ECEN3713 Network Analysis Lecture

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ECEN3713 Network AnalysisLecture 25 11
April 2006Dr. George Scheets

• Exam 2 Results Hi 89, Lo 30, Ave.
  60.23Standard Deviation 22.05
• Quiz 8 Results Hi 9, Lo 2, Ave. 4.42
• Standard Deviation 2.56
• Read Chapter 16.1 - 16.4
• Problems 15.26, 15.28, 15.31
• Thursday's Quiz
• Active Filters
• Thursday's AssignmentProblems 15.58, 16.1, 16,2

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ECEN3713 Network AnalysisLecture 27 18
April 2006Dr. George Scheets

• Read Chapter 16.5 - 16.9
• Problems 16.11, 16.13, 16.16
• Thursday's Quiz
• Chapter 16
• Final Exam, Thursday, 4 May, 1400-1550
• Comprehensive, Open Book Notes
• Thursday's AssignmentProblems 16.23, 16.24,
  16.29, 16.33

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ECEN3713 Network AnalysisLecture 29 25
April 2006Dr. George Scheets

• Quiz 10 Results Hi 10, Lo 3, Ave. 5.65
• Standard Deviation 2.47
• Problems 16.34, 16.35, 16.41, 16.47
• Final Exam
• 200-350pm, Thursday, 4 May
• Office Hours
• Wednesday Office Hours 1400 - 1700 Thursday
  Office Hours 0930-1130, 1230-11400

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ECEN3713 Network AnalysisLecture 30 27
April 2006Dr. George Scheets

• Final Exam
• 200-350pm, Thursday, 4 May
• Office Hours
• Wednesday Office Hours 1400 - 1700 Thursday
  Office Hours 0930-1130, 1230-11400

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How do S-Domain poles zeroes affect frequency
domain plots?

• Real Pole
• Causes H(s) to "blow up"
• Causes H(j?) to break down
• Real Zero
• Causes H(s) to be 0
• Causes H(j?) to break up
• Complex Conjugate Pole Pairs
• Cause H(s) to "blow up" in two symmetrical
  places
• Cause H(j?) to have bulges

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Single Real Pole, Two Real Poles 1/(s3),
1/(s3)2
H(?)
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Single Real Pole, Two Real Poles 1/(s3),
3/(s3)2
H(?)
Note 2nd order system has sharper
roll-off. Also, 3 dB break point has moved.
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Complex Conjugate Poles, real 01/(s2 100)
1/(s j10)(s j10)
H(?)
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Complex Conjugate Poles, real gt 01/(s2 4s
104) 1/(s 2 j10)(s 2 j10)
H(?)
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Complex Conjugate Poles, real gt 01/(s2 10s
125) 1/(s 5 j10)(s 5 j10)
H(?)
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Correlation
x(t) y(t) dt

• Tells how "alike" x(t) and y(t) are
• If evaluates positive
• if x(t1) is positive, y(t1) tends to be positive
  t1 an arbitrary time
• x(t) and y(t) are similar, i.e. there is a lot
  of y(t) in x(t)

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Correlation
x(t) y(t) dt

• If evaluates negative
• if x(t1) is positive, y(t1) tends to be negative
  vice-versa
• x(t) and y(t) are similar but opposites
• If evaluates 0
• x(t) y(t) are not related (uncorrelated)no
  predictability

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Laplace Transform
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F(s) f(t) e-st dt
0-

• F(3) tells how alike f(t) and e-3t are
• Over the time interval 0- to infinity

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Fourier Transform
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F(?) f(t) e-j?t dt
- 8

• F(3) tells how alike f(t) and e-j3t are
• Over the time interval -8 to 8

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Fourier Series
T
an 2/T f(t) cos(n?ot) dt
0

• a3 tells how alike f(t) and cos(3?ot) are
• Over one period, T
• 1/T average
• 2 scaling factor to get power correct

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