This document provides an overview of computer number systems. It discusses how bits and binary numbers work, with 0s and 1s representing values through place values like in decimal. Common number bases like binary, octal, hexadecimal, and how they map to decimal are explained. Larger data quantities like bytes, kilobytes and megabytes are defined. Methods for representing negative numbers like sign-magnitude, one's complement, and two's complement are covered. Binary addition and subtraction are demonstrated, along with how flags work. Fractions are discussed but deemed more complex than the scope of this overview.
A digital system can understand positional number system only where there are only a few symbols called digits and these symbols represent different values depending on the position they occupy in the number.
A digital system can understand positional number system only where there are only a few symbols called digits and these symbols represent different values depending on the position they occupy in the number.
Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases.
Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language.
In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete.
In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples.
The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint
programming, probabilistic programming
Research on the inductive synthesis of recursive functional programs started in the early 1970s and was brought onto firm theoretical foundations with the seminal THESIS system of Summers[6] and work of Biermann.[7] These approaches were split into two phases: first, input-output examples are transformed into non-recursive programs (traces) using a small set of basic operators; second, regularities in the traces are searched for and used to fold them into a recursive program. The main results until the mid 1980s are surveyed by Smith.[8] Due to
Number System, Conversion, Decimal to Binary, Decimal to Octal, Decimal to Binary, Decimal to HexaDecimal, Binary to Decimal, Octal to Decimal, Hexadecimal to Decimal, Binary to Octal, Binary to Hexadecimal, Octal to Hexadecimal, BCD, Binary Addition
Reviewing number systems involves understanding various ways in which numbers can be represented and manipulated. Here's a brief overview of different number systems:
Decimal System (Base-10):
This is the most common number system used by humans.
It uses 10 digits (0-9) to represent numbers.
Each digit's position represents a power of 10.
For example, the number 245 in decimal represents (2 * 10^2) + (4 * 10^1) + (5 * 10^0).
Binary System (Base-2):
Used internally by almost all modern computers.
It uses only two digits: 0 and 1.
Each digit's position represents a power of 2.
For example, the binary number 1011 represents (1 * 2^3) + (0 * 2^2) + (1 * 2^1) + (1 * 2^0) in decimal, which equals 11.
Octal System (Base-8):
Less commonly used, but still relevant in some computer programming contexts.
It uses eight digits: 0 to 7.
Each digit's position represents a power of 8.
For example, the octal number 34 represents (3 * 8^1) + (4 * 8^0) in decimal, which equals 28.
Hexadecimal System (Base-16):
Widely used in computer science and programming.
It uses sixteen digits: 0 to 9 followed by A to F (representing 10 to 15).
Each digit's position represents a power of 16.
Often used to represent memory addresses and binary data more compactly.
For example, the hexadecimal number 2F represents (2 * 16^1) + (15 * 16^0) in decimal, which equals 47.
Each number system has its own advantages and applications. Decimal is intuitive for human comprehension, binary is fundamental in computing due to its simplicity for electronic systems, octal and hexadecimal are often used for human-readable representations of binary data in programming, particularly when dealing with memory addresses and byte-oriented data.
Understanding these number systems is essential for various fields such as computer science, electrical engineering, and mathematics, as they provide different perspectives on how numbers can be represented and manipulated.
Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases.
Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language.
In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete.
In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples.
The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint
programming, probabilistic programming
Research on the inductive synthesis of recursive functional programs started in the early 1970s and was brought onto firm theoretical foundations with the seminal THESIS system of Summers[6] and work of Biermann.[7] These approaches were split into two phases: first, input-output examples are transformed into non-recursive programs (traces) using a small set of basic operators; second, regularities in the traces are searched for and used to fold them into a recursive program. The main results until the mid 1980s are surveyed by Smith.[8] Due to
Number System, Conversion, Decimal to Binary, Decimal to Octal, Decimal to Binary, Decimal to HexaDecimal, Binary to Decimal, Octal to Decimal, Hexadecimal to Decimal, Binary to Octal, Binary to Hexadecimal, Octal to Hexadecimal, BCD, Binary Addition
Reviewing number systems involves understanding various ways in which numbers can be represented and manipulated. Here's a brief overview of different number systems:
Decimal System (Base-10):
This is the most common number system used by humans.
