Mealy and Moore machines are types of finite state machines. A Mealy machine's output depends on its present state and input, while a Moore machine's output depends only on its present state. Mealy machines can be converted to Moore machines by breaking states with multiple outputs into multiple states, and vice versa by combining states with the same output. Both machine types have advantages and uses, with Mealy machines being faster but more expensive, and Moore machines being simpler but slower.
Moore and Mealy machines are two types of finite state machines. A Mealy machine's output depends on the current state and input, and its output size equals its input size. A Moore machine's output depends only on the current state, and its output size is one larger than its input size. Mealy machines are defined as tuples including states, inputs, outputs, transitions, and an output function. Moore machines are similarly defined except the output function maps states to outputs rather than states and inputs. Examples of Moore and Mealy machine applications include elevators, compilers, SRAM, and vending machines.
This document discusses Mealy and Moore machines, which are two types of finite state machines (FSM). It provides details on each:
- Mealy machines produce outputs on transitions between states based on both the present state and input. Moore machines produce outputs based only on the present state.
- Both can be described using tuples but the output functions differ - Mealy machines have an output transition function while Moore machines have an output function.
- Advantages of each include Mealy machines having faster response times while Moore machines are cheaper and easier to use. Disadvantages include Mealy machines being more expensive and states becoming unmanageable.
- Examples of uses include processors for Mealy machines and SRAM
Synchronous state machines. Moore and Mealy state machines (FSM)Mumbi Chishimba
A synchronous state machine is a machine whose transition between states occurs on the triggering edge of a clock signal. There are two types of synchronous finite state machines: Moore and Mealy machines. A Moore machine's output is solely dependent on its current state, while a Mealy machine's output depends on both its current state and current inputs. Moore machines are generally faster and easier to compose than Mealy machines. Mealy machines can have a lower response time but require more clock cycles to change state and are more expensive to produce when the number of possible states grows large. Both Moore and Mealy state machines are important concepts in digital design that can be used to model algorithms or processes simply or complexly.
This document discusses finite state machines (FSMs), specifically Moore and Mealy machines. It defines FSMs as circuits with a combinational block and memory block that can exist in multiple states, transitioning between states based on inputs. Moore machines output depends solely on the current state, while Mealy machines output depends on both the current state and inputs. Moore machines are safer since output only changes at clock edges, while Mealy machines are faster since output relies on inputs. Choosing between them depends on factors like whether synchronous/asynchronous operation is needed and whether speed or safety is a higher priority.
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.
The document discusses Mealy and Moore machine models. Mealy machines have an output function that depends on the present state and input, while Moore machines have an output function that depends only on the present state. The document provides examples of converting between Mealy and Moore machine representations.
The document discusses finite automata including nondeterministic finite automata (NFAs) and deterministic finite automata (DFAs). It provides examples of NFAs and DFAs that recognize particular strings, including strings containing certain substrings. It also gives examples of DFA state machines and discusses using finite automata to recognize regular languages.
Finite automaton discussed so far, is just associated with the RE or the language.
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.
Moore and Mealy machines are two types of finite state machines. A Mealy machine's output depends on the current state and input, and its output size equals its input size. A Moore machine's output depends only on the current state, and its output size is one larger than its input size. Mealy machines are defined as tuples including states, inputs, outputs, transitions, and an output function. Moore machines are similarly defined except the output function maps states to outputs rather than states and inputs. Examples of Moore and Mealy machine applications include elevators, compilers, SRAM, and vending machines.
This document discusses Mealy and Moore machines, which are two types of finite state machines (FSM). It provides details on each:
- Mealy machines produce outputs on transitions between states based on both the present state and input. Moore machines produce outputs based only on the present state.
- Both can be described using tuples but the output functions differ - Mealy machines have an output transition function while Moore machines have an output function.
- Advantages of each include Mealy machines having faster response times while Moore machines are cheaper and easier to use. Disadvantages include Mealy machines being more expensive and states becoming unmanageable.
- Examples of uses include processors for Mealy machines and SRAM
Synchronous state machines. Moore and Mealy state machines (FSM)Mumbi Chishimba
A synchronous state machine is a machine whose transition between states occurs on the triggering edge of a clock signal. There are two types of synchronous finite state machines: Moore and Mealy machines. A Moore machine's output is solely dependent on its current state, while a Mealy machine's output depends on both its current state and current inputs. Moore machines are generally faster and easier to compose than Mealy machines. Mealy machines can have a lower response time but require more clock cycles to change state and are more expensive to produce when the number of possible states grows large. Both Moore and Mealy state machines are important concepts in digital design that can be used to model algorithms or processes simply or complexly.
