(New page: This is the original code: <pre> F0 = 13; T0 = 1/F0; Ts = 0.07; t = 0:Ts:13*T0; x = real(exp(j*(2*pi*F0*t-pi/2))); plot(t,x) <\pre> This code is wrong because the sampling frequency, ...)
 
Line 9: Line 9:
 
x  = real(exp(j*(2*pi*F0*t-pi/2)));
 
x  = real(exp(j*(2*pi*F0*t-pi/2)));
 
plot(t,x)
 
plot(t,x)
<\pre>
+
</pre>
  
 
This code is wrong because the sampling frequency, Ts, is to large to get an accurate recreation of the
 
This code is wrong because the sampling frequency, Ts, is to large to get an accurate recreation of the
Line 21: Line 21:
 
x  = real(exp(j*(2*pi*F0*t-pi/2)));
 
x  = real(exp(j*(2*pi*F0*t-pi/2)));
 
plot(t,x)
 
plot(t,x)
<\pre>
+
</pre>
 
Explain what the bug is, and modify the above code to fix this bug. Post your answer on a Rhea page.
 
Explain what the bug is, and modify the above code to fix this bug. Post your answer on a Rhea page.

Revision as of 07:16, 10 September 2008

This is the original code:


F0 = 13;
T0 = 1/F0;
Ts = 0.07;
t  = 0:Ts:13*T0;
x  = real(exp(j*(2*pi*F0*t-pi/2)));
plot(t,x)

This code is wrong because the sampling frequency, Ts, is to large to get an accurate recreation of the signal. By Nyquist's theorem, the descrete sampling frequency must be twice the continuous frequency in order to avoid unwanted artifacts. In other words Ts=.5*T0.

F0 = 13;
T0 = 1/F0;
Ts = .5*T0;
t  = 0:Ts:13*T0;
x  = real(exp(j*(2*pi*F0*t-pi/2)));
plot(t,x)

Explain what the bug is, and modify the above code to fix this bug. Post your answer on a Rhea page.

Alumni Liaison

Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett