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The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

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Page 1: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Digital DelugeLearning in Retirement

David CollProfessor Emeritus

Department of Systems and Computer Engineering

Winter 2009

Page 2: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009
Page 3: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Welcome to “The Digital Deluge”

• My name is David Coll and

• In the next few weeks I’m going to try and give you a feel for what’s going on in a world awash in digital information.

• We’ll look at – the jargon, – the sources, – the volumes, – the technology, – the societal impact, and – how some are coping.

Page 4: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• This is a lecture series about the flood of information created by new technologies.

• The problem in offering a course about this subject is that there is way too much information to handle.

• But – we have ways of coping

• For example

• Clear and simple instructions …

Page 5: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009
Page 6: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

First - My Co-ordinates

• Professor Emeritusin the Department of Systems and Computer Engineering, Carleton University

[email protected]• http://www.sce.carleton.ca/faculty/coll.htm• 613-225-4229

Page 7: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Information

• The world is awash in INFORMATION.

• Our senses: sight, hearing, smell, touch, and taste are bombarded with inputs from all sides

• Information is essential for our survival.

• But …

• Let’s narrow it down somewhat

Page 8: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• We might limited ourselves to information that comes to us through a medium: mediated communications – Print

• newspapers, magazines, books, flyers …

– Posters– Radio– Television– Recordings – music, images, videos– The Telephone

Page 9: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• As well as through

• the Internet and

• the World Wide Web

• We will exclude information We will exclude information communicated to us through communicated to us through spectral channels by mediums spectral channels by mediums wearing star-studded gowns and wearing star-studded gowns and too many bracelets …too many bracelets …

Page 10: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Introduction

• The rapid growth and deployment of digital information processing and communications technology is changing our world, the way we do business, and the way we live.

• Information be it text, data, images, music, speech, or television is acquired, stored, processed, communicated, received, and presented in digital form, i.e., as numbers.

Page 11: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• The resulting torrent of information is

so vast that some have dubbed it the “The Digital Deluge”.

• del·uge – 1

• a: an overflowing of the land by water • b: a drenching rain

– 2: an overwhelming amount or number, as in:received a deluge of offers

Page 12: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Deluge from Various Perspectives

The extent, rapidity of acceptance, and pervasiveness of digital technology is almost (?) beyond comprehension.

Page 13: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Digital Deluge

• “Data is omnipresent. Everywhere we go, we encounter it, in our business transactions, subscription lists, email, etc.”

• “To put the growth of data in perspective, every 18 months the processing capacity of the world doubles, but at the same time data has been doubling every nine months.”

• http://itmanagement.earthweb.com/datbus/article.php/1495861

Page 14: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Digital Deluge

• “One of the scariest statistics in recent memory is IDC’s prediction that the amount of digital data between now and 2010, reaching a whopping 988 billion gigabytes.”

• “(That’s a figure so huge, it would be helpful to have one of those goofy yet helpful references to put it into context. How many football fields it would fill, for example.)”

• http://www.itbusinessedge.com/blogs/tve/?p=98

Page 15: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

ScienceDaily (Nov. 8, 2007)

• “Most people have a few gigabytes of files on their PC.

• In the next decade, astronomers expect to be processing 10 million gigabytes of data every hour from the Square Kilometre Array telescope”.

• http://www/sciencedaily.com/releases/2007/11/071107091524.htm

Page 16: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• “When the Large Hadron Collider [at CERN] starts running in 2008, it will be the biggest scientific instrument in the world.

• Thousands of scientists across the planet will be clamouring for access to the streams of data that will come out of the instrument.

• The LHC will produce about 15 Petabytes of data every year, which is more than 1000 [times] the amount of information in book form printed every year around the world”.

• If written to CDs, the stack of CDs would be about 20 km in height!

