This document discusses stacks and their applications. It defines a stack as a Last In First Out (LIFO) data structure where newly added items are placed on top. The core stack operations of PUSH, POP, and PEEK are described. An array implementation of stacks is presented and animations demonstrate push and pop operations. Applications of stacks like checking for balanced braces, converting infix to postfix notation, and postfix calculators are explained with examples. Pseudocode provides an algorithm for infix to postfix conversion using a stack.
The document discusses implementation of stacks. It describes stacks as linear data structures that follow LIFO principles. Key stack operations like push and pop are outlined. Stacks are often implemented using arrays or linked lists. Examples of stack applications include recursion handling, expression evaluation, parenthesis checking, and backtracking problems. Conversion from infix to postfix notation using a stack is also demonstrated.
A stack is a data structure where items can only be inserted and removed from one end. The last item inserted is the first item removed (LIFO). Common examples include stacks of books, plates, or bank transactions. Key stack operations are push to insert, pop to remove, and functions to check if the stack is empty or full. Stacks can be used to implement operations like reversing a string, converting infix to postfix notation, and evaluating arithmetic expressions.
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.
The document discusses applications of stacks, including reversing strings and lists, Polish notation for mathematical expressions, converting between infix, prefix and postfix notations, evaluating postfix and prefix expressions, recursion, and the Tower of Hanoi problem. Recursion involves defining a function in terms of itself, with a stopping condition. Stacks can be used to remove recursion by saving local variables at each step.
This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.
This document discusses stacks and their applications. It defines a stack as a Last In First Out (LIFO) data structure where newly added items are placed on top. The core stack operations of PUSH, POP, and PEEK are described. An array implementation of stacks is presented and animations demonstrate push and pop operations. Applications of stacks like checking for balanced braces, converting infix to postfix notation, and postfix calculators are explained with examples. Pseudocode provides an algorithm for infix to postfix conversion using a stack.
The document discusses implementation of stacks. It describes stacks as linear data structures that follow LIFO principles. Key stack operations like push and pop are outlined. Stacks are often implemented using arrays or linked lists. Examples of stack applications include recursion handling, expression evaluation, parenthesis checking, and backtracking problems. Conversion from infix to postfix notation using a stack is also demonstrated.
A stack is a data structure where items can only be inserted and removed from one end. The last item inserted is the first item removed (LIFO). Common examples include stacks of books, plates, or bank transactions. Key stack operations are push to insert, pop to remove, and functions to check if the stack is empty or full. Stacks can be used to implement operations like reversing a string, converting infix to postfix notation, and evaluating arithmetic expressions.
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.
The document discusses applications of stacks, including reversing strings and lists, Polish notation for mathematical expressions, converting between infix, prefix and postfix notations, evaluating postfix and prefix expressions, recursion, and the Tower of Hanoi problem. Recursion involves defining a function in terms of itself, with a stopping condition. Stacks can be used to remove recursion by saving local variables at each step.
This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.
The document discusses different notation styles for representing arithmetic expressions, including infix, prefix, and postfix notations. It provides examples of converting expressions between these notations. Infix notation is the conventional style that humans use, but prefix and postfix notations are better suited for computer parsing. The document also covers parsing expressions, operator precedence, and the steps to convert between infix and prefix and infix and postfix notations.
This document describes an implementation of a stack using an array in C. It includes functions to push elements onto the stack, pop elements off the stack, and display the elements currently in the stack. The main module contains a menu that allows the user to choose these stack operations and includes error handling for invalid inputs or overflow/underflow of the stack.
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
This document discusses double-ended queues or deques. Deques allow elements to be added or removed from either end. There are two types: input restricted deques where elements can only be inserted at one end but removed from both ends, and output restricted deques where elements can only be removed from one end but inserted from both ends. Deques can function as stacks or queues depending on the insertion and removal ends. The document describes algorithms for common deque operations like insert_front, insert_back, remove_front, and remove_back. It also lists applications of deques like palindrome checking and task scheduling.
Binary search is an algorithm that finds the position of a target value within a sorted array. It works by recursively dividing the array range in half and searching only within the appropriate half. The time complexity is O(log n) in the average and worst cases and O(1) in the best case, making it very efficient for searching sorted data. However, it requires the list to be sorted for it to work.
