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A Moore machine is a finite state machine where the next state is determined by the current state and input, and the output depends only on the present state. A Moore machine can be described with 6 tuples: the set of states Q, initial state Q0, input symbols Σ, output alphabet O, transition function δ from Q x Σ to Q, and output function λ from Q to O. An example Moore machine is shown with its state diagram, transition table, and how it produces an output of 1110 for the input 010 by transitioning through states q0, q1, and q2.
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Finite automaton discussed so far, is just associated with the RE or the language.
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Equivalent machines Two machines are said to be equivalent if they print the same output string when the same input string is run on them.
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2. It provides examples of alphabets, strings, and languages in automata. It also explains the key components of finite automata, DFAs, Mealy machines, Moore machines, and PDAs.
3. The document solves examples of DFAs that do or do not accept certain strings and compares the differences between Mealy machines and Moore machines.
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There is a question whether does there exist an FA which generates an output string corresponding to each input string ? The answer is yes. Such machines are called machines with output.
There are two types of machines with output.
Moore machine
Mealy machine
Equivalent machines Two machines are said to be equivalent if they print the same output string when the same input string is run on them.
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2. It provides examples of alphabets, strings, and languages in automata. It also explains the key components of finite automata, DFAs, Mealy machines, Moore machines, and PDAs.
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State machines are models that can represent systems whose outputs depend on their input history rather than just the most recent input. They have a set of possible internal states, inputs, outputs, and functions that determine the next state and current output based on the current state and input. There are different types of state machines, including finite state machines with a finite number of states, Mealy machines where the output depends on the current state and input, and Moore machines where the output depends only on the current state. State machines can be used to model systems in many domains like user interfaces, DNA sequences, and spacecraft operations.
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A state machine is any device storing the status of something at a given time. The status changes based on inputs, providing the resulting output for the implemented changes. A finite state machine has finite internal memory.
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1. A Moore machine is a finite state machine where the output depends only on the current state. It was defined to have states, input and output alphabets, transition and output tables.
2. A Mealy machine is also a finite state machine but its output depends on both the current state and the current input. It was defined to have states, input and output alphabets, and a pictorial representation showing state transitions and outputs.
3. Examples were given of both Moore and Mealy machines to illustrate their workings and how to determine the output string for a given input string. This included machines for complementing binary numbers and incrementing binary numbers.
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5. A program accepts strings with equal numbers of 0s and 1s.
6. A program counts the numbers of 0s and 1s in a given string.
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The document summarizes Moore and Mealy machines. [1] Moore machines have output associated with states, while Mealy machines have output associated with state transitions. [2] A Moore machine can be converted to an equivalent Mealy machine by defining the Mealy machine's output function based on the Moore machine's state output function. [3] Conversely, a Mealy machine can be converted to an equivalent Moore machine by splitting states with multiple transition outputs into multiple states, each with a single associated output.
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1. A Moore machine is a finite state machine where the output depends only on the current state. It was defined to have states, input and output alphabets, transition and output tables.
2. A Mealy machine is also a finite state machine but its output depends on both the current state and the current input. It was defined to have states, input and output alphabets, and a pictorial representation showing state transitions and outputs.
3. Examples were given of both Moore and Mealy machines to illustrate their workings and how to determine the output string for a given input string. This included machines for complementing binary numbers and incrementing binary numbers.
