The document discusses how real numbers are represented in IEEE standard form using 32 bits divided into three sections - a sign bit, 8-bit exponent, and 23-bit number. It provides the 5 steps to convert a real number into its IEEE representation: 1) calculate the binary form, 2) normalize it, 3) set the sign bit, 4) store the exponent as an 8-bit binary after adding 127, and 5) store the remaining bits of the normalized form. It asks to represent 25.010 in this standard form.
Math1003 1.15 - Integers and 2's Complementgcmath1003
The document discusses how integers are stored in computers using two's complement format. Integers and real numbers are stored differently, with integers using binary representations. Early computers stored integers in 8 bits, but now use 32 bits. Negative integers are represented by taking the two's complement of the binary representation of the positive integer of the same magnitude. This two's complement format addresses issues with representing both positive and negative zero that arose with earlier sign-magnitude representation of integers.
BCS Certificate Level Examination. Computer and Network Technology (CNT) subject. Fundamentals of Computer Science. Data Representation in Computers. Learn about decimal, binary, octal and hexadecimal number systems and conversion between systems. Learn about binary addition and subtraction. For a complete subject coverage including Information Systems and Software Developments subjects, please visit to https://www.bcsonlinelectures.com/
1. Digital systems represent information in binary form and use binary logic elements like logic gates to process data. Quantities are stored as binary values in storage elements like flip-flops.
2. There are different number systems like binary, decimal, and other bases. Converting between them involves procedures like partitioning into groups or dividing and accumulating remainders.
3. Representing negative numbers in binary involves sign-magnitude, 1's complement, or 2's complement systems. The 2's complement is most common for computer arithmetic due to its simplicity.
This is the second lesson of Computer and Network Technology subject of BCS HEQ Certificate Level exam.
Subject: Computer and Network Technology (CNT)
Chapter: Fundamentals
Lesson: Data Representation in Computers
This lesson discuss about how integers, floating point numbers and characters are handled by modern computers.
For more lessons please visit https://www.bcsonlinelectures.com website.
FPGA Based Decimal Matrix Code for Passive RFID TagIJERA Editor
In this paper, Decimal Matrix Code is developed for RFID passive tag. The proposed DMC uses the decimal algorithm to obtain the maximum error detection and correction capability. The Encoder-Reuse Technique is used to minimize the area overhead of extra circuits without disturbing the complete encoding and decoding processes. ERT uses DMC encoder itself to be part of the decoder. The Simulation results reveals that the Decimal Matrix Code is effective than existing Matrix and Hamming odes in terms of Error Correction Capability. Xilinx ISE 14.7 Software is used for the simulation outputs. The complete design is verified and tested on Spartan-6 FPGA board. The performance of system is measured in terms of power, area and delay. The Synthesis result shows that, the power required for complete design of Decimal Matrix Code is 0.1mW with a delay of 3.109ns.
The document discusses various methods for representing numeric data in a computer system, including binary, decimal, fixed-point, and floating-point representations. It describes word length in bits and bytes and how numbers are stored in memory in big-endian and little-endian formats. Signed number representations like sign-magnitude, one's complement, and two's complement are also summarized. Various decimal coding schemes such as BCD, ASCII, excess-three, and two-out-of-five are defined.
The document discusses different number systems used in digital computers including binary, decimal, octal, and hexadecimal systems. It describes the characteristics of each system such as the base and digits used. Methods for converting between these different number systems are presented, including using division or grouping bits. The representation of signed integers as binary numbers is also covered, comparing sign-magnitude, one's complement, and two's complement representations. Binary addition is demonstrated with examples.
Computers represent data using binary digits (bits) that can have a value of 0 or 1. Data is stored digitally as patterns of bits. Different numbering systems like binary, decimal, and hexadecimal use different symbols but the same positional notation approach. Converting between numbering systems involves repeatedly dividing the number by the base and recording the remainders as the digits of the new number.
Math1003 1.15 - Integers and 2's Complementgcmath1003
The document discusses how integers are stored in computers using two's complement format. Integers and real numbers are stored differently, with integers using binary representations. Early computers stored integers in 8 bits, but now use 32 bits. Negative integers are represented by taking the two's complement of the binary representation of the positive integer of the same magnitude. This two's complement format addresses issues with representing both positive and negative zero that arose with earlier sign-magnitude representation of integers.
BCS Certificate Level Examination. Computer and Network Technology (CNT) subject. Fundamentals of Computer Science. Data Representation in Computers. Learn about decimal, binary, octal and hexadecimal number systems and conversion between systems. Learn about binary addition and subtraction. For a complete subject coverage including Information Systems and Software Developments subjects, please visit to https://www.bcsonlinelectures.com/
1. Digital systems represent information in binary form and use binary logic elements like logic gates to process data. Quantities are stored as binary values in storage elements like flip-flops.
2. There are different number systems like binary, decimal, and other bases. Converting between them involves procedures like partitioning into groups or dividing and accumulating remainders.
3. Representing negative numbers in binary involves sign-magnitude, 1's complement, or 2's complement systems. The 2's complement is most common for computer arithmetic due to its simplicity.
This is the second lesson of Computer and Network Technology subject of BCS HEQ Certificate Level exam.
Subject: Computer and Network Technology (CNT)
Chapter: Fundamentals
Lesson: Data Representation in Computers
This lesson discuss about how integers, floating point numbers and characters are handled by modern computers.
For more lessons please visit https://www.bcsonlinelectures.com website.
