The document discusses the pumping lemma for regular sets. It states that for any regular language L, there exists a constant n such that any string w in L of length at least n can be broken down into sections xyz such that y is not empty, xy is less than or equal to n, and xykz is in L for all k. The pumping lemma can be used to show a language is not regular by finding a string that does not satisfy the lemma conditions. Examples are provided to demonstrate how to use the pumping lemma to prove languages are not regular.
Regular expressions-Theory of computationBipul Roy Bpl
Regular expressions are a notation used to specify formal languages by defining patterns over strings. They are declarative and can describe the same languages as finite automata. Regular expressions are composed of operators for union, concatenation, and Kleene closure and can be converted to equivalent non-deterministic finite automata and vice versa. They also have an algebraic structure with laws governing how expressions combine and simplify.
The document discusses the pumping lemma for regular and context-free languages. It states that for regular languages, any string of length greater than n can be broken down into uvxyz where pumping uvixyiz for i >= 0 keeps the string in the language. For context-free languages, any string can be broken into five parts where pumping the second and fourth parts keeps the string in the language. Examples are given demonstrating how pumping works for strings generated by a context-free grammar.
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.
The document discusses the equivalence between context-free grammars (CFGs) and pushdown automata (PDAs). It states that for any CFG, an equivalent PDA can be constructed to accept the language generated by the grammar, and vice versa. This allows a programming language to be specified by a CFG and implemented with a PDA in a compiler. The document also provides procedures for converting between CFGs and PDAs, including an example of constructing a PDA from a given CFG.
This document discusses automata theory and focuses on grammars, languages, and finite state machines. It defines key terminology like alphabets, strings, languages, and regular expressions. It explains Chomsky's hierarchy of formal languages from type-3 regular languages to type-0 recursively enumerable languages. The document also discusses finite state automata (FSA), deterministic finite automata (DFA), non-deterministic finite automata (NFA), context-free grammars, pushdown automata, and Turing machines. Examples of grammars, languages, and finite state machines are provided to illustrate these concepts.
Context-free languages can be described using context-free grammars, which are recursive rules that generate strings in a language. An example grammar is presented that generates strings of 1s and 0s separated by # symbols. Context-free grammars consist of variables, terminals, rules with variables on the left-hand side replacing with strings on the right-hand side, and a starting variable. Context-free languages can be recognized by pushdown automata using an extra stack. Regular languages are a subset of context-free languages. Context-free languages have closure properties including union, concatenation, and homomorphism. Derivation trees can represent grammar derivations and Backus-Naur form is a notation for compactly representing
The document discusses the pumping lemma for regular sets. It states that for any regular language L, there exists a constant n such that any string w in L of length at least n can be broken down into sections xyz such that y is not empty, xy is less than or equal to n, and xykz is in L for all k. The pumping lemma can be used to show a language is not regular by finding a string that does not satisfy the lemma conditions. Examples are provided to demonstrate how to use the pumping lemma to prove languages are not regular.
Regular expressions-Theory of computationBipul Roy Bpl
Regular expressions are a notation used to specify formal languages by defining patterns over strings. They are declarative and can describe the same languages as finite automata. Regular expressions are composed of operators for union, concatenation, and Kleene closure and can be converted to equivalent non-deterministic finite automata and vice versa. They also have an algebraic structure with laws governing how expressions combine and simplify.
The document discusses the pumping lemma for regular and context-free languages. It states that for regular languages, any string of length greater than n can be broken down into uvxyz where pumping uvixyiz for i >= 0 keeps the string in the language. For context-free languages, any string can be broken into five parts where pumping the second and fourth parts keeps the string in the language. Examples are given demonstrating how pumping works for strings generated by a context-free grammar.
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.
The document discusses the equivalence between context-free grammars (CFGs) and pushdown automata (PDAs). It states that for any CFG, an equivalent PDA can be constructed to accept the language generated by the grammar, and vice versa. This allows a programming language to be specified by a CFG and implemented with a PDA in a compiler. The document also provides procedures for converting between CFGs and PDAs, including an example of constructing a PDA from a given CFG.
This document discusses automata theory and focuses on grammars, languages, and finite state machines. It defines key terminology like alphabets, strings, languages, and regular expressions. It explains Chomsky's hierarchy of formal languages from type-3 regular languages to type-0 recursively enumerable languages. The document also discusses finite state automata (FSA), deterministic finite automata (DFA), non-deterministic finite automata (NFA), context-free grammars, pushdown automata, and Turing machines. Examples of grammars, languages, and finite state machines are provided to illustrate these concepts.
