This document discusses trees and their applications in discrete mathematics. It begins with an introduction to trees, including defining different types of trees such as rooted trees, m-ary trees, and binary trees. It then covers applications of trees such as binary search trees, decision trees, and game trees. Next, it discusses different tree traversal algorithms like preorder, inorder, and postorder traversal and how they can be used to represent expressions. Finally, it provides examples of evaluating expressions represented as trees.
Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.
Recursive descent parsing is a top-down parsing method that uses a set of recursive procedures associated with each nonterminal of a grammar to process input and construct a parse tree. It attempts to find a leftmost derivation for an input string by creating nodes of the parse tree in preorder starting from the root. While simple to implement, recursive descent parsing involves backtracking and is not as fast as other methods, with limitations on error reporting and lookahead. However, it can be constructed easily from recognizers by building a parse tree.
Syntax directed translation allows semantic information to be associated with a formal language by attaching attributes to grammar symbols and defining semantic rules. There are several types of attributes including synthesized and inherited. Syntax directed definitions specify attribute values using semantic rules associated with grammar productions. Evaluation of attributes requires determining an order such as a topological sort of a dependency graph. Syntax directed translation schemes embed program fragments called semantic actions within grammar productions. Actions can be placed inside or at the ends of productions. Various parsing strategies like bottom-up can be used to execute the actions at appropriate times during parsing.
This document provides an introduction to asymptotic analysis of algorithms. It discusses analyzing algorithms based on how their running time increases with the size of the input problem. The key points are:
- Algorithms are compared based on their asymptotic running time as the input size increases, which is more useful than actual running times on a specific computer.
- The main types of analysis are worst-case, best-case, and average-case running times.
- Asymptotic notations like Big-O, Omega, and Theta are used to classify algorithms based on their rate of growth as the input increases.
- Common orders of growth include constant, logarithmic, linear, quadratic, and exponential time.
The document discusses automata and formal languages. It begins by defining an automaton as a theoretical self-propelled computing device that follows a predetermined sequence of operations automatically. It then defines a language as a set of strings chosen from an alphabet. There are two types of finite automata: deterministic finite automata (DFA) and nondeterministic finite automata (NDFA). A DFA is defined by a 5-tuple including states, transitions, start and accepting states. The document provides examples of DFAs and how strings are processed. It also discusses epsilon-NFAs and provides steps to convert an NFA to a DFA.
This document describes binary search and provides an example of how it works. It begins with an introduction to binary search, noting that it can only be used on sorted lists and involves comparing the search key to the middle element. It then provides pseudocode for the binary search algorithm. The document analyzes the time complexity of binary search as O(log n) in the average and worst cases. It notes the advantages of binary search are its efficiency, while the disadvantage is that the list must be sorted. Applications mentioned include database searching and solving equations.
This document summarizes graph coloring using backtracking. It defines graph coloring as minimizing the number of colors used to color a graph. The chromatic number is the fewest colors needed. Graph coloring is NP-complete. The document outlines a backtracking algorithm that tries assigning colors to vertices, checks if the assignment is valid (no adjacent vertices have the same color), and backtracks if not. It provides pseudocode for the algorithm and lists applications like scheduling, Sudoku, and map coloring.
A graph G is composed of vertices V connected by edges E. It can be represented using an adjacency matrix or adjacency lists. Graph search algorithms like depth-first search (DFS) and breadth-first search (BFS) are used to traverse the graph and find paths between vertices. DFS recursively explores edges until reaching the end of a branch before backtracking, while BFS explores all neighbors at each level before moving to the next.
This document defines context-free grammars (CFGs) and context-free languages (CFLs). A CFG is defined by a 4-tuple specifying variables, terminals, productions, and a start variable. Derivations in a CFG involve replacing variables according to productions. A language is context-free if some CFG generates it. Examples of CFGs that generate canonical CFLs like {anbn} are given. Parse trees illustrate derivations, and leftmost/rightmost derivations are defined. Ambiguous grammars that generate the same string in different ways are discussed.
Semantic analysis is a pass by a compiler that adds semantic information to the parse tree and performs certain checks based on this information. It logically follows the parsing phase, in which the parse tree is generated, and logically precedes the code generation phase, in which executable code is generated.