• http://gridcafe.web.cern.ch/gridcafe/GridatCERN/gridatcern.html

Page 17: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

From IDC Report

• “With a compound annual growth rate of almost 60%, the digital universe is growing faster and is projected to be nearly 1.8 zettabytes (1,800 exabytes) in 2011, a 10-fold increase over the next five years

• “At 281 billion gigabytes (281 exabytes), the digital universe in 2007 was 10% bigger than originally estimated

Page 18: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• IDC’s new research shows the digital universe is growing more rapidly than original estimates as a result of accelerated growth in worldwide shipments of– digital cameras, – digital surveillance cameras, and – digital televisions

• as well as – a better understanding of information

replication trends.

• They didn’t mention traffic to and from cell phones , iPhones, Blackberries and the ilk..

Page 19: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• The digital universe in 2007 was equal to almost 45 gigabytes (GB) of digital information for every person on earth – or the equivalent of over 17 billion 8 GB iPhones.

• Other fast-growing corners of the digital universe include those related to – Internet access in emerging countries– sensor-based applications– data centers supporting “cloud computing”– social networks comprised of digital content

created by many millions of online users.

Page 20: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Digital Deluge

• What’s it all about and what can we do about it?

• Maybe we can not only survive the Digital Deluge, but use it to our advantage.

• Hey! Didn’t Noah’s kids learn to water ski behind the Arc?

Page 21: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Let’s Start with Five Questions

• Where does information come from?

• How does it get to be “digital”?

• How does it get to us?

• Why is it a problem?

• What is being done to cope with it?

Page 22: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Where Does the Information Come From?

Page 23: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Information Generators

• Arts

• Literature

• History

• Language

• Education

• Religion

• Science and Technology

• Government/Bureaucracy

• Commerce

Page 24: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• Administration

• Medicine and Pharmacology

• Geography and Cartography

• Entertainment

• News

• …

• …

• …

Page 25: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Traditional Sources

• Media– Print: Books, Newspapers– Film– Radio and TV Broadcast

• Accumulated Knowledge– Libraries– Repositories– Folk Lore

Page 26: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• Publications– Books, Newspapers, Magazines,

Professional Journals

• Radio and TV

• Personal Communications and Correspondence

• Sensors and Instrumentation

• .

Page 27: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• Libraries and Repositories– Publications, Reference Material,

Dictionaries, Encyclopedia, Catalogs

• Personal Creation– Records

• Bills, Accounts, Investments, Wills, Warranties, Contracts, …

– Libraries • Books, Magazines, Clippings, …

– Diaries and Writings– Recordings

• Audio, Photo, Film, Video• Computer Files

Page 28: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

How Does It Get To Us?

• Purchase and Own• Delivery Services

– Mail, Newspapers, Magazines• Broadcast Services

– Radio– Television

• Message Services– Telegraph– Teletype

• Data Communications– File Transfers, Data Collection– Transactions

Page 29: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Where did this “Deluge” come from?

• The major reason the world is awash in information in digital form is the

COMPUTER

• Actually the ability to represent and process information on digital form

• But, that is only half the story.

Page 30: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• An isolated computer is just a fancy adding machine or typewriter with memory

• So, the deluge results from the fact that all the world’s computers are connected!– Well, most all.

• The ability to communicate information is an essential factor behind the deluge, and

• Communications is done “digitally”..

Page 31: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

What’s the Problem?

• There is so much information/data*.– Entertainment, medical, scientific

research, educational, literature, news, personal …

• We are not always sure about just what “information” there is in the data

*“information” often implies “knowledge” whereas we are talking about the volumes of data that are generated – whether they make sense or not.

Page 32: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Culprit?

• A mixture of – The Internet

• networked computers, ubiquitous communications,

– Applications• the WWW, information processing

– and Smart Machines

• Enabled by the evolution of common digital technologies throughout.

Page 33: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

So What’s New?

• A common technological basis for the– acquisition, – distribution and – handling

• of information in digital formats.

• A basis that has very recently reached levels of maturity that have unleashed information in a flood of incomprehensible immensity.

Page 34: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• That’s the Punch Line

• But Why The Deluge Now?