Stacks are linear data structures that only allow insertion and deletion of elements from one end, called the top. Elements are inserted via a push operation and deleted via a pop operation. Stacks can be implemented using arrays, with a pointer tracking the top element. Common applications of stacks include reversing the order of elements and evaluating mathematical expressions by treating them as a postfix notation.
The document discusses heap data structures and their use in priority queues and heapsort. It defines a heap as a complete binary tree stored in an array. Each node stores a value, with the heap property being that a node's value is greater than or equal to its children's values (for a max heap). Algorithms like Max-Heapify, Build-Max-Heap, Heap-Extract-Max, and Heap-Increase-Key are presented to maintain the heap property during operations. Priority queues use heaps to efficiently retrieve the maximum element, while heapsort sorts an array by building a max heap and repeatedly extracting elements.
The document introduces stacks and discusses their implementation and applications. It defines a stack as a data structure that follows LIFO order, where elements can only be added and removed from one end. Stacks have two main implementations - using arrays and linked lists. Common applications of stacks include undo/redo in editors, browser history, and evaluating postfix expressions.
The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
The document discusses heap sort, which is a sorting algorithm that uses a heap data structure. It works in two phases: first, it transforms the input array into a max heap using the insert heap procedure; second, it repeatedly extracts the maximum element from the heap and places it at the end of the sorted array, reheapifying the remaining elements. The key steps are building the heap, processing the heap by removing the root element and allowing the heap to reorder, and doing this repeatedly until the array is fully sorted.
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
This document discusses arrays in C++. It begins by introducing arrays and their need, then describes the different types of arrays including single, two, and multi-dimensional arrays. It explains how arrays are stored in contiguous memory locations and indexed starting from zero. The document also covers array initialization, unsized array initialization, and using strings as arrays in C++.
Queue is a first-in first-out (FIFO) data structure where elements can only be added to the rear of the queue and removed from the front of the queue. It has two pointers - a front pointer pointing to the front element and a rear pointer pointing to the rear element. Queues can be implemented using arrays or linked lists. Common queue operations include initialization, checking if empty/full, enqueue to add an element, and dequeue to remove an element. The document then describes how these operations work for queues implemented using arrays, linked lists, and circular arrays. It concludes by providing exercises to implement specific queue tasks.
This document defines key terminology used in tree data structures. It explains that a tree is a hierarchical data structure composed of nodes connected by edges. The root node is the top node, with child nodes connected below and leaf nodes at the bottom with no children. Other terms defined include parent, sibling, internal and external nodes, degree, level, height, depth, path, and subtree.
Hashing is the process of converting a given key into another value. A hash function is used to generate the new value according to a mathematical algorithm. The result of a hash function is known as a hash value or simply, a hash.
The document discusses stacks and queues. It defines stacks as LIFO data structures and queues as FIFO data structures. It describes basic stack operations like push and pop and basic queue operations like enqueue and dequeue. It then discusses implementing stacks and queues using arrays and linked lists, outlining the key operations and memory requirements for each implementation.
Data structures are schemes for organizing data in computer memory. Common data structures include lists, arrays, stacks, queues, heaps, trees, and graphs. The organization of data affects program performance for different tasks. Programmers choose which data structures to use based on the nature of the data and the processes that need to be performed on that data.
Introductiont To Aray,Tree,Stack, QueueGhaffar Khan
This document provides an introduction to data structures and algorithms. It defines key terminology related to data structures like entities, fields, records, files, and primary keys. It also describes common data structures like arrays, linked lists, stacks, queues, trees, and graphs. Finally, it discusses basic concepts in algorithms like control structures, complexity analysis, and examples of searching algorithms like linear search and binary search.
The document discusses different notation styles for representing arithmetic expressions, including infix, prefix, and postfix notations. It provides examples of converting expressions between these notations. Infix notation is the conventional style that humans use, but prefix and postfix notations are better suited for computer parsing. The document also covers parsing expressions, operator precedence, and the steps to convert between infix and prefix and infix and postfix notations.
This document describes an implementation of a stack using an array in C. It includes functions to push elements onto the stack, pop elements off the stack, and display the elements currently in the stack. The main module contains a menu that allows the user to choose these stack operations and includes error handling for invalid inputs or overflow/underflow of the stack.
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
This document discusses double-ended queues or deques. Deques allow elements to be added or removed from either end. There are two types: input restricted deques where elements can only be inserted at one end but removed from both ends, and output restricted deques where elements can only be removed from one end but inserted from both ends. Deques can function as stacks or queues depending on the insertion and removal ends. The document describes algorithms for common deque operations like insert_front, insert_back, remove_front, and remove_back. It also lists applications of deques like palindrome checking and task scheduling.
