(New page: This page will talk about the important property of sampling theorem. Consider the samples <math>x(nT)</math> for n = 0,-1,1,-2,2,... if <math>T < \frac{1}{2} \left (\frac{2\pi}{w_m} \ri...)
 
 
Line 3: Line 3:
 
Consider the samples <math>x(nT)</math> for n = 0,-1,1,-2,2,...
 
Consider the samples <math>x(nT)</math> for n = 0,-1,1,-2,2,...
  
if <math>T < \frac{1}{2} \left (\frac{2\pi}{w_m} \right )\,</math>
+
if
 +
 
 +
:<math>T < \frac{1}{2} \left (\frac{2\pi}{w_m} \right )\,</math>
 +
 
 +
then <math>x(t)</math> can be uniquely recovered from its samples. In this case, T is the sampling period, and <math>\frac{2\pi}{T}</math> (or <math>w_s</math>) is the sampling frequency.
 +
 
 +
The important fact is that
 +
 
 +
:<math>T < \frac{1}{2}\frac{2\pi}{w_m}</math>
 +
 
 +
:<math>2w_m < \frac{2\pi}{T}</math>
 +
 
 +
but we mentioned before that <math>w_s = \frac{2\pi}{T}</math> therefore,
 +
 
 +
:<math>2w_m < w_s</math>
 +
 
 +
In which <math>2w_m</math> is called the Nyquist rate.

Latest revision as of 15:28, 10 November 2008

This page will talk about the important property of sampling theorem.

Consider the samples $ x(nT) $ for n = 0,-1,1,-2,2,...

if

$ T < \frac{1}{2} \left (\frac{2\pi}{w_m} \right )\, $

then $ x(t) $ can be uniquely recovered from its samples. In this case, T is the sampling period, and $ \frac{2\pi}{T} $ (or $ w_s $) is the sampling frequency.

The important fact is that

$ T < \frac{1}{2}\frac{2\pi}{w_m} $
$ 2w_m < \frac{2\pi}{T} $

but we mentioned before that $ w_s = \frac{2\pi}{T} $ therefore,

$ 2w_m < w_s $

In which $ 2w_m $ is called the Nyquist rate.

Alumni Liaison

BSEE 2004, current Ph.D. student researching signal and image processing.

Landis Huffman