FPGA Based Decimal Matrix Code for Passive RFID TagIJERA Editor
In this paper, Decimal Matrix Code is developed for RFID passive tag. The proposed DMC uses the decimal algorithm to obtain the maximum error detection and correction capability. The Encoder-Reuse Technique is used to minimize the area overhead of extra circuits without disturbing the complete encoding and decoding processes. ERT uses DMC encoder itself to be part of the decoder. The Simulation results reveals that the Decimal Matrix Code is effective than existing Matrix and Hamming odes in terms of Error Correction Capability. Xilinx ISE 14.7 Software is used for the simulation outputs. The complete design is verified and tested on Spartan-6 FPGA board. The performance of system is measured in terms of power, area and delay. The Synthesis result shows that, the power required for complete design of Decimal Matrix Code is 0.1mW with a delay of 3.109ns.
The document discusses various methods for representing numeric data in a computer system, including binary, decimal, fixed-point, and floating-point representations. It describes word length in bits and bytes and how numbers are stored in memory in big-endian and little-endian formats. Signed number representations like sign-magnitude, one's complement, and two's complement are also summarized. Various decimal coding schemes such as BCD, ASCII, excess-three, and two-out-of-five are defined.
The document discusses different number systems used in digital computers including binary, decimal, octal, and hexadecimal systems. It describes the characteristics of each system such as the base and digits used. Methods for converting between these different number systems are presented, including using division or grouping bits. The representation of signed integers as binary numbers is also covered, comparing sign-magnitude, one's complement, and two's complement representations. Binary addition is demonstrated with examples.
Computers represent data using binary digits (bits) that can have a value of 0 or 1. Data is stored digitally as patterns of bits. Different numbering systems like binary, decimal, and hexadecimal use different symbols but the same positional notation approach. Converting between numbering systems involves repeatedly dividing the number by the base and recording the remainders as the digits of the new number.
The document discusses different methods of representing data in computers, including:
1. Binary representation of numbers using 0s and 1s. This allows integers and floating point numbers to be stored.
2. Text representation using character encoding standards like ASCII and Unicode which assign binary codes to letters, numbers and symbols.
3. Graphic representations including bitmapped images and vector graphics. Bitmaps store color values for each pixel while vectors store mathematical descriptions of shapes.
This document discusses data representation in computers. It covers:
- Numbering systems used in computers, including binary and hexadecimal.
- Procedures for converting between decimal, binary, and hexadecimal numbers.
- Signed integer representation, discussing signed magnitude, one's complement, and two's complement notation.
- Examples of adding signed binary integers using signed magnitude representation and how overflow can cause errors.
Digital systems represent quantities using symbols called digits that can take various forms such as binary, octal, and hexadecimal. The binary number system uses two symbols, 0 and 1, and is important for digital circuits. Decimal numbers can be converted to binary by repeatedly dividing the number by two and writing the remainders as binary digits. Real numbers are represented internally using a mantissa and exponent in binary form. Character encoding schemes like ASCII and ISCII assign numeric codes to letters and symbols to allow text to be represented digitally, with Unicode now providing a standard coding that supports many languages.
This document discusses different methods of representing data in a computer, including numeric data types, number systems, and encoding schemes. It covers binary, decimal, octal, and hexadecimal number systems. Methods for representing signed and unsigned integers are described, such as signed-magnitude, 1's complement, and 2's complement representations. Floating point number representation with a sign bit, exponent field, and significand is also summarized. Conversion between different number bases and data encodings like binary-coded decimal are explained through examples.
The document discusses data representation in computers. It explains that:
1) Data is stored in binary form using bits, with the basic unit being a byte made up of 8 bits. Each byte can represent one character using ASCII codes.
2) When a user types a letter on a keyboard, it is converted to its ASCII binary code and stored in memory. This allows the letter to be processed and displayed on an output device.
3) Common units for measuring data size are kilobytes, megabytes, and gigabytes which are powers of 1024, 1024^2, and 1024^3 bytes respectively. Clock speed of a processor, measured in Hertz, determines how fast it can process data.
This document discusses different data types in MATLAB including:
- Logical, character, integer, floating point, structure, cell, and user-defined data types.
- Integer types include uint8, int8, uint16 etc. depending on the bit size and whether they are signed or unsigned.
- Floating point numbers can be single or double precision, with double being the default type in MATLAB. Complex numbers are represented as doubles with real and imaginary parts.
This document discusses binary codes and their use in digital systems. It begins by defining code as the symbolic representation of discrete information elements. It then discusses various types of binary codes, including binary codes, decimal codes, Gray codes, error detection codes, and alphanumeric codes. It also discusses binary storage in registers and how information is transferred between registers in a computer's memory and processor units.
This document outlines the course content for a Higher Computing course, which is divided into 3 main units: Computer Systems (40 hours), Software Development (40 hours), and Artificial Intelligence (40 hours). The Computer Systems unit covers topics like data representation, computer structure, networking, and computer software across 5 sections. Specific lessons in the Data Representation section discuss how numbers, text, and images are stored in binary and how storage capacities are measured. Graphics representation and compression techniques are also introduced. Students will complete assessments including end of unit tests, coursework tasks, and a written exam.
The document discusses how computers represent data using binary numbers (1s and 0s). It explains that binary is used because it provides an easy way to represent two states (on/off) in storage devices. It then discusses how different numbers of bits (binary digits) can be used to represent different numbers in binary, and provides examples of converting between binary and decimal numbers. Finally, it briefly introduces the concept of data compression for reducing the size of files.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Data representation computer architecturestudy cse
Digital computers represent all information internally as binary patterns of 1s and 0s. There are several common data representation schemes that determine how different types of data like integers, floating point numbers, characters, etc. are mapped to and interpreted from these binary patterns. The choice of representation depends on factors like the type and range of values, required precision, and hardware support. Standardized formats like IEEE 754 are used to allow portability of floating point data across systems.
This document discusses different methods for representing data in computers, including numeric and character representations. It covers representing signed and unsigned integers using methods like sign-magnitude, 1's complement, and 2's complement. It also discusses floating point number representation using the IEEE standard. Finally, it discusses character representation using ASCII and Unicode encoding schemes.