This document discusses finite state machines (FSMs), specifically Moore and Mealy machines. It defines FSMs as circuits with a combinational block and memory block that can exist in multiple states, transitioning between states based on inputs. Moore machines output depends solely on the current state, while Mealy machines output depends on both the current state and inputs. Moore machines are safer since output only changes at clock edges, while Mealy machines are faster since output relies on inputs. Choosing between them depends on factors like whether synchronous/asynchronous operation is needed and whether speed or safety is a higher priority.
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.
The document discusses Mealy and Moore machine models. Mealy machines have an output function that depends on the present state and input, while Moore machines have an output function that depends only on the present state. The document provides examples of converting between Mealy and Moore machine representations.
The document discusses finite automata including nondeterministic finite automata (NFAs) and deterministic finite automata (DFAs). It provides examples of NFAs and DFAs that recognize particular strings, including strings containing certain substrings. It also gives examples of DFA state machines and discusses using finite automata to recognize regular languages.
Finite automaton discussed so far, is just associated with the RE or the language.
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.
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
This document discusses finite state machines (FSMs) and compares Moore and Mealy machines. Moore machines output depends only on the current state, while Mealy machines output depends on both the current state and input. It provides examples of Mealy machine applications like traffic lights and vending machines. Mealy machines have advantages like reducing the number of states needed and generating faster output that changes immediately with input changes.
Finite-State Machine
The document discusses finite-state machines (FSM), which model sequential logic circuits. It describes two types of FSMs: Mealy and Moore machines. Mealy machines output depends on the present state and input, changing asynchronously with the clock. Moore machines' output depends only on the present state, changing synchronously with state changes and clock. The document provides an example of designing an FSM to output 0 if an even number of 1's have been received on the input, and 1 for odd. It shows solutions as both a Mealy and Moore machine using state transition tables and logic diagrams.
This document provides an introduction to finite automata. It defines key concepts like alphabets, strings, languages, and finite state machines. It also describes the different types of automata, specifically deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). DFAs have a single transition between states for each input, while NFAs can have multiple transitions. NFAs are generally easier to construct than DFAs. The next class will focus on deterministic finite automata in more detail.
This document discusses finite state machines (FSMs), which are mathematical models used to model the behavior of systems that can be in one of a finite number of states. The document defines FSMs, describes their components and representations, compares deterministic and non-deterministic FSMs, and discusses their applications in areas like software engineering, hardware design, and modeling reactive systems. FSMs are a fundamental concept in automata theory and computational modeling.
This document provides an overview of Turing machines. It introduces Turing machines as a simple mathematical model of a computer proposed by Alan Turing in 1936. A Turing machine consists of a tape divided into cells, a read/write head, finite states, and a transition function. The transition function defines how the machine moves between states and reads/writes symbols on the tape based on its current state and tape symbol. Turing machines can accept languages and compute functions. Variations include multi-tape, non-deterministic, multi-head, offline, multi-dimensional, and stationary-head Turing machines. The properties of Turing machines include their ability to recognize any language generated by a phrase-structure grammar and the Church
Object oriented programming (OOP) addresses limitations of procedural programming by dividing programs into objects that encapsulate both data and behaviors. OOP supports features like inheritance, polymorphism, and abstraction. Inheritance allows new classes to inherit attributes and behaviors from parent classes, polymorphism allows the same message to be interpreted differently depending on the object receiving it, and abstraction focuses on essential characteristics without implementation details. These features help make programs more modular, reusable, and maintainable.
This document discusses converting non-deterministic finite automata (NFA) to deterministic finite automata (DFA). NFAs can have multiple transitions with the same symbol or no transition for a symbol, while DFAs have a single transition for each symbol. The document provides examples of NFAs and their representations, and explains how to systematically construct a DFA that accepts the same language as a given NFA by considering all possible state combinations in the NFA. It also notes that NFAs and DFAs have equal expressive power despite their differences, and discusses minimizing DFAs and relationships to other automata models.
The document discusses solving the 8 queens problem using backtracking. It begins by explaining backtracking as an algorithm that builds partial candidates for solutions incrementally and abandons any partial candidate that cannot be completed to a valid solution. It then provides more details on the 8 queens problem itself - the goal is to place 8 queens on a chessboard so that no two queens attack each other. Backtracking is well-suited for solving this problem by attempting to place queens one by one and backtracking when an invalid placement is found.