The document discusses Strassen's algorithm for matrix multiplication. It begins by explaining traditional matrix multiplication that has a time complexity of O(n3). It then explains how the divide and conquer strategy can be applied by dividing the matrices into smaller square sub-matrices. Strassen improved upon this by reducing the number of multiplications from 8 to 7 terms, obtaining a time complexity of O(n2.81). His key insight was applying different equations on the sub-matrix multiplication formulas to minimize operations.
Lexical analysis is the first phase of compilation. It reads source code characters and divides them into tokens by recognizing patterns using finite automata. It separates tokens, inserts them into a symbol table, and eliminates unnecessary characters. Tokens are passed to the parser along with line numbers for error handling. An input buffer is used to improve efficiency by reading source code in blocks into memory rather than character-by-character from secondary storage. Lexical analysis groups character sequences into lexemes, which are then classified as tokens based on patterns.
The document discusses code optimization techniques in compilers. It covers the following key points:
1. Code optimization aims to improve code performance by replacing high-level constructs with more efficient low-level code while preserving program semantics. It occurs at various compiler phases like source code, intermediate code, and target code.
2. Common optimization techniques include constant folding, propagation, algebraic simplification, strength reduction, copy propagation, and dead code elimination. Control and data flow analysis are required to perform many optimizations.
3. Optimizations can be local within basic blocks, global across blocks, or inter-procedural across procedures. Representations like flow graphs, basic blocks, and DAGs are used to apply optimizations at
Prim's and Kruskal's algorithms are greedy algorithms used to find minimum spanning trees in graphs. Prim's algorithm builds the spanning tree by repeatedly adding the shortest edge that connects to the current tree. Kruskal's algorithm builds the tree by repeatedly adding the shortest edge that does not create a cycle. Both algorithms are used for applications like network design, cluster analysis, and map routing. The key difference is that Prim's starts from a node while Kruskal's starts from the minimum edge, making Prim's faster for dense graphs and Kruskal's faster for sparse graphs.
This document discusses asymptotic notations and their use in analyzing the time complexity of algorithms. It introduces the Big-O, Big-Omega, and Big-Theta notations for describing the asymptotic upper bound, lower bound, and tight bound of an algorithm's running time. The document explains that asymptotic notations allow algorithms to be compared by ignoring lower-order terms and constants, and focusing on the highest-order term that dominates as the input size increases. Examples are provided to illustrate the different orders of growth and the notations used to describe them.
implementation of travelling salesman problem with complexity pptAntaraBhattacharya12
This document discusses the travelling salesman problem and its implementation using complexity analysis algorithms. It introduces the travelling salesman problem, which aims to find the shortest route for a salesman to visit each city once and return to the starting point. It describes using graphs and dynamic programming to model and solve the problem. An algorithm is presented that uses dynamic programming to solve the travelling salesman problem in polynomial time by breaking it down into subproblems. Applications including routing software for delivery vehicles are discussed.
1. Several sorting algorithms are compared including quicksort, heapsort, mergesort, insertion sort, selection sort, and bubble sort.
2. For most algorithms, the best, average, and worst case time complexities are listed as O(n log(n)), O(n log(n)), and O(n^2) respectively except for some cases like selection sort and bubble sort that have average and worst case complexities of O(n^2).
3. For space complexity, many algorithms have worst case complexities of O(n) except for mergesort which uses additional space and has a worst case complexity of O(n).
Elliptic curve cryptography (ECC) uses elliptic curves over finite fields for encryption, digital signatures, and key exchange. The key sizes are smaller than RSA for the same security level. Its security relies on the assumed hardness of solving the discrete logarithm problem over elliptic curves. ECC defines elliptic curves with parameters over Galois fields GF(p) for prime p or binary fields GF(2m). Points on the curves along with addition and doubling formulas are used to perform scalar multiplications for cryptographic operations.
The document discusses evaluation of expressions and the conversion between infix and postfix notations. It provides examples of:
1) Evaluating expressions using the order of operations and precedence of operators. Scenarios are worked through step-by-step.
2) Converting infix notation expressions to equivalent postfix notation expressions using a stack-based algorithm.
3) Evaluating postfix notation expressions using a stack to pop operands and operators in order.
The document describes splay trees, a type of self-adjusting binary search tree. Splay trees differ from other balanced binary search trees in that they do not explicitly rebalance after each insertion or deletion, but instead perform a process called "splaying" in which nodes are rotated to the root. This splaying process helps ensure search, insert, and delete operations take O(log n) amortized time. The document explains splaying operations like zig, zig-zig, and zig-zag that rotate nodes up the tree, and analyzes how these operations affect the tree's balance over time through a concept called the "rank" of the tree.
what is Parsing
different types of parsing
what is parser and role of parser
what is top-down parsing and bottom-up parsing
what is the problem in top-down parsing
design of top-down parsing and bottom-up parsing
examples of top-down parsing and bottom-up parsing
The document discusses finite automata and theory of computation. It begins by defining finite automata as abstract machines that can have a finite number of states and read finite input strings, as well as the basic components of an automaton. It then explains the differences between deterministic finite automata (DFAs) and non-deterministic finite automata (NFAs), and provides examples of transition diagrams for visualizing state transitions in automata. The document aims to introduce the basics of finite automata as part of the theory of computation.
Information and data security pseudorandom number generation and stream cipherMazin Alwaaly
Information And Data Security Pseudorandom Number Generation and Stream Cipher seminar
Mustansiriya University
Department of Education
Computer Science
Polynomial reppresentation using Linkedlist-Application of LL.pptxAlbin562191
Linked lists are useful for dynamic memory allocation and polynomial manipulation. They allow for efficient insertion and deletion by changing only pointers, unlike arrays which require shifting elements. Linked lists can represent polynomials by storing coefficient, exponent, and link fields in each node. Polynomial addition using linked lists involves traversing both lists simultaneously and adding coefficients of matching exponents or duplicating unmatched terms into the new list.
This document provides information about the course "Data Structures and Algorithms" taught at Matrusri Engineering College. It includes the course objectives, outcomes, units and topics covered. The topics covered include algorithms complexity, linear data structures like stacks and queues, non-linear structures like trees and graphs. It also discusses various searching and sorting techniques. Specifically, it provides detailed information about tree data structures - their properties, types, operations, representations and traversal algorithms.
A graph G is composed of vertices V connected by edges E. It can be represented using an adjacency matrix or adjacency lists. Graph search algorithms like depth-first search (DFS) and breadth-first search (BFS) are used to traverse the graph and find paths between vertices. DFS recursively explores edges until reaching the end of a branch before backtracking, while BFS explores all neighbors at each level before moving to the next.
This document defines context-free grammars (CFGs) and context-free languages (CFLs). A CFG is defined by a 4-tuple specifying variables, terminals, productions, and a start variable. Derivations in a CFG involve replacing variables according to productions. A language is context-free if some CFG generates it. Examples of CFGs that generate canonical CFLs like {anbn} are given. Parse trees illustrate derivations, and leftmost/rightmost derivations are defined. Ambiguous grammars that generate the same string in different ways are discussed.
Semantic analysis is a pass by a compiler that adds semantic information to the parse tree and performs certain checks based on this information. It logically follows the parsing phase, in which the parse tree is generated, and logically precedes the code generation phase, in which executable code is generated.
The document discusses Strassen's algorithm for matrix multiplication. It begins by explaining traditional matrix multiplication that has a time complexity of O(n3). It then explains how the divide and conquer strategy can be applied by dividing the matrices into smaller square sub-matrices. Strassen improved upon this by reducing the number of multiplications from 8 to 7 terms, obtaining a time complexity of O(n2.81). His key insight was applying different equations on the sub-matrix multiplication formulas to minimize operations.
Lexical analysis is the first phase of compilation. It reads source code characters and divides them into tokens by recognizing patterns using finite automata. It separates tokens, inserts them into a symbol table, and eliminates unnecessary characters. Tokens are passed to the parser along with line numbers for error handling. An input buffer is used to improve efficiency by reading source code in blocks into memory rather than character-by-character from secondary storage. Lexical analysis groups character sequences into lexemes, which are then classified as tokens based on patterns.
The document discusses code optimization techniques in compilers. It covers the following key points:
1. Code optimization aims to improve code performance by replacing high-level constructs with more efficient low-level code while preserving program semantics. It occurs at various compiler phases like source code, intermediate code, and target code.
2. Common optimization techniques include constant folding, propagation, algebraic simplification, strength reduction, copy propagation, and dead code elimination. Control and data flow analysis are required to perform many optimizations.
3. Optimizations can be local within basic blocks, global across blocks, or inter-procedural across procedures. Representations like flow graphs, basic blocks, and DAGs are used to apply optimizations at
Prim's and Kruskal's algorithms are greedy algorithms used to find minimum spanning trees in graphs. Prim's algorithm builds the spanning tree by repeatedly adding the shortest edge that connects to the current tree. Kruskal's algorithm builds the tree by repeatedly adding the shortest edge that does not create a cycle. Both algorithms are used for applications like network design, cluster analysis, and map routing. The key difference is that Prim's starts from a node while Kruskal's starts from the minimum edge, making Prim's faster for dense graphs and Kruskal's faster for sparse graphs.
This document discusses asymptotic notations and their use in analyzing the time complexity of algorithms. It introduces the Big-O, Big-Omega, and Big-Theta notations for describing the asymptotic upper bound, lower bound, and tight bound of an algorithm's running time. The document explains that asymptotic notations allow algorithms to be compared by ignoring lower-order terms and constants, and focusing on the highest-order term that dominates as the input size increases. Examples are provided to illustrate the different orders of growth and the notations used to describe them.
implementation of travelling salesman problem with complexity pptAntaraBhattacharya12
This document discusses the travelling salesman problem and its implementation using complexity analysis algorithms. It introduces the travelling salesman problem, which aims to find the shortest route for a salesman to visit each city once and return to the starting point. It describes using graphs and dynamic programming to model and solve the problem. An algorithm is presented that uses dynamic programming to solve the travelling salesman problem in polynomial time by breaking it down into subproblems. Applications including routing software for delivery vehicles are discussed.
1. Several sorting algorithms are compared including quicksort, heapsort, mergesort, insertion sort, selection sort, and bubble sort.
2. For most algorithms, the best, average, and worst case time complexities are listed as O(n log(n)), O(n log(n)), and O(n^2) respectively except for some cases like selection sort and bubble sort that have average and worst case complexities of O(n^2).
3. For space complexity, many algorithms have worst case complexities of O(n) except for mergesort which uses additional space and has a worst case complexity of O(n).
Elliptic curve cryptography (ECC) uses elliptic curves over finite fields for encryption, digital signatures, and key exchange. The key sizes are smaller than RSA for the same security level. Its security relies on the assumed hardness of solving the discrete logarithm problem over elliptic curves. ECC defines elliptic curves with parameters over Galois fields GF(p) for prime p or binary fields GF(2m). Points on the curves along with addition and doubling formulas are used to perform scalar multiplications for cryptographic operations.
The document discusses evaluation of expressions and the conversion between infix and postfix notations. It provides examples of:
1) Evaluating expressions using the order of operations and precedence of operators. Scenarios are worked through step-by-step.
2) Converting infix notation expressions to equivalent postfix notation expressions using a stack-based algorithm.
3) Evaluating postfix notation expressions using a stack to pop operands and operators in order.
The document describes splay trees, a type of self-adjusting binary search tree. Splay trees differ from other balanced binary search trees in that they do not explicitly rebalance after each insertion or deletion, but instead perform a process called "splaying" in which nodes are rotated to the root. This splaying process helps ensure search, insert, and delete operations take O(log n) amortized time. The document explains splaying operations like zig, zig-zig, and zig-zag that rotate nodes up the tree, and analyzes how these operations affect the tree's balance over time through a concept called the "rank" of the tree.
what is Parsing
different types of parsing
what is parser and role of parser
what is top-down parsing and bottom-up parsing
what is the problem in top-down parsing
design of top-down parsing and bottom-up parsing
examples of top-down parsing and bottom-up parsing
The document discusses finite automata and theory of computation. It begins by defining finite automata as abstract machines that can have a finite number of states and read finite input strings, as well as the basic components of an automaton. It then explains the differences between deterministic finite automata (DFAs) and non-deterministic finite automata (NFAs), and provides examples of transition diagrams for visualizing state transitions in automata. The document aims to introduce the basics of finite automata as part of the theory of computation.
Information and data security pseudorandom number generation and stream cipherMazin Alwaaly
Information And Data Security Pseudorandom Number Generation and Stream Cipher seminar
Mustansiriya University
Department of Education
Computer Science
Polynomial reppresentation using Linkedlist-Application of LL.pptxAlbin562191
Linked lists are useful for dynamic memory allocation and polynomial manipulation. They allow for efficient insertion and deletion by changing only pointers, unlike arrays which require shifting elements. Linked lists can represent polynomials by storing coefficient, exponent, and link fields in each node. Polynomial addition using linked lists involves traversing both lists simultaneously and adding coefficients of matching exponents or duplicating unmatched terms into the new list.
This document provides information about the course "Data Structures and Algorithms" taught at Matrusri Engineering College. It includes the course objectives, outcomes, units and topics covered. The topics covered include algorithms complexity, linear data structures like stacks and queues, non-linear structures like trees and graphs. It also discusses various searching and sorting techniques. Specifically, it provides detailed information about tree data structures - their properties, types, operations, representations and traversal algorithms.
The document discusses binary trees and their implementation and traversal methods. It defines a binary tree as a tree where each node has at most two children. It describes the common traversal orders of binary trees as inorder, preorder and postorder. It also discusses breadth first traversal and storing binary trees using node structures. Expression trees are described as binary trees used to represent mathematical expressions where leaves are operands and internal nodes are operators.
This Presentation will Clear the idea of non linear Data Structure and implementation of Tree by using array and pointer and also Explain the concept of Binary Search Tree (BST) with example
The document defines and provides examples of trees and binary trees. It discusses key tree terminology like root, parent, child, ancestor, descendant, leaf, internal nodes. It also covers properties of binary trees like their recursive definition, minimum and maximum heights, and the relationship between number of nodes, leaves and internal nodes. Common tree ADTs are described including methods for accessing, querying and updating tree structure and elements.
The document discusses various tree data structures including binary trees and their terminology. It defines a tree as a set of nodes connected by links/branches where one node is designated as the root. Key terms discussed include child, parent, leaf, root, and level. The document also covers different ways to represent trees using arrays and linked lists and how to traverse trees using preorder, inorder, and postorder traversal algorithms.
Trees are hierarchical data structures that can represent relationships between data items. They are useful for representing organizational charts, file systems, and programming environments. Key tree concepts include the root node, internal and leaf nodes, ancestors and descendants, subtrees, depth, height, and degree. Common tree operations include traversing the tree using preorder, inorder, and postorder traversal methods, evaluating expression trees, and using trees for data compression through Huffman coding. Huffman coding assigns variable-length binary codes to characters based on their frequency, allowing more common characters to have shorter codes to reduce the overall file size.
The document discusses trees and binary trees. It defines trees and binary trees, describes their terminology like root, leaf nodes, levels etc. It explains different representations of binary trees using arrays and linked lists. It also covers operations on binary search trees like insertion, deletion and searching. Tree traversals namely preorder, inorder and postorder are also explained.
The document discusses binary search trees and their properties. It explains that a binary search tree is a binary tree where every node's left subtree contains values less than the node's value and the right subtree contains greater values. Operations like search, insert, delete can be done in O(h) time where h is the height of the tree. The height is O(log n) for balanced trees but can be O(n) for unbalanced trees. The document also provides examples of using a binary search tree to sort a set of numbers in O(n log n) time by building the BST and doing an inorder traversal.
The document discusses various tree data structures and their terminology. It begins by defining a tree as a set of nodes connected in a parent-child relationship, with one root node and multiple disjoint subtree structures. Key terminology introduced includes root, child, parent, leaf nodes, siblings, ancestors, and height/depth. Binary trees are defined as having at most two children per node. Common traversal orders of trees - preorder, inorder, and postorder - are explained along with examples. Finally, algorithms for traversing binary trees using stacks are presented for preorder and inorder traversal.
Trees are useful for representing hierarchically ordered data like directory structures and family trees. A tree is a collection of nodes connected by branches, with each node having at most one parent. It is defined recursively as a root node with zero or more subtrees. Binary trees restrict nodes to having at most two children. Trees can be traversed in preorder, inorder, or postorder. Binary search trees organize nodes to keep all left descendants less than the parent and all right descendants greater than the parent. Graphs generalize trees by allowing many-to-many relationships between nodes.
