The presentation topic is on computational models. It will be presented by Md. Touhidur Rahman. The document discusses four computational models: finite automata (DFA and NFA), context free grammar, pushdown automata, and Turing machines. Finite automata are defined using states, symbols, transitions, a start state, and accepting states. Context free grammars are defined using variables, terminals, productions, and a start symbol. Pushdown automata extend NFAs with a stack. Turing machines are formally defined but also described at a higher level for understanding.
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.
This document provides an introduction to finite automata and regular languages. It defines key concepts such as alphabets, strings, languages, deterministic finite automata (DFAs), and the transition function. DFAs are represented using graphs or transition tables. The extended transition function describes the state reached based on an input string. The language of a DFA is the set of strings that reach a final state. Proofs are used to show two language descriptions are equivalent. Regular languages can be recognized by DFAs and have certain properties, while some languages are non-regular. Examples demonstrate regular and non-regular languages.
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.
recognizer for a language, Deterministic finite automata, Non-deterministic finite automata, conversion of NFA to DFA, Regular Expression to NFA, Thomsons Construction
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.
Here is the NFA in formal notation:
Q = {q0, q1, q2}
Σ = {a, b}
q0 = q1 (the initial state)
F = {q0} (the single accepting state)
q0
The transition function δ is:
a
q1 → {q0, q2}
q2 → {q0}
b
This NFA accepts ε, a, baba, baa, and aa since there is a path from the initial state q1 to the accepting state q0 under those inputs. It does not accept b, bb, or babba since there is no path from
Deterministic Finite State Automata (DFAs) are machines that read input strings and determine whether to accept or reject them based on their state transitions. A DFA is defined as a 5-tuple (Q, Σ, δ, q0, F) where Q is a finite set of states, Σ is a finite input alphabet, q0 is the starting state, F is the set of accepting states, and δ is the transition function that maps a state and input symbol to the next state. The language accepted by a DFA is the set of strings that cause the DFA to enter an accepting state. Nondeterministic Finite State Automata (NFAs) are similar but δ maps to sets of states rather
This document contains questions and answers related to finite automata theory. It begins with definitions of finite automata and their uses in text processing, compiler design, and hardware design. It then provides the formal definition of a deterministic finite automaton (DFA) and explains how a DFA processes strings using the transition function. Examples of DFAs are given for specific languages. Extended transition functions are described, along with examples of using them to represent languages. Specific languages are also given with their corresponding DFA diagrams and formal definitions.
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.
This document provides an introduction to finite automata and regular languages. It defines key concepts such as alphabets, strings, languages, deterministic finite automata (DFAs), and the transition function. DFAs are represented using graphs or transition tables. The extended transition function describes the state reached based on an input string. The language of a DFA is the set of strings that reach a final state. Proofs are used to show two language descriptions are equivalent. Regular languages can be recognized by DFAs and have certain properties, while some languages are non-regular. Examples demonstrate regular and non-regular languages.
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.
recognizer for a language, Deterministic finite automata, Non-deterministic finite automata, conversion of NFA to DFA, Regular Expression to NFA, Thomsons Construction
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.
Here is the NFA in formal notation:
Q = {q0, q1, q2}
Σ = {a, b}
q0 = q1 (the initial state)
F = {q0} (the single accepting state)
q0
The transition function δ is:
a
q1 → {q0, q2}
q2 → {q0}
b
This NFA accepts ε, a, baba, baa, and aa since there is a path from the initial state q1 to the accepting state q0 under those inputs. It does not accept b, bb, or babba since there is no path from
Deterministic Finite State Automata (DFAs) are machines that read input strings and determine whether to accept or reject them based on their state transitions. A DFA is defined as a 5-tuple (Q, Σ, δ, q0, F) where Q is a finite set of states, Σ is a finite input alphabet, q0 is the starting state, F is the set of accepting states, and δ is the transition function that maps a state and input symbol to the next state. The language accepted by a DFA is the set of strings that cause the DFA to enter an accepting state. Nondeterministic Finite State Automata (NFAs) are similar but δ maps to sets of states rather
This document contains questions and answers related to finite automata theory. It begins with definitions of finite automata and their uses in text processing, compiler design, and hardware design. It then provides the formal definition of a deterministic finite automaton (DFA) and explains how a DFA processes strings using the transition function. Examples of DFAs are given for specific languages. Extended transition functions are described, along with examples of using them to represent languages. Specific languages are also given with their corresponding DFA diagrams and formal definitions.