Binary search is an algorithm that finds the position of a target value within a sorted array. It works by recursively dividing the array range in half and searching only within the appropriate half. The time complexity is O(log n) in the average and worst cases and O(1) in the best case, making it very efficient for searching sorted data. However, it requires the list to be sorted for it to work.
Stacks are linear data structures that only allow insertion and deletion of elements from one end, called the top. Elements are inserted via a push operation and deleted via a pop operation. Stacks can be implemented using arrays, with a pointer tracking the top element. Common applications of stacks include reversing the order of elements and evaluating mathematical expressions by treating them as a postfix notation.
The document discusses heap data structures and their use in priority queues and heapsort. It defines a heap as a complete binary tree stored in an array. Each node stores a value, with the heap property being that a node's value is greater than or equal to its children's values (for a max heap). Algorithms like Max-Heapify, Build-Max-Heap, Heap-Extract-Max, and Heap-Increase-Key are presented to maintain the heap property during operations. Priority queues use heaps to efficiently retrieve the maximum element, while heapsort sorts an array by building a max heap and repeatedly extracting elements.
The document introduces stacks and discusses their implementation and applications. It defines a stack as a data structure that follows LIFO order, where elements can only be added and removed from one end. Stacks have two main implementations - using arrays and linked lists. Common applications of stacks include undo/redo in editors, browser history, and evaluating postfix expressions.
The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
The document discusses heap sort, which is a sorting algorithm that uses a heap data structure. It works in two phases: first, it transforms the input array into a max heap using the insert heap procedure; second, it repeatedly extracts the maximum element from the heap and places it at the end of the sorted array, reheapifying the remaining elements. The key steps are building the heap, processing the heap by removing the root element and allowing the heap to reorder, and doing this repeatedly until the array is fully sorted.
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
This document discusses arrays in C++. It begins by introducing arrays and their need, then describes the different types of arrays including single, two, and multi-dimensional arrays. It explains how arrays are stored in contiguous memory locations and indexed starting from zero. The document also covers array initialization, unsized array initialization, and using strings as arrays in C++.
Queue is a first-in first-out (FIFO) data structure where elements can only be added to the rear of the queue and removed from the front of the queue. It has two pointers - a front pointer pointing to the front element and a rear pointer pointing to the rear element. Queues can be implemented using arrays or linked lists. Common queue operations include initialization, checking if empty/full, enqueue to add an element, and dequeue to remove an element. The document then describes how these operations work for queues implemented using arrays, linked lists, and circular arrays. It concludes by providing exercises to implement specific queue tasks.
This document defines key terminology used in tree data structures. It explains that a tree is a hierarchical data structure composed of nodes connected by edges. The root node is the top node, with child nodes connected below and leaf nodes at the bottom with no children. Other terms defined include parent, sibling, internal and external nodes, degree, level, height, depth, path, and subtree.
Hashing is the process of converting a given key into another value. A hash function is used to generate the new value according to a mathematical algorithm. The result of a hash function is known as a hash value or simply, a hash.
The document discusses stacks and queues. It defines stacks as LIFO data structures and queues as FIFO data structures. It describes basic stack operations like push and pop and basic queue operations like enqueue and dequeue. It then discusses implementing stacks and queues using arrays and linked lists, outlining the key operations and memory requirements for each implementation.
Data structures are schemes for organizing data in computer memory. Common data structures include lists, arrays, stacks, queues, heaps, trees, and graphs. The organization of data affects program performance for different tasks. Programmers choose which data structures to use based on the nature of the data and the processes that need to be performed on that data.
Introductiont To Aray,Tree,Stack, QueueGhaffar Khan
This document provides an introduction to data structures and algorithms. It defines key terminology related to data structures like entities, fields, records, files, and primary keys. It also describes common data structures like arrays, linked lists, stacks, queues, trees, and graphs. Finally, it discusses basic concepts in algorithms like control structures, complexity analysis, and examples of searching algorithms like linear search and binary search.
This document defines and compares common data structures like lists, stacks, and queues. It describes their abstract definitions, common operations, and different implementation methods. Lists can be implemented with arrays or linked nodes and support insertion, deletion, and retrieval of elements. Stacks and queues follow last-in first-out and first-in first-out rules respectively.
This document discusses linear arrays and algorithms for common operations on arrays such as traversing, inserting, deleting, and sorting elements. It defines a linear array as a list of homogeneous data elements with a lower and upper bound. Algorithms provided include traversing from the lower to upper bound, inserting an element at a specified position by shifting other elements and incrementing the size, deleting an element by shifting elements and decrementing the size, and sorting an array using bubble sort by making multiple passes to swap adjacent elements in descending order.
Simple graph types include line charts, area charts, column charts, bar charts, segmented bar/column charts, pie charts, and doughnut charts. Line charts show changes over time and compare variables. Area charts show changes in volume over time. Column charts compare data at specific times or between nominal categories. Bar charts emphasize comparisons. Segmented charts show proportional relationships and sub-elements over time. Pie charts compare parts to a whole. Doughnut charts are like pie charts but can show multiple data series.
Line graphs are best for displaying continuous changes in a dependent variable in response to an independent variable. They show the relationship between two variables, with the dependent variable plotted on the y-axis and independent variable on the x-axis. Other common graphs include bar graphs, which display data collected by counting, and pie charts, which show the distribution of parts within a whole quantity. Organizing and presenting data through written reports and oral presentations are important science skills that allow scientists to share their results.
A list is a sequential data structure that allows additions and removals at any position, unlike stacks and queues. Common list operations include adding and removing nodes, updating node contents, checking if the list is empty/full, and initializing/destroying the list. Lists can be implemented using arrays (for static storage) or linked nodes (for dynamic storage). Array lists allow constant-time access but linear-time insertion/removal. Linked lists have linear-time access but constant-time insertion/removal. Both use probes and previous references to traverse the list during operations.
Stack and Queue.pptx university exam preparationRAtna29
Queues and stacks are dynamic while arrays are static. So when we require dynamic memory we use queue or stack over arrays. Stacks and queues are used over arrays when sequential access is required. To efficiently remove any data from the start (queue) or the end (stack) of a data structure
The document describes implementing a queue using an array. It provides algorithms for enQueue() and deQueue() operations. EnQueue() inserts elements at the rear by incrementing rear and checking for full. DeQueue() deletes elements from the front by incrementing front and checking for empty. The queue uses front and rear pointers to manage insertion and deletion of elements based on FIFO principle using an underlying fixed-size array.
This document discusses stack applications and operations. It provides examples of how stacks can be used for reversing data, converting decimal to binary, evaluating arithmetic expressions by converting infix to postfix notation, and backtracking. Basic stack operations include push, pop, and peeking at the top element. Stacks follow LIFO order and are commonly implemented using arrays or linked lists. Common applications include reversing strings or files, evaluating arithmetic expressions, and backtracking to achieve goals.
The document discusses the stack data structure. A stack is a collection of elements that follow the LIFO (last-in, first-out) principle. Elements can be inserted and removed from the top of the stack only. Stacks have common applications like undo functions in text editors and web browser history. Formally, a stack is an abstract data type that supports push, pop, top, is_empty and length operations. The document provides examples and explanations of stack operations and applications like infix to postfix conversion, expression evaluation, balancing symbols, function calls and reversing a string.
The document discusses stacks and queues as data structures. It defines a stack as a linear data structure that follows the LIFO principle with insertion and deletion occurring at one end. Key stack operations are described as push, pop, peek, isEmpty and isFull. Queue is defined as a linear structure that follows the FIFO principle with insertion at the rear and deletion at the front. Common queue operations are enqueue, dequeue, peek, isEmpty and isFull. Array and linked list implementations of stacks and queues are also covered.
Stacks are linear data structures that follow the LIFO (last in, first out) principle. Elements can only be inserted or removed from one end, called the top. Common stack operations include push to add an element and pop to remove an element. Stacks have many applications, such as converting infix notation to postfix notation and evaluating postfix expressions.
The document discusses stacks, which are linear data structures that follow the LIFO (last-in, first-out) principle. A stack has two main operations - push, which adds an element to the top of the stack, and pop, which removes the top element. Some key applications of stacks include implementing undo/redo features, converting infix expressions to postfix notation, and solving recursive backtracking problems. The document also describes how to evaluate expressions in postfix notation using a stack. Elements are pushed onto the stack as they are encountered, and when an operator is reached, elements are popped off to perform the operation before pushing the result back on.