Context-free languages can be described using context-free grammars, which are recursive rules that generate strings in a language. An example grammar is presented that generates strings of 1s and 0s separated by # symbols. Context-free grammars consist of variables, terminals, rules with variables on the left-hand side replacing with strings on the right-hand side, and a starting variable. Context-free languages can be recognized by pushdown automata using an extra stack. Regular languages are a subset of context-free languages. Context-free languages have closure properties including union, concatenation, and homomorphism. Derivation trees can represent grammar derivations and Backus-Naur form is a notation for compactly representing
Push Down Automata (PDA) | TOC (Theory of Computation) | NPDA | DPDAAshish Duggal
Push Down Automata (PDA) is part of TOC (Theory of Computation)
From this presentation you will get all the information related to PDA also it will help you to easily understand this topic. There is also one example.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
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.
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.
This file contains the concepts of Class P, Class NP, NP- completeness, Travelling Salesman Person problem, Clique Problem, Vertex cover problem, Hamiltonian problem, FFT and DFT.
This document discusses two-way deterministic finite automata (2DFA). 2DFA can read input symbols multiple times by moving the read head back and forth, unlike DFA which reads once from left to right. The document provides an example of a 2DFA that accepts strings where the number of a's is divisible by 3 and the number of b's is even. It notes that while 2DFA may use more memory than DFA, some problems can be solved more simply with 2DFA than DFA. The document also formally defines 2DFA and compares their capabilities to DFA and Turing machines.
The document discusses pushdown automata (PDA). It defines a PDA as a 7-tuple that includes a set of states, input alphabet, stack alphabet, initial/start state, starting stack symbol, set of final/accepting states, and a transition function. PDAs operate on an input tape with a stack, and can accept languages that finite automata cannot, such as anbn. The document provides examples of designing PDAs for specific languages and converting between context-free grammars and PDAs.
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.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
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 describes three methods for minimizing a deterministic finite automaton (DFA): the partitioning method, the equivalence theorem, and Myhill-Nerode theorem. The partitioning method iteratively partitions the states into equivalences classes until no further partitions can be made. The equivalence theorem removes unreachable and equivalent states by comparing the transitions of each state pair. The Myhill-Nerode theorem marks state pairs where one is final and one is not, then iteratively marks additional pairs based on their transitions until no more can be marked, with unmarked pairs becoming equivalent states.
The document discusses symbol tables, which are data structures used by compilers to track semantic information about identifiers, variables, functions, classes, etc. It provides details on:
- How various compiler phases like lexical analysis, syntax analysis, semantic analysis, code generation utilize and update the symbol table.
- Common data structures used to implement symbol tables like linear lists, hash tables and how they work.
- The information typically stored for different symbols like name, type, scope, memory location etc.
- Organization of symbol tables for block-structured vs non-block structured languages, including using multiple nested tables vs a single global table.
recognizer for a language, Deterministic finite automata, Non-deterministic finite automata, conversion of NFA to DFA, Regular Expression to NFA, Thomsons Construction
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
This document discusses deterministic finite automata (DFA) minimization. It defines the components of a DFA and provides an example of a non-minimized DFA that accepts strings with 'a' or 'b'. The document then introduces an algorithm to minimize a DFA by identifying redundant states that are not necessary to recognize the language. The algorithm works by iteratively labeling states as distinct or equivalent based on their transitions and whether they are accepting states. This process combines equivalent states to produce a minimized DFA with the smallest number of states.
The document discusses simplifying context-free grammars through three steps:
1) Eliminating useless symbols by removing productions that can never be used to derive strings from the starting variable.
2) Eliminating null productions by removing productions with the empty string on the right-hand side.
3) Eliminating unit productions by removing productions where a single non-terminal symbol produces a single terminal symbol.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
The document discusses constructing a directed acyclic graph (DAG) to represent the computation of values in a basic block of code. It describes how to build the DAG by processing each statement and creating nodes for operators and values. The DAG makes it possible to analyze the code block to optimize computations by removing duplicate subexpressions and determine which values are used inside and outside the block.
Automata theory - describes to derives string from Context free grammar - derivation and parse tree
normal forms - Chomsky normal form and Griebah normal form
The document discusses syntax analysis and parsing. It defines context-free grammars and different types of grammars. It also discusses derivation, parse trees, ambiguity in grammars and different parsing techniques like top-down and bottom-up parsing.
Push Down Automata (PDA) | TOC (Theory of Computation) | NPDA | DPDAAshish Duggal
Push Down Automata (PDA) is part of TOC (Theory of Computation)
From this presentation you will get all the information related to PDA also it will help you to easily understand this topic. There is also one example.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
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.
