Line 3: Line 3:
 
The z-transform converts a discrete-time signal into a complex frequency domain representation.
 
The z-transform converts a discrete-time signal into a complex frequency domain representation.
  
* <math>X(z) = \sum_{n=-\infty}^\infty (x[n]*z^{-n})</math>
+
* <math> X(z) = \sum_{n=-\infty}^\infty (x[n]z^{-n})</math>
 +
 
 +
Some Properties:
 +
 
 +
Linearity:
 +
 
 +
* <math> ax1[n]+bx2[n] = aX1(z)+bX2(z) </math>
 +
 
 +
Time-Shifting:
 +
 
 +
* <math> x[n-k] = z^{-k}X(z) </math>
 +
 
 +
Scaling in Z domain:
 +
 
 +
* <math> a^{n}Y(z) = X(a^{-1}Z) </math>
 +
 
 +
Time Reversal:
 +
 
 +
* <math> x[-n] = X(z^{-1}) </math>
 +
 
 +
Convolution:
 +
 
 +
* x1[n]* x2[n] = X1(z)X2(z)

Revision as of 11:05, 8 September 2009

The Z-Transform

The z-transform converts a discrete-time signal into a complex frequency domain representation.

  • $ X(z) = \sum_{n=-\infty}^\infty (x[n]z^{-n}) $

Some Properties:

Linearity:

  • $ ax1[n]+bx2[n] = aX1(z)+bX2(z) $

Time-Shifting:

  • $ x[n-k] = z^{-k}X(z) $

Scaling in Z domain:

  • $ a^{n}Y(z) = X(a^{-1}Z) $

Time Reversal:

  • $ x[-n] = X(z^{-1}) $

Convolution:

  • x1[n]* x2[n] = X1(z)X2(z)

Alumni Liaison

Have a piece of advice for Purdue students? Share it through Rhea!

Alumni Liaison