Data Structure- Stack operations may involve initializing the stack, using it and then de-initializing it. Apart from these basic stuffs, a stack is used for the following two primary operations −
PUSH, POP, PEEP
STACK ( LIFO STRUCTURE) - Data StructureYaksh Jethva
Stack which is known as LIFO structure.Which is type of the Linear data structure and it is Non-Primitive data structure.
Definition:Non primitive data structure are not a basic data structure and depends on other primitive data structure (Integer,float etc).
Non primitive data structure can't be operated by machine level instruction directly.
The document discusses stacks and their implementation and use in evaluating arithmetic expressions. It defines a stack as a collection of elements that can only be inserted or removed from one end, called the top. Stacks follow a last-in, first-out (LIFO) principle. Arrays can be used to implement stacks, with a pointer tracking the current top element. Common stack operations like push, pop, peek, etc. are described. Stacks are useful for converting infix expressions to postfix and then evaluating them, as operators are processed after their operands in postfix notation.
The document discusses stacks and their applications. It provides 3 key points:
1. A stack is an abstract data type that follows LIFO (last-in, first-out) principles with push and pop operations. Functions like stack_full and stack_empty are used to implement stacks.
2. Stacks have applications in converting infix notation to postfix notation and evaluating postfix expressions. The algorithms pop and push operators and operands to convert between notations and perform calculations.
3. Examples show converting the infix expression (A * B + (C - D / E)) to postfix and evaluating the postfix expression 50, 60, +, 20, 10, -, *.
The document discusses stacks and their applications. It provides 3 key points:
1. A stack is an abstract data type that follows LIFO (last-in, first-out) principles with push and pop operations. Functions like stack_full and stack_empty are used to implement stacks.
2. Stacks have applications in converting infix notation to postfix notation and evaluating postfix expressions. The algorithms pop and push operators and operands to produce the postfix form or calculate values.
3. Examples show converting the infix expression (A * B + (C - D / E)) to postfix AB*C(D-F/)++ and evaluating the postfix form of True, False, NOT, AND,
The document discusses stacks and their implementation. It defines stacks as ordered collections where insertions and deletions occur at one end. Common stack operations like push and pop are described. Stacks can be implemented using arrays or linked lists. Various stack applications are mentioned like checking balanced symbols.
A stack is a linear data structure that follows the LIFO (last in, first out) principle. Elements can only be inserted or removed from one end, called the top. Stacks have common operations like push to add an element and pop to remove the top element. Stacks have many applications including evaluating arithmetic expressions in postfix notation, implementing recursion, and solving puzzles like Towers of Hanoi. The document discusses stack implementations using arrays and linked lists and provides examples of stack applications.
Application of Stack For Expression Evaluation by Prakash Zodge DSY 41.pptxPrakash Zodge
In short...The stack organization is very effective in evaluating arithmetic expressions. Expressions are usually represented in what is known as Infix notation, in which each operator is written between two operands (i.e., A + B)....
The document discusses stacks and their implementation using linked lists. It defines a stack as a last-in, first-out data structure that allows insertion and deletion of elements only at one end, called the top. Key stack operations like push, pop and peek are described along with their algorithms. Linked list and array implementations of stacks are presented. Common applications of stacks like balancing symbols, postfix notation calculators, and infix to postfix conversion are also covered.
The document introduces stacks and their operations. It defines a stack as a last-in, first-out data structure that can only be accessed at one end. The key stack operations are push, which adds an item to the top, and pop, which removes an item from the top. It provides examples of stack representations using arrays and linked lists, and algorithms for implementing push and pop. The document also discusses postfix notation for arithmetic expressions and algorithms for evaluating expressions in postfix notation using a stack.
The section provides brief introduction to stack and its operations (PUSH and POP) along with the implementation of stack in real scenario to convert infix expression to postfix expression.
Stack and its Applications : Data Structures ADTSoumen Santra
Stacks are a data structure that follow the last-in, first-out (LIFO) principle. Elements are inserted and removed from the same end called the top of the stack. Common stack operations include push to add an element, pop to remove an element, peek to view the top element, and isEmpty to check if the stack is empty. Stacks have various applications like representing function call stacks, evaluating mathematical expressions, and solving puzzles like the Towers of Hanoi. They can be implemented using arrays or linked lists.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
2. ∗ Stacks are linear lists.
∗ All deletions and insertions occur at one
end of the stack known as the TOP.
∗ Data going into the stack first, leaves
out last.