1. Automata theory is the study of abstract machines and the problems they are able to solve. It is closely related to formal language theory as automata are often classified by the formal languages they can recognize.
2. A finite automaton is an abstract machine that consists of a finite number of states. It reads an input string and based on its current state and the next input symbol, transitions to the next state according to its transition function. If it ends in an accepting state, the input is accepted.
3. Deterministic finite automata (DFAs) are a type of finite automaton where the transition function maps each state-symbol pair to a unique next state. DFAs can be represented
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 provides an introduction to automata theory and finite automata. It defines an automaton as an abstract computing device that follows a predetermined sequence of operations automatically. A finite automaton has a finite number of states and can be deterministic or non-deterministic. The document outlines the formal definitions and representations of finite automata. It also discusses related concepts like alphabets, strings, languages, and the conversions between non-deterministic and deterministic finite automata. Methods for minimizing deterministic finite automata using Myhill-Nerode theorem and equivalence theorem are also introduced.
This document provides an overview of a seminar on finite automata. It discusses key topics including deterministic finite automata (DFA), non-deterministic finite automata (NFA), regular expressions, and regular languages. It defines these concepts, provides examples, and discusses applications such as text searching using regular expressions.
The document discusses finite automata and deterministic finite automata (DFAs) in particular. It defines DFAs using a 5-tuple notation and explains that a DFA has a single possible state transition for each input symbol. The document provides examples of constructing DFAs to recognize specific languages and describes the step-by-step process of using a DFA to determine if a string is accepted by the language. It also gives examples of building DFAs for languages containing certain substrings and strings with a minimum length.
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 various concepts related to finite automata. It begins by defining a finite automaton as a mathematical model of a system with discrete inputs and outputs that can be in a finite number of states. A finite automaton consists of a finite set of states and transitions between states based on input symbols. The document then discusses formal languages, the functions of a head pointer and finite control, the two main types of finite automata (DFA and NFA), ways to represent automata, definitions of languages and transitions, regular expressions and languages, two-way finite automata, epsilon closure, equivalence of NFAs and DFAs, Moore and Mealy machines, and applications of finite automata such as lexical analysis.
Introduction to the theory of computationprasadmvreddy
This document provides an introduction and overview of topics in the theory of computation including automata, computability, and complexity. It discusses the following key points in 3 sentences:
Automata theory, computability theory, and complexity theory examine the fundamental capabilities and limitations of computers. Different models of computation are introduced including finite automata, context-free grammars, and Turing machines. The document then provides definitions and examples of regular languages and context-free grammars, the basics of finite automata and regular expressions, properties of regular languages, and limitations of finite state machines.
The document discusses the theory of computation and defines key concepts related to automata theory. It defines a finite automaton as an automaton with a finite number of states. A Moore machine is defined as an automaton where the output depends only on the state, while a Mealy machine is one where the output depends on both the state and input. A finite automaton is formally defined as a 5-tuple consisting of a set of states, alphabet, transition function, initial state, and set of final/accepting states. Related concepts like alphabet, string, and language are also defined.
The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
This document provides an overview of automata and formal languages. It defines key terms like automata, finite automata, deterministic finite automaton (DFA), non-deterministic finite automaton (NDFA), and grammars. It also explains the differences between DFAs and NDFAs, and between Mealy and Moore machines. Formal definitions of these concepts are given using common notation like tuples.
NFA Non Deterministic Finite Automata by Mudasir khushikMudsaraliKhushik
NFA Non Deterministic Finite Automata.
Basics
tuples of NFA
NFA Examples
Transition Table and man more things which you want to understand like Language, Rules, Alphabets, Descriptive Method, Regular Expression, String, and Finite Automata.
Finite automata are machines that recognize languages. They have a finite set of states, transitions between states based on input symbols, a start state, and final states. They accept words in the language by reaching a final state and reject other words. An example FA is given that recognizes words over the alphabet {a,b} with the structure (a+b)*b(a+b)*. Finite automata always transition deterministically based on their current state and input symbol. Various examples of finite automata are constructed to recognize specific languages over given alphabets.