This document discusses theory of computation and finite automata. It begins by defining theory of computation as dealing with the logic of computation using abstract machines called automata. It then defines basic terminology like symbols, alphabets, strings, and languages. Next, it introduces finite automata as the simplest machines that recognize patterns using a finite set of states. Deterministic finite automata and nondeterministic finite automata are described as the two types of finite automata, differing in their transition functions. Transition diagrams and tables are also presented as ways to represent finite automata.
This document discusses finite state machines and their applications. It describes three types of finite state automata: 1) without output, 2) with output on transition (Mealy machine), and 3) with output on state (Moore machine). It then provides examples of Mealy and Moore machines, including ones that invert input, divide by 2, and act as a vending machine.
This document provides an overview of Turing machines including:
- Turing machines are mathematical models that can perform any computation like a computer and have an infinite tape with a tape head.
- A Turing machine is defined by 7 tuples including states, alphabets, transition function, and blank symbols.
- Multitape Turing machines have multiple tapes that can be accessed independently by separate heads.
- The instantaneous description specifies the current state, tape symbol under the head, and full tape configuration.
- Transition diagrams visually represent the transition function that specifies how the tape and state change based on the current symbol.
This document provides an overview of the Turing machine. It describes the Turing machine as an abstract computational model invented by Alan Turing in 1936. A Turing machine consists of an infinite tape divided into cells, a tape head that reads and writes symbols on the tape, and a state table that governs the machine's behavior. The document then explains the formal definition of a Turing machine, provides an example of how it works, discusses properties like decidability and recognizability, and covers modifications like multi-tape and non-deterministic Turing machines. It concludes by discussing the halting problem and explaining how Turing machines demonstrate the power and applications of computational theory.
The document discusses the accumulator register in a CPU. It describes the accumulator as a short-term storage register for arithmetic and logic operations. It contains details about the inputs and outputs to the accumulator from other registers like the data register and input register. It also explains the different microoperations that can be performed on the accumulator like addition, transfer, complement, and shift operations. The control gates for these microoperations are also defined.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)Mohammad Ilyas Malik
The term "Automata" is derived from the Greek word "αὐτόματα" which means "self-acting". An automaton (Automata in plural) is an abstract self-propelled computing device which follows a predetermined sequence of operations automatically.
The document discusses compilers and their role in translating high-level programming languages into machine-readable code. It notes that compilers perform several key functions: lexical analysis, syntax analysis, generation of an intermediate representation, optimization of the intermediate code, and finally generation of assembly or machine code. The compiler allows programmers to write code in a high-level language that is easier for humans while still producing efficient low-level code that computers can execute.
Automata theory studies abstract computing devices and the types of tasks they are capable of. Alan Turing pioneered this field in the 1930s by studying Turing machines. The theory examines questions like which tasks can and cannot be performed by different models of machines. It also considers the distinction between what is computable versus the complexity of computation. Common concepts include finite automata, formal languages, and the Chomsky hierarchy for classifying language types. Proofs in automata theory involve techniques like deduction, induction, contradiction, and establishing results by definition.
The document discusses timing and control in basic computers. It describes two types of control organizations: hardwired control and microprogram control. Hardwired control implements control logic with gates and flip-flops, allowing for fast operation. Microprogram control stores control information in a control memory that programs required microoperations. The document also provides details on the components and functioning of a hardwired control unit, including an instruction register, control logic gates, decoders, and sequence counter used to control the timing of registers based on clock pulses.
Turing machines are a simple mathematical model of computation that was introduced by Alan Turing in 1936. A Turing machine consists of a finite set of states, an infinite tape divided into cells, and a head that can read and write symbols on the tape. It operates based on a transition function that changes the state and head position based on the current state and symbol. Turing machines can be used as language acceptors by accepting inputs that cause them to halt in an accepting state, or as transducers by treating the initial tape as input and final tape as output. Variations include multi-tape, non-deterministic, multi-head, and multi-dimensional Turing machines. Turing machines are useful for determining decid
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.
The document compares Mealy and Moore finite state machines. Mealy machines have outputs that are a function of both the present state and inputs, while Moore machines have outputs that are a function only of the present state. The document provides examples of Mealy and Moore machines that detect a "10" sequence and a "111" sequence. It discusses their state diagrams, circuit implementations, and timing diagrams. The timing diagram shows that Mealy machines can have invalid or "glitchy" outputs when the inputs change asynchronously, while Moore machines always have valid outputs.