It uses 10 digits (0-9) to represent numbers.
Each digit's position represents a power of 10.
For example, the number 245 in decimal represents (2 * 10^2) + (4 * 10^1) + (5 * 10^0).
Binary System (Base-2):
Used internally by almost all modern computers.
It uses only two digits: 0 and 1.
Each digit's position represents a power of 2.
For example, the binary number 1011 represents (1 * 2^3) + (0 * 2^2) + (1 * 2^1) + (1 * 2^0) in decimal, which equals 11.
Octal System (Base-8):
Less commonly used, but still relevant in some computer programming contexts.
It uses eight digits: 0 to 7.
Each digit's position represents a power of 8.
For example, the octal number 34 represents (3 * 8^1) + (4 * 8^0) in decimal, which equals 28.
Hexadecimal System (Base-16):
Widely used in computer science and programming.
It uses sixteen digits: 0 to 9 followed by A to F (representing 10 to 15).
Each digit's position represents a power of 16.
Often used to represent memory addresses and binary data more compactly.
For example, the hexadecimal number 2F represents (2 * 16^1) + (15 * 16^0) in decimal, which equals 47.
Each number system has its own advantages and applications. Decimal is intuitive for human comprehension, binary is fundamental in computing due to its simplicity for electronic systems, octal and hexadecimal are often used for human-readable representations of binary data in programming, particularly when dealing with memory addresses and byte-oriented data.
Understanding these number systems is essential for various fields such as computer science, electrical engineering, and mathematics, as they provide different perspectives on how numbers can be represented and manipulated.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
4. The Significance of the Bit
A bit (Binary digIT) is merely 0 or 1
It is a unit of information since you cannot communicate
with anything less than two states
The use of binary encoding dates back to the 1600s with
Jacquard's loom, which created textiles using card
templates with holes that allowed needles through
5. Bits and Computers
The nice thing about a bit is that, with only two states, it is
easy to embody in physical machinery
Each bit is simply a switch and computers moved from
vacuum tubes to transistors for this
e-
6. Decimal Number System
Most computers count in binary, which we can easily
understand from the decimal so ingrained in us
35462
7. Decimal Number System
Most computers count in binary, which we can easily
understand from the decimal so ingrained in us
35462
2x100
8. Decimal Number System
Most computers count in binary, which we can easily
understand from the decimal so ingrained in us
35462
2x100
6x101
+
9. Decimal Number System
Most computers count in binary, which we can easily
understand from the decimal so ingrained in us
35462
2x100
6x101
+
4x102
+
10. Decimal Number System
Most computers count in binary, which we can easily
understand from the decimal so ingrained in us
35462
2x100
6x101
+
4x102
+
5x103
+
11. Decimal Number System
Most computers count in binary, which we can easily
understand from the decimal so ingrained in us
35462
2x100
6x101
+
4x102
+
5x103
+
3x104
+
12. Binary
Binary is exactly the same, only instead of ten
digits/states (0 to 9) we have just two, so the base
becomes 2:
10110
0x20
1x21
+
1x22
+
0x23
+
1x24
+
13. Binary
Binary is exactly the same, only instead of ten
digits/states (0 to 9) we have just two, so the base
becomes 2:
10110b
= 22d
0x20
1x21
+
1x22
+
0x23
+
1x24
+
14. Binary
Binary is exactly the same, only instead of ten
digits/states (0 to 9) we have just two, so the base
becomes 2:
10110b
= 22d
0x20
1x21
+
1x22
+
0x23
+
1x24
+
Most Significant
Bit (MSB)
Least Significant
Bit (LSB)
15. Works for Fractional Numbers too...
35.462
2x10-3
6x10-2
+
4x10-1
+
5x100
+
3x101
+
19. Representable Numbers
With d decimal digits, we can represent 10d
different
values, usually the numbers 0 to (10d
-1) inclusive
In binary with n bits this becomes 2n
values, usually
the range 0 to (2n
-1)
Computers usually assign a set number of bits
(physical switches) to an instance of a type.