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.
This file contains the concepts of Class P, Class NP, NP- completeness, Travelling Salesman Person problem, Clique Problem, Vertex cover problem, Hamiltonian problem, FFT and DFT.
This document discusses two-way deterministic finite automata (2DFA). 2DFA can read input symbols multiple times by moving the read head back and forth, unlike DFA which reads once from left to right. The document provides an example of a 2DFA that accepts strings where the number of a's is divisible by 3 and the number of b's is even. It notes that while 2DFA may use more memory than DFA, some problems can be solved more simply with 2DFA than DFA. The document also formally defines 2DFA and compares their capabilities to DFA and Turing machines.
The document discusses pushdown automata (PDA). It defines a PDA as a 7-tuple that includes a set of states, input alphabet, stack alphabet, initial/start state, starting stack symbol, set of final/accepting states, and a transition function. PDAs operate on an input tape with a stack, and can accept languages that finite automata cannot, such as anbn. The document provides examples of designing PDAs for specific languages and converting between context-free grammars and PDAs.
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.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
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 describes three methods for minimizing a deterministic finite automaton (DFA): the partitioning method, the equivalence theorem, and Myhill-Nerode theorem. The partitioning method iteratively partitions the states into equivalences classes until no further partitions can be made. The equivalence theorem removes unreachable and equivalent states by comparing the transitions of each state pair. The Myhill-Nerode theorem marks state pairs where one is final and one is not, then iteratively marks additional pairs based on their transitions until no more can be marked, with unmarked pairs becoming equivalent states.
The document discusses symbol tables, which are data structures used by compilers to track semantic information about identifiers, variables, functions, classes, etc. It provides details on:
- How various compiler phases like lexical analysis, syntax analysis, semantic analysis, code generation utilize and update the symbol table.
- Common data structures used to implement symbol tables like linear lists, hash tables and how they work.
- The information typically stored for different symbols like name, type, scope, memory location etc.
- Organization of symbol tables for block-structured vs non-block structured languages, including using multiple nested tables vs a single global table.
recognizer for a language, Deterministic finite automata, Non-deterministic finite automata, conversion of NFA to DFA, Regular Expression to NFA, Thomsons Construction
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
This document discusses deterministic finite automata (DFA) minimization. It defines the components of a DFA and provides an example of a non-minimized DFA that accepts strings with 'a' or 'b'. The document then introduces an algorithm to minimize a DFA by identifying redundant states that are not necessary to recognize the language. The algorithm works by iteratively labeling states as distinct or equivalent based on their transitions and whether they are accepting states. This process combines equivalent states to produce a minimized DFA with the smallest number of states.
The document discusses simplifying context-free grammars through three steps:
1) Eliminating useless symbols by removing productions that can never be used to derive strings from the starting variable.
2) Eliminating null productions by removing productions with the empty string on the right-hand side.
3) Eliminating unit productions by removing productions where a single non-terminal symbol produces a single terminal symbol.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
The document discusses constructing a directed acyclic graph (DAG) to represent the computation of values in a basic block of code. It describes how to build the DAG by processing each statement and creating nodes for operators and values. The DAG makes it possible to analyze the code block to optimize computations by removing duplicate subexpressions and determine which values are used inside and outside the block.
Automata theory - describes to derives string from Context free grammar - derivation and parse tree
normal forms - Chomsky normal form and Griebah normal form
The document discusses syntax analysis and parsing. It defines context-free grammars and different types of grammars. It also discusses derivation, parse trees, ambiguity in grammars and different parsing techniques like top-down and bottom-up parsing.
The document discusses various topics related to formal languages and automata theory including:
- Definitions of alphabets, strings, regular expressions, and formal languages. Regular expressions can be used to represent regular languages.
- Four types of grammars (Type-0 to Type-3) with Type-3 grammars generating regular languages and Type-2 grammars generating context-free languages.
- Components of a grammar including nonterminal symbols, terminal symbols, rules, and a starting symbol.
- Turing machines and their components including states, tape alphabet, transition function, initial/final states, and blank symbol.
- Decidability and reducibility. The halting problem is un
The document discusses compiler theory, automata, and language. It provides an overview of compilers and how they translate source code into target code by using a two-stage process of analysis and synthesis. The analysis stage includes lexical, syntactic, and semantic analysis, while the synthesis stage includes generating intermediate code, optimizing code, and generating target code. It also discusses how automata and formal language theory relate to compiler design and implementation.
