Introduction to Computer Science (I1100) Data Storage
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1 Data Storage 145
2 Data types Data comes in different forms Data Numbers Text Audio Images Video 146
3 Data inside the computer All data types are transformed into a uniform representation when they are stored in a computer and transformed back to their original form when retrieved. This universal representation is called bit pattern. Bit: (binary digit) is the smallest unit of data that can be stored in a computer and has value of 0 or 1. (based on switches on/off) To represent different types of data, we use bit pattern, a sequence (string of bits). A bit pattern with 8 bits is called a byte. 147
4 Storing numbers A number is changed to the binary system before being stored in the computer s memory. However, 2 issues need to be handled: 1. How to store the sign of a number? 2. How to show the decimal point? 148
5 Storing Integers Integers are whole numbers (without a fractional part). Example: An integer can be thought of as a number in which the position of the decimal point is fixed: the decimal point is to the right of the least significant bit (rightmost). A fixed-point representation is used to store an integer (the decimal point is assumed but not stored) Memory location Decimal point (assumed position) 149
6 Storing Integers An unsigned integer is an integer that can never be negative and can take only 0 or positive values. Its range is between 0 and positive infinity. Since no computer can possibly represent all the integers in this range, most computers define a constant called the maximum unsigned integer. Maximum unsigned integer = 2 n -1 where n is the number of bits allocated to represent an unsigned integer. 150
7 Storing unsigned integers To store an unsigned integer, follow these steps: 1. The integer is changed to binary 2. If the number of bits is less than n, 0s are added to the left of the binary integer so that there is a total of n bits. If the number of bits is greater than n, the integer cannot be stored. Overflow. 151
8 Storing unsigned integers Example Store 7 in a 8-bit memory location using unsigned representation. Solution: 1. Change the integer to binary : (111) 2 2. Add five 0s to the left to make a total of 8 bits: ( ) 2 152
9 Storing unsigned integers Overflow In a n-bit memory, we can store an unsigned integer between 0 and 2 n -1. Example: in a 4-bit memory, the larger integer that can be stored is 2 4-1=15 What if we store 20? 20 = (10100) 2 The computer drops the leftmost bit and keeps the rightmost 4 bits (0100) 2 =4 153
10 Storing signed integers All computers uses two s complement representation to store a signed integer in a n-bit memory. In this method, the available range of an unsigned integer is divided into two equal subranges. The first subrange is used to represent positive integers the second subrange is used to represent negatives integers 154
11 Storing signed integers Example If n=4, the range is 0000 to This range is divided into two halves: 0000 to 0111, and 1000 to 1111 The first leftmost bit determines the sign. If the leftmost bit is 0, the integer is positive if the leftmost bit is 1, the integer is negative 155
12 Storing signed integers Two complement operation Copy bits from the right until a 1 is copied, then flip the rest of the bits. Example
13 Storing an integer in two s complement format To store an integer in two s complement format, follow these steps: 1. The integer is changed to n-binary format 2. If the integer is positive or zero, it is stored as it is If the integer is negative, the computer takes the two s complement of the integer and stores it. 157
14 Retrieving an integer in two s complement format To retrieve an integer in two s complement format, follow these steps: 1. If the leftmost bit is 1, the computer applies the two s complement operation to the integer If the leftmost bit is 0, no operation is applied 2. The computer changes the integer to decimal. 158
15 Storing signed integers Two complement operation - EXAMPLE Store the integer 28 in a 8-bit memory location using two s complement representation. Solution: The integer is positive. After decimal to binary transformation no more action is needed. We need to add 3 extra 0s to the left to the integer to make it 8 bits. 28 = ( ) 2 159
16 Storing signed integers Two complement operation - EXAMPLE Store the integer -28 in a 8-bit memory location using two s complement representation. Solution: The integer is negative. After decimal to binary transformation, the computer applies the two s complement operation. Change 28 to binary = ( ) 2 apply two s complement operation = ( ) 2 160
17 Retrieving signed integers Two complement operation - EXAMPLE Retrieve the integer that is stored as ( ) 2 in memory using two s complement representation. Solution: The leftmost bit is 1, so the integer is negative. The integer needs to be two s complemented before changing to decimal ( ) 2 apply two s complement operation = ( ) 2 integer changed to decimal = 26 sign is added =