• Because we can!

Page 35: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

ELECTRONICS COMMUNICATIONS

DIGITAL TECHNOLOGY

HARDWARE SOFTWARE

Enabling Technologies

Page 36: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• But, first

• Let us pause for a short break ….

Page 37: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

What will we do?

• We’ll look at sources of the information explosion in areas that affect us:– Mobile telephones– Digital cameras – Music: iPods, CDs, DVDs– HDTV – The Internet and Web Applications – Libraries and other repositories – Smart terminals– Home automation – Scientific Research, and so on.

Page 38: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• We will look at what “digital” means– How the adoption of digital technology is

used to create information, store and process it, communicate and display it.

• Why we are deluged with digital information, i.e., – What’s behind the sudden onslaught of

information, where this surge is coming from, how it’s to be handled, and what the future holds.

Page 39: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• Then, hopefully we can discuss some ways of coping, and even enjoying the deluge –

• Let’s start with some definitions

Page 40: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Terminology

• Misuse of terminology can lead to lead to misunderstanding.

• Being precise in one's terminology can be important.

• It is also important to understand the historical origin of terms, to better understand their meaning, and to realize how different points of view can modify meaning.

Page 41: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Scale and Scope of the Deluge

• How deep is the flood?• If we are in water that is 6 feet deep we

have a feel for what sort of situation we are facing

• But, what’s an exafoot?• What are these funny names for data rates

and volumes?

Page 42: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• The basic digital (binary) unit is the bit, a single binary digit with values 0 or 1.

• In Digital Communications, one usually refers to data rates in terms of bits per second, or bps.

• Data rates, often mistakenly termed bandwidth, are expressed as the number of bits transmitted per second– as in 56 kbits per second (kbps) or 1.544

megabits per sec (mbps)

Page 43: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Common Data Rates

• Data Rate speeds derive from teletype and telephone services

• Originally, everything was a multiple of the telegraph speed of 75 bits per second– 300, 1200, 2400, 4800, 9600 bps

• Then multiples of the basic telephone sampling speed of 64,000 kbps took over, giving services at the T1 TDM carrier speed of 1.544 Mbps, and multiples of it.

Page 44: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Common Optical FibreTransmission Rates – in Megabytes per second.

OpticalOptical Line RatesLine Rates

OC-1OC-1 51.8451.84

OC-3OC-3 155.52155.52

OC-12OC-12 622.08622.08

OC-48OC-48 2,488.322,488.32

OC-192OC-192 9,953.289,953.28

OC-768OC-768 39,813.1239,813.12

Page 45: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Optical Fibre Transmission Rates- in Megabytes per second- MBps

• The OC192 rate is 9,953.28 megabits per second, or almost 10,000 Mbps = 10 gigabits per second.

• This number is 192 times 51.84 mbps• 51.84 = 810 x 8,000 x 8, or 810 x 64,000• The number of bits per second in a basic

SONET frame used for the transmission of 810 voice channels.

Page 46: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Prefix Symbol Base 10 Base 2

yotta Y +24 +80

zetta Z +21 +70

exa E +18 +60

peta P +15 +50

tera T +12 +40

giga G +09 +30

mega M +06 +20

kilo k +03 +10

hecto h +02 -

deka da +01 -

deci d -01 -

centi c -02 -

milli m -03 -

micro -06 -

nano n -09 -

pico p -12 -

femto f -15 -

atto -18 -

zepto z -21 -

yocto y -24 -

In communications, electronics, and physics, multipliers are defined in powers of 10 from 10-24 to 1024, proceeding in increments of three orders of magnitude (103 or 1,000).

In IT and data storage, multipliers are defined in powers of 2 from 210 to 280, proceeding in increments of ten orders of magnitude (210 or 1,024).

