Computing

Computing

1. algorithims

   • An algorithm is a step-by-step procedure for solving problems. It is a set of instructions that can be followed by humans and computers.
   • Flowcharts
     • Flowcharts are visual ways of showing an algorithims

2. Abstraction and decompostition

   • Abstraction
     • Abstraction is the process of filtering out – ignoring - the characteristics of patterns that we don't need in order to concentrate on those that we do. It is also the filtering out of specific details. From this we create a representation (idea) of what we are trying to solve.
   • Decompisition
     • is the process of breaking down a problem into small simple simple steps

3. Inputs and Outputs

   • Inputs
     • Inputs are what the user tells the computer
   • Outputs
     • Outputs are what the computer shows the user based on there input
   • E.g a user inputs their user name and password and if it is incorrect the computer will output that it is incorrect

4. Representing Text, graphics and sound

   • All data has to represented in binary. All images and sounds can be broken down into 1s and 0s
   • Ascii code - at first different manufactureers had different binary representations but when they realised they needed to share data they came up with ascii code. ASCII stands for American Standard Code for Information Interchange. it is 7 bit and the 128 combinations represent the English language
     • The problem with ASCII was it could only represent the English language. so they came up with unicode which is a 32 bit language. unicode can have 4,294,967,296 characters which is easily enough for every character in every language.
   • Computers also represent images in binary code
     • an image is made up of many small dots of colour known as picture elements or pixels. each is pixel is represented by number of bits. the size of the image is determine by number of pixels in the height and width eg 670 x 420. as the number of pixels used to repreasewnt an image falls so does the resolution, al ow resolution can make a picture difficult to see.
       • colour depth is important for the quality of an image. colour depth is how many bits are used to encode an image. more bits means an increase range of colour. images today have a much better colour depth than the first pcs because memory space has become a lot larger meaning they can now store larger image files
   • Computers can use binary to represent sound.
     • Sound is caused by vibrations travelling through a medium such as air, water or metal. These vibrations compress and then pull apart the air molecules thus causing changes in air pressure. If these vibrations enter our ears and cause tiny sensory hairs to vibrate, then we can hear the sound. When we speak, our vocal cords vibrate and cause the air to vibrate. Musical instruments function in the same way: the strings of a guitar, the reed in a clarinet or the air in a flute are all made to vibrate. Vibrations travel out in waves; these are called sound waves.
       • Sampling rate is the number of samples taken per second. the higher the sampling rate the better the sound quality. sample resolution is the number of bits used to represent the sound, more bits = better sound.
         • to calculate the file size of a recording you use the formulae sample rate x sample resolution x length in seconds

5. Data compression

   • Huffman encoding
     • Huffman coding, also known as ‘Huffman encoding’ or ‘Huffman compression’, is an algorithm for lossless compression based on the frequency of the characters or symbols in the file. It ensures that the more common characters have fewer bits to represent them than the less common characters that need more bits to identify them. Therefore the overall size of the file is reduced. It is therefore called a ‘variable-length coding system’ because the codes for different characters have different lengths. During the encoding, a binary tree, called a Huffman tree, is built up.
   • Run length encoding
     • Run length encoding (RLE) works by reducing the physical size of a repeating string of characters. This repeating string, called a run, is encoded into two bytes. The first byte represents the number of characters in the run and the second gives the character. For example, look at the following string: aaabbbbbbccccccccc This string has a length of 18 bytes. Using run length encoding, this could be compressed to: 3a6b9c It could be reduced to 6 bytes. Run length encoding provides very good compression ratios where there are long runs of a particular character or value. A black and white image, where there are long runs of either black or white, will encode very well. However, a colour photograph, where there are short runs of many different colours, will not encode as well.

6. Computer systems

   • Embedded systems