The document discusses data representation in computers, specifically floating point numbers. It explains that floating point representation uses three fields - a sign bit, exponent field, and significand field - to represent numbers in scientific notation. The IEEE 754 standard defines common floating point formats like single and double precision that specify the number of bits used for each field. The document provides examples of how different numbers are represented in a simplified 14-bit floating point format and discusses how operations like addition and multiplication are performed on floating point values.
The document provides lecture notes on digital logic design that cover the following topics:
1. It introduces the concepts of binary systems, number bases, binary arithmetic, complements and binary codes.
2. Boolean algebra and gate level logic minimization techniques such as Karnaugh maps are discussed.
3. The design of combinational logic circuits including adders, decoders and multiplexers is examined.
4. Sequential logic circuits including latches, flip-flops, shift registers and finite state machines are explored.
5. Memory systems such as RAM, ROM and cache are covered.
This document discusses various methods of data representation in computers, including:
1. Numeric and non-numeric data types. Computers represent numeric data like integers and real numbers, as well as non-numeric data like letters and symbols.
2. Positional number systems like binary, decimal, octal and hexadecimal are used for efficient internal representation in computers. Conversion between different bases is also covered.
3. Fixed point number representation including signed magnitude, 1's complement, and 2's complement representations. Floating point number representation separates the mantissa and exponent is also discussed.
Digital computers represent data by means of an easily identified symbol called a digit. The data may
contain digits, alphabets or special character, which are converted to bits, understandable by the computer.
In Digital Computer, data and instructions are stored in computer memory using binary code (or
machine code) represented by Binary digIT’s 1 and 0 called BIT’s.
The number system uses well-defined symbols called digits.
Number systems are classified into two types:
o Non-positional number system
o Positional number system
This document discusses different number systems used in computers including fixed-point, floating-point, and binary coded decimal (BCD) systems. It explains that fixed-point systems have a constant number of integer and fractional bits, while floating-point systems allow representation of very large and small numbers using a sign bit, exponent bits, and mantissa bits according to the IEEE 754 standard. BCD systems encode each decimal digit with 4 bits and are commonly used where values need to be displayed.
This document discusses number systems and data representation in computers. It covers topics like binary, decimal, hexadecimal, and ASCII number systems. Some key points covered include:
- Computers use the binary number system and positional notation to represent data precisely.
- Different number systems have different bases (like binary base-2, decimal base-10, hexadecimal base-16).
- Methods for converting between number systems like binary to decimal and hexadecimal to decimal.
- Signed and unsigned integers, ones' complement, twos' complement representation of negative numbers.
- ASCII encoding of characters and how to convert between character and numeric representations.
1. The document discusses different types of codes used to represent digital data including weighted, non-weighted, alphanumeric, error detection, error correction, and binary codes.
2. It describes various binary codes like BCD, Gray, EBCDIC, and ASCII codes explaining how they represent numeric and alphanumeric data.
3. Specific codes discussed in detail include BCD, excess-3, Gray, and ASCII codes explaining their binary representations of decimal numbers and characters.
The document discusses scientific notation and how it is used to write very large and very small numbers in a standardized way. Scientific notation expresses numbers as the product of a number between 1 and 10 and a power of 10. This allows numbers with many zeros to be written more concisely than in standard decimal form. The document provides examples of how various numbers are written in scientific notation, including the distance from Earth to the moon, the number of stars in the universe, and the size of modern computer chips.
The document discusses the decimal number system. It explains that decimal numbers are composed of digits in different place values that are powers of ten, with the place value increasing by factors of ten from right to left. This place value system allows very large and small numbers to be represented. The document uses the numbers 1764 and 1359.24 to illustrate how digits in each place value (thousands, hundreds, tens, ones, tenths, hundredths) represent that value when multiplied by the corresponding power of ten.
The document discusses different methods of representing data in computers, including:
1. Binary representation of numbers using 0s and 1s. This allows integers and floating point numbers to be stored.
2. Text representation using character encoding standards like ASCII and Unicode which assign binary codes to letters, numbers and symbols.
3. Graphic representations including bitmapped images and vector graphics. Bitmaps store color values for each pixel while vectors store mathematical descriptions of shapes.
This document discusses data representation in computers. It covers:
- Numbering systems used in computers, including binary and hexadecimal.
- Procedures for converting between decimal, binary, and hexadecimal numbers.
- Signed integer representation, discussing signed magnitude, one's complement, and two's complement notation.
- Examples of adding signed binary integers using signed magnitude representation and how overflow can cause errors.
Digital systems represent quantities using symbols called digits that can take various forms such as binary, octal, and hexadecimal. The binary number system uses two symbols, 0 and 1, and is important for digital circuits. Decimal numbers can be converted to binary by repeatedly dividing the number by two and writing the remainders as binary digits. Real numbers are represented internally using a mantissa and exponent in binary form. Character encoding schemes like ASCII and ISCII assign numeric codes to letters and symbols to allow text to be represented digitally, with Unicode now providing a standard coding that supports many languages.
This document discusses different methods of representing data in a computer, including numeric data types, number systems, and encoding schemes. It covers binary, decimal, octal, and hexadecimal number systems. Methods for representing signed and unsigned integers are described, such as signed-magnitude, 1's complement, and 2's complement representations. Floating point number representation with a sign bit, exponent field, and significand is also summarized. Conversion between different number bases and data encodings like binary-coded decimal are explained through examples.
The document discusses data representation in computers. It explains that:
1) Data is stored in binary form using bits, with the basic unit being a byte made up of 8 bits. Each byte can represent one character using ASCII codes.