Page 47: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

But, how many is that in base 10?*

• Kilo = 1 thousand = 103

• Mega = 1 million = 106

• Giga = 1 billion = 109

• Tera = 1 trillion = 1012

• Exa = 1 quadrillion = 1015

• Peta = 1 quintillion = 1018 • Zetta = 1 sextillion = 1021

* By the “US short scale”: check http://en.wikipedia.org/wiki/Names_of_large_numbers

Page 48: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Computer Memory and Processing

• In digital computers, the basic element of information storage is the byte, an 8-bit binary number.

• The basic storage element is a word, and is usually 8, 16, 32 or 64 bits long, or 1, 2, 4, or 8 bytes.

• It is common to measure the size of a computer memory in terms of powers of 2,

• e.g.• 1 Kilobyte = 210 = 1024 bytes.

Page 49: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Counting Bytes

• Kilobyte: 1 KB = 210 = 1024 bits ~ 1 x 103

• Megabyte: 1MB = 220 bits, or 1,048,576 bits ~ 1 x 106

• Gigabyte: 1GB = 230 bits = 1,073,741,824 bits• Terabyte: 1TB = 240 bits = 1,099,511,627,776

bits• Exabyte: 1 EB =250 bits =

1,125,899,906,842,624 bits ~ 1 x 1015

• Petabyte 1 PB = 260 bits = 1,152,921,504,606,846,976 bits ~ 1 x 1018

Page 50: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Bits and Bytes

• Transmission speeds invariably refer to bits per second

• e.g. 1.544 Mbps

• File sizes refer to bytes • e.g. 1 Megabyte of RAM = 1024 Kilobytes

or 1,048,576 bits; but communications rarely, if ever, refers to bits at all.

Page 51: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The “Digital” Thing: What’s the Big Idea, Anyway?

• ““DigitalDigital” ” is a word (mis)used to is a word (mis)used to describe information (often called describe information (often called datadata) in ) in discretediscrete formform, and the , and the subsequent processing of that subsequent processing of that information, information,

as opposed to information that occurs as opposed to information that occurs in a in a continuouscontinuous, or “, or “analoganalog” form.” form.

Page 52: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• DigitalDigital means means discretediscrete (like whole numbers) (like whole numbers) and and AnalogAnalog means means continuouscontinuous (like physical (like physical properties such as temperature, volume, properties such as temperature, volume, etc.). etc.).

• The term The term analoganalog comes from early comes from early computers (circa WWII) used to solve computers (circa WWII) used to solve differential equations with continuous differential equations with continuous variablesvariables, ,

• as contrasted with as contrasted with discrete state machines discrete state machines (like an elevator controller) built from open-(like an elevator controller) built from open-or-closed switches or on-off digital circuitsor-closed switches or on-off digital circuits

Page 53: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Definitions (from whatis.com)

AnalogAnalog

• Using physical representationUsing physical representation

• Relating to a system, device that Relating to a system, device that represents data variation by a represents data variation by a measurable physical quality measurable physical quality such as such as temperature, volume, distance, temperature, volume, distance, weight, pressure …weight, pressure …

• Which is Which is continuouscontinuous in time or space in time or space and valueand value

Page 54: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Definitions

DigitalDigital

• Representing data as numbersRepresenting data as numbers– ProcessingProcessing– Operating on Operating on – StoringStoring– Transmitting Transmitting – Displaying Displaying

• Data in the form of numerical digits, as Data in the form of numerical digits, as in a digital computerin a digital computer

Page 55: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• Representing a physical quantityRepresenting a physical quantity– such as sound, light, or electricitysuch as sound, light, or electricity

• by means of by means of samplessamples– taken at taken at discrete times discrete times (or (or placesplaces))– and given and given numerical valuesnumerical values

• usually in the binary systemusually in the binary system

– as in a digital audio recordingas in a digital audio recording– or in digital televisionor in digital television– or in digital photographyor in digital photography

Page 56: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

In Communications

• AnalogAnalog is used to refer to systems is used to refer to systems with signals that are with signals that are continuouscontinuous in in value and timevalue and time– such as AM and FM, where the such as AM and FM, where the

electrical signals are representations electrical signals are representations of the information signals.of the information signals.