∗ Stacks are also known as LIFO data
structures (Last-In, First-Out).
Quick Introduction
3. ∗ PUSH – Adds an item to the top of a stack.
∗ POP – Removes an item from the top of the
stack and returns it to the user.
∗ PEEP – Find an item from the top of the
stack.
∗ Change(Update) – User can change the
contents of the specific element.
Basic Stack Operations
4. ∗ Reversing Data: We can use stacks to reverse data.
(example: files, strings)
Very useful for finding palindromes.
Consider the following pseudo code:
1) read (data)
2) loop (data not EOF and stack not full)
1) push (data)
2) read (data)
3) Loop (while stack not Empty)
1) pop (data)
2) print (data)
Stack Applications
5. ∗ Converting Decimal to Binary: Consider the following code
1) Read (number)
2) Loop (number > 0)
1) digit = number modulo 2
2) print (digit)
3) number = number / 2
// from Data Structures by Gilbert and Frozen
The problem with this code is that it will print the binary
number backwards. (ex: 19 becomes 11001000 instead of 00010011. )
To remedy this problem, instead of printing the digit right away, we can
push it onto the stack. Then after the number is done being converted, we
pop the digit out of the stack and print it.
Stack Applications
6. ∗ Evaluating arithmetic expressions.
∗ Prefix: + a b
∗ Infix: a + b (what we use in grammar school)
∗ Postfix: a b +
∗ In high level languages, infix notation cannot be used
to evaluate expressions. We must analyze the
expression to determine the order in which we
evaluate it. A common technique is to convert a infix
notation into postfix notation, then evaluating it.
Stack Applications
8. PUSH Algorithm
PUSH(S, Top, x)
Step 1: [Check For Stack Overflow]
if Top >= N
then write("Stack Overflow")
Exit
Step 2: [Increment Top pointer By
Value One]
Top Top +1
Step 3: [Perform Insertion]
s[Top] X
Step 4: [Finished]
Exit
N
Top 0
9. PUSH Algorithm
PUSH(S, Top, x)
Step 1: [Check For Stack Overflow]
if Top >= N
then write("Stack Overflow")
Exit
Step 2: [Increment Top pointer By
Value One]
Top Top +1
Step 3: [Perform Insertion]
s[Top] X
Step 4: [Finished]
Exit
N
Top A
10. PUSH Algorithm
PUSH(S, Top, x)
Step 1: [Check For Stack Overflow]
if Top >= N
then write("Stack Overflow")
Exit
Step 2: [Increment Top pointer By
Value One]
Top Top +1
Step 3: [Perform Insertion]
s[Top] X
Step 4: [Finished]
Exit
N
Top
A
B
11. PUSH Algorithm
PUSH(S, Top, x)
Step 1: [Check For Stack Overflow]
if Top >= N
then write("Stack Overflow")
Exit
Step 2: [Increment Top pointer By
Value One]
Top Top +1
Step 3: [Perform Insertion]
s[Top] X
Step 4: [Finished]
Exit
N
A
B
Top C
12. PUSH Algorithm
PUSH(S, Top, x)
Step 1: [Check For Stack Overflow]
if Top >= N
then write("Stack Overflow")
Exit
Step 2: [Increment Top pointer By
Value One]
Top Top +1
Step 3: [Perform Insertion]
s[Top] X
Step 4: [Finished]
Exit
N
Top
A
B
C
D
13. PUSH Algorithm
PUSH(S, Top, x)
Step 1: [Check For Stack Overflow]
if Top >= N
then write("Stack Overflow")
Exit
Step 2: [Increment Top pointer By
Value One]
Top Top +1
Step 3: [Perform Insertion]
s[Top] X
Step 4: [Finished]
Exit
N
Top
A
B
C
D
Stack Overflow
15. POP Algorithm
N
Top
A
B
C
D
POP(S , Top)
Step 1: [Check For Stack Underflow Or
Check Whether Stack Is Empty]
if (Top = 0) OR (Top = -1)
then write("Stack Underflow")
Exit
Step 2: [Decrement Top pointer /
Remove The Top Information]
Value S[Top]
Top Top - 1
Step 3: [Return From Top Element Of The
Stack]
Write(value)
Step 4: [Finished]
