COMPUTER NETWORKS and INTERNETS Chapter 6 Information sources and signals

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

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

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

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

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 https://www.youtube.com/watch?v=ThrK2spjrLs

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

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

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

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

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

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

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

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

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

The end