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
This document discusses finite state machines (FSMs) and compares Moore and Mealy machines. Moore machines output depends only on the current state, while Mealy machines output depends on both the current state and input. It provides examples of Mealy machine applications like traffic lights and vending machines. Mealy machines have advantages like reducing the number of states needed and generating faster output that changes immediately with input changes.
Finite-State Machine
The document discusses finite-state machines (FSM), which model sequential logic circuits. It describes two types of FSMs: Mealy and Moore machines. Mealy machines output depends on the present state and input, changing asynchronously with the clock. Moore machines' output depends only on the present state, changing synchronously with state changes and clock. The document provides an example of designing an FSM to output 0 if an even number of 1's have been received on the input, and 1 for odd. It shows solutions as both a Mealy and Moore machine using state transition tables and logic diagrams.
This document provides an introduction to finite automata. It defines key concepts like alphabets, strings, languages, and finite state machines. It also describes the different types of automata, specifically deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). DFAs have a single transition between states for each input, while NFAs can have multiple transitions. NFAs are generally easier to construct than DFAs. The next class will focus on deterministic finite automata in more detail.
This document discusses finite state machines (FSMs), which are mathematical models used to model the behavior of systems that can be in one of a finite number of states. The document defines FSMs, describes their components and representations, compares deterministic and non-deterministic FSMs, and discusses their applications in areas like software engineering, hardware design, and modeling reactive systems. FSMs are a fundamental concept in automata theory and computational modeling.
This document provides an overview of Turing machines. It introduces Turing machines as a simple mathematical model of a computer proposed by Alan Turing in 1936. A Turing machine consists of a tape divided into cells, a read/write head, finite states, and a transition function. The transition function defines how the machine moves between states and reads/writes symbols on the tape based on its current state and tape symbol. Turing machines can accept languages and compute functions. Variations include multi-tape, non-deterministic, multi-head, offline, multi-dimensional, and stationary-head Turing machines. The properties of Turing machines include their ability to recognize any language generated by a phrase-structure grammar and the Church
Object oriented programming (OOP) addresses limitations of procedural programming by dividing programs into objects that encapsulate both data and behaviors. OOP supports features like inheritance, polymorphism, and abstraction. Inheritance allows new classes to inherit attributes and behaviors from parent classes, polymorphism allows the same message to be interpreted differently depending on the object receiving it, and abstraction focuses on essential characteristics without implementation details. These features help make programs more modular, reusable, and maintainable.
This document discusses converting non-deterministic finite automata (NFA) to deterministic finite automata (DFA). NFAs can have multiple transitions with the same symbol or no transition for a symbol, while DFAs have a single transition for each symbol. The document provides examples of NFAs and their representations, and explains how to systematically construct a DFA that accepts the same language as a given NFA by considering all possible state combinations in the NFA. It also notes that NFAs and DFAs have equal expressive power despite their differences, and discusses minimizing DFAs and relationships to other automata models.
The document discusses solving the 8 queens problem using backtracking. It begins by explaining backtracking as an algorithm that builds partial candidates for solutions incrementally and abandons any partial candidate that cannot be completed to a valid solution. It then provides more details on the 8 queens problem itself - the goal is to place 8 queens on a chessboard so that no two queens attack each other. Backtracking is well-suited for solving this problem by attempting to place queens one by one and backtracking when an invalid placement is found.
This document discusses theory of computation and finite automata. It begins by defining theory of computation as dealing with the logic of computation using abstract machines called automata. It then defines basic terminology like symbols, alphabets, strings, and languages. Next, it introduces finite automata as the simplest machines that recognize patterns using a finite set of states. Deterministic finite automata and nondeterministic finite automata are described as the two types of finite automata, differing in their transition functions. Transition diagrams and tables are also presented as ways to represent finite automata.
This document discusses finite state machines and their applications. It describes three types of finite state automata: 1) without output, 2) with output on transition (Mealy machine), and 3) with output on state (Moore machine). It then provides examples of Mealy and Moore machines, including ones that invert input, divide by 2, and act as a vending machine.
This document provides an overview of Turing machines including:
- Turing machines are mathematical models that can perform any computation like a computer and have an infinite tape with a tape head.
- A Turing machine is defined by 7 tuples including states, alphabets, transition function, and blank symbols.
- Multitape Turing machines have multiple tapes that can be accessed independently by separate heads.
- The instantaneous description specifies the current state, tape symbol under the head, and full tape configuration.
- Transition diagrams visually represent the transition function that specifies how the tape and state change based on the current symbol.
