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<math>a_{0} = \frac{1}{4} \sum_{n=0}^{3} x[n] = \frac{1}{4}(0+1+2+1)</math>
 
<math>a_{0} = \frac{1}{4} \sum_{n=0}^{3} x[n] = \frac{1}{4}(0+1+2+1)</math>
  
<font size="4">
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<font size="4.5">
'''<math>a_{0} = 1</math>'''
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<math>a_{0} = 1</math>
 
</font>
 
</font>
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<math>a_{1} = \frac{1}{4} \sum_{n=0}^{3} x[n] e^{-j \frac{\pi}{2} n} = \frac{1}{4}[(0)(1)+(1)(-j)+(2)(-1)+(1)(j)]</math>
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<math>a_{1} = -\frac{1}{2}</math>
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<math>a_{2} = \frac{1}{4} \sum_{n=0}^{3} x[n] e^{-j \pi n} = \frac{1}{4}[(0)(1)+(1)(-1)+(2)(1)+(1)(-1)]</math>
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<font size="4.5">
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<math>a_{2} = 0</math>
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</font>
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<math>a_{3} = \frac{1}{4} \sum_{n=0}^{3} x[n] e^{-j \frac{3 \pi}{2} n} = \frac{1}{4}[(0)(1)+(1)(j)+(2)(-1)+(1)(-j)]</math>
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<math>a_{3} = -\frac{1}{2}</math>

Latest revision as of 13:40, 24 September 2008

Periodic DT Signal

The following plot shows two periods of the periodic DT signal $ x[n] $, a sawtooth:

SawDTJP ECE301Fall2008mboutin.jpg

Fourier Series Coefficients

$ a_{k} = \frac{1}{N} \sum_{n=0}^{N-1} x[n] e^{-jk \frac{2 \pi}{N} n} $

From the plot above, N = 4:

$ a_{k} = \frac{1}{4} \sum_{n=0}^{3} x[n] e^{-jk \frac{\pi}{2} n} $

and:

$ x[0] = 0 $

$ x[1] = 1 $

$ x[2] = 2 $

$ x[3] = 1 $

$ x[4] = 0 $

Therefore:

$ a_{0} = \frac{1}{4} \sum_{n=0}^{3} x[n] = \frac{1}{4}(0+1+2+1) $

$ a_{0} = 1 $


$ a_{1} = \frac{1}{4} \sum_{n=0}^{3} x[n] e^{-j \frac{\pi}{2} n} = \frac{1}{4}[(0)(1)+(1)(-j)+(2)(-1)+(1)(j)] $

$ a_{1} = -\frac{1}{2} $


$ a_{2} = \frac{1}{4} \sum_{n=0}^{3} x[n] e^{-j \pi n} = \frac{1}{4}[(0)(1)+(1)(-1)+(2)(1)+(1)(-1)] $

$ a_{2} = 0 $


$ a_{3} = \frac{1}{4} \sum_{n=0}^{3} x[n] e^{-j \frac{3 \pi}{2} n} = \frac{1}{4}[(0)(1)+(1)(j)+(2)(-1)+(1)(-j)] $

$ a_{3} = -\frac{1}{2} $

Alumni Liaison

Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang