This document provides an overview of agile methodology and compares it to traditional waterfall development. It describes waterfall development as a sequential process with distinct phases completed one after another. Agile approaches like Scrum and Kanban are presented as more iterative and adaptive alternatives that focus on delivering working software frequently in short cycles through self-organizing cross-functional teams. Key aspects of Scrum like sprints, daily stand-ups, and product backlogs are defined. Kanban emphasizes visualizing and limiting work in progress to optimize flow. Both aim to incorporate feedback and respond rapidly to changes over rigidly following pre-defined plans.
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 summarizes the Turing machine. It describes a Turing machine as having three main elements: an input/output tape, a read/write head that moves bidirectionally along the tape, and a finite state control. It operates by examining the symbol under the head along with its current state to determine the symbol to write, the head's movement, and the next state. The document then provides a formal definition of a Turing machine as a 7-tuple and describes some variations including those with different tape configurations and those that are nondeterministic or have multiple tapes/heads.
HiperLAN was developed as a wireless local area network standard by ETSI to provide higher data rates than early 802.11 standards. HiperLAN Type 1 achieved data rates up to 2 Mbps for ad hoc networking. HiperLAN Type 2 was later developed to provide connection-oriented service up to 54 Mbps, with quality of service guarantees, security, and flexibility. It uses OFDM in the 5 GHz spectrum for robust transmission. While early products only achieved 25 Mbps, the standard provides a framework for higher speeds as technologies advance. HiperLAN is intended to complement wired networks by providing wireless connectivity in hotspot areas like offices, homes, and public places.
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.
Recognizing Opportunities and generating Ideas.Fahad Mahmood
The document discusses techniques for recognizing opportunities and generating ideas for new businesses. It describes three approaches entrepreneurs use to identify opportunities: observing trends, solving problems, and finding gaps in the marketplace. Personal characteristics like prior experience, social networks, cognitive factors, and creativity can help entrepreneurs recognize opportunities. The document also outlines techniques for generating ideas like brainstorming, focus groups, and research. It emphasizes that developing new ideas is important for entrepreneurial success.
The document discusses the Post Correspondence Problem (PCP) and shows that it is undecidable. It defines PCP as determining if there is a sequence of string pairs from two lists A and B that match up. It then defines the Modified PCP (MPCP) which requires the first pair to match. It shows how to reduce the Universal Language Problem to MPCP by mapping a Turing Machine and input to lists A and B, and then how to reduce MPCP to PCP. Finally, it discusses Rice's Theorem and how properties of recursively enumerable languages are undecidable.
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
This document provides an overview of Turing machines. It introduces Turing machines as a simple mathematical model of a computer proposed by Alan Turing in 1936. A Turing machine consists of a tape divided into cells, a read/write head, finite states, and a transition function. The transition function defines how the machine moves between states and reads/writes symbols on the tape based on its current state and tape symbol. Turing machines can accept languages and compute functions. Variations include multi-tape, non-deterministic, multi-head, offline, multi-dimensional, and stationary-head Turing machines. The properties of Turing machines include their ability to recognize any language generated by a phrase-structure grammar and the Church
The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.
This document discusses variants of Turing machines that increase their computational power and proves that any language accepted by a variant Turing machine is also accepted by a standard Turing machine. It covers variants such as multiple tapes, two-way infinite tapes, and nondeterminism. For each variant, it provides a proof that the standard Turing machine is equally powerful by simulating the variant with the standard model.
The document discusses solving the 8 queens problem using backtracking. It begins by explaining backtracking as an algorithm that builds partial candidates for solutions incrementally and abandons any partial candidate that cannot be completed to a valid solution. It then provides more details on the 8 queens problem itself - the goal is to place 8 queens on a chessboard so that no two queens attack each other. Backtracking is well-suited for solving this problem by attempting to place queens one by one and backtracking when an invalid placement is found.
This document provides information about Post Machines, including:
- Post Machines are a variant of Turing Machines based on Emil Post's model that uses a queue instead of a tape.
- Post Machines can accept some context-free and non-context-free languages, just like Turing Machines. There is a proof that any language accepted by a Post Machine is also accepted by an equivalent Turing Machine and vice versa.
- Post Machines operate by reading and adding symbols to the queue/store and have read, add, and halt states but no push states like a pushdown automaton. They must terminate in an accept or reject state.
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.
NP completeness. Classes P and NP are two frequently studied classes of problems in computer science. Class P is the set of all problems that can be solved by a deterministic Turing machine in polynomial time.
This document discusses the process of converting a context-free grammar (CFG) into Chomsky normal form (CNF) in multiple steps. It first recalls the definition of CNF and the theorem that every context-free language minus the empty string has a CFG in CNF. It then outlines the steps to convert a CFG to CNF: 1) remove epsilon productions, 2) remove unit rules, 3) break productions with more than two variables into chains of productions with two variables, and 4) ensure all productions are in the forms A->BC or A->a. The document provides two examples showing the full conversion process.
This document presents an overview of the Floyd-Warshall algorithm. It begins with an introduction to the algorithm, explaining that it finds shortest paths in a weighted graph with positive or negative edge weights. It then discusses the history and naming of the algorithm, attributed to researchers in the 1950s and 1960s. The document proceeds to provide an example of how the algorithm works, showing the distance and sequence tables that are updated over multiple iterations to find shortest paths between all pairs of vertices. It concludes with discussing the time and space complexity, applications, and references.
The document discusses code generation in compilers. It describes the main tasks of the code generator as instruction selection, register allocation and assignment, and instruction ordering. It then discusses various issues in designing a code generator such as the input and output formats, memory management, different instruction selection and register allocation approaches, and choice of evaluation order. The target machine used is a hypothetical machine with general purpose registers, different addressing modes, and fixed instruction costs. Examples of instruction selection and utilization of addressing modes are provided.
Decision properties of reular languagesSOMNATHMORE2
This document discusses decision properties of regular languages. It defines regular languages as those that can be described by regular expressions and accepted by finite automata. It explains that decision properties are algorithms that take a formal language description and determine properties like emptiness, finiteness, membership in the language, and equivalence to another language. The key decision properties - emptiness, finiteness, membership, and equivalence - are then defined along with the algorithms to determine each. Examples are provided to illustrate the algorithms. Applications of decision properties in areas like data validation and parsing are also mentioned.
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.
This document discusses converting non-deterministic finite automata (NFA) to deterministic finite automata (DFA). NFAs can have multiple transitions with the same symbol or no transition for a symbol, while DFAs have a single transition for each symbol. The document provides examples of NFAs and their representations, and explains how to systematically construct a DFA that accepts the same language as a given NFA by considering all possible state combinations in the NFA. It also notes that NFAs and DFAs have equal expressive power despite their differences, and discusses minimizing DFAs and relationships to other automata models.
Bottom-up parsing builds a derivation by working from the input sentence back toward the start symbol S. It is preferred in practice and also called LR parsing, where L means tokens are read left to right and R means it constructs a rightmost derivation. The two main types are operator-precedence parsing and LR parsing, which covers a wide range of grammars through techniques like SLR, LALR, and LR parsing. LR parsing reduces a string to the start symbol by inverting productions through identifying handles and replacing them.
The document discusses window to viewport transformation. It defines a window as a world coordinate area selected for display and a viewport as a rectangular region of the screen selected for displaying objects. Window to viewport mapping requires transforming coordinates from the window to the viewport. This involves translation, scaling and another translation. Steps include translating the window to the origin, resizing it based on the viewport size, and translating it to the viewport position. An example transforms a sample window to a viewport through these three steps.
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
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
The document discusses the Post Correspondence Problem (PCP) and shows that it is undecidable. It defines PCP as determining if there is a sequence of string pairs from two lists A and B that match up. It then defines the Modified PCP (MPCP) which requires the first pair to match. It shows how to reduce the Universal Language Problem to MPCP by mapping a Turing Machine and input to lists A and B, and then how to reduce MPCP to PCP. Finally, it discusses Rice's Theorem and how properties of recursively enumerable languages are undecidable.
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
This document provides an overview of Turing machines. It introduces Turing machines as a simple mathematical model of a computer proposed by Alan Turing in 1936. A Turing machine consists of a tape divided into cells, a read/write head, finite states, and a transition function. The transition function defines how the machine moves between states and reads/writes symbols on the tape based on its current state and tape symbol. Turing machines can accept languages and compute functions. Variations include multi-tape, non-deterministic, multi-head, offline, multi-dimensional, and stationary-head Turing machines. The properties of Turing machines include their ability to recognize any language generated by a phrase-structure grammar and the Church
The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.
This document discusses variants of Turing machines that increase their computational power and proves that any language accepted by a variant Turing machine is also accepted by a standard Turing machine. It covers variants such as multiple tapes, two-way infinite tapes, and nondeterminism. For each variant, it provides a proof that the standard Turing machine is equally powerful by simulating the variant with the standard model.
The document discusses solving the 8 queens problem using backtracking. It begins by explaining backtracking as an algorithm that builds partial candidates for solutions incrementally and abandons any partial candidate that cannot be completed to a valid solution. It then provides more details on the 8 queens problem itself - the goal is to place 8 queens on a chessboard so that no two queens attack each other. Backtracking is well-suited for solving this problem by attempting to place queens one by one and backtracking when an invalid placement is found.
This document provides information about Post Machines, including:
- Post Machines are a variant of Turing Machines based on Emil Post's model that uses a queue instead of a tape.
- Post Machines can accept some context-free and non-context-free languages, just like Turing Machines. There is a proof that any language accepted by a Post Machine is also accepted by an equivalent Turing Machine and vice versa.
- Post Machines operate by reading and adding symbols to the queue/store and have read, add, and halt states but no push states like a pushdown automaton. They must terminate in an accept or reject state.
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.
NP completeness. Classes P and NP are two frequently studied classes of problems in computer science. Class P is the set of all problems that can be solved by a deterministic Turing machine in polynomial time.
This document discusses the process of converting a context-free grammar (CFG) into Chomsky normal form (CNF) in multiple steps. It first recalls the definition of CNF and the theorem that every context-free language minus the empty string has a CFG in CNF. It then outlines the steps to convert a CFG to CNF: 1) remove epsilon productions, 2) remove unit rules, 3) break productions with more than two variables into chains of productions with two variables, and 4) ensure all productions are in the forms A->BC or A->a. The document provides two examples showing the full conversion process.
This document presents an overview of the Floyd-Warshall algorithm. It begins with an introduction to the algorithm, explaining that it finds shortest paths in a weighted graph with positive or negative edge weights. It then discusses the history and naming of the algorithm, attributed to researchers in the 1950s and 1960s. The document proceeds to provide an example of how the algorithm works, showing the distance and sequence tables that are updated over multiple iterations to find shortest paths between all pairs of vertices. It concludes with discussing the time and space complexity, applications, and references.
The document discusses code generation in compilers. It describes the main tasks of the code generator as instruction selection, register allocation and assignment, and instruction ordering. It then discusses various issues in designing a code generator such as the input and output formats, memory management, different instruction selection and register allocation approaches, and choice of evaluation order. The target machine used is a hypothetical machine with general purpose registers, different addressing modes, and fixed instruction costs. Examples of instruction selection and utilization of addressing modes are provided.
Decision properties of reular languagesSOMNATHMORE2
This document discusses decision properties of regular languages. It defines regular languages as those that can be described by regular expressions and accepted by finite automata. It explains that decision properties are algorithms that take a formal language description and determine properties like emptiness, finiteness, membership in the language, and equivalence to another language. The key decision properties - emptiness, finiteness, membership, and equivalence - are then defined along with the algorithms to determine each. Examples are provided to illustrate the algorithms. Applications of decision properties in areas like data validation and parsing are also mentioned.
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.
This document discusses converting non-deterministic finite automata (NFA) to deterministic finite automata (DFA). NFAs can have multiple transitions with the same symbol or no transition for a symbol, while DFAs have a single transition for each symbol. The document provides examples of NFAs and their representations, and explains how to systematically construct a DFA that accepts the same language as a given NFA by considering all possible state combinations in the NFA. It also notes that NFAs and DFAs have equal expressive power despite their differences, and discusses minimizing DFAs and relationships to other automata models.
Bottom-up parsing builds a derivation by working from the input sentence back toward the start symbol S. It is preferred in practice and also called LR parsing, where L means tokens are read left to right and R means it constructs a rightmost derivation. The two main types are operator-precedence parsing and LR parsing, which covers a wide range of grammars through techniques like SLR, LALR, and LR parsing. LR parsing reduces a string to the start symbol by inverting productions through identifying handles and replacing them.
The document discusses window to viewport transformation. It defines a window as a world coordinate area selected for display and a viewport as a rectangular region of the screen selected for displaying objects. Window to viewport mapping requires transforming coordinates from the window to the viewport. This involves translation, scaling and another translation. Steps include translating the window to the origin, resizing it based on the viewport size, and translating it to the viewport position. An example transforms a sample window to a viewport through these three steps.
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
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 discusses deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). It provides examples of DFAs that accept certain languages over various alphabets. It also defines NFAs formally and provides examples of NFAs that accept languages. The key differences between DFAs and NFAs are that NFA transition functions can map a state-symbol pair to multiple possible next states, whereas DFA transition functions map to exactly one next state.
1. The document discusses deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). DFAs have a single transition function, while NFAs have a transition function that can map a state-symbol pair to multiple possible next states.
2. Examples are given of DFAs and NFAs that accept certain languages over various alphabets. The DFA examples use transition diagrams to represent the transition functions, while the NFA examples explicitly define the transition functions.
3. Key properties of DFAs and NFAs are summarized, including their definitions as 5-tuples and how languages are accepted by looking for paths from the starting to a final state.
The document discusses Deterministic Finite Automata (DFAs) and Nondeterministic Finite Automata (NFAs). It defines the key components of a DFA/NFA including states, alphabet, transition function, initial state, and accepting states. It provides examples of DFAs and NFAs and their transition diagrams. It also discusses how to determine if a string is accepted by a DFA/NFA and the language recognized by a DFA/NFA.
This document describes pushdown automata (PDA) and how they are used to recognize context-free languages. It provides definitions of PDA, including their components and transition function. An example PDA is given for the language of balanced parentheses. The document also discusses how PDA can accept by final state or empty stack, and how PDA are equivalent to context-free grammars. It describes how to convert between PDA that accept by final state vs empty stack, and how to construct a PDA from a given context-free grammar.
16 PDA push down autometa push down.pptxnandan543979
While a recent study shows that the projected global shortage of health workers by 2030 has reduced from 18 million to 10 million, the aging of the population induces an increased health need and further widens this gap. An additional 1.8 million health workers are needed in fifty-four countries
The document describes pushdown automata (PDA) which are analogous to context-free languages in the same way that finite automata are analogous to regular languages. A PDA has states, input symbols, stack symbols, transition functions, an initial state, initial stack symbol, and accepting states. The transition function specifies state transitions based on the current state, input symbol, and top of stack symbol and can modify the stack. The document provides examples of PDAs for languages of the form wwr and balanced parentheses and discusses how PDAs work by changing their instantaneous descriptions as the input is processed and stack is modified.
- An NFA can be converted to an equivalent DFA using the subset construction. This involves constructing states of the DFA that correspond to subsets of states of the NFA.
- The subset construction results in an exponential blow-up in the number of states between the NFA and DFA. There is an NFA with n+1 states that requires a DFA with at least 2^n states.
- An epsilon-NFA (ε-NFA) allows epsilon transitions and can be converted to an equivalent DFA using a similar subset construction approach.
This document introduces finite automata and regular languages. It discusses that regular languages can be described by finite automata, regular expressions, and regular grammars. It then defines deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs) formally, provides examples of each, and shows their equivalence in terms of the languages they accept. It also discusses extending the transition function to strings, regular expressions, converting between DFAs/NFAs and regular expressions, and properties of regular expressions.
The document discusses context-free languages and pushdown automata. It defines context-free grammars and languages, and provides examples of grammars and the strings they generate. It also defines pushdown automata formally as a 6-tuple with states, input alphabet, stack alphabet, transition function, start state, and accept states. Pushdown automata are similar to finite automata but have an additional stack which allows them to recognize some non-regular languages.
This document discusses pushdown automata (PDA) and provides examples. It can be summarized as:
(1) A PDA is like a finite automaton but with an additional stack that allows it to remember an infinite amount of information. Transitions can push symbols onto or pop symbols off the stack.
(2) Examples show how a PDA can recognize languages like {0^n 1^n} that require counting, which finite automata cannot do. The stack is used to match 0s and 1s.
(3) The formal definition of a PDA specifies its states, alphabet, stack alphabet, initial/accepting configurations, and transition function defining stack operations.
This document discusses pushdown automata (PDA) and provides examples. It can be summarized as follows:
(1) A PDA is like a finite automaton but with an additional stack that allows it to remember an unlimited amount of information. This makes PDAs more powerful than finite automata.
(2) The document defines PDA formally and explains their transition function and how it incorporates stack operations. Examples are provided to recognize languages like 0^n 1^n that require unlimited memory.
(3) Sample computations are shown step-by-step to demonstrate how PDAs process input strings and manipulate their stack according to the transition function. Negative examples are also included to show strings that would
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.
The document discusses pushdown automata (PDA) and provides examples of how PDA can be used to recognize certain languages. It begins by explaining that PDA extend finite state automata by adding a stack, allowing them to "remember" an infinite amount of information. Several examples are then provided to illustrate how PDA work, including languages of balanced parentheses, strings with equal numbers of 0s and 1s, and strings of the form wcwr. The formal definition of a PDA is given, and transition diagrams are used to visualize example computations.
1. The document describes the process of converting a regular expression (RE) = (ab+c)* to an equivalent deterministic finite automaton (DFA).
2. It starts with the equivalent non-deterministic finite automaton with epsilon transitions (NFA-ε) for the given RE.
3. It then constructs the DFA by calculating the epsilon-closure of states and determining the transitions between states for each symbol in the alphabet.
4. The resulting DFA has 4 states - A, B, C, D and the transition table and diagram are shown.
Here are the steps to construct an NFA-ε equivalent to the given regular expressions:
1. ε + a b (a+b)*
- States: q0, q1, q2
- Transitions: q0 -> q1 on ε, q0 -> q2 on a, q1 -> q1 on a/b, q1 -> q2 on b
- Start state: q0
- Final states: q2
2. (0+1)* (00+11)
- States: q0, q1, q2, q3
- Transitions: q0 -> q0 on 0/1, q0 -> q1 on 0/1
The document describes how to convert a given NFA-ε into an equivalent DFA. It finds the ε-closure of each state in the NFA to create the states of the DFA. It then determines the transitions between these DFA states on each input symbol by taking the ε-closure of the NFA state transitions. This results in a DFA transition table and diagram that is equivalent to the original NFA.
The document describes the process of converting a non-deterministic finite automaton (NFA) to a deterministic finite automaton (DFA). It provides an example of an NFA with initial state q0 and shows the steps to obtain the transition function δ' and states of the equivalent DFA. The resulting DFA has states A, B, and C, with transitions between them labeled 0 or 1. It also provides the transition table for the equivalent DFA.
The document provides instructions for drawing deterministic finite automata (DFAs) and non-deterministic finite automata (NFAs) to accept various languages over given alphabets. It includes examples of constructing DFAs to accept languages ending or starting with particular strings, or containing certain substrings. It also gives examples of NFAs accepting languages based on string length, substring position, or relationships between symbols. The document discusses epsilon closures in NFAs.
Theory of computation is the study of how problems can be solved using formal mathematical models of computation that reflect real-world computers. It includes automata theory, which studies abstract machines and how they can be used to solve problems. Models of computation are represented using tools like flow charts, state transition diagrams, and transition tables. Theory of computation has applications in areas like text processing, web browsing, compiler design, and artificial intelligence. It builds on fields like set theory, model theory, and computability theory. Key topics covered include finite automata, regular expressions and languages, context-free grammars and languages, and the undecidability of some computational problems.
1) An epsilon-NFA (ε-NFA) is converted to a non-deterministic finite automaton (NFA) by taking the epsilon-closure of each state.
2) The initial state and number of states remain the same, but the final states and transitions may change.
3) The procedure takes the epsilon-closure of the source states for each transition and uses it as the target of the corresponding transition in the new NFA.
The document describes the steps of SLR parsing:
1. Create an augmented grammar by adding a new start symbol S' and the production S' -> S.
2. Generate kernel items by introducing dots in productions.
3. Find the closure of kernel items.
4. Compute the goto table from the closure sets.
5. Construct the parsing table from the goto table and closure sets.
6. Parse input strings using the parsing table and stack.
This document describes the steps to construct a LALR parser from a context-free grammar:
1. Create an augmented grammar by adding a new start symbol and productions.
2. Generate kernel items by introducing dots in productions and adding second components. Then take closures of the items.
3. Compute the goto function to fill the parsing table.
4. Construct the CLR parsing table from the items and gotos.
5. Shrink the CLR parser by merging equivalent states to produce the more efficient LALR parsing table with fewer states.
The TRB AJE35 RIIM Coordination and Collaboration Subcommittee has organized a series of webinars focused on building coordination, collaboration, and cooperation across multiple groups. All webinars have been recorded and copies of the recording, transcripts, and slides are below. These resources are open-access following creative commons licensing agreements. The files may be found, organized by webinar date, below. The committee co-chairs would welcome any suggestions for future webinars. The support of the AASHTO RAC Coordination and Collaboration Task Force, the Council of University Transportation Centers, and AUTRI’s Alabama Transportation Assistance Program is gratefully acknowledged.
This webinar overviews proven methods for collaborating with USDOT University Transportation Centers (UTCs), emphasizing state departments of transportation and other stakeholders. It will cover partnerships at all UTC stages, from the Notice of Funding Opportunity (NOFO) release through proposal development, research and implementation. Successful USDOT UTC research, education, workforce development, and technology transfer best practices will be highlighted. Dr. Larry Rilett, Director of the Auburn University Transportation Research Institute will moderate.
For more information, visit: https://aub.ie/trbwebinars
Design of Variable Depth Single-Span Post.pdfKamel Farid
Hunched Single Span Bridge: -
(HSSBs) have maximum depth at ends and minimum depth at midspan.
Used for long-span river crossings or highway overpasses when:
Aesthetically pleasing shape is required or
Vertical clearance needs to be maximized
Deepfake Phishing: A New Frontier in Cyber ThreatsRaviKumar256934
n today’s hyper-connected digital world, cybercriminals continue to develop increasingly sophisticated methods of deception. Among these, deepfake phishing represents a chilling evolution—a combination of artificial intelligence and social engineering used to exploit trust and compromise security.
Deepfake technology, once a novelty used in entertainment, has quickly found its way into the toolkit of cybercriminals. It allows for the creation of hyper-realistic synthetic media, including images, audio, and videos. When paired with phishing strategies, deepfakes can become powerful weapons of fraud, impersonation, and manipulation.
This document explores the phenomenon of deepfake phishing, detailing how it works, why it’s dangerous, and how individuals and organizations can defend themselves against this emerging threat.
David Boutry - Specializes In AWS, Microservices And PythonDavid Boutry
With over eight years of experience, David Boutry specializes in AWS, microservices, and Python. As a Senior Software Engineer in New York, he spearheaded initiatives that reduced data processing times by 40%. His prior work in Seattle focused on optimizing e-commerce platforms, leading to a 25% sales increase. David is committed to mentoring junior developers and supporting nonprofit organizations through coding workshops and software development.