This document provides an overview of the Turing machine. It describes the Turing machine as an abstract computational model invented by Alan Turing in 1936. A Turing machine consists of an infinite tape divided into cells, a tape head that reads and writes symbols on the tape, and a state table that governs the machine's behavior. The document then explains the formal definition of a Turing machine, provides an example of how it works, discusses properties like decidability and recognizability, and covers modifications like multi-tape and non-deterministic Turing machines. It concludes by discussing the halting problem and explaining how Turing machines demonstrate the power and applications of computational theory.
The document discusses the accumulator register in a CPU. It describes the accumulator as a short-term storage register for arithmetic and logic operations. It contains details about the inputs and outputs to the accumulator from other registers like the data register and input register. It also explains the different microoperations that can be performed on the accumulator like addition, transfer, complement, and shift operations. The control gates for these microoperations are also defined.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)Mohammad Ilyas Malik
The term "Automata" is derived from the Greek word "αὐτόματα" which means "self-acting". An automaton (Automata in plural) is an abstract self-propelled computing device which follows a predetermined sequence of operations automatically.
The document discusses compilers and their role in translating high-level programming languages into machine-readable code. It notes that compilers perform several key functions: lexical analysis, syntax analysis, generation of an intermediate representation, optimization of the intermediate code, and finally generation of assembly or machine code. The compiler allows programmers to write code in a high-level language that is easier for humans while still producing efficient low-level code that computers can execute.
Automata theory studies abstract computing devices and the types of tasks they are capable of. Alan Turing pioneered this field in the 1930s by studying Turing machines. The theory examines questions like which tasks can and cannot be performed by different models of machines. It also considers the distinction between what is computable versus the complexity of computation. Common concepts include finite automata, formal languages, and the Chomsky hierarchy for classifying language types. Proofs in automata theory involve techniques like deduction, induction, contradiction, and establishing results by definition.
The document discusses timing and control in basic computers. It describes two types of control organizations: hardwired control and microprogram control. Hardwired control implements control logic with gates and flip-flops, allowing for fast operation. Microprogram control stores control information in a control memory that programs required microoperations. The document also provides details on the components and functioning of a hardwired control unit, including an instruction register, control logic gates, decoders, and sequence counter used to control the timing of registers based on clock pulses.
Turing machines are a simple mathematical model of computation that was introduced by Alan Turing in 1936. A Turing machine consists of a finite set of states, an infinite tape divided into cells, and a head that can read and write symbols on the tape. It operates based on a transition function that changes the state and head position based on the current state and symbol. Turing machines can be used as language acceptors by accepting inputs that cause them to halt in an accepting state, or as transducers by treating the initial tape as input and final tape as output. Variations include multi-tape, non-deterministic, multi-head, and multi-dimensional Turing machines. Turing machines are useful for determining decid
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.
The document compares Mealy and Moore finite state machines. Mealy machines have outputs that are a function of both the present state and inputs, while Moore machines have outputs that are a function only of the present state. The document provides examples of Mealy and Moore machines that detect a "10" sequence and a "111" sequence. It discusses their state diagrams, circuit implementations, and timing diagrams. The timing diagram shows that Mealy machines can have invalid or "glitchy" outputs when the inputs change asynchronously, while Moore machines always have valid outputs.
A Mealy machine is a finite state machine that generates an output based on its current state and input. In contrast, a Moore machine's output depends only on its current state. The use of a Mealy machine often reduces the number of states needed compared to an equivalent Moore machine.
This document discusses finite state machine (FSM) design. It begins by motivating FSMs as a generalization of counters where outputs are a function of state rather than just the state itself. FSMs are used to implement circuits that control other circuits and make decisions based on their state and inputs. The chapter then provides an overview of the concepts to be covered, including defining the FSM, representing state transitions, Moore and Mealy machines, and using word problems and case studies. It describes the basic six-step design process and covers the concept of the state machine in more detail, including timing, state diagrams, and communicating between state machines.
A state machine is a system that can be described by a set of states that the system transitions through. It has a set of inputs, outputs, and memory. There are two main types - Mealy machines where the output is a function of the state and inputs, and Moore machines where the output is only a function of the state. To design a state machine, you first understand the problem, draw a state diagram, reduce states if possible, assign states, and design the circuit from the state table. An example is a serial adder where each bit is added sequentially using states to track the carry.
The document discusses state machines and finite state machines. It describes two models for representing sequential circuits: Moore circuits where the output depends only on the present state, and Mealy circuits where the output depends on both the present state and inputs. The state diagram and state table are introduced as ways to represent a state machine pictorially and tabularly. Methods like state reduction, state assignment, and implementation using flip-flops are also covered. Examples of state machines like adders, detectors, generators and counters are provided.
