1. COMPUTER NETWORKS and INTERNETS
Chapter 6
Information sources and signals

2. introduction
Previous chapters gave the framework for data communications.
The chapter begins an exploration of data communications in more detail

3. Sources of information
An input signal can arise from
Transducer such as a microphone
Video Camera
Sensors – Measuring devices (thermometers and scales
Receiver such as an Ethernet interface
We use the term signal processing to describe the recognition and transformation of signals

4. Analog and digital signals
Analog – Characterized by a continuous mathematical function – the input changes from one value to the next moving through all the possible values.
Digital – Fixed set of valid values

5. Sine waves (analog) Y - Axis (Vertical) is the Voltage
Fundamental because sine waves characterize many natural phenomena
Examples
Audible tones
Radio waves
Light energy
Y - Axis (Vertical) is the Voltage
X - Axis (Horizontal) is the time

6. Sine wave characteristics
Three important characteristics are used in networks:
Frequency – Number of oscillations per unit (typically 1 sec)
Millisecond (ms) – Khz (10^3)
Microsecond (us) – MHz (10^6)
Nanosecond (ns) – Ghz (10^9)
Picosecond (ps) – THz (10^12)
Amplitude – Difference between the maximum and minimum signal
Phase – shifting from reference line

7. Fourier analysis Multiple sine waves can be added together
Result is known as a composite wave
Corresponds to combining multiple signals (e.g., playing two musical tones at the same time)
Mathematician named Fourier discovered how to decompose an arbitrary composite wave into individual sine waves
Fourier analysis provides the mathematical basis for signal processing

8. Definition of analog bandwidth
Time domain – a graph of a signal as a function of time
Decompose a signal into a set of sine waves and take the
difference between the highest and lowest frequency
Easy to compute from a frequency domain plot
Example signal with bandwidth of 4 Kilohertz (KHz):
Difference highest to lowest
Highest 5
Lowest 1
Difference 4 Khz

9. Digital signals and signal levels
A digital signal level can represent multiple bits

10. Converting digital to analog
Approximate digital signal with composite of sine wave

11. Converting digital to analog
Three steps taken during conversion (Pulse Code Modulation)
Sampling each measurement
Quantizing the samples (converting into small integer
Encode in a specific format

12. Converting digital to analog
Example sampling using eight levels
Under sampling – Too few samples
Over sampling – Too many samples (additional bandwidth)

13. Sampling rate and nyquist theorem
How many samples should be taken per second?
Mathematician named Nyquist discovered the answer.
sampling rate = 2 × f max where f max is the highest frequency in the composite signal

14. Nonlinear encoding A-law Linear sampling does not work well for voice
Researchers created nonlinear sampling that modify dynamic range to reproduce sounds to which the human ear is sensitive
Mu-law (μ-law)
Used in North America and Japan
More dynamic range, but more sensitive to noise
A-law
Used in Europe
Less sensitive to noise, but less dynamic range

15. Synchronization errors and line coding
Synchronization errors occurs when receiver and sender disagree about bit boundaries (clocks differ)
Line coding techniques prevent synchronization errors

16. The end