The document provides information about a course on the theory of automata. It includes details such as the course title, prerequisites, duration, lectures, laboratories, and topics to be covered. The topics include finite automata, deterministic finite automata, non-deterministic finite automata, regular expressions, properties of regular languages, context-free grammars, pushdown automata, and Turing machines. It also lists reference books and textbooks, and the marking scheme for the course.
The document describes the syllabus for the course "Formal Languages and Automata Theory". It contains:
- 8 units covering topics like introduction to finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, and more.
- Details of each unit including hours, chapters covered from the textbook, and topics discussed.
- Information about internal assessment and exams, including marks distribution.
- Names of two recommended textbooks and their relevant chapters.
- A table of contents listing the topics covered in each unit and their page numbers.
The document discusses Noam Chomsky's classification of grammars and languages, which divides them into four classes based on the type of automaton that can recognize them: recursively enumerable grammars recognized by a Turing machine, context-sensitive grammars recognized by a linear bounded automaton, context-free grammars recognized by a pushdown automaton, and regular grammars recognized by a finite state automaton. It then provides details on finite state automata, including how deterministic (DFA) and nondeterministic (NFA) finite automata work, how to define, construct, and use them to accept or reject strings and describe languages.
This document discusses finite automata and provides definitions and examples. It defines deterministic finite automata (DFA) and nondeterministic finite automata (NFA) and their components. It describes how strings are processed by DFAs using transition functions. Notations for finite automata like transition diagrams and tables are presented. The reasons for nondeterminism and how to convert NFAs to equivalent DFAs are summarized. Examples of finite automata design are provided.
The document discusses lexical analysis and lexical analyzer generators. It provides background on why lexical analysis is a separate phase in compiler design and how it simplifies parsing. It also describes how a lexical analyzer interacts with a parser and some key attributes of tokens like lexemes and patterns. Finally, it explains how regular expressions are used to specify patterns for tokens and how tools like Lex and Flex can be used to generate lexical analyzers from regular expression definitions.
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.
This document discusses regular languages and finite automata. It begins with an overview of regular expressions and the introduction to the theory of finite automata. It then explains that finite automata are used in text processing, compilers, and hardware design. The document goes on to define automata theory and discusses deterministic finite automata (DFA) and nondeterministic finite automata (NFA). It notes that while NFA allow epsilon transitions and multiple state transitions, DFA and NFA have equivalent computational power.
1. Automata theory is the study of abstract machines and the problems they are able to solve. It is closely related to formal language theory as automata are often classified by the formal languages they can recognize.
2. A finite automaton is an abstract machine that consists of a finite number of states. It reads an input string and based on its current state and the next input symbol, transitions to the next state according to its transition function. If it ends in an accepting state, the input is accepted.
3. Deterministic finite automata (DFAs) are a type of finite automaton where the transition function maps each state-symbol pair to a unique next state. DFAs can be represented
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 provides an introduction to automata theory and finite automata. It defines an automaton as an abstract computing device that follows a predetermined sequence of operations automatically. A finite automaton has a finite number of states and can be deterministic or non-deterministic. The document outlines the formal definitions and representations of finite automata. It also discusses related concepts like alphabets, strings, languages, and the conversions between non-deterministic and deterministic finite automata. Methods for minimizing deterministic finite automata using Myhill-Nerode theorem and equivalence theorem are also introduced.
This document provides an overview of a seminar on finite automata. It discusses key topics including deterministic finite automata (DFA), non-deterministic finite automata (NFA), regular expressions, and regular languages. It defines these concepts, provides examples, and discusses applications such as text searching using regular expressions.
The document discusses finite automata and deterministic finite automata (DFAs) in particular. It defines DFAs using a 5-tuple notation and explains that a DFA has a single possible state transition for each input symbol. The document provides examples of constructing DFAs to recognize specific languages and describes the step-by-step process of using a DFA to determine if a string is accepted by the language. It also gives examples of building DFAs for languages containing certain substrings and strings with a minimum length.
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 various concepts related to finite automata. It begins by defining a finite automaton as a mathematical model of a system with discrete inputs and outputs that can be in a finite number of states. A finite automaton consists of a finite set of states and transitions between states based on input symbols. The document then discusses formal languages, the functions of a head pointer and finite control, the two main types of finite automata (DFA and NFA), ways to represent automata, definitions of languages and transitions, regular expressions and languages, two-way finite automata, epsilon closure, equivalence of NFAs and DFAs, Moore and Mealy machines, and applications of finite automata such as lexical analysis.