The document discusses binary trees and binary search trees. It begins with definitions of tree, binary tree, and binary search tree. It describes the key properties and terminology used for trees including nodes, degrees, heights, paths, etc. It then covers various tree traversal methods like preorder, inorder and postorder traversal. Operations for binary search trees like searching, insertion and deletion of nodes are explained along with algorithms. Different representations of binary trees using arrays and linked lists are also presented.
This document discusses binary trees, including:
- The differences between binary trees and regular trees in how subtrees are ordered.
- Common data structures used to represent binary trees in code.
- Examples of binary trees used for arithmetic expressions and decision making.
- Properties of binary trees like the maximum number of nodes at each depth and for trees of a given height.
- The concepts of full and complete binary trees and how they are structured and labeled.
The document discusses trees and binary trees. Some key points:
- Trees have basic operations like traversals, inserting and deleting nodes, and searching.
- A tree has a root node and hierarchical parent-child relationships between nodes.
- A binary tree restricts each node to have no more than two children. Binary trees can be balanced, complete, or full.
- Tree traversal algorithms like preorder, inorder, and postorder recursively visit nodes in different orders.
This document defines and provides examples of trees and binary trees. It begins by defining trees as hierarchical data structures with nodes and edges. It then provides definitions for terms like path, forest, ordered tree, height, and multiway tree. It specifically defines binary trees as having two children per node. The document gives examples and properties of binary trees, including full, complete, and binary search trees. It also explains linear and linked representations of binary trees and different traversal methods like preorder, postorder and inorder. Finally, it provides examples of insertion and deletion operations in binary search trees.
The document discusses trees and graphs data structures. It begins with introducing different types of trees like binary trees, binary search trees, threaded binary trees, and their various traversal algorithms like inorder, preorder and postorder traversals. It then discusses tree operations like copying trees, testing for equality. It also covers the satisfiability problem and how binary trees can be used to represent logical expressions to solve this problem. Finally, it discusses threaded binary trees where null links in a binary tree are replaced with threads, and how this allows for efficient inorder traversal in linear time.
The document discusses different types of binary trees and tree traversal methods. It defines binary trees and outlines their key properties. It also describes different types of binary trees such as strictly binary trees, complete binary trees, and almost complete binary trees. Finally, it discusses two methods for traversing trees - depth-first traversal and breadth-first traversal, and covers preorder, inorder and postorder traversal techniques for binary trees.
This document provides information about budgerigars, including their food, cage cleaning, pairing, behavior, and advantages of keeping them as pets. Budgerigars, or budgies, should be fed regular bird food, calcium during egg laying, and live foods like leaves. Their cage needs dry cleaning weekly and wet cleaning can harm their health. Lovebirds are vocal, preen each other, and feed each other to bond. Keeping budgies can reduce loneliness and foster affection between family members.
National Assessment and Accreditation Council (NAAC)
Criteria 3 Research, Innovations and Extension
Key Indicators (KIs)
Quantitative Metrics - QnM
Standard Operating Procedure (SOP) for Data Validation
An overview about Artificial intelligence and its patterns, different tools, framework,industry examples, demo. The deviation from conventional approach.
The document discusses the future of blogging. It emphasizes that blogging is about publishing smart content rather than just publishing a lot of content. It covers topics like choosing a blogging platform, the importance of passion in blogging, customizing blogs, promoting blogs through social media, and ways to monetize blogs such as through ads, affiliate links, or selling digital products. It also lists some of the highest earning blogs as examples.
Socrates - Most Important of his ThoughtsMANISH T I
Socrates was a famous ancient Greek philosopher known for his teachings about knowledge, virtue, and the "examined life." Some of his most famous quotes emphasize knowing yourself, the importance of questioning beliefs rather than accepting them blindly, and seeking wisdom rather than wealth or power. He believed that true wisdom comes from acknowledging how little we actually understand about life.
Technical writing involves publishing research results in journals to advance the field and receive credit. The process begins with experiments, literature review to identify gaps, and developing a manuscript with an introduction, methods, results and discussion. Key questions to consider are whether the research significantly advances knowledge and is interesting to peers. Thoroughly reviewing previous work and selecting the appropriate journal helps get published. References and citations must properly attribute prior work to avoid plagiarism.
