(→Chapter 7) |
(→Chapter 8) |
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==Chapter 8== | ==Chapter 8== | ||
+ | #Complex Exponential and Sinusoidal Amplitude Modulation (AM) <math> y(t) = x(t)c(t) </math> | ||
+ | ##<math>c(t) = e^{\omega_c t + \theta_c}</math> | ||
8.1, 8.2, 8.3, 8.5, 8.8, 8.10, 8.11, 8.12, 8.21, 8.23 | 8.1, 8.2, 8.3, 8.5, 8.8, 8.10, 8.11, 8.12, 8.21, 8.23 | ||
+ | |||
==Chapter 9== | ==Chapter 9== | ||
9.2, 9.3, 9.4, 9.6, 9.8, 9.9, 9.21, 9.22 | 9.2, 9.3, 9.4, 9.6, 9.8, 9.9, 9.21, 9.22 |
Revision as of 01:00, 5 December 2008
Suggested problems from Oppenheim and Willsky
Contents
[hide]Chapter 7
- Sampling
- Impulse Train Sampling
- The Sampling Theorem and the Nyquist
- Signal Reconstruction Using Interpolation: the fitting of a continuous signal to a set of sample values
- Sampling with a Zero-Order Hold (Horizontal Plateaus)
- Linear Interpolation (Connect the Samples)
- Undersampling: Aliasing
- Processing CT Signals Using DT Systems (Vinyl to CD)
- Analog vs. Digital: The Show-down (A to D conversion -> Discrete-Time Processing System -> D to A conversion
- Sampling DT Signals (CD to MP3 albeit a complicated sampling algorithm, MP3 is less dense signal)
7.1, 7.2, 7.3, 7.4, 7.5, 7.7, 7.10, 7.22, 7.29, 7.31, 7.33
Chapter 8
- Complex Exponential and Sinusoidal Amplitude Modulation (AM) $ y(t) = x(t)c(t) $
- $ c(t) = e^{\omega_c t + \theta_c} $
8.1, 8.2, 8.3, 8.5, 8.8, 8.10, 8.11, 8.12, 8.21, 8.23
Chapter 9
9.2, 9.3, 9.4, 9.6, 9.8, 9.9, 9.21, 9.22
Chapter 10
10.1, 10.2, 10.3, 10.4, 10.6, 10.7, 10.8, 10.9, 10.10, 10.11, 10.13, 10.15, 10.21, 10.22, 10.23, 10.24, 10.25, 10.26, 10.27, 10.30, 10.31, 10.32, 10.33, 10.43, 10.44.
Note: If a problem states that you should use “long division”, feel free to use the geometric series formula instead.