Computer numbers
Computer numbers are the basic units used and manipulated in a digital computer. The term binary is used to describe the raw data as it is stored in a computer. The smallest unit of the binary system is the "bit" which defines only a 1 or a 0. Multiple bits are used to define larger numbers.
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[edit] Overview
Even if the computer data represents symbols or characters the computer hardware will still treat it as a binary number. It is up to the software program to interpret the information correctly.
The computer uses a binary numbering system as shown below on the left as compared to the decimal system we use today. Each binary digit is called a 'bit' in computer parlance. A bit can only have a value of 1 or 0 (on or off) so to count past 1 you need more than 1 bit.
[edit] table


[edit] Hexadecimal
Hexadecimal (also called base16 or abbreviated to hex) numbers is a number system used to simplify the display and manipulation of numbers used in computers. As can be seen in the table it can take up to 4 binary digits to represent the symbols used in the decimal system. Working with binary digits can really be cumbersome so computer folks often work in hexadecimal. Hexadecimal is similar to decimal but adds 6 more characters to the number system (A, B, C, D, E, F) to fill out all the 16 combinations possible with 4 binary digits (bits). Two hexadecimal numbers can represent 8 bits, called a byte, that can count from 0 to 255 in decimal.
It is up to the program and computer to understand what a byte represents. It could be a binary number or a character or even interpreted as a logical representation of data such as true or false. However, it can sometimes be confusing as to what numbering system is in use. Hexadecimal numbers include a small x at the beginning to indicate that the number is being shown in hexadecimal thus x10 is equal to a decimal 16. In this notation the letters AF can be in either upper or lower case. The x may be preceded with a 0.
[edit] Implications of binary
In decimal we use powers of ten thus we see progressions as 10, 100, 1000, 10000, etc. In binary we see powers of two thus the progression is 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, etc. This is why computer terminology for a thousand bytes is really 1024 instead of 1000.
It is easy to translate decimal integers into the equivalent binary but not so for fractions. There are many decimal fractions that have no exact binary equivalent. Binary fractions are actually division by 2 so fractions like 1/2, 1/4, 1/8 are easy to do but others are more difficult. Binary numbers also get long quickly and become more difficult to deal with. One choice is to group them in 4's and create bytes. For really big numbers or really small numbers in decimal we typically resort to scientific notation. For binary the answer is to convert them to a floatingpoint number which may also lose some accuracy.
Note that computer numbers are often used even to identify the binary value of characters or such as ASCII values or for any binary information since it is easier to identify values in Hexadecimal than to use pure binary. Sometimes binary can also be represented in groups of 3 bits using Octal notation which is simply the numbers from 0 to 7 as shown in the table.
Note that we have referred to this binary data as numbers but it may also be interpreted as printable characters or any other form of data. The computer understands it as numbers but the program or user can interpret it differently.
[edit] Byte
The term byte is used to collect a number of binary bits into a character. Generally the term Byte is used to represent 8 bits, two hexadecimal digits. Thus a single byte (2^{8}) has 256 possible values ranging from 0 to 255. When abbreviated the term bit is in lower case while Byte is in upper case so a transfer rate of 300 b/s would indicate bits per second while 300 B/s would be 8 times faster. Memory and Disk capacity is normally specified in Bytes.
[edit] Word
To carry the analogy of one Byte representing a character a bit further multiple bytes can represent a word. A computer word is the generally the size of an instruction or meaningful memory location. Computers are often identified by the size of their word. A native 32 bit computer means the word size is 32 bits or 4 bytes. A 32 bit word can hold a number up to 4 Gig (2 Gig for a signed number). To represent a larger number you might use a double word or perhaps a 64 bit computer.
There are implications to the word size as it effects limits. For example a 32bit word limits the capacity of a computer to access storage directly using one word to 4 GB. Normally numbers in a computer are signed. This means that they could be positive or negative numbers and one bit is reserved for the sign, leaving 31 bits for the number itself. This effectively halves the size of the number that can be stored in a word to 2 Gigabytes. This, for example, is the cause of the need to change the format of an SD card when the capacity exceeded 2 GB. This also is the cause of a future problem where the date stored in computer files will overflow. Time in a computer is generally stored in total seconds in one word from with a finite date. In Unix this was Jan 1 1970 UT. Dates are computed using the number of seconds in the computer clock word and this will overflow after 2030. Negative time is assigned to dates prior to 1970.
[edit] Converting Hexadecimal to decimal
In the discussion to follow see the table above to translate the individual characters to their decimal equivalent.
In our decimal system each digit is multiplied by powers of 10 to achieve larger numbers. For hexadecimal you would need to multiply by powers of 16 instead.
Beginning right to left, take the decimal equivalent digits and add them together after multiplying by the appropriate value. For the right digit multiply by 1. For the second digit multiply by 16. For each successive digit multiply by 256, 4096, 65536, 1048576, etc. using successive powers of 16. Thus hex 2A would be 10 x 1 + 2 x 16 = 42.
[edit] Hexadecimal translation tables
Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  