An integer is often 32 bits, so can represent
positive integers from 0 to 4,294,967,295 incl.
Or a range of negative and positive integers...
20. Other Common Bases
Higher bases make for shorter numbers that are easier for
humans to manipulate. e.g.
6654733d
=11001011000101100001101b
We traditionally choose powers-of-2 bases because this
corresponds to whole chunks of binary
21. Other Common Bases
Higher bases make for shorter numbers that are easier for
humans to manipulate. e.g.
6654733d
=11001011000101100001101b
We traditionally choose powers-of-2 bases because this
corresponds to whole chunks of binary
Octal is base-8 (8=23
digits, which means 3 bits per digit)
6654733d
=011-001-011-000-101-100-001-101b
= 31305415o
22. Other Common Bases
Higher bases make for shorter numbers that are easier for
humans to manipulate. e.g.
6654733d
=11001011000101100001101b
We traditionally choose powers-of-2 bases because this
corresponds to whole chunks of binary
Octal is base-8 (8=23
digits, which means 3 bits per digit)
6654733d
=011-001-011-000-101-100-001-101b
= 31305415o
Hexadecimal is base-16 (16=24
digits so 4 bits per digit)
Our ten decimal digits aren't enough, so we add 6 new
ones: 0,1,2,3,4,5,6,7,8,9,A,B,C,D,E,F
6654733d
=0110-0101-1000-1011-0000-1101b
=658B0Dh
23. Other Common Bases
Higher bases make for shorter numbers that are easier for
humans to manipulate. e.g.
6654733d
=11001011000101100001101b
We traditionally choose powers-of-2 bases because this
corresponds to whole chunks of binary
Octal is base-8 (8=23
digits, which means 3 bits per digit)
6654733d
=011-001-011-000-101-100-001-101b
= 31305415o
Hexadecimal is base-16 (16=24
digits so 4 bits per digit)
Our ten decimal digits aren't enough, so we add 6 new
ones: 0,1,2,3,4,5,6,7,8,9,A,B,C,D,E,F
6654733d
=0110-0101-1000-1011-0000-1101b
=658B0Dh
Because we constantly slip between binary and hex, we have
a special marker for it
Prefix with '0x' (zero-x). So 0x658B0D=6654733d
, 0x123=291d
24. Bytes
A byte was traditionally the number of bits needed to
store a character of text
A de-facto standard of 8 bits has now emerged
256 values
0 to 255 incl.
Two hex digits to describe
0x00=0, 0xFF=255
Check: what does 0xBD represent?
25. Bytes
A byte was traditionally the number of bits needed to
store a character of text
A de-facto standard of 8 bits has now emerged
256 values
0 to 255 incl.
Two hex digits to describe
0x00=0, 0xFF=255
Check: what does 0xBD represent?
B → 11 or 1011
D → 13 or 1101
Result is 11x161
+13x160
= 189 or 10111101
26. Larger Units
Strictly the SI units since 1998 are:
Kibibyte (KiB)
1024 bytes (closest power of 2 to 1000)
Mebibyte (MiB)
1,048,576 bytes
Gibibyte (GiB)
1,073,741,824 bytes
27. Larger Units
Strictly the SI units since 1998 are:
Kibibyte (KiB)
1024 bytes (closest power of 2 to 1000)
Mebibyte (MiB)
1,048,576 bytes
Gibibyte (GiB)
1,073,741,824 bytes
but these haven't really caught on so we tend to still use the SI
Kilobyte, Megabyte, Gigabyte. This leads to lots of confusion
since technically these are multiples of 1,000.
29. Unsigned Integer Addition
Addition of unsigned integers works the same way as
addition of decimal (only simpler!)
0 + 0 = 0
0 + 1 = 1
1 + 0 = 1
1 + 1 = 0, carry 1
Only issue is that computers have fixed sized types so we
can't go on adding forever...