Introduction to the theory of computationprasadmvreddy
This document provides an introduction and overview of topics in the theory of computation including automata, computability, and complexity. It discusses the following key points in 3 sentences:
Automata theory, computability theory, and complexity theory examine the fundamental capabilities and limitations of computers. Different models of computation are introduced including finite automata, context-free grammars, and Turing machines. The document then provides definitions and examples of regular languages and context-free grammars, the basics of finite automata and regular expressions, properties of regular languages, and limitations of finite state machines.
The document discusses the theory of computation and defines key concepts related to automata theory. It defines a finite automaton as an automaton with a finite number of states. A Moore machine is defined as an automaton where the output depends only on the state, while a Mealy machine is one where the output depends on both the state and input. A finite automaton is formally defined as a 5-tuple consisting of a set of states, alphabet, transition function, initial state, and set of final/accepting states. Related concepts like alphabet, string, and language are also defined.
The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
This document provides an overview of automata and formal languages. It defines key terms like automata, finite automata, deterministic finite automaton (DFA), non-deterministic finite automaton (NDFA), and grammars. It also explains the differences between DFAs and NDFAs, and between Mealy and Moore machines. Formal definitions of these concepts are given using common notation like tuples.
NFA Non Deterministic Finite Automata by Mudasir khushikMudsaraliKhushik
NFA Non Deterministic Finite Automata.
Basics
tuples of NFA
NFA Examples
Transition Table and man more things which you want to understand like Language, Rules, Alphabets, Descriptive Method, Regular Expression, String, and Finite Automata.
Finite automata are machines that recognize languages. They have a finite set of states, transitions between states based on input symbols, a start state, and final states. They accept words in the language by reaching a final state and reject other words. An example FA is given that recognizes words over the alphabet {a,b} with the structure (a+b)*b(a+b)*. Finite automata always transition deterministically based on their current state and input symbol. Various examples of finite automata are constructed to recognize specific languages over given alphabets.
The document provides information about a course on the theory of automata. It includes details such as the course title, prerequisites, duration, lectures, laboratories, and topics to be covered. The topics include finite automata, deterministic finite automata, non-deterministic finite automata, regular expressions, properties of regular languages, context-free grammars, pushdown automata, and Turing machines. It also lists reference books and textbooks, and the marking scheme for the course.
The document describes the syllabus for the course "Formal Languages and Automata Theory". It contains:
- 8 units covering topics like introduction to finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, and more.
- Details of each unit including hours, chapters covered from the textbook, and topics discussed.
- Information about internal assessment and exams, including marks distribution.
- Names of two recommended textbooks and their relevant chapters.
- A table of contents listing the topics covered in each unit and their page numbers.
The document discusses Noam Chomsky's classification of grammars and languages, which divides them into four classes based on the type of automaton that can recognize them: recursively enumerable grammars recognized by a Turing machine, context-sensitive grammars recognized by a linear bounded automaton, context-free grammars recognized by a pushdown automaton, and regular grammars recognized by a finite state automaton. It then provides details on finite state automata, including how deterministic (DFA) and nondeterministic (NFA) finite automata work, how to define, construct, and use them to accept or reject strings and describe languages.
This document discusses finite automata and provides definitions and examples. It defines deterministic finite automata (DFA) and nondeterministic finite automata (NFA) and their components. It describes how strings are processed by DFAs using transition functions. Notations for finite automata like transition diagrams and tables are presented. The reasons for nondeterminism and how to convert NFAs to equivalent DFAs are summarized. Examples of finite automata design are provided.
The document discusses lexical analysis and lexical analyzer generators. It provides background on why lexical analysis is a separate phase in compiler design and how it simplifies parsing. It also describes how a lexical analyzer interacts with a parser and some key attributes of tokens like lexemes and patterns. Finally, it explains how regular expressions are used to specify patterns for tokens and how tools like Lex and Flex can be used to generate lexical analyzers from regular expression definitions.
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.