2) When a user types a letter on a keyboard, it is converted to its ASCII binary code and stored in memory. This allows the letter to be processed and displayed on an output device.
3) Common units for measuring data size are kilobytes, megabytes, and gigabytes which are powers of 1024, 1024^2, and 1024^3 bytes respectively. Clock speed of a processor, measured in Hertz, determines how fast it can process data.
This document discusses different data types in MATLAB including:
- Logical, character, integer, floating point, structure, cell, and user-defined data types.
- Integer types include uint8, int8, uint16 etc. depending on the bit size and whether they are signed or unsigned.
- Floating point numbers can be single or double precision, with double being the default type in MATLAB. Complex numbers are represented as doubles with real and imaginary parts.
This document discusses binary codes and their use in digital systems. It begins by defining code as the symbolic representation of discrete information elements. It then discusses various types of binary codes, including binary codes, decimal codes, Gray codes, error detection codes, and alphanumeric codes. It also discusses binary storage in registers and how information is transferred between registers in a computer's memory and processor units.
This document outlines the course content for a Higher Computing course, which is divided into 3 main units: Computer Systems (40 hours), Software Development (40 hours), and Artificial Intelligence (40 hours). The Computer Systems unit covers topics like data representation, computer structure, networking, and computer software across 5 sections. Specific lessons in the Data Representation section discuss how numbers, text, and images are stored in binary and how storage capacities are measured. Graphics representation and compression techniques are also introduced. Students will complete assessments including end of unit tests, coursework tasks, and a written exam.
The document discusses how computers represent data using binary numbers (1s and 0s). It explains that binary is used because it provides an easy way to represent two states (on/off) in storage devices. It then discusses how different numbers of bits (binary digits) can be used to represent different numbers in binary, and provides examples of converting between binary and decimal numbers. Finally, it briefly introduces the concept of data compression for reducing the size of files.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Data representation computer architecturestudy cse
Digital computers represent all information internally as binary patterns of 1s and 0s. There are several common data representation schemes that determine how different types of data like integers, floating point numbers, characters, etc. are mapped to and interpreted from these binary patterns. The choice of representation depends on factors like the type and range of values, required precision, and hardware support. Standardized formats like IEEE 754 are used to allow portability of floating point data across systems.
This document discusses different methods for representing data in computers, including numeric and character representations. It covers representing signed and unsigned integers using methods like sign-magnitude, 1's complement, and 2's complement. It also discusses floating point number representation using the IEEE standard. Finally, it discusses character representation using ASCII and Unicode encoding schemes.
The document discusses data representation in computers, specifically floating point numbers. It explains that floating point representation uses three fields - a sign bit, exponent field, and significand field - to represent numbers in scientific notation. The IEEE 754 standard defines common floating point formats like single and double precision that specify the number of bits used for each field. The document provides examples of how different numbers are represented in a simplified 14-bit floating point format and discusses how operations like addition and multiplication are performed on floating point values.
The document provides lecture notes on digital logic design that cover the following topics:
1. It introduces the concepts of binary systems, number bases, binary arithmetic, complements and binary codes.
2. Boolean algebra and gate level logic minimization techniques such as Karnaugh maps are discussed.
3. The design of combinational logic circuits including adders, decoders and multiplexers is examined.
4. Sequential logic circuits including latches, flip-flops, shift registers and finite state machines are explored.
5. Memory systems such as RAM, ROM and cache are covered.
This document discusses various methods of data representation in computers, including:
1. Numeric and non-numeric data types. Computers represent numeric data like integers and real numbers, as well as non-numeric data like letters and symbols.
2. Positional number systems like binary, decimal, octal and hexadecimal are used for efficient internal representation in computers. Conversion between different bases is also covered.
3. Fixed point number representation including signed magnitude, 1's complement, and 2's complement representations. Floating point number representation separates the mantissa and exponent is also discussed.
Digital computers represent data by means of an easily identified symbol called a digit. The data may
contain digits, alphabets or special character, which are converted to bits, understandable by the computer.
In Digital Computer, data and instructions are stored in computer memory using binary code (or
machine code) represented by Binary digIT’s 1 and 0 called BIT’s.
The number system uses well-defined symbols called digits.
Number systems are classified into two types:
o Non-positional number system
o Positional number system
This document discusses different number systems used in computers including fixed-point, floating-point, and binary coded decimal (BCD) systems. It explains that fixed-point systems have a constant number of integer and fractional bits, while floating-point systems allow representation of very large and small numbers using a sign bit, exponent bits, and mantissa bits according to the IEEE 754 standard. BCD systems encode each decimal digit with 4 bits and are commonly used where values need to be displayed.
This document discusses number systems and data representation in computers. It covers topics like binary, decimal, hexadecimal, and ASCII number systems. Some key points covered include:
- Computers use the binary number system and positional notation to represent data precisely.
- Different number systems have different bases (like binary base-2, decimal base-10, hexadecimal base-16).
- Methods for converting between number systems like binary to decimal and hexadecimal to decimal.
- Signed and unsigned integers, ones' complement, twos' complement representation of negative numbers.
- ASCII encoding of characters and how to convert between character and numeric representations.
1. The document discusses different types of codes used to represent digital data including weighted, non-weighted, alphanumeric, error detection, error correction, and binary codes.
2. It describes various binary codes like BCD, Gray, EBCDIC, and ASCII codes explaining how they represent numeric and alphanumeric data.
3. Specific codes discussed in detail include BCD, excess-3, Gray, and ASCII codes explaining their binary representations of decimal numbers and characters.
The document discusses scientific notation and how it is used to write very large and very small numbers in a standardized way. Scientific notation expresses numbers as the product of a number between 1 and 10 and a power of 10. This allows numbers with many zeros to be written more concisely than in standard decimal form. The document provides examples of how various numbers are written in scientific notation, including the distance from Earth to the moon, the number of stars in the universe, and the size of modern computer chips.