18 Storing reals A real is a number with an integral part and a fractional part. The fixed-point representation can be used, however the result may not be accurate. Example: * if we reserve 2 digits to the right of the decimal point, the system will store as 1.00 * if we reserve 6 digits to the left of the decimal point, the system will store as As a rule: real numbers with very large integral parts or very small fractional parts should not be stored in fixed-point representation. 162
19 Storing reals Floating-point representation This representation allows the decimal point to float: we can have different numbers of digits to the left or right of the decimal point. In this representation, either decimal or binary, a number is made up of 3 sections Sign Shifter Fixed-point number 163
20 Storing texts We represent each symbol with a bit pattern. Example: CATS C A T S How many bits are needed in a bit-pattern to represent a symbol in a language? It depends on the number of symbols in that language. The relation is logarithmic If we need 2 symbols the length is 1 bit (log 2 2 = 1) If we need 4 symbols the length is 2 bits (log 2 4 = 2) 164
21 Number of symbols and bit pattern length Number of Symbols Bit pattern length Number of Symbols Bit pattern length , ,294,967, ASCII CODE UNICODE 165
22 ASCII Table 166
23 Unicode 167
24 Storing Audio Audio is a representation of sound or music. Audio is not countable. Audio is an entity that changes with time. We measure its intensity at each moment. Storing audio in computer memory = storing the intensity of an audio signal over a period of time. Audio is an example of analog data (text, numbers are digital data) Even if we are able to measure all its values in a period of time, we cannot store these as we would need infinite number of memory locations. 168
25 Storing Audio 169
26 Storing Audio Step 1 : Sampling Sampling rate 40,000 samples per second 170
27 Storing Audio Step 2 : Quantization The value for each sample is a real number. Quantization = use an unsigned integer for each sample 171
28 Storing Audio Step 3 : Encoding Encoding : Quantized sample need to be encoded as bit patterns 172
29 Storing Audio Bit depth (B) : number of bits allocated for each sample (nowadays 16, 32 bits) Bit rate : we need to store S*B bits for each second of audio (S = number of samples per second) Example: if we use 40,000 samples per second and 16bits per each sample, the bit rate is R = 40,000*16=640,000 bits per second = 640 kilobits per second 173
30 MP3 Dominant standard for storing audio is MP3 (MPEG layer 3). This standard is a modification of the MPEG (Motion Picture Experts Group) compression method used for video. It uses 44,100 samples per second and 16 bits per sample. The result is a signal with a bit rate of 705,600 bits per second, which is compressed using a compression method that discards information that cannot be detected by the human ear (called lossy compression) 174
31 Storing Images Images are stored in computers using 2 different techniques: raster images and vector images (we will not cover vector images). 175
32 Raster images / Bitmap images Used to store an analog image such as a photograph. Photograph = analog data, the intensity of data (color) varies in space. Data must be sampled. Here it is called Scanning. Samples are called pixels (picture elements). The whole image is divided into small pixels where each pixel have a single intensity value. 176
33 Resolution Just like audio sampling, we need to decide how many pixels we need to record for each square. Scanning rate in image processing is called resolution. 177
34 Color Depth Color depth = number of bits to represent a pixel, depends on how a pixel s color is handled by different encoding techniques. Our eyes have different types of photoreceptor cells where some respond to the 3 primary colors red, green and blue (called RGB). 178
35 Color encoding technique True-color True-color uses 24 bits to encode a pixel. Each of the primary colors (RGB) are represented by 8 bits (each color is represented by 3 decimals between 0 and 255) Color Red Green Blue Color Red Green Blue Black Yellow Red Cyan Green Magenta Blue White True-color scheme can encode 2 24 = 16,776,226 colors (the color intensity of each pixel is one of these values). 179
36 Color encoding technique Indexed color Many applications do not need such large range of colors. The indexed color (palette color) uses only a portion of these colors (normally 256 colors only) The index scheme uses only 8 bits to store the sample pixel. 180
37 JPEG GIF JPEG (Joint Photographic Experts Group) uses the True-Color scheme, but compresses the image to reduce the number of bits. GIF (Graphic Interchange Format) uses the indexed color scheme. 181
38 Storing Video Video is a representation if images (called frames) over time. Movie = series of frames shown one after another to create the illusion of motion. Video = representation of information that changes in space (single image) and in time (a series of image) Each image or frame is transformed into a set of bit patterns and stored. The combination represents the video. Today video is normally compressed. Example: MPEG is a common video compression technique. 182
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