1. 1 Part I: Machine Architecture 4 A major process in the development of a science is the construction of theories that are confirmed or rejected by experimentation. 4 In some cases these theories lie dormant for extended periods, waiting for technology to develop to the point that they can be tested. 4 In other cases the capabilities of current technology influence the concerns of the science.

2. 2 Ch. 1 Data Storage 4 Storage of bits. 4 Main memory. 4 Mass storage. 4 Coding information for storage. 4 The binary system. 4 Storing integers. 4 Storing Fractions. 4 Communication errors.

3. 3 Storage of bits 4 Today’s computers represent information as patterns of bits. 4 Gates are devices that produce the output of a Boolean operation when given the operation’s input values. 4 A flip-flop is a circuit that has one of two output values (i.e., 0 or 1), the output will flip or flop between two values under control of external stimuli.

4. 4 Storage of Bits 4 A flip-flop is ideal for the storage of a bit within a computer (Fig 1.3 and 1.4). A flip- flop loses data when its power is turned off. 4 Cores, a donut-shaped rings of magnetic material, are obsolete today due to their size and power requirements. 4 A magnetic or laser storage device is commonly used when longevity is important. 4 Hexadecimal notation (Fig. 1.6).

5. 5 Main Memory 4 Cells - a typical cell size is 8 or called byte. 4 MB = 1,048,576 (2 ** 20) bytes, KB and GB. 4 Address is used to identify individual cells in a main memory. 4 Random access memory (RAM). 4 Read only memory (ROM). 4 Most significant bit (MSB) and least significant bit (LSB).

6. 6 Mass Storage 4 Secondary memory. 4 Storing large units of data (called files). 4 Mass storage systems are slow due to mechanical motion requirement. 4 On-line Vs. off-line operations.

7. 7 Mass Storage 4 Mass Storage Disk storage. –Floppy disk and hard disk –Track, sector, seek time, latency time (rotation delay), access time, transfer time –Milliseconds Vs. nanoseconds 4 Compact disks and CD-ROM. –A single spiral track 4 Tape storage.

8. 8 Mass Storage 4 Physical Vs. logical records. 4 Buffer. 4 Main memory and mass storage. 4 Main memory, magnetic disk, compact disk and magnetic tape exhibit decreasing degrees of random access to data.

9. 9 Representing Text 4 American Standard Code for Information Interchange (ASCII) - 8-bit codes. –Appendix A –Figure 1.12 4 Unicode - 16-bit codes; allow to represent most common Chinese and Japanese symbols. 4 International Standards Organization (ISO) - 32-bit codes.

10. 10 Representing Numeric Values 4 Using 16 bits, the largest number we can store in ASCII is - 4 Binary notation (Figures 1.14 and 1.16). –Given 16 bits, the largest number we can store is - 4 A particular value may be represented by several different bit patterns; a particular bit pattern may be given several interpretations.

11. 11 Representing Images 4 Bit map representation –An image is considered as a collection of pixel a pixel can be black or white, represented by a bit a pixel can be a color, represented by three bytes –A typical photograph consists of 1280 rows of 1024 pixels requires several megabytes of storage image compression 4 Vector representation provides a means of scaling.

12. 12 The Binary System 4 Binary addition. 4 Fractions in binary. – Radix point (same as decimal point in decimal notation) – Figure 1.18 – Example of addition

13. 13 Storing Integers in Computers 4 Two’s complement notation. –Figure 1.19 –Sign bit –How to decode a bit pattern? 4 Addition in two’s complement notation. –Addition of any combination of signed numbers can be accomplished using the same algorithm simplify circuit design –Figure 1.21

14. 14 Storing Integers in Computers 4 Overflow problem. –Limit to the size of the values that can be represented –5 + 4 = -7 –Addition of two positive (negative) values appears to be negative (positive) 4 Excess notation. –Figures 1.22 and 1.23 excess 8 (4) notation for bit patterns of length 4 (3)

15. 15 Storing Fractions in Computers 4 Floating-point notation. – Sign bit, exponent field, mantissa field – Exponent expressed in excess notation – 01101011 = - – 1.125 = - – 0.375 = - – All nonzero values have a mantissa starting with 1

16. 16 Storing Fractions in Computers 4 Round-off errors. –Mantissa field is not large enough 2.625 = - –Order of computation 2.5 + 0.125 + 0.125 = - –Nonterminating representation 0.1 = - change the unit of measure from dollar to cent for a dime

17. 17 Data Compression 4 Run-length encoding. –A bit pattern consists of 253 1’s, followed by 118 0’s 4 Relative encoding. –Each data block is coded in terms of its relationship to the previous block

18. 18 Data Compression 4 Frequency-dependent encoding. –More frequently used characters are represented by shorter bit patterns –Huffman codes 4 Adaptive dictionary encoding. –Lempel-Ziv encoding –ABAABQB (5,4,A) (0,0,D) (8,6,B)

19. 19 Data Compression 4 GIF. –Each pixel is represented by a single byte 4 JPEG. –Human eyes are more sensitive to changes in brightness than color –Each four-pixel block is represented by six values rather than 12 values 4 MPEG.

20. 20 Communication Errors 4 How can you make sure the information you received is correct? 4 Coding techniques for error detection and correction. – Parity bits. – Error-correcting codes. Figures 1.28 and 1.29 Hamming distance of at least five is able to detect up to - errors and correct up to - errors