1. Chapter 3 Sampling

2. Zero-order hold interpolation
Nearest neighbor interpolation
(3-14)
Fig. 3-9.

3. In frequency domain Induces distortion in the reconstructed signal
Resolution error inside Nyquist frequency
Aliasing error beyond Nyquist frequency
Ideal low-pass filter
Zero-order hold interpolation function
Fig

4. Applying low-pass filtering for smoothing
Smoothing filter
Fig

5. First-order hold interpolation
Linear interpolation
(3-15)
Fig

6. Comparison of interpolation functions
Ideal interpolation function
Zero-order hold interpolation function
First-order hold interpolation function
Fig

7. Analog to Digital Conversion
Digital signal processing
Anti-aliasing filter (pre-filter)
To reduce aliasing and to limit within Nyquist frequency
A/D converter
Converting the analog input signal into digital form
D/A converter
Converting processed digital signal back into analog form
Smoothing filter
Smoothing the reconstructed signal and removing unwanted high frequency components
Anti-aliasing filter
(LPF)
A/D
converter
Digital
processors
D/A
Smoothing
filter
Fig

8. Analog to digital conversion process
Sampling
Converting analog signal into discrete-time signal
Quantizer
Each discrete-time signal sample is quantized into one of levels
Encoder
Encoding the discrete levels into distinct binary word,
each of length B bits
Lowpass
filter
Sample and hold
Quantizer
Encoder
Fig

9. Quantization Quantization error (3-16) x(t) and Quantization
Fig

10. Max. quantization error
If q is least-significant bit(LSB)
Quantization error using probability density function
Average and variance
(3-17)
Fig

11. Quantization interval
Variance of quantization error
Signal-to-noise ratio
(3-18)
(3-19)
(3-20)

12. Example 3-1
Mean-square quantization error(MSQE)