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7249424
Data Representation
Description
Mind Map on Untitled, created by Elliott Powell on 13/12/2016.
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Mind Map by
Elliott Powell
, updated more than 1 year ago
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Created by
Elliott Powell
about 8 years ago
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Resource summary
Data Representation
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Data Representation
Images as binary
Images are stored as a series of pixels
We mostly use bitmap images made up of tiny little dots called pixels
The colour of a pixel is represented in a binary code
Black and white images only use two colours meaning they need on,y 1 bit to represent each pixel
To make a great range of shades and colours increasing the number of bits for each pixel
Increasing the colour depth and resolution increases the file size
Colour depth is the number of bits used for each pixels
Total number of colours = 2n (n = number of bits per pixel)
The resolution is the density of pixels in an image e.g how many pixels are within a certain area
The higher the resolution, the more pixels in a certain area and that means the better quality of image
Increasing the resolution or the colour depth means there are bits in an image, improving the image quality but also the file size
Devices need metadata to display images
Metadata is the information stored in an image file which helps the computer recreate the image on screen from the binary data in each pixel
Metadata includes a image... File format, height, width, colour depth and resolution
Without metadata we wouldn't be able to display images on a screen as intended
Sorting sound
Converting analogue to digital is called sampling
All sound is recorded into a microphone as an analogue signal
Analogue signals are pieces of changing data
Sampling intervals
The gaps between each of the points where the analogue is sampled
Sampling frequency (or sample rate) is how many samples you take in per second
Bit rate is the number of bits used per second of audio
Bit rate = sampling frequency x sample size
Increasing the sample frequency means the analogue recording is sampled more often the sampled sound will be better quality
Increasing sample frequency and sample size will increase the bit size
Characters as Binary
Binary can be used to represent characters
Alphanumeric characters are used to make words and strings including upper and lower case letter, digits 0-9 and a range of symbols
Computers cannot directly process all these symbols so they have to turn them into binary
Characters Sets
A collection of characters that a computer recognises from their binary Representation
ASCII The most commonly used character set in the English speaking world
Every ASCII character is given a 7bit binary code meaning that it can represent 128 different characters
EXTENDED ASCII
Gives an 8 but binary code which allows for 256 characters to be represented
Particularly useful for languages that include ascents
UNICODE
Tries to cover every possible character
Uses 16 to 32 bit binary codes
Covers every single language
Binary
Counting in binary is like counting in denary
Binary only uses two digits 0 and 1 (base 2)
The place values in binary increase in powers of 2 ( 8,4,2,1)
Binary numbers are easier to convert using tables.
Convert binary to denary by subtracting
We add Binary numbers using column addition
Binary numbers only uses 0s and 1s therefore it's easy to do 0+0 = 0 etc but for 1+1 we write 10
Sometimes we can come across overflow errors were the final binary number has too many numbers, the computer stores them elsewhere
Binary shifts
Binary shifts can be used to multiply or to divide by 2
Gaps at the beginning it ends are filled with 0s
Lefts shifts multiply meaning we add 0s on the right
Right shifts divide meaning we add 0s to the left
Hexadecimal Numbers
Hexadecimal numbers are shorter than binary numbers
Hexadecimal uses 16 different digits hence why it's called base 16
0-9,A-F
Convert Binary to Hex by splitting it into nibbles
For Hex to Binary, use each characters Denary value
For the opposite way convert each hex character into binary
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