Page 57: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Amplitude Modulation (AM)

)2cos()](1[)( tftmkAts cac

Page 58: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Phase or Frequency Modulation (FM)

))(2cos()(

))(2cos()(

0

dmktfAts

tmktfAtst

fcc

cc

Page 59: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

In Communications

• DigitalDigital is used to refer to is used to refer to discrete-discrete-state, discrete-time state, discrete-time signals that can signals that can take on only specific values at specific take on only specific values at specific times;times;

• such as such as – sampled/quantized signals, sampled/quantized signals, – pulse modulated signals, pulse modulated signals,

• and to data communication signals in and to data communication signals in general.general.

Page 60: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Digital Modulation: Discrete in Time and Value

Page 61: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Parameters of Information Sources & Systems

• AnalogAnalog (continuous functions of time, (continuous functions of time, space, weight, …)space, weight, …)– voice, audio, image, video, temperature voice, audio, image, video, temperature

• Bandwidth – frequency (harmonics) rangeBandwidth – frequency (harmonics) range• Statistics – amplitude distribution, power, Statistics – amplitude distribution, power,

spectrum (frequency content, harmonics)spectrum (frequency content, harmonics)

• DigitalDigital (sets of numbers): (sets of numbers):– ASCII characters, computer words, …ASCII characters, computer words, …

• Bit Rate – bps, kbps, Mbps, Gbps, Tbps, Bit Rate – bps, kbps, Mbps, Gbps, Tbps, Ebps, … Ebps, …

Page 62: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

How does Information Become “Digital”?How does Information Become “Digital”?

Page 63: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Digital Representation• Information that is naturally discrete, such Information that is naturally discrete, such

as state of a light switch (on-off), integers, as state of a light switch (on-off), integers, or text can be represented by binary or text can be represented by binary numbers in obvious ways.numbers in obvious ways.

• Text (as generated on a keyboard) is often Text (as generated on a keyboard) is often represented by 8-bit binary numbers.represented by 8-bit binary numbers.

• Speech may be represented by a pressure Speech may be represented by a pressure wave, which is continuous – in time and wave, which is continuous – in time and value – and has to be value – and has to be sampled and sampled and quantized to quantized to be represented digitally.be represented digitally.

Page 64: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Discrete Information

• Some information, such as numerals Some information, such as numerals and characters is discrete and can be and characters is discrete and can be represented “digitally” easilyrepresented “digitally” easily

• Take characters of the English Take characters of the English Language for exampleLanguage for example

• The American Standard Code for The American Standard Code for Information Interchange Information Interchange (ASCII)(ASCII) is the is the binary representation used in teletype binary representation used in teletype messaging and adopted as a universal messaging and adopted as a universal computer character representation. computer character representation.

Page 65: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

“A” = 11000001

“a” = 11100001

10001101 = CR10001010 = LF

10000001 = SOH

10000010 = STX

10000100 = EOT

10000011 = ETX

“%” = 10100101

Formatting

Messaging

Page 66: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Serendipity

• Early minicomputers such as Digital Early minicomputers such as Digital Equipment Corporation (DEC) PDP Equipment Corporation (DEC) PDP machines used machines used teletypewritersteletypewriters as as terminalsterminals

• They hadThey had– keyboards that generated ASCII code keyboards that generated ASCII code

wordswords– printers that accepted ASCIII code printers that accepted ASCIII code

words and words and – punched paper tape I/O that could be punched paper tape I/O that could be

used to save and replay messages.used to save and replay messages.

Page 67: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• The ASCII code set includingThe ASCII code set including– text formattingtext formatting

• CR and LFCR and LF

– and message formattingand message formatting• SOH, STX ETX, EOTSOH, STX ETX, EOT

• Became the way computer Became the way computer communications over leased and dial-communications over leased and dial-up telephone lines startedup telephone lines started

• Except for a bunch of computer geeks Except for a bunch of computer geeks who used Sun Microsystems who used Sun Microsystems workstations which had a different workstations which had a different communications scheme built-in.communications scheme built-in.

