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<math> x_{p}(t) = x(t)p(t) = x(t)\sum^{\infty}_{n=-\infty}\delta(t-nT)</math> | <math> x_{p}(t) = x(t)p(t) = x(t)\sum^{\infty}_{n=-\infty}\delta(t-nT)</math> | ||
| − | <math> = \sum^{\infty}_{n=-\infty} | + | <math> = \sum^{\infty}_{n=-\infty}x(t)delta(t-nT)</math> |
| − | <math> = \sum^{\infty}_{n=-\infty} | + | <math> = \sum^{\infty}_{n=-\infty}x(nT)delta(t-nT)</math> |
Above diagram is the sampling process. | Above diagram is the sampling process. | ||
Revision as of 16:33, 9 November 2008
Sampling theorem
Here is a signal, x(t) with X(w) = 0 when |W| > Wm.
With sampling period T, samples of x(t),x(nT), can be obtained , where n = 0 +-1, +-2, ....
The sampling frequency is $ \frac{2\pi}{T} $. It is called Ws.
If Ws is greater than 2Wm, x(t) can be recovered from its samples.
Here, 2Wm is called the "Nyquist rate".
To recover, first we need a filter with amplited T when |W| < Wc.
Wc has to exist between Wm and Ws-Wm.
Here is a diagram.
x(t) ------> multiply ---------> $ x_{p}(t) $
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$ p(t) = \sum^{\infty}_{n=-\infty}\delta(t-nT) $
$ x_{p}(t) = x(t)p(t) = x(t)\sum^{\infty}_{n=-\infty}\delta(t-nT) $ $ = \sum^{\infty}_{n=-\infty}x(t)delta(t-nT) $ $ = \sum^{\infty}_{n=-\infty}x(nT)delta(t-nT) $
Above diagram is the sampling process.