この資料は、Roy FieldingのREST論文(第5章)を振り返り、現代Webで誤解されがちなRESTの本質を解説しています。特に、ハイパーメディア制御やアプリケーション状態の管理に関する重要なポイントをわかりやすく紹介しています。
This presentation revisits Chapter 5 of Roy Fielding's PhD dissertation on REST, clarifying concepts that are often misunderstood in modern web design—such as hypermedia controls within representations and the role of hypermedia in managing application state.
Construction Materials (Paints) in Civil EngineeringLavish Kashyap
This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
Jacob Murphy Australia - Excels In Optimizing Software ApplicationsJacob Murphy Australia
In the world of technology, Jacob Murphy Australia stands out as a Junior Software Engineer with a passion for innovation. Holding a Bachelor of Science in Computer Science from Columbia University, Jacob's forte lies in software engineering and object-oriented programming. As a Freelance Software Engineer, he excels in optimizing software applications to deliver exceptional user experiences and operational efficiency. Jacob thrives in collaborative environments, actively engaging in design and code reviews to ensure top-notch solutions. With a diverse skill set encompassing Java, C++, Python, and Agile methodologies, Jacob is poised to be a valuable asset to any software development team.
In this paper, the cost and weight of the reinforcement concrete cantilever retaining wall are optimized using Gases Brownian Motion Optimization Algorithm (GBMOA) which is based on the gas molecules motion. To investigate the optimization capability of the GBMOA, two objective functions of cost and weight are considered and verification is made using two available solutions for retaining wall design. Furthermore, the effect of wall geometries of retaining walls on their cost and weight is investigated using four different T-shape walls. Besides, sensitivity analyses for effects of backfill slope, stem height, surcharge, and backfill unit weight are carried out and of soil. Moreover, Rankine and Coulomb methods for lateral earth pressure calculation are used and results are compared. The GBMOA predictions are compared with those available in the literature. It has been shown that the use of GBMOA results in reducing significantly the cost and weight of retaining walls. In addition, the Coulomb lateral earth pressure can reduce the cost and weight of retaining walls.
This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.
2. 2
Pushdown Automata (PDA)
• Informally:
– A PDA is an NFA-ε with a stack.
– Transitions are modified to accommodate stack operations.
• Questions:
– What is a stack?
– How does a stack help?
• A DFA can “remember” only a finite amount of information, whereas a PDA can
“remember” an infinite amount of (certain types of) information, in one memory-stack
3. 3
• Example:
{0n1n | 0=<n}
is not regular, but
{0n1n | 0≤n≤k, for some fixed k} is regular, for any
fixed k.
• For k=3:
L = {ε, 01, 0011, 000111}
0/1
q
0
q
7
0 q
1
1
1
q
2
1
q
5
0 q
3
1
1
q
4
0
1
0
0
0/1
q
6
0
4. 4
• In a DFA, each state remembers a finite amount of information.
• To get {0n1n | 0≤n} with a DFA would require an infinite number of states
using the preceding technique.
• An infinite stack solves the problem for {0n1n | 0≤n} as follows:
– Read all 0’s and place them on a stack
– Read all 1’s and match with the corresponding 0’s on the stack
• Only need two states to do this in a PDA
• Similarly for {0n1m0n+m | n,m≥0}
6. 6
Formal Definition of a PDA
• A pushdown automaton (PDA) is a seven-tuple:
M = (Q, Σ, Г, δ, q0, z0, F)
Q A finite set of states
Σ A finite input alphabet
Г A finite stack alphabet
q0 The initial/starting state, q0 is in Q
z0 A starting stack symbol, is in Г // need not always remain at the bottom of stack
F A set of final/accepting states, which is a subset of Q
δ A transition function, where
δ: Q x (Σ U {ε}) x Г –> finite subsets of Q x Г*
7. 7
• Consider the various parts of δ:
Q x (Σ U {ε}) x Г –> finite subsets of Q x Г*
– Q on the LHS means that at each step in a computation, a PDA must consider its’
current state.
– Г on the LHS means that at each step in a computation, a PDA must consider the
symbol on top of its’ stack.
– Σ U {ε} on the LHS means that at each step in a computation, a PDA may or may
not consider the current input symbol, i.e., it may have epsilon transitions.
– “Finite subsets” on the RHS means that at each step in a computation, a PDA may
have several options.
– Q on the RHS means that each option specifies a new state.
– Г* on the RHS means that each option specifies zero or more stack symbols that
will replace the top stack symbol, but in a specific sequence.
8. 8
• PDA transitions:
δ(q, a, z) = {(p1,γ1), (p2,γ2),…, (pm,γm)}
– Current state is q
– Current input symbol is a
– Symbol currently on top of the stack z
– Move to state pi from q
– Replace z with γi on the stack (leftmost symbol on top)
– Move the input head to the next input symbol
:
q
p
1
p
2
p
m
a/z/ γ1
a/z/ γ2
a/z/ γm
9. 9
• Two types of PDA transitions:
δ(q, ε, z) = {(p1,γ1), (p2,γ2),…, (pm,γm)}
– Current state is q
– Current input symbol is not considered
– Symbol currently on top of the stack z
– Move to state pi from q
– Replace z with γi on the stack (leftmost symbol on top)
– No input symbol is read
:
q
p
1
p
2
p
m
ε/z/ γ1
ε/z/ γ2
ε/z/ γm
17. Exercise
• Construct PDA for acceptance by empty stack.
1. L={a2n bn | n>=0 }
1. L = {0n1n2m3m | n>=1, m>=1}
2. L={0n1m0n | m, n>=1}.
3. L= { a i b j c k | i, j, k ≥ 0 and i + j = k }
• Construct PDA for acceptance by Final state
1. L = { a n b n c m | m, n ≥ 1 }
1. L = {0n1m2m3n | n>=1, m>=1}
2. L={an b2n | n>=0 }
– L={an b3n | n>=1 }
1. L={a3n bn | n>=0 }
17
18. 18
• Definition: Let M = (Q, Σ, Г, δ, q0, z0, F) be a PDA. The language
accepted by empty stack, denoted LE(M), is the set
LE(M) = {w | (q0, w, z0) |—* (p, ε, ε) for some
p in Q}
• Definition: Let M = (Q, Σ, Г, δ, q0, z0, F) be a PDA. The language
accepted by final state, denoted LF(M), is the set
LE(M)= {w | (q0, w, z0) |—* (p, ε, γ) for some p in F and γ in
Г*}
• Definition: Let M = (Q, Σ, Г, δ, q0, z0, F) be a PDA. The language
accepted by empty stack and final state, denoted L(M), is the set
LE(M)= {w | (q0, w, z0) |—* (p, ε, ε) for some p in F}
Language Acceptance
21. Problem 1: CFG - PDA
Convert the grammar S-> aAA, A->aS/bS/a to a PDA that accepts the same language
by empty stack
Solution :
Let G be a CFG and G=(V,T,P,S) where V={S,A} T={a,b}
p: S->aAA, A->aS/bS/a.
To find the equivalent PDA
Q={q0}
∑=T={a,b}
Г=VUT={S,A,a,b}
F=Ø
Transition function for PDA:
– For each variable S,A
δ(q0,Є,S)={(q0,aAA)}
δ(q0,Є,A)={(q0,aS),(q0,bS),(q0,a)}
For each terminal a,b
δ(q0,a,a)={q0,Є}
δ(0,b,b)={q0,Є}
21
22. Problem 2: CFG - PDA
• Convert the grammar S-> 0S1/A, A->1A0/S/є to a PDA that accepts the same language by
empty stack.
Solution :
Let G be a CFG and G=(V,T,P,S) where V={S,A} T={a,b}
p: S->aAA, A->aS/bS/a.
To find the equivalent PDA
Q={q0}
∑=T={0,1}
Г=VUT={S,A,0,1}
F=Ø
Transition function for PDA:
– For each variable S,A
δ(q0,Є,S)={(q0,0S1), (q0,A)}
δ(q0,Є,A)={(q0,1A0),(q0,S),(q0, Є)}
For each terminal 0,1
δ(q0,0,0)={q0,Є}
δ(q0,1,1)={q0,Є}
22
23. Problem 3: CFG – PDA
Construct PDA for the given CFG, and test whether 0104 is acceptable by this PDA.
S → 0BB B → 0S | 1S | 0
23
Solution:
The PDA can be given as:
1.A = {(q), (0, 1), (S, B, 0, 1), δ, q, S, ?}
The production rule δ can be:
R1: δ(q, ε, S) = {(q, 0BB)}
R2: δ(q, ε, B) = {(q, 0S) | (q, 1S) | (q, 0)}
R3: δ(q, 0, 0) = {(q, ε)}
R4: δ(q, 1, 1) = {(q, ε)}
Testing 0104 i.e. 010000 against PDA:
δ(q, 010000, S) ⊢ δ(q, 010000, 0BB)
⊢ δ(q, 10000, BB) R1
⊢ δ(q, 10000,1SB) R3
⊢ δ(q, 0000, SB) R2
⊢ δ(q, 0000, 0BBB) R1
⊢ δ(q, 000, BBB) R3
⊢ δ(q, 000, 0BB) R2
⊢ δ(q, 00, BB) R3
⊢ δ(q, 00, 0B) R2
⊢ δ(q, 0, B) R3
⊢ δ(q, 0, 0) R2
⊢ δ(q, ε) R3
ACCEPT
Thus 0104 is accepted by the PDA.
