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- From: jim@fuji.eng.yale.edu (James J. Szinger)
- Subject: Re: Energy within a Digitized Pulse
- In-Reply-To: liuc@madrone.eecs.ucdavis.edu's message of 29 Jul 92 03:43:27 GMT
- Message-ID: <JIM.92Jul29103519@fuji.eng.yale.edu>
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- Organization: Yale Univerity, Intelligent Sensors Lab, Elect. Eng.
- References: <15498@ucdavis.ucdavis.edu> <TeNmoB1w164w@gmp.lonestar.org>
- <15598@ucdavis.ucdavis.edu>
- Date: 29 Jul 92 10:35:19
- Lines: 27
-
- In article <15598@ucdavis.ucdavis.edu>
- liuc@madrone.eecs.ucdavis.edu (Chia-Liang Liu Ka -Leung Lau)
- writes:
- >In article <TeNmoB1w164w@gmp.lonestar.org> greg@gmp.lonestar.org (G.R. Basile) writes:
- >>
- >>I agree. What has piqued my curiousity is how to derive an exact solution
- >>of the power of the continuous time signal from the sampled data.
-
- >The information provided by a digitized signal and its FFT is
- >identical. Since you can not calculate the energy of a signal from
- >its samples EXACTLY, you can't calculate it from its FFT either.
-
- To me it is conceptually easy to obtain the EXACT power from the
- samples. Start by discretizing x(t) to get x[i]. By sampling at
- more than four times the highest frequency in x(t), the sampling
- criteria are satisfied for x^2(t). The power in x(t) is the
- integral from minus to plus infinity of x^2(t). But x^2(t) is
- equal to the sum of sinc functions weighted by x^2[i]. The
- integral of the sinc function can be found in a table of
- integrals, so the power simplifies to a weighted sum of x^2[i].
- It is left as an exercise for the reader to carefully derive the
- correct weights, but the principle is straight forward and it
- clearly can be done.
-
- Jim
- --
- James Szinger jim@fuji.eng.yale.edu
-