1. CS 101 – Sept. 11 Review linear vs. non-linear representations. Text representation Compression techniques Image representation –grayscale –File size issues –(Later, we’ll look at color)

2. Representing data Linear: text, image, audio, video Non-linear: networks or hierarchies –Examples: road system, genealogy, arithmetic expr. For expressions, it’s convenient to be able to express in linear/text format. –Postfix notation: eliminates the need for parentheses –Note that there’s one more number than operator: 5 6 * 1 2 3 + - 5 6 * 4 – 1 +

3. The joy of text ASCII code: –Contiguous (makes it easy to alphabetize) –Case sensitive –One byte per character ASCII table (p. 67) –‘A’ = 65 ‘a’ = 97 ‘0’ = 48 –Try this example: “Dog”

4. Unicode An extension of ASCII Incorporated into the Java language. Uses 16 bits instead of 8. Supports foreign alphabets; symbols (examples on p. 68)

5. Text Compression Goal is for a document to take up less space. Techniques –Keyword encoding: replace common words by special symbols like δ ↕ ╞ –Run-length encoding: replace repetitions with a number: “pppppppppppppp”  [14p] Also works well for compressing images, sound. –Huffman code: common letters should take up fewer bits.

6. Huffman code example Suppose you want to send a message and you know the only letters you need are A,D,E,L,N,P,S. A Huffman code might look like this table: ADELNPS 001100011010001000011

7. How to create code In CS we often use “trees” to help us solve problems. We’re given set of letters used in message, and their frequencies. –Ex. A=5, B=10, C=20, D=25, E=30 –Ex. P=5, N=10, D=10, L=15, A=20, S=20, E=30 Arrange frequencies in order Group the letters in pairs, always looking for the smallest sum of frequences  Create a tree!