This document discusses regular languages and finite automata. It begins with an overview of regular expressions and the introduction to the theory of finite automata. It then explains that finite automata are used in text processing, compilers, and hardware design. The document goes on to define automata theory and discusses deterministic finite automata (DFA) and nondeterministic finite automata (NFA). It notes that while NFA allow epsilon transitions and multiple state transitions, DFA and NFA have equivalent computational power.
The document discusses lexical analysis in compiler design. It covers the role of the lexical analyzer, tokenization, and representation of tokens using finite automata. Regular expressions are used to formally specify patterns for tokens. A lexical analyzer generator converts these specifications into a finite state machine (FSM) implementation to recognize tokens in the input stream. The FSM is typically a deterministic finite automaton (DFA) for efficiency, even though a nondeterministic finite automaton (NFA) may require fewer states.
The document discusses compilers and their components. It begins by defining a compiler as a program that translates programs written in one language into another language. It then describes the main components of a compiler: lexical analysis, syntax analysis, semantic analysis, code generation, and code optimization. The document provides details on each component and how they work together in the compilation process from a source language to a target language.
Automata theory - describes to derives string from Context free grammar - derivation and parse tree
normal forms - Chomsky normal form and Griebah normal form
This document provides information about non-deterministic finite automata (NFA) and deterministic finite automata (DFA). It defines what an automaton is and explains that a finite automaton has a finite number of states. An automaton can be represented as a 5-tuple containing the states, alphabet, transition function, initial state, and final states. It then discusses the differences between NFAs and DFAs, including that NFAs allow non-deterministic (multiple) transitions while DFAs only allow single deterministic transitions. The document provides an example of an NFA and DFA for the regular expression (a|b)*abb.
The document provides an introduction to theory of computation and automata. It defines key concepts such as symbols, alphabets, strings, languages, finite automata, deterministic finite automata (DFA), non-deterministic finite automata (NFA). It explains these concepts using examples and discusses their representation using transition diagrams, transition tables, and examples of DFAs recognizing specific languages.
This document contains lecture notes on finite automata from the Theory of Computation unit 1 course. Some key points include:
1. Finite automata are defined as 5-tuples (Q, Σ, δ, q0, F) where Q is a set of states, Σ is an input alphabet, δ is a transition function, q0 is the initial state, and F is a set of final states.
2. Deterministic finite automata (DFAs) have a single transition between states for each input symbol, while non-deterministic finite automata (NFAs) can have multiple transitions for a single input.
3. Regular expressions are used to describe the languages
Finite automata is a state machine that takes a string of symbols as input and changes states accordingly. It consists of a finite set of states, input symbols, a start state, final states, and a transition function. Finite automata can be deterministic (DFA) or nondeterministic (NFA). A DFA transitions to exactly one state for each input, while an NFA may transition to multiple states. NFAs allow empty string transitions while DFAs do not.
The document defines deterministic finite automata (DFA) and provides examples to illustrate their components and operation. It then discusses applications of finite automata in areas like compiler construction, hardware design, games, and more. The document also defines non-deterministic finite automata (NFA) and describes the process to convert an NFA to an equivalent DFA in 4 steps: 1) Set the starting state, 2) Determine new states and transitions, 3) Identify accepting states, 4) Make the starting state accepting if epsilon is allowed. An example NFA is converted to DFA using this process.
This document provides information about a course on the theory of automata, including:
- The course title is Theory of Computer Science [Automata] and is intended for students pursuing a BCS degree in their fifth semester.
- Key topics that will be covered include finite automata, regular expressions, context-free grammars, pushdown automata, and Turing machines.
- Reference textbooks and materials are listed to support student learning in the course over 18 weeks of lectures and labs.
This document discusses context-free languages and grammars (CFLs and CFGs). Some key points:
- CFLs are a class of languages larger than regular languages that can be defined using context-free grammars (CFGs). CFGs use recursive rules called productions.
- An example CFG is given that defines the language of binary palindromes using the productions: A => 0A0 | 1A1 | 0 | 1 | ε. Strings can be generated or recognized using this grammar.
- Parse trees can represent derivations in a CFG. Derivations, recursive inferences, and parse trees are interchangeable representations.
- Applications of CFLs/CFGs
This document discusses context-free languages and grammars (CFLs and CFGs). Some key points:
- CFLs are a class of languages larger than regular languages that can be defined using context-free grammars (CFGs). CFGs use recursive rules called productions.