The document discusses the sun and its impact on Earth. It provides background on the sun's lifespan, solar events like sunspots and flares, and how these events can impact systems on Earth like power grids, GPS, and satellites. High-resolution imaging of the sun is needed to better understand and predict solar activity and space weather in order to mitigate negative effects on life and technology on Earth.
The JPEG standard is a lossy image compression method that uses discrete cosine transform. It involves converting images from RGB to YIQ or YUV color spaces, subsampling the color channels, applying DCT to 8x8 blocks, quantizing the coefficients, run length encoding zero values, differential pulse code modulating DC coefficients, and entropy coding the data. Key aspects of JPEG include chroma subsampling to reduce color resolution, higher visual acuity for luminance over chrominance, and greater compression achieved through quantization and entropy coding DC and AC coefficients.
Colours have profound effects on our personalities and emotions. Red symbolizes passion and energy, pink brings calmness and romance, and blue represents trust and reliability. Each colour invokes different feelings and can be used in homes and businesses to create specific environments and impacts. Understanding how colours affect us can help us benefit from incorporating them into our daily lives.
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
Research Methodology - Methods of data collectionMANISH T I
The document discusses methods for collecting primary data, including observation, interviews, and surveys. It provides details on structured vs unstructured observation and interviews. Some key advantages of interviews are that more in-depth information can be obtained, the interviewer can overcome respondent resistance, and flexibility exists to restructure questions. However, interviews are also expensive and open to interviewer and respondent bias.
The document outlines 15 lessons from Lord Buddha that can help one live a better life, including accepting responsibility for your actions, avoiding harming others, being content with what you have, letting go of anger and resentment, practicing compassion, and gaining wisdom through understanding life's impermanence.
This document discusses image enhancement techniques. It begins by explaining that image enhancement aims to process an image to make it more suitable for a specific application than the original. Techniques can operate in the spatial domain on pixels, the frequency domain on Fourier transforms, or use combinations. Point and local operations modify pixel brightness values. Enhancement factors include dynamic range, bit-planes, and illumination. Morphological operators create structuring elements to perform operations like opening that can remove objects based on size.
This document defines and discusses research methodology. It describes research as a systematic search for knowledge through investigation. The objectives of research are to gain new insights into phenomena and to test hypotheses. Research can be motivated by various factors such as intellectual curiosity, a desire for career advancement, or a desire to solve problems. The document outlines different types of research including descriptive vs analytical, applied vs fundamental, quantitative vs qualitative, and conceptual vs empirical. It also discusses various research approaches like quantitative, qualitative, experimental, and simulation approaches. Finally, it discusses the significance of research for advancing knowledge and solving problems across various fields.
The document discusses first normal form (1NF) in databases. 1NF requires that each attribute contain atomic (non-divisible) values, and disallows composite attributes or attributes with multiple values. The example database violates 1NF by having a location attribute with composite values. There are three proposed solutions: 1) split the relation into two tables, 2) expand the key to separate tuples for each location, or 3) introduce additional attributes to store each location value separately.
This document summarizes a simple dictionary compression algorithm that operates in two passes. In the first pass, it analyzes the data file and creates a dictionary of unique bytes and their frequencies. In the second pass, it replaces each byte in the file with an index value from the dictionary, writing these values to the compressed file along with their bit lengths. Compression is achieved because the dictionary is sorted by frequency, allowing each byte to be represented by 4 to 11 bits rather than 8 bits. While compression is slow, decompression is not.
Data Compression - Text Compression - Run Length EncodingMANISH T I
Run-length encoding (RLE) replaces consecutive repeated characters in data with a single character and count. For example, "aaabbc" would compress to "3a2bc". RLE works best on data with many repetitive characters like spaces. It has limitations for natural language text which contains few repetitions longer than doubles. Variants include digram encoding which compresses common letter pairs, and differencing which encodes differences between successive values like temperatures instead of absolute values.
How to manage Multiple Warehouses for multiple floors in odoo point of saleCeline George
The need for multiple warehouses and effective inventory management is crucial for companies aiming to optimize their operations, enhance customer satisfaction, and maintain a competitive edge.
As of Mid to April Ending, I am building a new Reiki-Yoga Series. No worries, they are free workshops. So far, I have 3 presentations so its a gradual process. If interested visit: https://www.slideshare.net/YogaPrincess
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Blessings and Happy Spring. We are hitting Mid Season.
