Transcript Interdisciplinary Teaching Using HCI

Digital Media
Lecture 2: SemesterOverview
Georgia Gwinnett College
School of Science and Technology
Dr. Jim Rowan
The Big Question:
How do you take stuff found in the
real world…
 Store it as numbers on a
computer…
 So that it can be manipulated and
shared?

The answer:
It depends on what it is you are
trying to capture
 We will have to know about the
nature of the real world

– Some things can be counted
– Some things need to be measured

The details of this will come a bit
later
As previously seen using hexFiend:
Text, audio, images and videos are
all stored in a file as numbers on
the computer
 Some of this is meant for human
consumption (ASCII)
 Some of this is meant for program
consumption (the header)
 Some is used by the program to
save and represent the world



http://wiki.ggc.edu/wiki/It%27sAllJustBitsITEC2110WikiTextJrowan1
Let’s get started: It’s all just bits!
But first: Numbering systems!

Which is correct?
5 + 5 = 10
1 + 1 = 10
1 + 7 = 10
1 + F = 10
But first: Numbering systems!

Which is correct?

The answer is: It depends!
5 + 5 = 10 (in decimal)
1 + 1 = 10 (in binary)
1 + 7 = 10 (in octal)
1 + F = 10 (in hexadecimal)
But first: Numbering systems!

In this class we deal with
– Decimal
– Binary
– Hexadecimal

We will not be converting

We will not do math (a small lie)

We will learn how to count

http://wiki.ggc.edu/wiki/HowToCountLikeAnAlien

Let’s get started: Counting like an alien!
The process of counting is simple

No matter which numbering
system…
– You count starting with the first digit
– You continue to count through all the
digits available to you
– When you run out of digits, you go
back to the first digit
– Add 1 to the column to the left
How many things can you count if
you have 4 [
]positions?
your numbering system here

In decimal: 0000 -> 9999
– You can count 1000 things

In binary: 0000 -> 1111
– You can count 16 things

In hexadecimal: 0000 -> FFFF
– You can count 65,536 things
How many things can you count if
you have 4 [
]positions?
your numbering system here

The formula is:
– the number of digits in the numbering system raised to the
power of the number of positions you are using
– [#digits in the system] ** [# positions used together]

In decimal: 0000 -> 9999
10 ** 4 = 1000 things

In binary: 0000 -> 1111
2 ** 4 = 16 things

In hexadecimal: 0000 -> FFFF
16 ** 4 = 65,536 things
How do you convert stuff in the
real world into numbers that can
be placed on a computer?
It depends…
 It depends on whether the thing is
a discrete thing or a continuous
thing

Stuff (phenomena)
in the real world…

Can be discrete
– These either ARE or ARE NOT
– These can be counted
• The number of cars in a parking lot
• The number of beans in a jar

Can be continuous
– These have no breaks
– These must be measured
• The height of a wave
• The atmospheric pressure
Stuff (phenomena)
in the real world…

Discrete can be counted
– 5 cars
– 11,223 beans

Continuous must be MEASURED
– The height of a wave
• 3.76 feet from crest to trough
– The atmospheric pressure
• 30.02 inches of mercury
The problem is…

Most of the interesting stuff is
continuous!
– Sound is continuous compression
waves
– Light is continuous electromagnetic
waves

To store continuous phenomena on
a computer you must measure it
and store the measurement
Sampling:

The process of converting
continuous phenomena into
discrete so that you can store it as
a number on the computer
But before we talk about
sampling…
What this stuff means:








Bit: binary digit
Byte: 8 Bits
KB: kilo byte (1,000 bytes)
MB: mega byte (1,000,000 bytes)
GB: giga byte (1,000,000,000 bytes)
TB: tera byte (1,000,000,000,000
bytes)
KBPS: kilo (1,000) bits per second
MBPS: mega (1,000,000) bits per
second
What this stuff means:
Strictly speaking…

In computing the meanings of K, M,
G are powers of 2
K = 2 ** 10 = 1,024 not 1,000
M = 2 ** 20 = 1,048,576
not 1,000,000
G = 2 ** 30 = 1,073,710,825
not 1,000,000,000

But in this class, either will do
What this stuff means:
And finally…
In some classes B and b when
used in abbreviations mean Bytes
and bits respectively
 This can be confusing
 For this class…