This document describes several examples of VHDL code implementations for digital circuits, including:
1) Moore and Mealy finite state machines with 4 states each that demonstrate the use of two processes - one for combinational logic and one for synchronous elements.
2) A read-only memory (ROM) implemented using a constant array that can be indexed to read values from locations.
3) A waveform generator that uses a ROM to store output values for each time step, with a counter to step through the time steps.
4) An enhanced "smart" waveform generator that allows time steps to be extended over multiple clock cycles by adding a second ROM to store duration values and a delay counter.
This document provides an overview of finite state machines (FSM) including concepts, examples, and implementation using Unified Modeling Language (UML) state diagrams. It defines an FSM as an abstract mathematical model that represents the behavior of a system. Examples of systems that can be modeled as FSMs include traffic lights, text parsers, and characters in video games. The document discusses Mealy and Moore machines, state structure, advantages of UML state diagrams such nested states and extended states, and limitations of state diagrams. Commercial and open source tools for designing FSMs using state diagrams are also mentioned.
The document compares the topics of finite state machines covered in AS level to those in A2 level, noting that A2 level introduces new concepts like state transition diagrams, Mealy and Moore machines which output on state transitions, and deterministic and non-deterministic finite state automata. It also provides examples of finite state machines with outputs like a ballpoint pen and a vending machine, as well as machines without outputs like a basic combination lock.
PHP is a server-side scripting language used to build dynamic web applications. It allows developers to add interactivity to websites. Some key points:
- PHP scripts are executed on the server-side and allow generation of dynamic web page content.
- It supports many databases and is compatible with popular web servers like Apache and IIS.
- Basic PHP syntax involves opening and closing <?php ?> tags to embed PHP code in HTML documents.
- Variables, conditional statements, loops and functions allow building complex scripts.
- PHP can retrieve and process form data submitted from HTML forms.
Affirmative Action must be taken by covered employers to recruit and advance qualified minorities, women, persons with disabilities, and covered veterans. Affirmative Actions include training programs, outreach efforts, and other positive steps. These procedures should be incorporated into the company’s written personnel policies. Employers with written Affirmative Action programs must implement them, keep them on file and update them annually.
This document discusses recursive functions and growth functions. It defines recursive functions as functions defined in terms of previous values using initial conditions and recurrence relations. Examples of recursively defined sequences like Fibonacci are provided. Growth functions are defined using big-O notation to analyze how functions grow relative to each other. Common proof techniques like direct proof, indirect proof, proof by contradiction and induction are described. Walks and paths in trees are defined as sequences of alternating vertices and edges that begin and end at vertices. Deterministic finite automata are defined as 5-tuples with states, input alphabet, transition function, start state, and set of accepting/final states.
This document introduces finite-state machines as a computational model that is more restrictive than Turing machines. Finite-state machines can be used for pattern matching, modeling sequential logic circuits, and representing relationships in directed graphs. The document discusses various ways to represent finite-state machines, including as functional programs, imperative programs, feedback systems, tables, and graphs. An example edge detector machine is presented and modeled in different representations.
Affirmative action aims to remedy past discrimination by requiring federal contractors to develop affirmative action plans and track applicant demographics to identify and address any underrepresentation of women and minorities; federal contractors with 50+ employees and sufficient federal contracts must maintain written affirmative action plans for each establishment, but quotas are prohibited and affirmative action should not result in reverse discrimination.
Affirmative action plans are mandated by several executive orders and laws to protect women and minorities from discrimination in employment. They require companies with federal contracts to audit their human resources practices, identify any discrepancies impacting protected groups, and develop action plans and goals to address underrepresentation. The Office of Federal Contract Compliance Programs oversees compliance through audits and can impose penalties on non-compliant companies like fines and contract debarment. An effective affirmative action plan involves analyzing workforce data, identifying areas of underutilization, and setting good faith efforts and goals to promote diversity and equal opportunity.
The document discusses affirmative action, including its definition, arguments for and against it, and its history and current status. It provides background on how affirmative action originated from the civil rights movement to promote equal opportunity and address past discrimination. It now faces controversy as some argue it amounts to unfair racial preferences, while supporters see it as a way to promote diversity and compensate for historical inequalities.
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The Equal Employment Opportunity (EEO) Act, implemented by the Federal government, applies to private employers, labor organizations, employment agencies, and educational institutions under states and Federal governments. In this article, we briefly discuss the benefits, rights and responsibilities under the EEO Act.