This document discusses compiler theory, automata, and language. It begins by defining a compiler as a program that translates source code written in one language into another target language. It then discusses how automata and language theory relate to compiler concepts like lexical analysis, syntactic analysis, semantic analysis, and code generation. The compilation process involves two main stages - analysis and synthesis. Analysis breaks down the source code while synthesis generates intermediate code, performs optimizations, and ultimately generates the target code.
Theory of automata and formal language lab manualNitesh Dubey
The document describes several experiments related to compiler design including lexical analysis, parsing, and code generation.
Experiment 1 involves writing a program to identify if a given string is an identifier or not using a DFA. Experiment 2 simulates a DFA to check if a string is accepted by the given automaton. Experiment 3 checks if a string belongs to a given grammar using a top-down parsing approach. Experiment 4 implements recursive descent parsing to parse expressions based on a grammar. Experiment 5 computes FIRST and FOLLOW sets and builds a LL(1) parsing table for a given grammar. Experiment 6 implements shift-reduce parsing to parse strings. Experiment 7 generates intermediate code like Polish notation, 3-address code, and quadruples
This document introduces the key concepts in the theory of computation, including automata, formal languages, and grammars. It defines automata as abstract models that accept input, process it, and produce output. Formal languages are sets of strings formed from symbols according to rules, and grammars are sets of rules for generating the strings in a language. The document also reviews mathematical concepts needed to study computation and provides examples of operations on strings and languages.
Formal Languages and Automata Theory unit 4Srimatre K
The document discusses various topics related to context-free grammars including:
1. Normal forms like Chomsky normal form and Greibach normal form that put constraints on the structure of productions in a context-free grammar.
2. The pumping lemma for context-free languages and how it can be used to prove that a language is not context-free.
3. Closure properties of context-free languages like their closure under union, concatenation and Kleene star but not under intersection and complement.
4. Decision properties of context-free languages and how questions of emptiness, membership and finiteness can be solved.
5. An introduction to Turing machines as accepting devices for recursively enumerable
The document discusses lexical analysis, which is the first stage of syntax analysis for programming languages. It covers terminology, using finite automata and regular expressions to describe tokens, and how lexical analyzers work. Lexical analyzers extract lexemes from source code and return tokens to the parser. They are often implemented using finite state machines generated from regular grammar descriptions of the lexical patterns in a language.
This document discusses context free grammars (CFG). It defines the key components of a CFG including terminals, non-terminals, and productions. Terminals are symbols that cannot be replaced, non-terminals must be replaced, and productions are the grammatical rules. A CFG consists of an alphabet of terminals, non-terminals (including a start symbol S), and a finite set of productions that replace non-terminals with strings of terminals and/or non-terminals. Several examples are provided to illustrate how CFGs can define different context free languages.
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
FINITE STATE MACHINE AND CHOMSKY HIERARCHYnishimanglani
This document discusses finite state machines and the Chomsky hierarchy. It defines a finite state machine as a machine that has a finite number of states and can change states and produce outputs based on its current state and inputs. A finite state machine is formally defined by its states, inputs, outputs, transition function, and initial state. The document also explains the four types of grammars in the Chomsky hierarchy - type-3 (regular), type-2 (context-free), type-1 (context-sensitive), and type-0 (unrestricted) - and the languages and automata associated with each type.
The Chomsky hierarchy arranges formal languages based on the type of grammar needed to describe them. Type-0 languages are the most powerful, including all recursively enumerable languages and are described by unrestricted grammars. Type-1 languages are context-sensitive and described by context-sensitive grammars. Type-2 languages are context-free and accepted by pushdown automata using context-free grammars. Type-3, the least powerful type, are regular languages described by regular grammars and recognized by finite state automata. Each language type in the hierarchy includes all languages of less restrictive types.
The document discusses context free grammars and related concepts. It defines context free grammars and provides examples. It also discusses Chomsky hierarchy, classifying grammars into types 0-3 (unrestricted to regular) based on production rules. Formal languages generated by each grammar type are described along with their properties and closure properties. Context free grammars are defined in more detail, covering derivation, Backus-Naur form, and leftmost and rightmost derivations.
This document provides an introduction to programming in GWBASIC. It discusses that GWBASIC is a good starting point for beginners as it is easy to learn and has graphics capabilities. The key building blocks of a GWBASIC program are numbered line statements that contain variables, constants, operations, and control structures. The document outlines various statements and programming concepts in GWBASIC including numeric constants, variables, operations, control structures, input/output, and functions. It provides syntax examples for many of these programming elements.