Page 68: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Common Sense Digitization of Analog Information

• All continuous signals can be All continuous signals can be represented by a collection of represented by a collection of numbers to any degree of accuracy by numbers to any degree of accuracy by – sampling often enough sampling often enough and and – using enough quantization levels*using enough quantization levels* to to

represent the signal value at the represent the signal value at the sampling instants.sampling instants.

– ** determined by the number of digits in determined by the number of digits in the representationthe representation

Page 69: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Analog-to-Digital Conversion

• Two stage processTwo stage process

• SampleSample– Sampling TheoremSampling Theorem– Nyquist RateNyquist Rate

• QuantizeQuantize– Precision, SNR (% average Precision, SNR (% average errorerror))

– Note: a digital representation of an Note: a digital representation of an analog value always has erroranalog value always has error

Page 70: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Sampling Theorem

• Shannon’s Sampling Theorem states Shannon’s Sampling Theorem states that that – any bandlimited signal may be any bandlimited signal may be

represented by represented by samples taken at a rate samples taken at a rate of twice its highest frequencyof twice its highest frequency*, and *, and

– may be may be reconstructed reconstructed without errorwithout error if the if the appropriate interpolation functions are appropriate interpolation functions are used**.used**.

* Twice the highest frequency is called the * Twice the highest frequency is called the Nyquist RateNyquist Rate..

** Physically unrealizable sinx/x or (sinc) functions.** Physically unrealizable sinx/x or (sinc) functions.

Nerd AlertNerd Alert

Page 71: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Impulse Sampling

Page 72: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Reconstruction

Page 73: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Summary

• All signals can be represented by a All signals can be represented by a collection of numbers to any degree of collection of numbers to any degree of accuracy by sampling often enough accuracy by sampling often enough and using enough quantization levels and using enough quantization levels to represent the signal value at the to represent the signal value at the sampling instant.sampling instant.

Page 74: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Summary (for irrepressible nerds only)

• Shannon’s Sampling Theorem Shannon’s Sampling Theorem states that states that any strictly bandlimited function may be any strictly bandlimited function may be presented by sampling at a rate that is at presented by sampling at a rate that is at least twice as fast as the highest frequency least twice as fast as the highest frequency in the signal, and that it may be recovered in the signal, and that it may be recovered without distortion without distortion by passing the (impulse) by passing the (impulse) samples through an ideal low-pass filter samples through an ideal low-pass filter with a bandwidth equal to that of the signal.with a bandwidth equal to that of the signal.

Page 75: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Quantization

• For processing, storage or communication, samples with infinite precision must be quantized

• Such that a range, or interval, of values is represented by a single, finite precision, number

• For example, by a finite binary number.

Page 76: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Quantization

7

-3

12

7

5 5

-3-2

-4-3

-2

7 7

32

6

7

43

11

time

Page 77: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Reconstitution

time

-2

-3

-4

Quantum Boundary

Quantum Boundary

Reconstruction ValueActual Value

ERROR-3

7

-3

12

7

5 5

-3-2

-4-3

-2

7 7

32

6

7

43

11

Page 78: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Quantization Error (for nerds and audiophiles)

• The quantization error depends on the number of distinct quantization intervals used.

• If N binary digits are used, the number of distinct intervals is 2N.

• The signal-to-quantization-error ratio is about (6N + 1.8) dB.

Page 79: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Binary Representation

• Once information is discretized, or sampled, a number can be assigned to represent the value of each sample.

• The number can be expressed as a binary number, e.g., 2009 is

1024 + 512 + 256 + 128 +64 + 32 + 8 + 4 + 1

1x 210 + 1x 29 + 1x 28 + 1x 27 + 1x 26 +1x 25 +1x 23 + 1x 22 + 1x 20

11111101101

Page 80: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

SummarySummary

The basis of the Digital Deluge is The basis of the Digital Deluge is the universal adoption of a the universal adoption of a technology that can create, process, technology that can create, process, and communicate information that is and communicate information that is represented in digital form.represented in digital form.