Exit
16. POP Algorithm
N
A
B
C
POP(S , Top)
Step 1: [Check For Stack Underflow Or
Check Whether Stack Is Empty]
if (Top = 0) OR (Top = -1)
then write("Stack Underflow")
Exit
Step 2: [Decrement Top pointer /
Remove The Top Information]
Value S[Top]
Top Top - 1
Step 3: [Return From Top Element Of The
Stack]
Write(value)
Step 4: [Finished]
Exit
Top
17. POP Algorithm
N
A
B
POP(S , Top)
Step 1: [Check For Stack Underflow Or
Check Whether Stack Is Empty]
if (Top = 0) OR (Top = -1)
then write("Stack Underflow")
Exit
Step 2: [Decrement Top pointer /
Remove The Top Information]
Value S[Top]
Top Top - 1
Step 3: [Return From Top Element Of The
Stack]
Write(value)
Step 4: [Finished]
Exit
Top
18. POP Algorithm
N
A
POP(S , Top)
Step 1: [Check For Stack Underflow Or
Check Whether Stack Is Empty]
if (Top = 0) OR (Top = -1)
then write("Stack Underflow")
Exit
Step 2: [Decrement Top pointer /
Remove The Top Information]
Value S[Top]
Top Top - 1
Step 3: [Return From Top Element Of The
Stack]
Write(value)
Step 4: [Finished]
Exit
Top
19. POP Algorithm
N
POP(S , Top)
Step 1: [Check For Stack Underflow Or
Check Whether Stack Is Empty]
if (Top = 0) OR (Top = -1)
then write("Stack Underflow")
Exit
Step 2: [Decrement Top pointer /
Remove The Top Information]
Value S[Top]
Top Top - 1
Step 3: [Return From Top Element Of The
Stack]
Write(value)
Step 4: [Finished]
ExitTop 0
20. POP Algorithm
N
POP(S , Top)
Step 1: [Check For Stack Underflow Or
Check Whether Stack Is Empty]
if (Top = 0) OR (Top = -1)
then write("Stack Underflow")
Exit
Step 2: [Decrement Top pointer /
Remove The Top Information]
Value S[Top]
Top Top - 1
Step 3: [Return From Top Element Of The
Stack]
Write(value)
Step 4: [Finished]
ExitTop 0
Stack Underflow
22. PEEP Algorithm
N
Top
A
B
C
D
PEEP(S, Top, i)
Step 1: [Check For Stack Underflow]
if (Top - i + 1) <= 0
then write("Stack Underflow")
Exit
Step 2: [Return The ith Element From
The Top Of The Stack]
X S(Top - i + 1)
Step 3: [Finished]
Exit
Top D
24. CHANGE Algorithm
N
Top
A
B
C
D
CHANGE(S, Top, X, i)
Step 1: [Check For Stack Underflow]
if (Top - i + 1) < 0
then write("Stack Underflow")
Exit
Step 2: [Change The Element Value
From The Top Of The Stack]
S(Top - i + 1) X
Step 3: [Finished]
Exit
25. CHANGE Algorithm
N
Top
A
B
C
D1
CHANGE(S, Top, X, i)
Step 1: [Check For Stack Underflow]
if (Top - i + 1) < 0
then write("Stack Underflow")
Exit
Step 2: [Change The Element Value
From The Top Of The Stack]
S(Top - i + 1) X
Step 3: [Finished]
Exit
27. ∗ Rules:
∗ Operands immediately go directly to output
∗ Operators are pushed into the stack (including parenthesis)
- Check to see if stack top operator is less than current operator
- If the top operator is less, than push the current operator onto stack
- If the top operator is greater than the current, pop top operator and push onto
stack, push current operator onto stack
- Priority 2: * /
- Priority 1: + -
- Priority 0: (
If we encounter a right parenthesis, pop from stack until we get
matching left parenthesis. Do not output parenthesis.
Infix to Postfix Conversion
28. A + B * C - D / E
Infix Stack Postfix
(
a)A ( A
b)+ (+ A
c)B (+ AB
d)* (+* AB
e)C (+* ABC
f)- (- ABC*+
g)D (- ABC*+D
h)/ (-/ ABC*+D
i)E (-/ ABC*+DE
j)) ABC*+DE/-
Infix to Postfix Example
29. A + B * C - D / E
Label No. Postfix Stack
1 A A
2 B A B
3 C A B C
4 * A B * C
5 + A + B * C
6 D A + B * C D
7 E A + B * C D E
8 / A + B * C D / E
9 - A + B * C – D / E
Postfix Evaluation