111
+ 001
001
+ 001
Carry flag: Carry flag:
30. Modulo or Clock Arithmetic
Overflow takes us across the dotted
boundary
So 7+1=0 (overflow)
We say this is (7+1) mod 8
000
001
010
011
100
101
110
111
000
001
010
011
100
101
110
111
31. Negative Numbers
All of this skipped over the need to represent
negatives.
The naïve choice is to use the MSB to indicate +/-
1 in the MSB → negative
0 in the MSB → positive
This is the sign-magnitude technique
-7 = 1111
Negative
Normal positive
representation of 7
32. Difficulties with Sign-Magnitude
Has a representation of minus zero (10002
=-0) so
wastes one of our 2n
labels
Addition/subtraction circuitry must be designed
from scratch
1101
+ 0001
1110
Our unsigned addition alg.
33. Difficulties with Sign-Magnitude
Has a representation of minus zero (10002
=-0) so
wastes one of our 2n
labels
Addition/subtraction circuitry must be designed
from scratch
1101
+ 0001
1110
Our unsigned addition alg.
+13
+1
+14
Unsigned
interpretation
34. Difficulties with Sign-Magnitude
Has a representation of minus zero (10002
=-0) so
wastes one of our 2n
labels
Addition/subtraction circuitry must be designed
from scratch
1101
+ 0001
1110
-5
+1
-6
+13
+1
+14
Sign-mag
interpretation
Unsigned
interpretation
Our unsigned addition alg.
35. Alternatively...
Gives us two discontinuities and a
reversal of direction using normal
addition circuitry!!
000
001
010
011
100
101
110
111 000
001
010
011
100
101
110
111
36. Ones' Complement
The negative is the positive with all the bits flipped
7 → 0111 so -7 → 1000
Still the MSB is the sign
One discontinuity but still -0 :-(
000
001
010
011
100
101
110
111 000
001
010
011
100
101
110
111
37. Two's Complement
The negative is the positive with all the bits flipped
and 1 added (the same procedure for the inverse)
7 → 0111 so -7 → 1000+0001 → 1001
Still the MSB is the sign
One discontinuity and proper ordering
000
001
010
011
100
101
110
111
000
001
010
011
100
101
110
111
38. Two's Complement
Positive to negative: Invert all the bits and add 1
Negative to positive: Same procedure!!
1011 (-5) → 0100 → 0101 (+5)
0101 (+5) → 1010 → 1011 (-5)
42. So we can use the same circuitry for unsigned and 2s-
complement addition :-)
Well, almost.
Signed Addition
0100
+0100
1000
+4
+4
+8
Unsigned
Carry flag: 0
43. So we can use the same circuitry for unsigned and 2s-
complement addition :-)
Well, almost.
The problem is our MSB is now signifying the sign and our carry
should really be testing the bit to its right :-(
Signed Addition
0100
+0100
1000
+4
+4
-8
+4
+4
+8
2's-comp Unsigned
Carry flag: 0
44. So we can use the same circuitry for unsigned and 2s-
complement addition :-)
Well, almost.
The problem is our MSB is now signifying the sign and our carry
should really be testing the bit to its right :-(
So we introduce an overflow flag that indicates this problem
Signed Addition
0100
+0100
1000
+4
+4
-8
+4
+4
+8
2's-comp Unsigned
Carry flag: 0
Overflow: 1
45. Integer subtraction
Could implement the “borrowing”
algorithm you probably learnt in school
But why bother? We can just add the
2's complement instead.
0100
- 0011
0100
+1101
0001
→
46. Flags Summary
When adding/subtracting
Carry flag → overflow for unsigned integer
Overflow flag → overflow for signed integer
The CPU does not care whether it's
handling signed or unsigned integers
Down to our compilers/programs to
interpret the result
47. Fractional Numbers
Scientific apps rarely survive on integers alone, but
representing fractional parts efficiently is
complicated.
Option one: fixed point
Set the point at a known location. Anything to
the left represents the integer part; anything to
the right the fractional part
But where do we set it??
Option two: floating point
Let the point 'float' to give more capacity on its
left or right as needed
Much more efficient, but harder to work with
Very important: more in Numerical Methods
course