Page 81: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• So much for Digital RepresentationSo much for Digital Representation

• Now, let’s look at Digital Information Now, let’s look at Digital Information TechnologiesTechnologies

• But, firstBut, first

• Let us pause for a short break ….Let us pause for a short break ….

Page 82: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Let us look at the Digital Technologies

• CommunicationsCommunications

• ComputingComputing

Page 83: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Digital Communications

• We have We have – Sources of InformationSources of Information

• That create informationThat create information

– Destinations for InformationDestinations for Information• That use informationThat use information

• and we haveand we have– Communications NetworksCommunications Networks

• That provide connectivity between themThat provide connectivity between them

• We also have TerminalsWe also have Terminals– That interface (connect) the Sources That interface (connect) the Sources

and Destinations to the Networks.and Destinations to the Networks.

Page 84: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

A Taxonomy of Telecommunications

• SourcesSources

• ChannelsChannels

• DestinationsDestinations

ChannelsChannelsSourceSource

DestDest

DestDest

Term

Term

Term

Term

Term

Term

Page 85: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

What are “Digital” Communications?

• Modern Telecommunication Systems are designed to accept and deliver information made up of sequences of binary signals.

• These systems and the connections through them are enabled and controlled by computers.

Page 86: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

What is Special About NOW?Why the Deluge NOW?

• Realization of the Telecomm Dream

– Unified Communications• ubiquitous high speed, multimedia, reliable,

standardized networks– The All-IP Multimedia Network – The Internet and the WWW

– Ubiquitous Broadband Access• Wired (FTTP)• Wireless(Cellular/WLAN)

Page 87: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

More on Communications

• We will discuss communications later We will discuss communications later when we look at delivering digital when we look at delivering digital information.information.

Page 88: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Computers: Universal Digital Processing Machines

• Computers are universal digital Computers are universal digital machines that can machines that can – accept information in digital form accept information in digital form – store it store it – process it in many waysprocess it in many ways– output it to various devicesoutput it to various devices– display it display it – communicate it communicate it

• All under control of a set of pre-All under control of a set of pre-determined steps called a program.determined steps called a program.

Page 89: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

The Evolution of the Computer

• Intelligent Information Agents– Communications, Processing, Control– Programmable – Powerful Hardware: speed, memory– Handheld/Mobile– Robotic

• autonomous tasks• in touch with local environment

Page 90: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Terminals and Switches

– Terminal EquipmentTerminal Equipment• the sources and destinations of information, the sources and destinations of information,

are digital machines, i.e., computers, in the are digital machines, i.e., computers, in the broadest sense.broadest sense.

– Network Switches are also computers.Network Switches are also computers.

Page 91: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• Software Development– Highly evolutionary – Use of complex components– Standardization

Page 92: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Intelligent Agent: Telematics

Com

munications

Processor

A &

D I/O

Page 93: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

What are “Computers” Anyway?

• ComputersComputers are digital machines that can are digital machines that can acquire or create, store/retrieve, process, acquire or create, store/retrieve, process, display, and communicate information in the display, and communicate information in the form of finite binary numbers.form of finite binary numbers.

