2. Lecture 10: Binary Representation Intro to IT COSC1078 Introduction to Information Technology Lecture 10 Binary Representation James Harland james.harland@rmit.edu.au

3. Lecture 10: Binary RepresentationIntro to IT Introduction to IT 1 Introduction 2 Images 3 Audio 4 Video 5 Binary Representation WebTest 1, Assignment 1 6 Data Storage 7 Machine Processing 8 Operating Systems WebLearn Test 2 9 Processes Assignment 2 10 Internet 11 Internet Security WebLearn Test 3 12 Future of ITAssignment 3, Peer and Self Assessment

4. Lecture 10: Binary RepresentationIntro to IT Overview  Questions?  WebLearn Test 1  Binary Representation  Questions?

5. Lecture 10: Binary RepresentationIntro to IT Web Test 1  Week 5  Quizzes (practice tests) up now  Due by 11.59pm Sunday 22 nd August  Content will be on weeks 2-4 Images Audio Video

6. Lecture 10: Binary RepresentationIntro to IT Assignment 1  Use GIMP (or a similar tool) to perform some manipulations on an image  Address six issues in relation to this  Main emphasis is on process, not result!  SUBMIT VIA WEBLEARN  Due by 11.59pm Sunday 3 rd April JUST DO IT!

7. Lecture 10: Binary RepresentationIntro to IT Introduction

8. Lecture 10: Binary RepresentationIntro to IT Overview 01010100001010101010100110100010101001101001010010 100011100010101010100101111001001010…

9. Lecture 10: Binary RepresentationIntro to IT What do computers do?  Compute!  Input/Output  Processing  Memory

10. Lecture 10: Binary RepresentationIntro to IT History ……  Babbage’s Difference Engine (1849)  Babbage’s Analytical Engine (1837-1871, never built)  Turing’s Universal Machine (1936, mathematical model)  Turing digital Boolean-logic multiplier (1937)  Colossus (1943, destroyed 1945)  ENIAC (1946)  Von Neumann architecture (c. 1945)  EDVAC (1949)  CSIRAC (1949)

11. Lecture 10: Binary RepresentationIntro to IT Computer Memory Cells of 8 bits each (one byte) Most significant bit Least significant bit … … address

12. Lecture 10: Binary RepresentationIntro to IT Random Access Memory (RAM)  Random access means any cell can be accessed at any time (and in any order)  Volatile – contents cleared when machine is switched off  Very fast compared to other forms of memory  DRAM: dynamic RAM (replenishes charges constantly)  SDRAM: synchronous DRAM – faster still  Often have small very fast caches and registers

13. Lecture 9: Data Storage DevicesIntro to IT Magnetic Disk  Thin spinning metal disk with magnetic coating  Each disk contains a number of circular tracks  Often several disks stacked on top of each other  Cylinders made up of tracks made up of sectors  Can have very large storage this way  Slow access time!

14. Lecture 9: Data Storage DevicesIntro to IT Magnetic Disk (Hard Disk) Seek time: move heads from one track to another Latency time: half time for complete disk rotation Access time: seek time + latency time Transfer rate: rate data can be read from disk `Typical’ Hard disk Seek time: 2ms to 15ms Latency time: 8ms to 20ms Transfer rate: 0.5 GB per second Sounds fast, but is actually quite slow …

15. Lecture 9: Data Storage DevicesIntro to IT Optical Disks (CDs, DVDs) Laser readers rather than magnetic ones Disks more error-tolerant than magnetic ones TypeFeaturesDateStorage CD“compact disk”1984800MB DVDMultiple layers199515GB Blu-ray`blue laser’ (405 vs 650 nm) 2004100GB

16. Lecture 9: Data Storage DevicesIntro to IT Flash Drives  Disks of all sorts are slow compared to other circuits  Flash drives ‘write’ small electronic circuits  Eventually decay after many changes of data  Suitable for slow-changing data, not main memory  Portable and much more resilient than disks

17. Lecture 9: Data Storage DevicesIntro to IT Older Storage Types Magnetic tape `Floppy’ disk (5.25’’ disk) 3.5’’ disk

18. Lecture 10: Binary Representation Intro to IT Binary Codes “Meet me at Fred’s” 234 12.43434343 -620 0 0 111001

19. Lecture 10: Binary RepresentationIntro to IT ASCII  American Standard Code for Information Interchange  7-bit patterns to represent  letters (upper and lower case)  numbers ,., ; “ $ % @ * & ! ? …  Total of 128 different characters

20. Lecture 10: Binary RepresentationIntro to IT ASCII 01001000 H 01100101 e 01101100 l 01101111 o 00101110. Hello! Unicode: uses 16 bits, can do Chinese, Japanese & Hebrew characters

21. Lecture 10: Binary RepresentationIntro to IT Numbers Represented in binary notation 25 in ASCII is 00110010 00110101 8 bits per digit seems too much! Can represent 256 different numbers in 8 bits … Don’t want to add, multiply etc. in ASCII … Remember that 1 + 1 = 10 …

22. Lecture 10: Binary RepresentationIntro to IT Two’s Complement How do you store negative numbers? Bit patternValue 0113 0102 0011 0000 111 110-2 101-3 100-4

23. Lecture 10: Binary RepresentationIntro to IT Two’s Complement Bit patternValue 0113 0102 0011 0000 111 110-2 101-3 100-4 0 first means +ve (sign bit) 1 first means –ve +ve: Count from 0 up to 01 n-1 -ve: Start from 1 n down to 10 n-1 3 is 011, -3 is 101 2 is 010, -2 is 110 1 is 001, -1 is 111

24. Lecture 10: Binary RepresentationIntro to IT Two’s Complement Bit patternValue 0113 0102 0011 0000 111 110-2 101-3 100-4 1 + 2: add in obvious way 3 – 1: calculate as 3 + (-1) 011 + 111 = 1010 Answer is 010, ie 2. Can add and subtract with the same circuits

25. Lecture 10: Binary RepresentationIntro to IT Excess Notation Bit patternValue 1113 1102 1011 1000 011 010-2 001-3 000-4 A different encoding of the numbers “naive” bit pattern encodes 4 more than actual value 100 (looks like 4) encodes 0 101 (looks like 5) encodes 1 110 (looks like 6) encodes 2

26. Lecture 10: Binary RepresentationIntro to IT Floating Point sign bit Mantissa exponent 1 bit for sign 3 bits for exponent 4 bits for mantissa 100.101

27. Lecture 10: Binary RepresentationIntro to IT Floating Point 01011001 means +ve 0.1001 shifted 101 place = 1.001  Mantissa: digit sequence (1 st digit always 1)  Exponent: where to put the.  This is generally given in ‘excess’ notation  Binary form of 2.423 x 10 4

28. Lecture 10: Binary RepresentationIntro to IT Truncation Errors Beware adding small numbers to large ones! Finite length of encoding means that sometimes digits are lost Not often a problem, but can be …

29. Lecture 10: Binary RepresentationIntro to IT Parity Bits  Add a ‘parity bit’ to each byte  Odd parity: make total of 1s in all 9 bits odd  Even parity: make total of 1s in all 9 bits even  If parity is wrong, then an error has occurred

30. Lecture 10: Binary RepresentationIntro to IT Conclusion  Get Assignment and WebTest done this week  Do online quizzes later this week  Keep reading! (book particularly)