– When abbreviating communication
speeds the b (or B) means bits
– When abbreviating file size the b (or
B) means bytes
What this stuff means:
So…
Since kbps (or KBPS) is a
communication speed the b (or B)
means bits
 Since mb (or MB) is a file size the
b (or B) means bytes

Network access
Changing all the time
 Is getting faster and faster
 Is available in a variety of forms


In this class we will discuss a few
of them
http://wiki.ggc.edu/wiki/NetworkingIssues
Let’s get started: Networking issues
Network access

Can be symmetric
– The speed into the network is the
same as the speed out

But now asymmetric is fairly
common in the home
– The speed out of the network is faster
than the speed into the network

Unless you are running a server
– Servers usually have very high
speed, symmetric connections to the
network
Network access

ADSL example
– Asymmetric Digital Subscriber Line
• Speed in can be 640 kbps
• Speed out can be 6.1 mbps

Prehistoric example: dial up modem
– Asymmetric
• Speed in is 36,000 bps
• Speed out as high as 56,000 bps
Network access
If you are running a commercial
server (like you would have if you
were running an online business)
you may want faster service
 T1 and T3 are faster and
symmetric

– T1 can be 1.544 mbps
– T3 can be 44.7 mbps
And now… Sampling
How many samples do you need to
faithfully capture a continuous
phenomena?
 The answer:

– It depends! (of course!)

What does it depend on?
– It depends on the frequency of the
continuous phenomena you are trying
to capture
http://wiki.ggc.edu/wiki/SoundInTheRealWorldAndSampling
Capturing sound in the real world? Sampling!: Sound and Sampling
Sampling sound
Sound radiates
out from the
source like the
waves created
when you toss a
stone into a pond
 In the air it
travels at ~760
mph

How many samples are needed?
Now, an example to show how and
why the Nyquist rate works
 Below is a note played on a violin
and captured with an oscilloscope

A note played on a violin
Sampled at 625 samples per second
A note played on a violin
Sampled at 1250 samples per second
A note played on a violin
Sampled at 2500 samples per second
A note played on a violin
Sampled at 5000 samples per second
A note played on a violin
Sampled at 10,000 samples per second
A note played on a violin
Sampled at 20,000 samples per second
How many samples are needed?

If you take too few samples
– the sound quality will degrade
– but the file size will be small

If you take too many samples
– the sound quality will be excellent
– but the file size can get HUGE!

So…
– Where’s the sweet spot?
How many samples are needed?
Nyquist states that you need to
sample at twice the frequency of
the highest frequency you want to
capture and faithfully reproduce
 With humans

– Since some of us can hear 20,000 cps
– You would need to sample at
40,000 cps

CD quality? (with a little wiggle room)
– 44,100 samples per second
An example: Fields of Gold

We played Fields of Gold in class
– CD quality is:
• 44,100 samples per second
• 16 bits (2 bytes) per sample
– with 16 bits you can capture…
– 2**16 = 65,636 different levels

Looking at the file:
– It is 4 minutes and 59 seconds
– The file size is 1,201,173 bytes long

Does this make sense?
An example: Fields of Gold
4 minutes and 59 seconds
= 4 x 60 + 59 = 299 seconds
299 seconds x 44,100 sps
= 13,156,000 samples
13,156,000 samples x 2 bytes per sample
= 26,371,800 bytes
But this is stereo (two channels) so…
26,371,800 bytes x 2 channels…
= 52,743,600 bytes…
That’s ~52 MB… but we said that the music was
1.2 MB
How is this possible?
HMMMMMmmmm…
An example: Fields of Gold
Fields of Gold is an MP3…
It’s compressed!
If we had the original CD it would be
~52 MB in length
Types of compressed files

MP3 is lossy
– What you get back after compressing the
file is NOT exactly the same as the original
– But… it’s close enough
– Images and sound can use lossy
compression techniques (more later)

Zip is lossless
– What you get back is EXACTLY what you
started with
– Applications must be losslessly compressed
– All the 0s and 1s have to be exactly the
same or the program will not run
Further reading

http://en.wikipedia.org/wiki/Nyquist_ra
te

http://en.wikipedia.org/wiki/Sampling_
%28signal_processing%29

http://en.wikipedia.org/wiki/Mp3
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