PRIM'S ALGORITHM
•Download as PPTX, PDF•
0 likes•487 views
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. It was developed in 1930 by Czech mathematician Vojtěch Jarník but later rediscovered and published by computer scientists Robert C. Prim in 1957 and Edsger W. Dijkstra in 1959. The algorithm finds the minimum spanning tree by building it one edge at a time, each time choosing the next edge that connects an isolated vertex to a partially constructed minimum spanning tree.
Report
Share
Report
Share
Recommended
KRUSKAL'S algorithm from chaitra
The document summarizes Kruskal's algorithm for finding a minimum cost spanning tree in a graph. Kruskal's algorithm uses a heap data structure to extract the lowest cost edge from the graph. It adds edges to the spanning tree only if they do not create cycles. This results in a minimum cost spanning tree being built incrementally. The document provides an example graph and shows the minimum cost spanning tree obtained using Kruskal's algorithm on that graph.
Dijkstra’s algorithm
The document describes Dijkstra's algorithm for finding the shortest paths between nodes in a graph. It begins with an overview of how the algorithm works by solving subproblems to find the shortest path from the source to increasingly more nodes. It then provides pseudocode for the algorithm and analyzes its time complexity of O(E+V log V) when using a Fibonacci heap. Some applications of the algorithm include traffic information systems and routing protocols like OSPF.
Prims Algorithm
Prim's algorithm is a greedy algorithm used to find minimum spanning trees for weighted undirected graphs. It operates by building the spanning tree one vertex at a time, from an arbitrary starting vertex, at each step adding the minimum weight edge that connects the growing spanning tree to a vertex not yet included in the tree. The algorithm repeats until all vertices are added.
Kruskal Algorithm
Kruskal's algorithm is a minimum spanning tree algorithm that finds the minimum cost collection of edges that connect all vertices in a connected, undirected graph. It works by sorting the edges by weight and then sequentially adding the shortest edge that does not create a cycle. The algorithm uses a disjoint set data structure to track connected components in the graph. It runs in O(ElogE) time, where E is the number of edges.
Dijkstra.ppt
Dijkstra's algorithm finds the shortest paths between vertices in a graph with non-negative edge weights. It works by maintaining distances from the source vertex to all other vertices, initially setting all distances to infinity except the source which is 0. It then iteratively selects the unvisited vertex with the lowest distance, marks it as visited, and updates the distances to its neighbors if a shorter path is found through the selected vertex. This continues until all vertices are visited, at which point the distances will be the shortest paths from the source vertex.
Prims and kruskal algorithms
The document discusses minimum spanning trees (MST) and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm operates by building the MST one vertex at a time, starting from an arbitrary root vertex and at each step adding the cheapest connection to another vertex not yet included. Kruskal's algorithm finds the MST by sorting the edges by weight and sequentially adding edges that connect different components without creating cycles.
Dijkstra's Algorithm
Dijkstra's algorithm allows finding the shortest path between any two vertices in a graph. It works by overestimating the distance of each vertex from the starting point and then visiting neighbors to find shorter paths. The algorithm uses a greedy approach, finding the next best solution at each step. It maintains path distances in an array and maps each vertex to its predecessor in the shortest path. A priority queue is used to efficiently retrieve the closest vertex. The time complexity is O(E Log V) and space is O(V). Applications include social networks, maps, and telephone networks.
Prim Algorithm and kruskal algorithm
The document discusses minimum spanning trees and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm works by growing a spanning tree from an initial node, always adding the edge with the lowest weight that connects to a node not yet in the tree. Kruskal's algorithm sorts the edges by weight and builds up a spanning tree by adding edges in order as long as they do not form cycles. Both algorithms run on undirected, weighted graphs and produce optimal minimum spanning trees.
Recommended
KRUSKAL'S algorithm from chaitra
The document summarizes Kruskal's algorithm for finding a minimum cost spanning tree in a graph. Kruskal's algorithm uses a heap data structure to extract the lowest cost edge from the graph. It adds edges to the spanning tree only if they do not create cycles. This results in a minimum cost spanning tree being built incrementally. The document provides an example graph and shows the minimum cost spanning tree obtained using Kruskal's algorithm on that graph.
