An interactive guide to the Fourier transform (2012)
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An interactive guide to the Fourier transform (2012)
1–10 of 79 posts
Re: An interactive guide to the Fourier transform (2012)
#2previous submission with 79 comments
Re: An interactive guide to the Fourier transform (2012)
#3Reading this I realise Bartosz Ciechanowski's work has ruined my definition of "interactive" for blogs.
Re: An interactive guide to the Fourier transform (2012)
#4Adding epicycles to the circular orbits - an example before Fourier!
Re: An interactive guide to the Fourier transform (2012)
#5Request for someone to make an intuitive explanation of why the Fourier Transform is (almost) it's own inverse. I know the math proof from taking analysis, but the formula is too pretty and symmetrical for the explanation to be so technical.
Same for why it preserves L2 norm.
Re: An interactive guide to the Fourier transform (2012)
#6Reading this I realise Bartosz Ciechanowski's work has ruined my definition of "interactive" for blogs.
[deleted]
Re: An interactive guide to the Fourier transform (2012)
#7It would also be nice to have an understanding of its relation to the Laplace transform, something more than saying that the real component goes to zero.
Re: An interactive guide to the Fourier transform (2012)
#8Reading this I realise Bartosz Ciechanowski's work has ruined my definition of "interactive" for blogs.
Exactly what I was thinking!
Re: An interactive guide to the Fourier transform (2012)
#9It would also be nice to have an understanding of its relation to the Laplace transform, something more than saying that the real component goes to zero.
+1. Pop-sci explanations of the frequency domain and Fourier transform are too many already, but does anyone know if there's a similar physical interpretation or visualization of the Laplace transform and the S domain?
Re: An interactive guide to the Fourier transform (2012)
#10The short explanation of the DFT that I like the most is that it projects the signal vector on to the vector room with the exponential functions as basis vectors. Then you can see how much of the signal that each basis vector, corresponding to a frequency, can explain.
It's intuitive to see if you start with a 2D vector space (the regular euclidian plane) and a 2D vector, and then you can expand the definition of vector spaces to include orthogonal functions as bases.