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.
Definition of finite state automaton: computation. It is an abstract machine that can be in exactly one of a finite number of states at any given time. The FSM can change from one state to another in response to some external inputs; the change from one state to another is called a transition. A FSM is defined by a list of its states, its initial state, and the conditions for each transition.
the report contain
Introduction
The historical of finite state automaton
Types of FSA
The advantages and disadvantages of FSA
examples for FSA
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This document discusses finite state machines, specifically Moore and Mealy machines. It defines Moore machines as having output that depends only on the present state, while Mealy machines have output that depends on both the present state and input. The document then provides a 3-step process for converting a Moore machine to a Mealy machine by copying the Moore machine's state transitions and outputs to a Mealy machine transition table.
The document provides information about an upcoming lab, homework assignments, and midterm for a class. It discusses Moore and Mealy finite state machines and gives examples to illustrate the differences between them. Details are also given about the topics and format for the upcoming Midterm 2 exam.
This document discusses the conversion from a mealy machine to a moore machine. It defines mealy and moore machines, with mealy machines having outputs that depend on the present state and input, while moore machines have outputs that depend only on the present state. It provides steps to convert a mealy machine to a moore machine by breaking states with multiple outputs into multiple states with a single output each. An example conversion is shown by taking a mealy machine transition table and creating a transition table and diagram for the equivalent moore machine.
This document provides an introduction to automata theory and finite state machines. It defines automata as abstract machines that can process discrete inputs over time and transition between internal states. The document discusses the mathematical representation of finite automata using a 5-tuple and explains how the output of finite state machines can be determined using either Moore or Mealy models. The Moore model defines the output based solely on the current state, while the Mealy model defines the output based on both the current state and current input. Examples are given of applications that use finite state machines like traffic lights and vending machines.
- The document discusses the differences between Mealy and Moore models of sequential circuits.
- In a Mealy model, the output is a function of both the present state and input. In a Moore model, the output is a function of just the present state.
- Some key differences are that Mealy machines react faster to inputs but are more difficult to design, while Moore machines react more slowly but require less hardware and are easier to design.
This document summarizes sequential circuits and finite state machines. It discusses:
- 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
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.
This document outlines the syllabus for a course on formal languages and automata theory. The syllabus covers six units: (1) mathematical preliminaries, (2) finite automata and regular languages, (3) regular expressions and grammars, (4) context-free languages and pushdown automata, (5) Turing machines and variations, and (6) recursively enumerable and recursive sets. It also lists six course outcomes related to classifying languages and automata, formal relationships among models of computation, constructing grammars and automata, and demonstrating concepts in computability and decidability. The document provides examples and definitions for Moore and Mealy machines, and procedures for converting between the two models.
1) The document discusses state machine design and describes issues with traditional state diagrams and tables for representing finite state machines. 2) It introduces the state machine diagram model which adds notation for defining outputs and aims to address the scalability issues of traditional representations. 3) The state machine diagram model uses concepts like input conditions, transition conditions, output conditions, and output actions to concisely specify the behavior.
The Quine-McCluskey method is used to minimize Boolean functions expressed in sum-of-products form. It works by generating prime implicants from the minterms and then eliminating variables to find an essential prime implicant. The method uses the Boolean rule A + A' = 1 repeatedly. It can handle incompletely specified functions using don't care conditions. The main steps are: 1) generate prime implicants, 2) construct a prime implicant table, 3) reduce the table by removing essential prime implicants and applying row and column dominance, 4) solve the reduced table.
The document discusses Moore machines and Mealy machines. It defines what equivalent machines are and proves that for every Moore machine there exists an equivalent Mealy machine and vice versa. It demonstrates converting a Moore machine to an equivalent Mealy machine by shifting the output characters to transitions. Similarly, it shows converting a Mealy machine to an equivalent Moore machine by splitting states based on different output characters of incoming transitions. An example is provided for both conversions.
This document provides an introduction to automata theory and finite state machines. It defines an automata as a self-operating machine that performs a range of functions according to a predetermined set of coded instructions. There are four main types of automata: finite state machines, pushdown automata, Turing machines, and linear bound machines. Finite state machines are represented using a 5-tuple consisting of states, inputs, transitions, initial state, and final/accepting states. Examples of finite state machines for patterns like regular expressions and traffic lights are presented.
The document summarizes the topics covered in Lecture 19 of CSE370 including the differences between Moore and Mealy machines, examples of designing FSMs as Moore and Mealy machines including a parity checker, and provides details about the upcoming Midterm 2 exam covering material through simple FSM design.