Dijkstra’s algorithm
The document describes Dijkstra's algorithm for finding the shortest paths between nodes in a graph. It begins with an overview of how the algorithm works by solving subproblems to find the shortest path from the source to increasingly more nodes. It then provides pseudocode for the algorithm and analyzes its time complexity of O(E+V log V) when using a Fibonacci heap. Some applications of the algorithm include traffic information systems and routing protocols like OSPF.
Prims Algorithm
Prim's algorithm is a greedy algorithm used to find minimum spanning trees for weighted undirected graphs. It operates by building the spanning tree one vertex at a time, from an arbitrary starting vertex, at each step adding the minimum weight edge that connects the growing spanning tree to a vertex not yet included in the tree. The algorithm repeats until all vertices are added.
Kruskal Algorithm
Kruskal's algorithm is a minimum spanning tree algorithm that finds the minimum cost collection of edges that connect all vertices in a connected, undirected graph. It works by sorting the edges by weight and then sequentially adding the shortest edge that does not create a cycle. The algorithm uses a disjoint set data structure to track connected components in the graph. It runs in O(ElogE) time, where E is the number of edges.
Dijkstra.ppt
Dijkstra's algorithm finds the shortest paths between vertices in a graph with non-negative edge weights. It works by maintaining distances from the source vertex to all other vertices, initially setting all distances to infinity except the source which is 0. It then iteratively selects the unvisited vertex with the lowest distance, marks it as visited, and updates the distances to its neighbors if a shorter path is found through the selected vertex. This continues until all vertices are visited, at which point the distances will be the shortest paths from the source vertex.
Prims and kruskal algorithms
The document discusses minimum spanning trees (MST) and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm operates by building the MST one vertex at a time, starting from an arbitrary root vertex and at each step adding the cheapest connection to another vertex not yet included. Kruskal's algorithm finds the MST by sorting the edges by weight and sequentially adding edges that connect different components without creating cycles.
Dijkstra's Algorithm
Dijkstra's algorithm allows finding the shortest path between any two vertices in a graph. It works by overestimating the distance of each vertex from the starting point and then visiting neighbors to find shorter paths. The algorithm uses a greedy approach, finding the next best solution at each step. It maintains path distances in an array and maps each vertex to its predecessor in the shortest path. A priority queue is used to efficiently retrieve the closest vertex. The time complexity is O(E Log V) and space is O(V). Applications include social networks, maps, and telephone networks.
Prim Algorithm and kruskal algorithm
The document discusses minimum spanning trees and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm works by growing a spanning tree from an initial node, always adding the edge with the lowest weight that connects to a node not yet in the tree. Kruskal's algorithm sorts the edges by weight and builds up a spanning tree by adding edges in order as long as they do not form cycles. Both algorithms run on undirected, weighted graphs and produce optimal minimum spanning trees.
SINGLE-SOURCE SHORTEST PATHS
The document discusses different single-source shortest path algorithms. It begins by defining shortest path and different variants of shortest path problems. It then describes Dijkstra's algorithm and Bellman-Ford algorithm for solving the single-source shortest paths problem, even in graphs with negative edge weights. Dijkstra's algorithm uses relaxation and a priority queue to efficiently solve the problem in graphs with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but requires multiple relaxation passes to converge. Pseudocode and examples are provided to illustrate the algorithms.
Minimum spanning tree
The document discusses minimum spanning tree algorithms for finding low-cost connections between nodes in a graph. It describes Kruskal's algorithm and Prim's algorithm, both greedy approaches. Kruskal's algorithm works by sorting edges by weight and sequentially adding edges that do not create cycles. Prim's algorithm starts from one node and sequentially connects the closest available node. Both algorithms run in O(ElogV) time, where E is the number of edges and V is the number of vertices. The document provides examples to illustrate the application of the algorithms.
Single source stortest path bellman ford and dijkstra
This document discusses algorithms for finding shortest paths in weighted graphs:
- Dijkstra's algorithm finds single-source shortest paths in graphs with non-negative edge weights using a greedy approach and priority queue. It runs in O(ElogV) time with a Fibonacci heap.
- Bellman-Ford algorithm can handle graphs with negative edge weights by relaxing all edges V-1 times to detect negative cycles. It runs in O(VE) time.