The document discusses the decimal number system. It explains that decimal numbers are composed of digits in different place values that are powers of ten, with the place value increasing by factors of ten from right to left. This place value system allows very large and small numbers to be represented. The document uses the numbers 1764 and 1359.24 to illustrate how digits in each place value (thousands, hundreds, tens, ones, tenths, hundredths) represent that value when multiplied by the corresponding power of ten.
The document discusses different sets of numbers including natural numbers, integers, rational numbers, and real numbers. It defines the natural number set N as containing all positive whole numbers and 0. The integer set Z contains all natural numbers as well as their negative counterparts. Several examples are provided to demonstrate whether specific numbers belong to sets N and Z. The goal is to understand which set a given number would belong to.
This document outlines the syllabus and schedule for a mathematics course called MATH1003 for the computer industry. It introduces the instructor, Greg Rodrigo, and covers topics like number systems, sets, logic, Boolean algebra, equations, functions, and statistics. The schedule lists these topics to be covered over three sections during the semester.
The document discusses binary and hexadecimal number systems and their importance in computers. It describes a journey taken through different components of a computer network, including a theater, various buildings, switches, routers, and connections to other cities. The goal is to appreciate the role of these number systems in representing information transmitted through the network.
The document discusses properties of real numbers. It examines the commutative, associative, identity, and inverse properties through examples of addition, subtraction, multiplication and division. The commutative property states that the order of numbers does not matter for addition and multiplication. The associative property means grouping does not change the result for addition and multiplication. The identity property defines the numbers that leave other numbers unchanged when added or multiplied. And the inverse property establishes that adding or multiplying the opposite undoes the original operation.
The document discusses the binary number system. It explains that binary numbers are written using only 1s and 0s. The place values in binary are powers of 2, with the rightmost digit being 20 = 1, the next place being 21 = 2, and so on. This means the binary number 11012 represents 1*8 + 1*4 + 0*2 + 1*1 = 16 + 4 + 0 + 1 = 21. The document also shows how to write binary numbers with decimals by continuing the place values as negative powers of 2.
Math1003 1.7 - Hexadecimal Number Systemgcmath1003
The document discusses the hexadecimal number system. It notes that the hexadecimal number system has a base of 16 and the place values are powers of 16, ranging from 16^0 to 16^n. It explains that the symbols 0-9 represent their usual values, while A=10, B=11, C=12, D=13, E=14, and F=15. An example of converting the hexadecimal number 4D5816 to decimal is provided to illustrate the place value concept.
Math1003 1.8 - Converting from Binary and Hex to Decimalgcmath1003
The document discusses converting binary numbers to their decimal equivalent values. It provides an example of converting the binary number 11010012. It explains that in binary, the place values are powers of 2, with the place values doubling from right to left. To calculate the decimal equivalent, you add the place value of each digit that is a 1. In the example, the place values of the 1 digits are 64, 32, 16, and 8, so when added together they equal the decimal value of 120.
The document discusses binary addition and provides examples of applying the rules of binary addition. It begins by stating the goal of correctly applying the rules of binary addition. It then lists the 4 rules of binary addition and provides examples of applying each rule. It concludes by providing multiple multi-bit binary addition examples that demonstrate applying the rules to obtain the sum.
The document discusses several concepts related to errors that can occur when performing mathematical operations on a computer. It explains that computers have limited storage, so numbers are truncated or rounded. This can lead to truncation error when values are simply cut off. It also discusses overflow error, which occurs when a calculation produces a value that is too large for the computer's storage format. The goal is to explain and demonstrate the concepts of truncation, rounding, overflow, and conversion error.
The document appears to be a presentation about computer networking and binary/hexadecimal number systems. It includes diagrams showing connections between different buildings and network components on campus, with labels indicating switches, routers, and connections to external networks. The goal stated is to appreciate the importance of binary and hexadecimal number systems in the computer world.
Math1003 1.10 - Binary to Hex Conversiongcmath1003
This document provides an example of converting binary numbers to hexadecimal numbers. It shows that binary numbers are grouped into 4-bit groups starting from the decimal point moving left, then converted to their hexadecimal equivalent. So the binary number 1101100110101.101010011 would be converted to B 3 D . 5 11 in hexadecimal.
The document discusses exponents and the rules of exponentiation. It defines exponents, bases, and examples of exponents. It then outlines five rules of exponentiation and uses examples to illustrate each rule. It concludes by defining exponents of 0 and negative exponents.
The document discusses the importance of the order of operations when performing calculations. It provides examples of how different people could obtain different answers for the same calculation if an order of operations was not followed consistently. Having a set order of operations ensures that everyone "speaks the same language" when solving equations.
The document discusses the concepts of significant digits, accuracy, and precision in numbers. It defines significant digits as the non-zero digits in a number plus zeros between other significant digits. Leading and trailing zeros are not significant unless the number contains a decimal. The number of significant digits indicates the precision or level of detail in the value. Examples are provided to illustrate the rules for determining significant digits in different numbers.
Math1003 1.11 - Hex to Binary Conversiongcmath1003
The document provides instructions for converting hexadecimal numbers to binary numbers. It begins with an example conversion table that lists decimal, binary, and hexadecimal numbers from 0 to 15. It then works through converting the hexadecimal number 7E50.23C116 to binary. For each hexadecimal digit, it writes the corresponding 4-bit binary equivalent according to the conversion table. Once all digits are converted, the full binary representation is displayed.
Math1003 1.9 - Converting Decimal to Binary and Hexgcmath1003
The document discusses converting decimal numbers to binary and hexadecimal numbers. It provides examples of converting the decimal numbers 20, 26, and 39 to their binary equivalents. It also addresses that the largest number that can be represented with 6 bits is 63, which is equal to 26 - 1 and results from having all 1s in the 6 bit positions, each worth powers of 2.