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The document contains details of 7 experiments on designing programs for finite state machines:
1. A program is designed to accept strings ending with 101.
2. A program accepts strings with three consecutive 1s.
3. A program for a machine that accepts strings with a remainder of 0 when divided by 3.
4. A program accepts decimal numbers divisible by 2.
5. A program accepts strings with equal numbers of 0s and 1s.
6. A program counts the numbers of 0s and 1s in a given string.
7. A program finds the 2's complement of a given binary number.
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- Representing state using state equations, state tables, and state diagrams
- Analyzing sequential circuits using D, JK, and T flip-flops
- The difference between Mealy and Moore machines and examples of each
- Design procedures for state machines including state diagrams, next state tables, excitation tables, K-maps, and circuit implementation
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the report contain
Introduction
The historical of finite state automaton
Types of FSA
The advantages and disadvantages of FSA
examples for FSA
عمار عبد الكريم صاحب مبارك
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The document discusses Moore automata, which is a type of finite state machine where the next state is determined by the current state alone, independently of the input. In a Moore automata, the output at a given time depends only on the current state of the machine. It consists of 6 tuples: a finite set of states Q, input alphabet set Σ, output alphabet set Δ, transition function δ mapping state-input pairs to the next state, and output function λ mapping each state to an output. The output in response to an input string is determined by the sequence of states entered and the corresponding outputs according to λ. A Moore machine always produces an output for the empty input string based on the initial state.
NFA and DFA
This document provides information about non-deterministic finite automata (NFA) and deterministic finite automata (DFA). It defines NFA and DFA, describes their formal definitions and representations using graphs. Key differences between NFA and DFA are discussed. An example of an NFA is provided along with its transition diagram and table. The document concludes with thanks.
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This document discusses Moore machines and Mealy machines, which are two models of finite state machines that produce outputs. Moore machines output a character based on the current state, while Mealy machines output a character based on the current state and input. The document provides examples of constructing Moore and Mealy machines to perform tasks like outputting a 1 for certain input substrings or performing binary operations on an input string. It notes that Moore and Mealy machines are equivalent models, though one may require more or fewer states than the other for a given problem.
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This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Carrer goals.pptx and their importance in real life
Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.
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This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Artificial Intelligence, Data and Competition – OECD – June 2024 OECD discussion
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This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Artificial Intelligence, Data and Competition – LIM – June 2024 OECD discussion
Artificial Intelligence, Data and Competition – LIM – June 2024 OECD discussionOECD Directorate for Financial and Enterprise Affairs
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdf
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
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This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
Collapsing Narratives: Exploring Non-Linearity • a micro report by Rosie Wells
Insight: In a landscape where traditional narrative structures are giving way to fragmented and non-linear forms of storytelling, there lies immense potential for creativity and exploration.
'Collapsing Narratives: Exploring Non-Linearity' is a micro report from Rosie Wells.
Rosie Wells is an Arts & Cultural Strategist uniquely positioned at the intersection of grassroots and mainstream storytelling.
Their work is focused on developing meaningful and lasting connections that can drive social change.
Please download this presentation to enjoy the hyperlinks!
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XP 2024 presentation: A New Look to Leadership
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Mastering the Concepts Tested in the Databricks Certified Data Engineer Assoc...
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- 2. MOORE MACHINE MOORE MACHINE IS A FINITE STATE MACHINE IN WHICH THE NEXT STATE IS DECIDED BY THE CURRENT STATE AND CURRENT INPUT SYMBOL. THE OUTPUT SYMBOL AT A GIVEN TIME DEPENDS ONLY ON THE PRESENT STATE OF THE MACHINE.
- 3. TYPES OF TUPLES MOORE MACHINE CAN BE DESCRIBED BY 6 TUPLES (Q, Q0, ∑, O, Δ, Λ) WHERE, Q: finite set of states q0: initial state of machine ∑: finite set of input symbols O: output alphabet δ: transition function where Q × ∑ → Q λ: output function where Q → O
- 4. EXAMPLE THE STATE DIAGRAM FOR MOORE MACHINE IS: TRANSITION TABLE FOR MOORE MACHINE IS: In the above Moore machine, the output is represented with each input state separated by /. The output length for a Moore machine is greater than input by 1. Input: 010 Transition: δ (q0,0) => δ(q1,1) => δ(q1,0) => q2 Output: 1110(1 for q0, 1 for q1, again 1 for q1, 0 for q2)