. medical imaging, AI and deep learning applications), and also used in teaching. Usual pathway to that would be a PhD, postdoc research etc.
Hello sir, thanks a lot for such intuitive explanation. Could you please share the link to the ppts and animations it would be very helpful to make notes.
Many thanks for your feedback! The animations have web links provided in the video, I hope they can be located online? Otherwise for the PDF version, I would normally share that with local students only.
I accidentally hit the dislike button, and got a message that my feedback would be shared with the creator. It was a mistake. I corrected it to the "like" button, but I don't know if that stops the erroneous feedback.
All is fine, no notification/feedback received. Many thanks for the "like"!
M