This document is a lesson on variables in Visual Basic programming. It defines a variable as a location in memory that holds information during program execution. It explains that variables are declared to create them in memory and initialized to assign them a value. Different data types like byte, integer, and long can be used to store different kinds of numeric values in variables. The document provides examples of declaring, initializing, and performing math operations on variables.
The document discusses different methods of representing data in computers, including:
1. Binary representation of numbers using 0s and 1s. This allows integers and floating point numbers to be stored.
2. Text representation using character encoding standards like ASCII and Unicode which assign binary codes to letters, numbers and symbols.
3. Graphic representations including bitmaps which store color values for each pixel, and vectors which store graphical objects as mathematical descriptions.
The document contains examples of converting between binary and decimal numbers, identifying common flowchart shapes, and calculating the minimum number of bits needed to uniquely encode buttons or lights. Key concepts covered include binary, decimal, flowchart shapes, encoding, and the ASCII protocol.
Development of a static code analyzer for detecting errors of porting program...PVS-Studio
The article concerns the task of developing a program tool called static analyzer. The tool being developed is used for diagnosing potentially unsafe syntactic structures of C++ from the viewpoint of porting program code on 64-bit systems. Here we focus not on the problems of porting occurring in programs, but on the peculiarities of creating a specialized code analyzer. The analyzer is intended for working with the code of C/C++ programs.
This document discusses how to subnet class A, B, and C IP addresses. It explains that IP addresses are made up of 32 bits divided into four octets. It also provides information on binary numbering and how subnetting allows you to break up large networks into smaller, more manageable subnets. For each address class, it notes the default subnet mask and host range, and that bits need to be borrowed from the host portion of the mask to create additional subnets.
This document discusses bit manipulation in Java. It provides 3 key points:
1) Bit manipulation allows direct manipulation of binary bits in a number to perform operations like AND, OR, and shifts.
2) Java provides capabilities for bit-level manipulation through bitwise operators and the BitSet class.
3) Examples are given demonstrating bitwise operators and BitSet methods like set(), get(), and(), cardinality(), and more.
This document provides an introduction to an ICT class being taught by Ms. Lily. It covers various number systems including binary, decimal, octal, and hexadecimal. It discusses how binary numbers work using 1s and 0s to represent switches in an on or off position. Examples are provided of converting between binary and decimal numbers through addition and subtraction. Students are asked to provide answers to practice problems converting between number systems.
The document outlines the course content for an Intro to Computing course, which is divided into three main units on computer systems, software development, and artificial intelligence. The computer systems unit covers topics such as data representation, computer structure, networking, and representing graphics. Sample lesson plans describe how numbers, text, and images are stored in binary and how floating point numbers are represented using mantissa and exponent.
This document outlines the course content for a Higher Computing course, which is divided into 3 main units: Computer Systems, Software Development, and Artificial Intelligence. The Computer Systems unit covers topics like data representation, computer structure, networking, and computer software. It discusses how numbers, text, images, and other data are stored in binary and converted between binary and decimal. It also covers graphics representation, storage calculations, and compression techniques. Assessment includes end of unit tests, coursework tasks, and a written exam.
This document discusses IP addressing and subnetting. It begins by explaining that an IP address has two components: a network address and a host address. It then describes the three classes of IP addresses (A, B, C) and how they divide the 32 bits between the network and host portions. The document goes on to provide examples of how to determine the number of subnets, hosts, and subnet ranges when borrowing bits from the host portion to create additional subnets in a Class C network.
Beyond Floating Point – Next Generation Computer Arithmeticinside-BigData.com
John Gustafson from the National University of Singapore presented this talk at Stanford.
“A new data type called a “posit” is designed for direct drop-in replacement for IEEE Standard 754 floats. Unlike unum arithmetic, posits do not require interval-type mathematics or variable size operands, and they round if an answer is inexact, much the way floats do. However, they provide compelling advantages over floats, including simpler hardware implementation that scales from as few as two-bit operands to thousands of bits. For any bit width, they have a larger dynamic range, higher accuracy, better closure under arithmetic operations, and simpler exception-handling. For example, posits never overflow to infinity or underflow to zero, and there is no “Not-a-Number” (NaN) value. Posits should take up less space to implement in silicon than an IEEE float of the same size. With fewer gate delays per operation as well as lower silicon footprint, the posit operations per second (POPS) supported by a chip can be significantly higher than the FLOPs using similar hardware resources. GPU accelerators, in particular, could do more arithmetic per watt and per dollar yet deliver superior answer quality.”
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Watch the video presentation: http://wp.me/p3RLHQ-gjH
This document provides an overview of the history of computing and how computers store data. It discusses:
- Gottfried Leibniz inventing binary arithmetic in the 17th century, which became the basis for how computers represent numbers.
- How early computers used mechanical switches to represent 1s and 0s, with switches in the on position representing 1 and off representing 0.
- Each byte in a computer's memory being divided into eight bits, with each bit representing a digit in the binary number system.
- Larger numbers being stored across multiple bytes, with the maximum value storable in a single byte being 255 and across two bytes being 65,535.
- A brief history of
The document discusses fundamental data types in C including integer, floating point, character, and void types. It describes how variables must be declared before use and explains basic type modifiers like short, long, and unsigned. The summary also covers integer storage sizes and ranges, floating point precision and representation, and type conversions in C using casts and arithmetic promotion.
The document summarizes the IEEE floating-point representation used in Microsoft Visual C++. It describes that there are three main formats - real*4, real*8, and real*10 - which store the sign bit, exponent, and mantissa differently. Real*4 uses a single precision 32-bit format while real*8 uses a double precision 64-bit format. Exponents are stored in biased format, with the exponent bias allowing the representation of both positive and negative exponents. Due to the binary representation, some decimal values cannot be precisely represented in floating-point formats.
