This document discusses binary arithmetic and how it works using boolean logic. It provides examples of adding binary numbers and what happens in overflow conditions when the results exceed the bit limit. The key concepts covered are:
1) Binary addition employs the same process as decimal addition, carrying values to the next column when the sum exceeds the base.
2) Overflow occurs when the result of an addition does not fit in the allocated bit space, with part of the value lost. Programs can detect overflow as an error.
3) Binary operations can be represented using boolean logic functions like AND, OR and NOT. Single bit adders use these functions with inputs A, B, and a carry in to calculate outputs for the
Unit-1 Digital Design and Binary Numbers:Asif Iqbal
these slides contains general discerption about digital signals, binary numbers, digital numbers, and basic logic gates. it covers the first unit of AKTU syllabus.
Binary addition, Binary subtraction, Negative number representation, Subtraction using 1’s complement and 2’s complement, Binary multiplication and division, Arithmetic in octal, hexadecimal number system, BCD and Excess – 3 arithmetic
Unit-1 Digital Design and Binary Numbers:Asif Iqbal
these slides contains general discerption about digital signals, binary numbers, digital numbers, and basic logic gates. it covers the first unit of AKTU syllabus.
Binary addition, Binary subtraction, Negative number representation, Subtraction using 1’s complement and 2’s complement, Binary multiplication and division, Arithmetic in octal, hexadecimal number system, BCD and Excess – 3 arithmetic
The 8th Digital Learning session - this time on the Binary number system.
There are walkthroughs on how to carry out the following arithmetic actions in binary:
Conversion
Addition
Subtraction
Multiplication
Aimed at the BTEC Unit 26 Maths for I.T module but great for all related purposes.
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
UNIT-II ARITHMETIC FOR COMPUTERS
Addition and Subtraction – Multiplication – Division – Floating Point Representation – Floating Point Addition and Subtraction.
The 8th Digital Learning session - this time on the Binary number system.
There are walkthroughs on how to carry out the following arithmetic actions in binary:
Conversion
Addition
Subtraction
Multiplication
Aimed at the BTEC Unit 26 Maths for I.T module but great for all related purposes.
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
UNIT-II ARITHMETIC FOR COMPUTERS
Addition and Subtraction – Multiplication – Division – Floating Point Representation – Floating Point Addition and Subtraction.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
5941981.ppt
1. Binary Arithmetic
• Adding Binary numbers
• Overflow conditions
• How does it all work?
• AND, OR and NOT
2. Decimal Addition
• You are probably already familiar with adding
decimal numbers
7
+2
9
5
+1
6
2
+3
5
• You also know that when the addition of two
numbers exceeds the base, a value is “carried” over
to the next column
7
+ 5
1 2
1 Carry the “1”
3. Binary Addition
• Adding binary numbers employs the same
procedures as adding decimal numbers:
00
+00
00
01
+00
01
00
+01
01
• As with decimal addition, when the addition of two
numbers exceeds the base value, the value is
“carried” over to the next column
01
+01
10
1 Carry the “1”
5. More Binary Addition Examples
01111111
01000001
11000000
10001100
00111011
11000111
01111111
01001001
11001000
11111111
00000001
????????
• What happens when we get the following case?
(note: assume that we are limited to 8 bits)
6. Overflow
11111111
00000001
00000000
• In the following case, we have a condition called
“overflow”. The result may be somewhat unexpected
1 Carry the “1”
• When adding two numbers, it is possible that the
result will not fit within the space we have provided.
The addition completes, but part of the value is lost.
• Luckily, overflow is often considered to be an error
condition, so the program “knows” that this has
happened and can take appropriate action.
7. How does it all work?
• In the first week of lectures, we discussed vacuum
tubes, relays, and transistors
• As we went through the discussion, I asked, “How
could you add two numbers using switches?”
• I didn’t expect an answer, but I did show how switches
could be set up to implement the AND function and the
OR function.
8. AND, OR and NOT
• You may recall the “truth” tables for the AND and OR
functions:
0 1
0 0 0
1 0 1
0 1
0 0 1
1 1 1
A
B
A
B
AND OR
• The AND, OR and NOT functions are the basic
functions for “Boolean” Algebra
0 1
1 0
A
NOT (~)
9. Binary Arithmetic/AND and OR
• All binary arithmetic can be represented using
boolean algebra
• Each 1-bit adder has 3 inputs: A, B and CarryIn
• Each 1-bit adder has 2 output: C and CarryOut
CIN 0 0 0 0 1 1 1 1
A 0 0 1 1 0 0 1 1
B 0 1 0 1 0 1 0 1
C 0 1 1 0 1 0 0 1
COUT 0 0 0 1 0 1 1 1
10. Boolean Equations for Addition
CIN 0 0 0 0 1 1 1 1
A 0 0 1 1 0 0 1 1
B 0 1 0 1 0 1 0 1
C 0 1 1 0 1 0 0 1
COUT 0 0 0 1 0 1 1 1
C = (~A and B and ~CIN) or (A and ~B and ~CIN) or
(~A and ~B and CIN) or (A and B and CIN)
COUT= (A and B) or (A and CIN) or (B and CIN)