- An example CFG is given that defines the language of binary palindromes using the productions: A => 0A0 | 1A1 | 0 | 1 | ε. Strings can be generated or recognized using this grammar.
- Parse trees can represent derivations in a CFG. Derivations and parse trees are interchangeable representations. Leftmost and rightmost derivations may differ but define the same language.
Formal Languages and Automata Theory Unit 1Srimatre K
The document describes the minimization of a deterministic finite automaton (DFA) using the equivalence theorem. It involves partitioning the states into sets where states in the same set are indistinguishable based on their transitions. The initial partition P0 separates final and non-final states. Subsequent partitions P1, P2, etc. further split sets if states within are distinguishable. The process stops when the partition no longer changes, resulting in the minimized DFA with states merged within each final set. An example application of the steps to a 6-state DFA is also provided.
This document discusses lexical analysis using finite automata. It begins by defining regular expressions, finite automata, and their components. It then covers non-deterministic finite automata (NFAs) and deterministic finite automata (DFAs), and how NFAs can recognize the same regular languages as DFAs. The document outlines the process of converting a regular expression to an NFA using Thompson's construction, then converting the NFA to a DFA using subset construction. It also discusses minimizing DFAs using Hopcroft's algorithm. Examples are provided to illustrate each concept.
1. Regular expressions are used to compactly represent sets of strings and are the basis for specifying patterns in lexical analysis.
2. Finite state automata are constructed to recognize strings belonging to the language defined by a regular expression. For every regular expression there is a corresponding finite state automaton.
3. The input string is checked for membership in the regular language by tracing a path through the automaton corresponding to the regular expression. If a complete path is found that reads the input string, it is accepted.
This document discusses regular expressions and their properties. It begins by defining regular expressions and describing how they can be used to represent languages. It then discusses the languages associated with regular expressions and how to determine equivalence between regular expressions. The document also covers properties of regular expressions like precedence, algebraic laws, and identities. It provides examples of constructing finite automata from regular expressions and constructing regular expressions from finite automata. Finally, it discusses the pumping lemma and closure properties of regular languages.
The document discusses Frederick Winslow Taylor and the principles of scientific management, including optimizing jobs to increase productivity, standardization of work processes, and cooperation between workers and managers. It provides an overview of Taylor's theories and principles, how scientific management was implemented, criticisms of the approach, and how some of its concepts are still used in modern management practices today. The presentation is made by several students who each cover different aspects of Taylor's scientific management and its applications.
The document discusses different color models including RGB, CMY, and HSV. The RGB model is an additive color model used for displays with primary colors of red, green, and blue. The CMY model is a subtractive color model used in printing with primary colors of cyan, magenta, and yellow. RGB uses light to create colors and is used for digital devices while CMY uses ink on paper. Both models have their advantages and are used in different applications depending on whether light or reflected light is used to produce colors.
The document discusses Microsoft .NET, which is a development platform that allows applications to be built across multiple devices and operating systems using XML web services. It provides details on the components of .NET including .NET experiences, clients, services, servers, and tools. The document also covers ASP.NET, MVC, .NET security, and resources for learning more about the .NET framework.
The document discusses an operating system topic presented by the Ethernet group consisting of three members. It defines an operating system as the set of programs that controls a computer and provides an interface between the user and hardware. The functions of an operating system include process management, memory management, file management, and security management. It also discusses the differences between user mode and kernel mode in an operating system.
The document discusses the process of compiler design, including how a compiler translates source code from a programming language into machine code by performing analysis and synthesis. It describes the main components of a compiler as the scanner, parser, semantic routines, code generator, and optimizer. Additionally, it provides definitions and examples of nondeterministic finite automata (NFA) and deterministic finite automata (DFA) which are used in lexical analysis.
The document summarizes presentations from four students in the Ethernet Group on using the bisection method to find roots of equations. Student 1 presented on the bisection method algorithm. Student 2 used bisection method to find the root of x^3 - 2x - 3 = 0 to within 0.0001. Student 3 similarly used bisection method to find the root of x^3 - x - 2 = 0 to within 0.0001. Student 4 used bisection method to find the root of x^3 - 3x - 6 = 0 to within 0.0001, showing the iterative steps and calculations.
