1. Information Sources And Signals

2. Review: Composite Signals and Frequency Domain Representations
Time Domain Representation
Frequency Domain Representation 2 2

3. Signal Bandwidth A Measure of Signal Frequency Range
The difference between the highest and the lowest frequencies contained in a signal.

4. What Is the Bandwidth of This Signal?
2 - 1 = 1Hz

5. If a signal is decomposed into three sine waves with frequencies of 300, 700, and 1200 Hz, what is its bandwidth?

6. Why Should We Care about Bandwidth?
We need to know the bandwidth of a signal to make sure the communication channel is wide enough to transmit it.

7. An Analogy

8. In reality,
The bandwidth of a signal is much larger than what is allowed by a communication channel.
We need to chop off some frequency components of a signal so that it can be transmitted AND as much information as possible can be preserved.

9. Digital Signals

10. Digital Signals Use voltage to represent digital values
A positive voltage  a logical one (1)
Zero or a negative voltage  a logical zero (0)
+5 volts is usually what we use in computer hardware.
+5 or 0 -> 1 or 0
Two levels: 1 bit

11. Digital Signal Levels
Some physical mechanisms can support more than two signal levels.
For example, consider a system that uses four levels of voltage:
-5 volts, -2 volts, +2 volts, and +5 volts

12. Digital Signal Levels
More signal levels a system has, more bits need to be sent out per unit time.

13. Bits and Signal Levels Often we use bits to describe signal levels
How many bits can we represent using 4 levels?
-5, -2, 2, 5
How many bits can we represent using 8 levels?
How many levels do we need to represent n bits?

14. More Bits, Better?
More bits a system can deliver at a given time period, more information it can transfer.
Can we increase the signal levels as many as possible?
Mathematically, it is doable.
Practically, electronic systems cannot distinguish between signal levels (voltage levels) that differ by small amounts.

15. What’s the bandwidth of digital signals?
Fourier Analysis: =
Time Domain Representation
Frequency Domain Representation

16. Digital Signals The bandwidth of a digital signal is infinite!
Accurate representation of a digital signal requires an infinite set of sine waves.
Transmitting/reproducing digital signals is impractical
Engineers adopt a compromise:
generate analog waves that closely approximate the digital signal
approximation involves building a composite signal from only a few sine waves
the quality of approximation depends on the channel bandwidth 16 16

17. Bandwidth-Limited Signals
Having less bandwidth (harmonics) degrades the signal
8 sine waves
Lost!
Bandwidth
4 sine waves
Lost!
2 sine waves
Lost!
CN5E by Tanenbaum & Wetherall, © Pearson Education-Prentice Hall and D. Wetherall, 2011

18. Speed/Capacity of Data Transmittion
We use bit rate (bits per second) to measure the speed/capacity of transmission.
Two factors to consider when measuring the bit rate.
The number of signal levels
How many bits at each level?
How long does a system have to stay at a given level?
Should be long enough to guarantee the signal to be received.
We use Baud to measure how many times the signal can change per second

19. Baud Baud rate is confined by hardware. Some numbers (theoretical)
Dial-in (v.90): 56k
ISDN: 128k
DSL: 300k – 1,500k (1.5M)
Cable: 300k – 6,000k (6M) (could go higher)
T1: 1.5M
T3: 44M
100Base-T: 100M
Baud rates on real connections may be lower. 19 19

20. Bit Rate
If a system with two signal levels operates at 1000 baud, how many bits he system can transfer per second?
How about a system that operates at 2000 baud and has four signal levels 20 20

21. Synchronization and Agreement about Signals
Diverse signals and systems means different signal levels and baud rates.
Different signal levels + Different baud rates  Troubles!
The systems at both ends must be able to measure time precisely.
if one end transmits a signal with 10 elements per second, the other end must expect exactly 10 elements per second

22. Example of Synchronization Error

23. Synchronization and Agreement about Signals
Handshaking
At slow speeds, easy
At high speeds, many challenges
if one end transmits a signal with 109 elements per second, the other end must expect exactly 109 elements per second (not 109-1, not 109+1) 1
… 0
Um, how many zeros was that?

24. Manchester Encoding
For computers, detecting a transition in signal level is much easier than measuring the signal level
A transition from 0V to +5V  logical 1
A transition from +5V to 0V  logical 0
Transitions occur in the middle of each time slot

25. Converting an Analog Signal to Digital
Pulse code modulation

26. Converting an Analog Signal to Digital
The three steps used PCM
Sampling an analog signal.
Quantizing the sampled value.
Encoding the quantized value.
4.5
5.7
3.2 26 26

27. How many samples do we need?
too few samples: may only give a crude approximation of the original signal
too many samples: more digital data will be generated, which uses extra bandwidth

28. The Nyquist Theorem and Sampling Rate
A mathematician named Nyquist discovered exactly how much sampling is required:
fmax : the highest frequency in the composite signal.
Sample a signal at least twice as fast as the highest frequency that must be preserved.

29. Q: At what rate should we sample the following signal?

30. Example: Bit Rate of Telephone System
Audio bandwidth
Acceptable quality: preserving frequency up to 4k
Sampling rate (baud) = 2*4K = 8K
Quantization:
Reasonable quality reproduction: 8 bits / 256 levels

31. Digital Audio Audio frequency Sampling frequency 20 Hz – 20k
Varied from individual to individual
Teenbuzz:
Sampling frequency
MP3: 44.1kHz
DVD-audio: 48 kHz

32. Encoding and Data Compression
Data compression refers to a technique that reduces the number of bits required to represent data
Data compression is relevant to a communication system
because reducing the number of bits used to represent data reduces the time required for transmission
a communication system can be optimized by compressing data
Chapter 29 discusses compression in multimedia applications
There are two types of compression:
Lossy - some information is lost during compression
Lossless - all information is retained in the compressed version

33. Encoding and Data Compression
Lossy compression is generally used with data that a human consumes, such as an image, video/audio
The key idea is that the compression only needs to preserve details to the level of human perception
a change is acceptable if humans cannot detect the change
JPEG (used for images) compression or MPEG-3 (abbreviated MP3 and used for audio recordings) employ lossy compression
Lossless compression preserves the original data without missing anything
lossless compression can be used for documents or in any situation where data must be preserved exactly
when used for communication, a sender compresses the data before transmission and the receiver decompresses the result
arbitrary data can be compressed by a sender and decompressed by a receiver to recover an exact copy of the original 33 33

34. Friday Group research assignment 1 due at 11:59pm
Use class time to work on it