24. 24
CFG to PDA conversion
Formally, the given PDA is
M = (Q, Σ, Г, δ, q0, z0, F). Define CFG G=(V,
T, P, S), where
V=[p x q] for all p & q Є Q and x Є Σ,
T= Σ,
P=Set of Production rules constructed from δ
And S=Starting Symbol
25. Rules to construct P using δ
R1 – Production Rules for S
S 🡪 [q0 z0 p] for all p Є Q
R2 – Production Rules corresponding to the
transition move for pop operation
(q, a, z) = (p, ϵ)
[q z p] 🡪a
26. Cont…
R3 - Production Rules corresponding to the
transition move for push and read operation
(q, a, z) = (q’, z1z2…….zn)
[q z p] 🡪 a [q’ z1 q1] [q1 z2 q2] ………[qn zn p]
33. Final State to Empty Stack PDA
33
(p0, w,X0) |- (q0, w, Z0X0) |-* (q, ε , αX0) |- ( p, ε ,ε )
Example : Design a PDA to check for well-formed parentheses
34. Empty Stack to Final State PDA
34
(p0, w,X0) |- (q0, w, Z0X0) |-* (q, ε , X0) |- ( pf, ε ,ε )
Example : Design a PDA to check for well-formed parentheses
37. Is Npda more powerful
than DPDA?
• Power of NPDA is more than DPDA. It is
not possible to convert every NPDA to
corresponding DPDA. ... The languages
accepted by DPDA are called DCFL
(Deterministic Context Free Languages)
which are subset of NCFL (Non
Deterministic CFL) accepted by NPDA
37
39. Closure Properties of Context
Free Grammar
Context-free languages are closed under −
•Union- If L1 and L2 are CFL’s then L1ꓴL2 is
also CFL.
•Concatenation- If L1 and L2 are CFL’s then
L1L2 is also CFL.
•Kleene Star- If L1 is CFL then L*1 is also
CFL.
40. Closure under Union
• Begin with two grammars: G1 = (V1, Σ , P1, S1) and G2
= (V2, Σ , P2, S2), generating CFL’s L1 and L2
respectively.
• The new CFG Gx is made as:
– Σ remains the same
– Sx is the new start variable
– Vx = V1 ∪ V2 ∪ {Sx}
– Px = P1 ∪ P2 ∪ {Sx → S1|S2}
• Explanation: All we have done is augment the variable
set with a new start state and then allowed the new start
state to map to either of the two grammars. So, we’ll
generate strings from either L1 or L2, i.e. L1 ꓴ L2
41. Example
• Let L1 = { anbn , n > 0}. Corresponding
grammar G1 will have P: S1 → aAb|ab
• Let L2 = { cmdm , m ≥ 0}. Corresponding
grammar G2 will have P: S2 → cBb| ε
• Union of L1 and L2, L = L1 ∪ L2 = { anbn } ∪
{ cmdm }
• The corresponding grammar G will have the
additional production S → S1 | S2
42. Closure under Concatenation
• Begin with two grammars: G1 = (V1, Σ , P1, S1) and G2 =
(V2, Σ , P2, S2), generating CFL’s L1 and L2 respectively.
• The new CFG Gy is made as:
– Σ remains the same
– Sy is the new start variable
– Vy = V1 ꓴ V2 ꓴ {Sy}
– Py = P1 ꓴ P2 ꓴ {Sx → S1S2}
• Explanation: Again, all we have done is to augment the
variable set with a new start state, and then allowed the
new start state to map to the concatenation of the two
original start symbols. So, we will generate strings that
begin with strings from L1 and end with strings from L2,
i.e. L1L2 .
43. Example
• Let L1 = { anbn , n > 0}. Corresponding
grammar G1 will have P: S1 → aAb|ab
• Let L2 = { cmdm , m ≥ 0}. Corresponding
grammar G2 will have P: S2 → cBb| ε
• Concatenation of the languages L1 and L2, L =
L1L2 = { anbncmdm }
• The corresponding grammar G will have the
additional production S → S1 S2
44. Clouser under Kleene Star
• Begin with two grammars: G1 = (V1, Σ , P1, S1) and
G2 = (V2, Σ , P2, S2), generating CFL’s L1 and L2
respectively.
• The new CFG Gz is made as:
– Σ remains the same
– Sz is the new start variable
– Vz = V1 ꓴ {Sz}
– Pz = P1 ꓴ {Sz → S1Sz | ε}
• Explanation: Again we have augmented the variable
set with a new start state, and then allowed the new
start state to map to either S1Sz or ε. This means we
can generate strings with zero or more strings made
from expanding the variable S1, i.e. L*1 .
45. Example
• Let L = { anbn , n ≥ 0}. Corresponding
grammar G will have P: S → aAb| ε
• Kleene Star L1 = { anbn }*
• The corresponding grammar G1 will have
additional productions S1 → SS1 | ε
46. Context-free languages are not closed under −
•Intersection − If L1 and L2 are context free
languages, then L1 ∩ L2 is not necessarily
context free.
•Intersection with Regular Language − If L1 is a
regular language and L2 is a context free
language, then L1 ∩ L2 is a context free
language.
•Complement − If L1 is a context free language,
then L1’ may not be context free.