• A computer is an electronic machine A computer is an electronic machine composed of three partscomposed of three parts– Input/OutputInput/Output– MemoryMemory– Central Processor UnitCentral Processor Unit

Page 94: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

94

MEMORY

CENTRAL PROCESSING UNITC PU

INPUT OUTPUT

“BUS”

Page 95: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• Data in the form of binary words

• Can be moved– From the input unit

• to the memory or • to the processor

– From the memory• to the processor or • to the output unit

Page 96: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

Components

• The Memory consists of a number of storage spaces, – each having a unique address,

• in which data in the form of finite-length binary numbers can be stored

• Instructions, in the form of binary numbers, are also stored in the memory

Page 97: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

97

Instructions & the Basic Operation

• What the computer does is specified by INSTRUCTIONS

• An Instruction specifies:– what operation is to be performed

on – what data, and – where to put the result

• Instructions are coded as binary words and stored in the same memory as the data

Page 98: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

98

Word LengthAddress

000000

000001

000002

000003

000004

000005

1048576

Page 99: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

99

MemoryAddress Content

0000 101001110001 011010000010 110010010011 111111110100

0101

0110

0111

1000

1001

---

1111

Page 100: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• The Central Processor Unit (CPU) consists of – a Program Counter, which holds the

address of the instruction being executed.

– an Instruction Register, which holds the instruction being executed

– An Address Register, which holds the address of the operand

– An Arithmetic Logic Unit, which carries out the “current instruction” i.e. the instruction “being executed”

Page 101: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

101

CPU

Page 102: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

102

Instructions

• There are instructions to – Move data– Perform arithmetic or logical

operations on data, including comparisons

– Change the order in which instructions are carried out

– Control the machine and its peripherals

Page 103: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• The instructions stored in memory constitute a program.

• A single address computer instruction usually has two parts:– The Operation Code and the Operand– e.g., ADD B

• which, when executed– leaves the sum of the contents of the

Accumulator and the contents of Memory Location B in the Accumulator  

•  

Page 104: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

104

Instruction Format

OP CODE

OPERAND ADDRESS

Page 105: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• When an instruction is executed• It is moved

– from the location indicated by the Program Counter

• into the Instruction Register• The Program Counter is incremented by

one• The operation and operand are ascertained• and the operation is carried out.

Page 106: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

106

Instruction Cycle• FETCHFETCH

– Get the contents of the Memory Get the contents of the Memory Location whose address is in the Location whose address is in the Program Counter.Program Counter.

– Put the contents of the Operation Put the contents of the Operation Code Field in the Instruction Register.Code Field in the Instruction Register.

– Put the contents of the Address Field Put the contents of the Address Field in the Address Registerin the Address Register

– Increment the Program Counter. Increment the Program Counter. • EXECUTEEXECUTE

– Carry out the instruction in the Carry out the instruction in the Instruction Register on the data Instruction Register on the data referred to by the contents of the referred to by the contents of the Address RegisterAddress Register

Page 107: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

107

Addition

11100101

01111011

11100101

01111011

1 01100001

A

B

01100001

C

LDA A

ADD B

STA C

Page 108: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

108

THEN

WHAT DOES THE COMPUTER DO?

LDA A

ADA B

STA C

276

23

513

24

25

87

88

PC IR ACC AR

A

B

C

Page 109: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• What is the most important instruction?

• [Answer: HALT].

Page 110: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

110

Input-Output Units

• Input UnitsInput Units: keyboard, scanner, : keyboard, scanner, modem, external disk memory, modem, external disk memory, analog-to-digital converter, camera, analog-to-digital converter, camera, sensor.sensor.

• Output UnitsOutput Units: screen, printer, modem, : screen, printer, modem, external memory; digital-to-analog external memory; digital-to-analog converter, controller.converter, controller.

Page 111: The Digital Deluge Learning in Retirement David Coll Professor Emeritus Department of Systems and Computer Engineering Winter 2009

• OK, so that sort of explains how a OK, so that sort of explains how a

digital computer works: digital computer works:

• In a nutshellIn a nutshell– the set of operations required to solve the the set of operations required to solve the

problem at handproblem at hand• add up the bills, or print the characters in a add up the bills, or print the characters in a

book, or whatever, book, or whatever,

– are expressed in a program (a list of are expressed in a program (a list of instructions that when executed achieve instructions that when executed achieve the desired result); and the program is the desired result); and the program is ’run’, i.e., the individual instructions are ’run’, i.e., the individual instructions are carried out – one after another …carried out – one after another …