The document outlines a proposed student result management system with the following key points:
1. The system will manage student, subject, and marks information for classes 6-10 and generate tabulation sheets. It will also maintain the institute website.
2. The background discusses similar existing systems used in education sectors.
3. Performance will be evaluated based on user interfaces, validity checks, and software attributes like security, maintainability and portability.
The system aims to simplify and speed up the result preparation and management process for educational institutes.
This document discusses routing protocols in computer networks. It defines distance vector routing and link state routing, describing how each determines the best path. Distance vector routing uses hop count as a metric and shares routing tables, while link state routing shares information about network reachability and metrics. The document provides examples of how distance vector and Dijkstra's algorithm work through updating routing tables. It outlines characteristics of routers and compares advantages and disadvantages of the two routing approaches.
The document provides information about different generations of computers. It discusses Babbage's Analytical Engine as an early predecessor to modern computers. The first generation used vacuum tubes and were large, expensive machines. The second generation used transistors, making computers smaller, cheaper, and more reliable. The third generation used integrated circuits, making computers even smaller and faster. Later generations incorporated microprocessors, leading to the development of personal computers. The document also discusses some examples of specific early computers like the IAS and IBM 360.
This document discusses semiconductors and provides details about their properties and characteristics. It defines a semiconductor as a material with resistivity between that of a conductor and insulator. Some key points made in the document are:
- Semiconductors have properties like negative temperature coefficient of resistance and their conductivity can change when doped with impurities.
- Common semiconductors include silicon and germanium which form covalent bonds and have small energy gaps between valence and conduction bands.
- Semiconductors can be doped as n-type or p-type to introduce free electrons or holes that serve as majority carriers for current flow. A pn junction is formed when a p-type and n-
This document provides an introduction to the Open Systems Interconnection (OSI) model, which defines seven layers of network architecture: Physical, Data Link, Network, Transport, Session, Presentation, and Application. It describes each layer's responsibilities in moving data from one node or process to another. The Physical layer moves individual bits, the Data Link layer moves frames between nodes, the Network layer moves packets between hosts, the Transport layer moves segments between processes, and so on up to the Application layer which provides user-level services. An illustration is included showing how data is encapsulated and moves through each layer in an exchange between two systems.
This document contains examples of different types of tabular data presentations:
- Univariate tables use one variable to organize data into rows and columns, such as user numbers for different mobile operators.
- Bivariate tables use two variables, like the number of rich and middle class cellphone users of different brands.
- Multivariate tables use two or more variables, for example semester-wise results for students organized by department, age group, and school board performance.
Tabular presentations clearly organize data without losing information, and are useful for database design, cascading style sheets, Excel sheets, and representing data in word documents.
This document discusses production costs from the perspective of a group presenting on the topic. It defines costs and production costs, outlines the main types of costs including fixed, variable, average and marginal costs. It provides examples and formulas to calculate these different costs. The document also discusses elements that make up production costs, the relationship between different cost curves, how to estimate costs, and provides a summary of how costs and profits are considered from an economic and accounting perspective.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
Better Builder Magazine brings together premium product manufactures and leading builders to create better differentiated homes and buildings that use less energy, save water and reduce our impact on the environment. The magazine is published four times a year.
Online train ticket booking system project.pdfKamal Acharya
Rail transport is one of the important modes of transport in India. Now a days we
see that there are railways that are present for the long as well as short distance
travelling which makes the life of the people easier. When compared to other
means of transport, a railway is the cheapest means of transport. The maintenance
of the railway database also plays a major role in the smooth running of this
system. The Online Train Ticket Management System will help in reserving the
tickets of the railways to travel from a particular source to the destination.
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Paper Title
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation with Hybrid Beam Forming Power Transfer in WSN-IoT Applications
Authors
Reginald Jude Sixtus J and Tamilarasi Muthu, Puducherry Technological University, India
Abstract
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
Keywords
Signal to Noise Ratio (SNR), Bit Error Rate (BER), mm-Wave, MIMO, NOMA, deep learning, optimization.