K12 Tableau Tuesday - Algebra Equity and Access in Atlanta Public Schoolsdogden2
Algebra 1 is often described as a “gateway” class, a pivotal moment that can shape the rest of a student’s K–12 education. Early access is key: successfully completing Algebra 1 in middle school allows students to complete advanced math and science coursework in high school, which research shows lead to higher wages and lower rates of unemployment in adulthood.
Learn how The Atlanta Public Schools is using their data to create a more equitable enrollment in middle school Algebra classes.
The Pala kings were people-protectors. In fact, Gopal was elected to the throne only to end Matsya Nyaya. Bhagalpur Abhiledh states that Dharmapala imposed only fair taxes on the people. Rampala abolished the unjust taxes imposed by Bhima. The Pala rulers were lovers of learning. Vikramshila University was established by Dharmapala. He opened 50 other learning centers. A famous Buddhist scholar named Haribhadra was to be present in his court. Devpala appointed another Buddhist scholar named Veerdeva as the vice president of Nalanda Vihar. Among other scholars of this period, Sandhyakar Nandi, Chakrapani Dutta and Vajradatta are especially famous. Sandhyakar Nandi wrote the famous poem of this period 'Ramcharit'.
Geography Sem II Unit 1C Correlation of Geography with other school subjectsProfDrShaikhImran
The correlation of school subjects refers to the interconnectedness and mutual reinforcement between different academic disciplines. This concept highlights how knowledge and skills in one subject can support, enhance, or overlap with learning in another. Recognizing these correlations helps in creating a more holistic and meaningful educational experience.
The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
How to Set warnings for invoicing specific customers in odooCeline George
Odoo 16 offers a powerful platform for managing sales documents and invoicing efficiently. One of its standout features is the ability to set warnings and block messages for specific customers during the invoicing process.
How to Set warnings for invoicing specific customers in odooCeline George
Rooted & binary tree
1. Rooted & Binary Tree
Dr. Manish T I
Associate Professor
Dept of CSE
Adi Shankara Institute of Engineering & Technology, Kalady
[email protected]
2. • A Binary tree is defined as a tree in which there is
exactly one vertex of degree two, and each of the
remaining vertices is of degree one or three.
Introduction
• A Tree in which one vertex is distinguished from
all the others is called a Rooted tree
• A special class of rooted trees called Binary Rooted
trees.
3. VERTICES
a - Internal vertex
b - Pendant vertex
c- Internal vertex
d - Pendant vertex
e- Internal vertexe- Internal vertex
f - Pendant vertex
g - Pendant vertex
EDGES
1,2,3,4,5,6
4. Properties of Binary Tree
The number of vertices n in a binary tree is always odd.
n No: of Vertices in binary tree
p=(n+1)/2 No: of Pendant vertices in binary tree.
n-p-1 Number of vertices with degree three.
n-1 Number of edges .
p-1 Number of internal vertices in binary tree.
5. Properties of Binary Tree
From the previous diagram n=7 i.e. a odd number
Number of edges = 7-1 =6
No: of Pendant vertices in binary tree = (7+1)/2 =4
No: of vertices with degree three = 7-4-1 =2No: of vertices with degree three = 7-4-1 =2
Number of edges = 7-1 =6
Number of internal vertices in binary tree = 4-1 =3
6. Properties of Binary Tree
2k
Maximum number of vertices possible in k-level binary tree.
⌈ log2
(n+1) -1 ⌉ Minimum possible height of an n – vertex binary tree.⌈ ⌉
(n-1)/2 Maximum possible height of an n – vertex binary tree.
7. Properties of Binary Tree
From the previous diagram k=4
Max No: of vertices possible in k-level binary tree = 24
= 16
⌈⌈⌈⌈ ⌉⌉⌉⌉Min possible height of an 7 – vertex binary tree = ⌈⌈⌈⌈ log2 (7+1) -1 ⌉⌉⌉⌉
= ⌈⌈⌈⌈ log2 (8) -1 ⌉⌉⌉⌉ = ⌈⌈⌈⌈ 3 -1 ⌉⌉⌉⌉ = 2
Max possible height of an 7 – vertex binary tree = (7-1)/2 = 4