00  00  10  16  20  32  30  48  40  64  50  80  60  96  70  112  
01  01  11  17  21  33  31  49  41  65  51  81  61  97  71  113  
02  01  12  18  22  34  32  50  42  66  52  82  62  98  72  114  
03  03  13  19  23  35  33  51  43  67  53  83  63  99  73  115  
04  04  14  20  24  36  34  52  44  68  54  84  64  100  74  116  
05  05  15  21  25  37  35  53  45  69  55  85  65  101  75  117  
06  06  16  22  26  38  36  54  46  70  56  86  66  102  76  118  
07  07  17  23  27  39  37  55  47  71  57  87  67  103  77  119  
08  08  18  24  28  40  38  56  48  72  58  88  68  104  78  120  
09  09  19  25  29  41  39  57  49  73  59  89  69  105  79  121  
0A  10  1A  26  2A  42  3A  58  4A  74  5A  90  6A  106  7A  122  
0B  11  1B  27  2B  43  3B  59  4B  75  5B  91  6B  107  7B  123  
0C  12  1C  28  2C  44  3C  60  4C  76  5C  92  6C  108  7C  124  
0D  13  1D  29  2D  45  3D  61  4D  77  5D  93  6D  109  7D  125  
0E  14  1E  30  2E  46  3E  62  4E  78  5E  94  6E  110  7E  126  
0F  15  1F  31  2F  47  3F  63  4F  79  5F  95  6F  111  7F  127 
Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  Hex  Dec  

80  128  90  144  A0  160  B0  176  C0  192  D0  208  E0  224  F0  240  
81  129  91  145  A1  161  B1  177  C1  193  D1  209  E1  225  F1  241  
82  130  92  146  A2  162  B2  178  C2  194  D2  210  E2  226  F2  242  
83  131  93  147  A3  163  B3  179  C3  195  D3  211  E3  227  F3  243  
84  132  94  148  A4  164  B4  180  C4  196  D4  212  E4  228  F4  244  
85  133  95  149  A5  165  B5  181  C5  197  D5  213  E5  229  F5  245  
86  134  96  150  A6  166  B6  182  C6  198  D6  214  E6  230  F6  246  
87  135  97  151  A7  167  B7  183  C7  199  D7  215  E7  231  F7  247  
88  136  98  152  A8  168  B8  184  C8  200  D8  216  E8  232  F8  248  
89  137  99  153  A9  169  B9  185  C9  201  D9  217  E9  233  F9  249  
8A  138  9A  154  AA  170  BA  186  CA  202  DA  218  EA  234  FA  250  
8B  139  9B  155  AB  171  BB  187  CB  203  DB  219  EB  235  FB  251  
8C  140  9C  156  AC  172  BC  188  CC  204  DC  220  EC  236  FC  252  
8D  141  9D  157  AD  173  BD  189  CD  205  DD  221  ED  237  FD  253  
8E  142  9E  158  AE  174  BE  190  CE  206  DE  222  EE  238  FE  254  
8F  143  9F  159  AF  175  BF  191  CF  207  DF  223  EF  239  FF  255 
[edit] floatingpoint
Floatingpoint is the computer method of handling very large and very small numbers. It can deal with both positive and negative values. There is often hardware built into the computer specifically to deal with these numbers. To see how floatingpoint works consider the example 1024=2^{10}. In Hex this would be 400 or in binary it would be 100 0000 0000, a 1 with 10 zeros. If we stored the 1 and the 10 separately then we would need only 5 bits. One for the 1 and four for the 10. A floatingpoint number is stored exactly this way. If it were stored in a word (32 bits) we could devote 24 bits to the value and 8 bits for the exponent. We will actually use 7 bits for the exponent (0127) and one bit for a sign. (a negative exponent is for very small numbers) Thus we can represent numbers clear up to 3.4 times 10^{38} with about 7 digits of decimal accuracy using 24 bits. You might ask, what about leaving a bit for negative numbers? Since we don't need leading zeros in floating point we can assume the first digit is always a 1. Thus we don't need to store it and we can use that bit for the sign. For double precision floatingpoint we could use 64 bits.
Some of the image formats used today can represent each color with a floatingpoint number for HDR use but even single precision is overkill for this use. A half precision number has been developed using 16 bits (saving half the space). There are 11 bits reserved for the value and 4 digits are reserved for the exponent with 1 bit used for the exponent sign. Using 11 digits for precision is still more than the 10 bits per color (30 total bits) now be touted for high definition use. In addition the range, using the exponent, of over 65,000 distinct values provides brightness recording as good as the eye can see. Image formats supporting floatingpoint can also use half precision floatingpoint.
See https://en.wikipedia.org/wiki/Floatingpoint_arithmetic#Floatingpoint_numbers in wikipedia for more information.