The Chomsky hierarchy divides formal grammars into 4 types based on generative power. Type 0 grammars are the most powerful and unrestricted, recognizing recursively enumerable languages. Type 1 grammars are context-sensitive, Type 2 are context-free, and Type 3 grammars are regular and recognized by finite state automata. Each type is a subset of the previous type and less powerful in the types of languages they can generate.
This document discusses loops in Python. It introduces loops as a way to repeat instructions multiple times until a condition is met. The two main types of loops in Python are for loops, which iterate over a sequence, and while loops, which execute statements as long as a condition is true. It provides examples of for and while loops and covers else statements, loop control statements like break and continue, and some key points about loops in Python.
This is one of a type of Analog to Digital Converter (ADC).
Through this presentation, you will have a clear view of how an ADC works. This one specifies one of the types of Analog to Digital Convertor.
Through this presentation, you will get to know about Edge computing and explore the fields where it is needed.
You can start exploring the technical knowledge by seeing what industries are working on now-days
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
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
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
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1. ITM
Gwalior
1
INSTITUTE OF TECHNOLOGY AND MANAGEMENT
TOPIC: TYPES OF GRAMMAR
CS-501(A):Theory of computation
Presented to- Presented by-
Dr . Deepak Gupta Abhay Dhupar 0905CS191001
Associate Professor Abhay Singh 0905CS191002
(Dept. of CSE) Abhinav Goyal 0905CS191003
Abhinav Gupta 0905CS191004
2. GRAMMARS
• Noam Chomsky gave a mathematical model of grammar.
This model is used to write computer languages effectively.
• Grammar in theory of computation is a finite set of formal
rules that are generating syntactically correct sentences.
• The formal definition of grammar is that it is defined as four
tuples
ITM
Gwalior
2
3. CONT.
G=(V,T,P,S)
G is a grammar, which consists of a set of production rules. It is used to generate
the strings of a language.
T is the final set of terminal symbols. It is denoted by lower case letters.
V is the final set of non-terminal symbols. It is denoted by capital letters.
P is a set of production rules, which is used for replacing non-terminal symbols
(on the left side of production) in a string with other terminals (on the right side
of production).
S is the start symbol used to derive the string.
ITM
Gwalior
3
4. CONT.
• V = { S , A , B } => Non-Terminal symbols
• T = { a , b } => Terminal symbols
• P = { S → ABa , A → Ba , B → ab} => Production rules
• S = { S } => Start symbol
ITM
Gwalior
4
7. DIFFERENT TYPES OF GRAMMAR
Grammar can be divided on basis of –
Type of Production Rules
Number of Derivation Trees
Number of Strings
ITM
Gwalior
7
9. TYPES OF GRAMMAR
Grammar language Automata Production
Rules
Type 0 Recursively
enumerable
Turning machine No restriction
Type 1 Context-
sensitive
Linear-bounded Non-
deterministic machine
αAβ → αγβ
Type 2 Context-free Non-deterministic push down
Automata
A→γ
Type 3 Regular Finite Automata data A→αB
A→α
ITM
Gwalior
9
11. TYPE 0
ITM
Gwalior
11
Type 0 grammar language are recognized by turing machine.
Grammar Production in the form of
where
α is ( V + T)* V ( V + T)*
β is ( V + T )*.
In type 0 there must be at least one variable on Left side of production.
Ex -
Sab –> ba
A –> S.
12. TYPE 1
Type-1 grammars generate the context-sensitive languages.
The language generated by the grammar are recognized by the Linear
Bound Automata
In Type 1,
I. First of all Type 1 grammar should be Type 0.
II. Grammar Production in the form of
|α | <= | β |
i.e count of symbol in α is less than or equal to
Ex S –> AB
AB –> abc
B –> b
ITM
Gwalior
12
13. TYPE 2
Type-2 grammars generate the context-free languages. The language
generated by the grammar is recognized by a Pushdown automata.
In Type 2,
1. First of all it should be Type 1.
2. Left hand side of production can have only one variable.
|α| = 1.
Their is no restriction on β .
Ex -
S –> AB
A –> a
B –> b
ITM
Gwalior
13
14. TYPE 3
Type-3 grammars generate regular languages. These languages are
exactly all languages that can be accepted by a finite state automaton.
Type 3 is most restricted form of grammar.
It should be in the given form only :
V –> VT / T (left-regular grammar)
(or)
V –> TV /T (right-regular grammar)
Ex -
S –> a
ITM
Gwalior
14
15. APPLICATION OF GRAMMAR
• For defining programming languages
• For parsing the program by constructing syntax tree
• For translation of programming languages
• For describing arithmetic expressions
• For construction of compilers, etc.
ITM
Gwalior
15
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