This document provides an overview of data representation in computer systems. It discusses the fundamentals of numerical data representation using binary and other numeral systems, including signed and unsigned integers. It also covers character codes for representing human-readable text in computers. Error detection and correction techniques are introduced. The goals are to understand how computers store and manipulate numeric and character data internally.
- The document discusses various number systems like decimal, binary, octal and hexadecimal. It explains how each number system uses a different base or radix to represent numbers using symbols.
- Conversion methods between different number systems are also described, using techniques like successive division and multiplying place values.
- Character encoding standards like ASCII, ISCII and Unicode are introduced which allow representation of text in computers using numeric codes. UTF-8 encoding scheme of Unicode is also summarized briefly.
The document provides an introduction to computational thinking concepts including converting information to data, data types and encoding, and logic. It discusses how information is converted to continuous and discrete data, and how data is encoded through binary representations and bit strings. Different data types like numbers, text, colors, pictures and sound are also explained in terms of their encoding. The document then covers logic and computational thinking concepts like inductive and deductive logic, and how Boolean logic uses true/false propositions and logical operators.
This document provides an overview of data representation in computers. It discusses binary, decimal, hexadecimal, and floating point number systems. Binary numbers use only two digits, 0 and 1, and can represent values as sums of powers of two. Decimal uses ten digits from 0-9. Hexadecimal uses sixteen values from 0-9 and A-F. Negative binary integers can be represented using ones' complement or twos' complement methods. Twos' complement avoids multiple representations of zero and is commonly used in computers. Converting between number bases involves expressing the value in one base using the digits of another.
This document discusses how computers represent and manipulate data at the lowest levels. It covers:
1) How integers are represented in binary and how signed and unsigned integers work with 2's complement representation.
2) Data types like integers, real numbers, characters that are mapped to binary representations. Issues like byte ordering and memory addressing are also covered.
3) How instructions are encoded in binary machine code and the different instruction formats used in MIPS, including the R-format and I-format. Logical operations like shifting, AND, and OR that manipulate bits are also introduced.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
29. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers this is similar to
scientific notation
except that we use
Represent 25.010 in IEEE standard powers of 2
1. 25.010 = 11001.02
2. normalize 11001.02 1.1001 x 2 4
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
30. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
we moved the
point 4 positions to
the left, so our
Represent 25.010 in IEEE standard
exponent is 4
1. 25.010 = 11001.02
2. normalize 11001.02 1.1001 x 2 4
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
33. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 25.010
is a positive number
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
34. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit
4. store 4 in the exponent section
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
35. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0
Represent 25.010 in store standard
we want to IEEE
1. 25.010 = 11001.02negative and
both 1.1001 x 24
positive exponents
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit
4. store 4 in the exponent section
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
36. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0
Represent 25.0store itIEEE standard
we’ll 10 in so that
it corresponds to the
1. 25.010 = 11001.02 1.1001 x 24
following table
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit00000000 -127
4. store 4 in the exponent section
00000001 -126
00000010 -125
… …
01111111 0 Steps to represent a number in IEEE standard form
10000000 1 1. calculate the binary form of the number
10000001 2 2. calculate the normalized binary form
10000010 3 3. set the sign bit
10000011 4 4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
… …
11111111 128
MATH1003
37. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0
Represent 25.0store itIEEE standard
we’ll 10 in so that
it corresponds to the
1. 25.010 = 11001.02 1.1001 x 24
following table
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit00000000 -127
4. store 4 in the exponent section
00000001 -126
00000010 -125
… …
01111111 0 Steps to represent a number in IEEE standard form
10000000 1 1. calculate the binary form of the number
10000001 2 2. calculate the normalized binary form
10000010 3 3. set the sign bit
10000011 4 4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
… …
11111111 128
MATH1003
38. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0
Represent 25.010 in IEEE standard
to correspond to
1. 25.010 = 11001.02 we’ll add 127
the table, 1.1001 x 24
2. normalize 11001.02exponent x 24
to the = 1.1001
3. set the sign bit00000000 -127
4. store 4 in the exponent section
00000001 -126
00000010 -125
… …
01111111 0 Steps to represent a number in IEEE standard form
10000000 1 1. calculate the binary form of the number
10000001 2 2. calculate the normalized binary form
10000010 3 3. set the sign bit
10000011 4 4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
… …
11111111 128
MATH1003
39. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0
Represent 25.010 in IEEE standard
to correspond to
1. 25.010 = 11001.02 we’ll add 127
the table, 1.1001 x 24
2. normalize 11001.02exponent x 24
to the = 1.1001
3. set the sign bit00000000 -127 4 + 127 = 131
4. store 4 in the exponent section
00000001 -126
00000010 -125 131 = 100000112
… …
01111111 0 Steps to represent a number in IEEE standard form
10000000 1 1. calculate the binary form of the number
10000001 2 2. calculate the normalized binary form
10000010 3 3. set the sign bit
10000011 4 4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
… …
11111111 128
MATH1003
40. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 1 0 0 0 0 0 1 1
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 4 + 127 = 131
4. store 4 in the exponent section
131 = 100000112
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
41. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 1 0 0 0 0 0 1 1
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit
4. store 4 in the exponent section
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
42. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 1 0 0 0 0 0 1 1
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 1.1001
4. store 4 in the exponent section
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
43. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
the normalized form
0 1 0 0 0 0 0 1 1 will always have a 1 to the left
of the point, so let’s ignore the 1
Represent 25.010 when we use this number in a
(but
in IEEE standard
1. 25.010 = 11001.02 calculation, we’ll put it “back”) 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 1.1001
4. store 4 in the exponent section
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
44. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
the normalized form
0 1 0 0 0 0 0 1 1 will always have a 1 to the left
of the point, so let’s ignore the 1
Represent 25.010 when we use this number in a
(but
in IEEE standard
1. 25.010 = 11001.02 calculation, we’ll put it “back”) 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 1.1001
4. store 4 in the exponent section
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
45. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 1 0 0 0 0 0 1 1 1 0 0 1
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 1.1001
4. store 4 in the exponent section
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
46. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
we’ll
fill in the rest
with 0s
0 1 0 0 0 0 0 1 1 1 0 0 1
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 1.1001
4. store 4 in the exponent section
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
47. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
we’ll
fill in the rest
with 0s
0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Represent 25.010 in IEEE standard
1. 25.010 = 11001.02 1.1001 x 24
2. normalize 11001.02 = 1.1001 x 24
3. set the sign bit 1.1001
4. store 4 in the exponent section
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
57. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
Represent -34.210 in IEEE standard
1. 34.210 = 100010.00112 3410 = 1000102
.2 x 2 = 0.4
.4 x 2 = 0.8
.8 x 2 = 1.6
.6 x 2 = 1.2
.2 x 2 = 0.4
Steps to represent a number in IEEE standard form
34.210 = 100010.00112
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
64. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
1 1
Represent -34.210 in IEEE standard
1. 34.210 = 100010.00112 1.000100011 x 25
2. normalized as 1.000100011 x 25
3. set the sign bit
4. store 5 in the exponent section as (5 + 127 = 132) 100001002
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
65. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
1 1 0 0 0 0 1 0 0 0
Represent -34.210 in IEEE standard
1. 34.210 = 100010.00112 1.000100011 x 25
2. normalized as 1.000100011 x 25
3. set the sign bit
4. store 5 in the exponent section as (5 + 127 = 132) 100001002
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
66. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
1 1 0 0 0 0 1 0 0 0
Represent -34.210 in IEEE standard
1. 34.210 = 100010.00112 1.000100011 x 25
2. normalized as 1.000100011 x 25
3. set the sign bit
4. store 5 in the exponent section as (5 + 127 = 132) 100001002
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
67. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0
Represent -34.210 in IEEE standard
1. 34.210 = 100010.00112 1.000100011 x 25
2. normalized as 1.000100011 x 25
3. set the sign bit
4. store 5 in the exponent section as (5 + 127 = 132) 100001002
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
68. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
we’ll fill in the
Real Numbers rest with the repeating
pattern
1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0
Represent -34.210 in IEEE standard
1. 34.210 = 100010.00112 1.000100011 x 25
2. normalized as 1.000100011 x 25
3. set the sign bit
4. store 5 in the exponent section as (5 + 127 = 132) 100001002
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
69. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
we’ll fill in the
Real Numbers rest with the repeating
pattern
1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0
Represent -34.210 in IEEE standard
1. 34.210 = 100010.00112 1.000100011 x 25
2. normalized as 1.000100011 x 25
3. set the sign bit
4. store 5 in the exponent section as (5 + 127 = 132) 100001002
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
79. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
Represent 0.02510 in IEEE standard
1. 0.02510 = 0.00000112 .025 x 2 = 0.05
.05 x 2 = 0.1
.1 x 2 = 0.2
.2 x 2 = 0.4
.4 x 2 = 0.8
.8 x 2 = 1.6
.6 x 2 = 1.2
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
80. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
Represent 0.02510 in IEEE standard
1. 0.02510 = 0.00000112 .025 x 2 = 0.05
.05 x 2 = 0.1
.1 x 2 = 0.2
.2 x 2 = 0.4
.4 x 2 = 0.8
.8 x 2 = 1.6
.6 x 2 = 1.2
.2 x 2 = 0.4
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
81. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
Represent 0.02510 in IEEE standard
1. 0.02510 = 0.00000112 .025 x 2 = 0.05
.05 x 2 = 0.1
.1 x 2 = 0.2
.2 x 2 = 0.4
.4 x 2 = 0.8
.8 x 2 = 1.6
.6 x 2 = 1.2
.2 x 2 = 0.4
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
0.02510 = 0.00000112
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
89. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 0
Represent 0.02510 in IEEE standard
1. 0.02510 = 0.00000112 1.1001 x 2-6
2. normalized as 1.1001 x 2-6
3. set the sign bit
4. store -6 in the exponent section as (-6 + 127 = 121) 011110012
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
90. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 0 1 1 1 1 0 0 1 1
Represent 0.02510 in IEEE standard
1. 0.02510 = 0.00000112 1.1001 x 2-6
2. normalized as 1.1001 x 2-6
3. set the sign bit
4. store -6 in the exponent section as (-6 + 127 = 121) 011110012
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
91. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 0 1 1 1 1 0 0 1 1
Represent 0.02510 in IEEE standard
1. 0.02510 = 0.00000112 1.1001 x 2-6
2. normalized as 1.1001 x 2-6
3. set the sign bit
4. store -6 in the exponent section as (-6 + 127 = 121) 011110012
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003
92. 10110100101011010100101010111010101111011011101111011101110111101110111011110111111010110100101011110110110101111011010100111111011010100110101001
Real Numbers
0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0
Represent 0.02510 in IEEE standard
1. 0.02510 = 0.00000112 1.1001 x 2-6
2. normalized as 1.1001 x 2-6
3. set the sign bit
4. store -6 in the exponent section as (-6 + 127 = 121) 011110012
5. store the normalized binary form
Steps to represent a number in IEEE standard form
1. calculate the binary form of the number
2. calculate the normalized binary form
3. set the sign bit
4. store the exponent (+127, store as an 8-bit binary)
5. store the normalized binary form without the first 1
MATH1003