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Here's where you can reach us : ijcnc@airccse.org or ijcnc@aircconline.com
Cricket management system ptoject report.pdfKamal Acharya
The aim of this project is to provide the complete information of the National and
International statistics. The information is available country wise and player wise. By
entering the data of eachmatch, we can get all type of reports instantly, which will be
useful to call back history of each player. Also the team performance in each match can
be obtained. We can get a report on number of matches, wins and lost.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
4. Finite Automata - DFA & NFA
Definition: A deterministic finite automaton (DFA) consists of
• 1. a finite set of states (often denoted Q)
• 2. a finite set of symbols (alphabet)
• 3. a transition function that takes as argument a state and a
• symbol and returns a state (often denoted )
• 4. a start state often denoted q0
• 5. a set of final or accepting states (often denoted F)
5. Finite Automata - DFA & NFA
How to present a DFA? With a transition table
• The ! indicates the start state: here q0
• The indicates the final state(s) (here only one final state q1)
• This defines the following transition diagram
7. Finite Automata - DFA & NFA
How to present a NFA? With a transition table
8. Context Free Grammar
What is a grammar?:
A grammar consists of one or more variables that represent
classes of strings (i.e., languages)
There are rules that say how the strings in each class are
constructed. The construction can use :
1. symbols of the alphabet
2. strings that are already known to be in one of the
classes
3. or both
9. Context Free Grammar
Definition of Context-Free Grammar:
A CFG (or just a grammar) G is a tuple G = (V, T, P, S) where
1. V is the (finite) set of variables (or nonterminal or syntactic
categories). Each variable represents a language, i.e., a set of
strings .
2. T is a finite set of terminals, i.e., the symbols that form the
strings of the language being defined.
3. P is a set of production rules that represent the recursive
definition of the language.
4. S is the start symbol that represents the language being
defined. Other variables represent auxiliary classes of
strings that are used to help define the language of the start
symbol.
10. Context Free Grammar
Examples 1(CFG): The grammar:
1. E → I
2. E → E + E
3. E → E ∗ E
4. E → (E)
5. I → a
6. I → b
7. I → Ia
8. I → Ib
9. I → I 0
10. I → I 1
The Grammar for expression is started formally as,
Grammar G 1 = ( {E, I }, T, P, E)
where: T = { +, ∗, (, ), a, b, 0, 1 } is the set of symbol and P is
the set of productions
11. Context Free Grammar
Derivation:
The sequence of substitutions to generate a string is called a
derivation.
Derivation both are two types:
1.Left-Most Derivation.
2.Right-Most Derivation.
Left-Most Derivation:
A derivation which always replace the leftmost variable in each step is
called a leftmost derivation.
Examples of Left-Most derivation to Previous Examples1:
Derivation of a ∗ (a + b00) by G 1
E ⇒ E ∗ E ⇒ I ∗ E ⇒ a ∗ E ⇒ a ∗ (E) ⇒ a ∗ (E + E) ⇒ a ∗ (I + E) ⇒ a ∗
(a + E) ⇒ a ∗ (a + I) ⇒ a ∗ (a + I0) ⇒ a ∗ (a + I00) ⇒ a ∗ (a + b00)
We can conclude that E ∗ ⇒lm a ∗ (a + b00).
12. Context Free Grammar
Right-Most Derivation:
Rightmost derivation Always replace the rightmost variable by
one of its rule-bodies.
Examples of Right-Most derivation to Previous Examples 1:
E ⇒E ∗ E ⇒ E ∗ (E) ⇒ E ∗ (E + E) ⇒ E ∗ (E + I) ⇒rm E
∗ (E + I0) ⇒ E ∗ (E + I00) ⇒ E ∗ (E + b00) ⇒ E ∗ (I + b00) ⇒ E ∗
(a + b00) ⇒ I ∗ (a + b00) ⇒ a ∗ (a + b00)
We can conclude that E ∗ ⇒rm a ∗ (a + b00).
13. Push Down Automata
Definition:
A pushdown automata (PDA) is essentially an -NFA with a
stack.
• On a transition, the PDA:
1. Consumes an input symbol.
2. Goes to a new state (or stays in the old).
3. Replaces the top of the stack by any string (does nothing,
pops the
stack, or pushes a string onto
the stack)
16. Turing Machine
Definition:
We can give a formal description to a particular TM by
specifying each of its seven components.
This way a TM can become cumbersome.
Note: To avoid this we use higher level descriptions which are
precise enough for the purpose of understanding
However, every higher level description is actually just a
short hand for its formal counterpart.
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