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.
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.
The document discusses programmable logic controllers (PLCs) and industrial robots. It defines PLCs as digitally operating electronic devices that use programmable memory to implement logic functions to control machines and processes. PLCs have input/output modules, memory, and peripherals. The document also discusses ladder logic diagrams used to program PLCs to control sequences like having a robot unload parts from one machine and load parts into another machine.
This document summarizes a lecture on finite state machine (FSM) design. It discusses Moore and Mealy machines, FSM word problems, state minimization, state assignment, and implementation. It provides examples of an odd parity checker, vending machine, and traffic light controller FSM designs. It also compares alternative representations like Algorithmic State Machines and describes analyzing and reverse engineering Moore and Mealy machines.
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2. What is (Finite State Machine)FSM?
A finite state machine is a machine that has many states and has a logical way
of changing from one state to the other under guiding rules.
3. Types of FSM
Without output (answer true or false)
1. Finite State Automata
- With output
2. Mealy machine
- output on transition
3. Moore machine
- output on state
4. Mealy Machine
A Mealy Machine is an FSM whose output depends on the present state as well
as the present input. In Mealy machine every transition for a particular input
symbol has a fixed output.
5. Mealy Machine(Cont.)
• It can be described by a 6 tuple (Q, ∑, O, δ, X, q0) where −
1. Q is a finite set of states.
2. ∑ is a finite set of symbols called the input alphabet.
3. O is a finite set of symbols called the output alphabet.
4. δ is the input transition function where δ: Q × ∑ → Q
5. X is the output transition function where X: Q → O
6. q0 is the initial state from where any input is processed (q0 ∈ Q).
8. Moore Machine:
• In Moore machine. the value of output function is depend on the present
state only. Moore machine is described by 6-tuples - (Q, Σ, Δ, δ, λ, q0)
where
1. Q = Finite non-empty set of states;
2. Σ = Set of input alphabets.
3. Δ = Set of output alphabets.
4. δ = Transition function mapping Q X Σ → Q
5. λ = Output function mapping Q → Δ
6. q0 = Initial state..
9. Moore Transition Table:
• There is no concept of final state in Moore machines we consider output
for each state.
10. Moore Machine to Mealy Machine
1. Input: Moore Machine
2. Output: Mealy Machine
3. Step 1: Take a blank Mealy Machine transition table format.
4. Step 2: Copy all the Moore Machine transition states into this table format..
5. Step 3: Check the present states and their corresponding outputs in the Moore
Machine state table; if for a state Qi output is m, copy it into the output columns
of the Mealy Machine state table wherever Qi appears in the next state..
11. Example:
• Let us consider Moore Table
Present State Next State
a=0 a=1
Output
a d b 1
b a d 0
c c c 0
d b a 1
14. Mealy Machine To Moore Machine
1. Input: Mealy Machine
2. Output: Moore Machine
3. Step 1: Calculate the number of different outputs for each state (Qi) that are
available in the state table of the Mealy machine.
4. Step 2: If all the outputs of Qi are same, copy state Qi. If it has n distinct
outputs, break Qi into n states as Qin where n = 0, 1, 2.......
5. Step 3: If the output of the initial state is 1, insert a new initial state at the
beginning which gives 0 output.
15. Example:
• Let us consider the following Mealy Machine.
Here, states ‘a’ and ‘d’ give only 1 and 0 outputs respectively, so we retain states ‘a’ and ‘d’. But
states ‘b’ and ‘c’ produce different outputs 1and0. So, we divide b into b0, b1 and c into c0, c1.
18. Advantages of Mealy and Moore state
Machines
• Moore machines are cheap
• They are easy to use
• Moore state machines are very fast
• Mealy machines are reactive i.e. they have a low response time (they are fast)
19. Disadvantages of Mealy and Moore state
Machines
• Mealy state machines are expensive to produce
• Number of states can become unmanageable (they become too many)
20. Uses of Mealy and Moore state Machines
• Mealy state machines are used in processors due to their property of having
many states
• Mealy state machines are also used to provide a rudimentary mathematical
model for cipher machines
• A Moore state machine is used as a right enable in SRAM because of its
speed.
• It is used in SRAM because SRAM needs a level-sensitive control (signal has
to be asserted for an amount of time)
21. Conclusion
• In conclusion, Mealy and Moore state machines are very important concepts
in digital design
• These state machine can be used in the design of mathematical algorithms
• Mealy and Moore state machines can come in both simple (having one input
and output) to complex (having many inputs and outputs) types
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