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Signal Processing

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  • $\begingroup$ You mentioned a term "we are effectively rotating the signal," .. but what i have studied & understand is that in Orthogonal transform of N signal samples with twiddle factor matrix.. instead of signal we use to rotate the basis vectors. If we have divided 2*Pi frequency space into 10 points (i.e doing 10 point dft) then we will rotate through different basis vector(represent different frequencies) & found cordinate of point(i.e particular sample) with respect to different basis vector fourier.eng.hmc.edu/e102/lectures/orthogonalTransform/….. $\endgroup$ Commented Jun 2, 2017 at 11:09
  • $\begingroup$ continue from above ....and if we have different cordinate w.r.t to different frequency basis then we can have estimate how much frequency component is in each sample ... what i have got is right ? $\endgroup$ Commented Jun 2, 2017 at 11:09
  • $\begingroup$ or you want to say is N*N matrix of twiddle factor is an bank of filter... which is capable to extract the different frequency content in each sample of signal ? $\endgroup$ Commented Jun 2, 2017 at 11:11
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    $\begingroup$ See this post where I explain the similarity between homodyning the signal (rotating the signal) and homdyning the coefficients (rotating the coefficient, which instead moves the filter to the signal: dsp.stackexchange.com/questions/41228/… $\endgroup$ Commented Jun 2, 2017 at 11:16