- Examples are provided to illustrate the relaxation process and execution of both algorithms on sample graphs. Key properties like handling of negative weights and cycles are also explained.
Minimum spanning Tree
Kruskal's algorithm finds a minimum spanning tree by growing it from a forest of trees. It works by repeatedly selecting the lowest cost edge that connects two different trees without forming a cycle and adding it to the spanning tree. This continues until only one tree remains, which is the minimum spanning tree. The algorithm considers edges in order of increasing weight, merging components with safe edges. It runs in O(ElogV) time by using a priority queue to track the lowest cost remaining edge.
Shortest path algorithm
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
Prim's Algorithm on minimum spanning tree
Prim's algorithm is used to find the minimum spanning tree of a connected, undirected graph. It works by continuously adding edges to a growing tree that connects vertices. The algorithm maintains two lists - a closed list of vertices already included in the minimum spanning tree, and a priority queue of open vertices. It starts with a single vertex in the closed list. Then it selects the lowest cost edge that connects an open vertex to a closed one, adds it to the tree and updates the lists. This process repeats until all vertices are in the closed list and connected by edges in the minimum spanning tree. The algorithm runs in O(E log V) time when using a binary heap priority queue.
Shortest Path in Graph
The document discusses algorithms for finding shortest paths in graphs. It describes Dijkstra's algorithm and Bellman-Ford algorithm for solving the single-source shortest paths problem and Floyd-Warshall algorithm for solving the all-pairs shortest paths problem. Dijkstra's algorithm uses a priority queue to efficiently find shortest paths from a single source node to all others, assuming non-negative edge weights. Bellman-Ford handles graphs with negative edge weights but is slower. Floyd-Warshall finds shortest paths between all pairs of nodes in a graph.
Dijkstra s algorithm
Dijkstra's algorithm is a solution to the single-source shortest path problem in graph theory. It finds the shortest paths from a source vertex to all other vertices in a weighted graph where all edge weights are non-negative. The algorithm uses a greedy approach, maintaining a set of vertices whose final shortest path from the source vertex has already been determined.
Prim's algorithm
Cover the Prim's algorithm for Minimum Spanning Tree with the help of an example given in cormen book
Kruskal’s algorithm
The purpose of making this presentation is to explain Kruskal's Algorithm in a simple and attractive way. However, the content of this presentation is taken from more than one resources and merged at one place for the better understanding of students. It also contains an example which will definitely help students to understand it more quickly.
Dijkstra's algorithm presentation
The solution to the single-source shortest-path tree problem in graph theory. This slide was prepared for Design and Analysis of Algorithm Lab for B.Tech CSE 2nd Year 4th Semester.
Kruskal & Prim's Algorithm
This presentation summarizes Kruskal's algorithm and Prim's algorithm for finding minimum spanning trees in graphs. Kruskal's algorithm works by selecting the shortest edge in a network that does not create a cycle, repeating until all vertices are connected. Prim's algorithm starts with any vertex and selects the shortest edge connected to vertices already in the tree, repeating until all vertices are connected. Both algorithms are demonstrated on a example network to connect five villages to a market town, finding the minimum total edge length of 18 using either algorithm by connecting edges of length 2, 3, 4, 4, and 5.
Kruskal's algorithm
It is a algorithm used to find a minimum cost spanning tree for connected weighted undirected graph.This algorithm first appeared in Proceedings of the American Mathematical Society in 1956, and was written by Joseph Kruskal.
minimum spanning tree
- A minimum spanning tree (MST) connects all nodes in a graph with the minimum total edge weight while avoiding cycles.
- There are different algorithms that can find an MST, such as Kruskal's algorithm and Prim's algorithm, which were introduced in the document.
- Kruskal's algorithm works by sorting the edges by weight and building the MST by adding the shortest edges that do not create cycles. Prim's algorithm grows the MST from an initial node by always adding the shortest edge connected to the current MST.
Minimum spanning tree
This document discusses minimum spanning trees and algorithms to find them. It begins by introducing the problem of laying cable in a new neighborhood along certain paths. It then defines spanning trees and minimum spanning trees. Two algorithms are described - Prim's and Kruskal's. Prim's grows the minimum spanning tree by adding one edge at a time, while Kruskal's grows a forest by adding edges until a single tree remains. The document applies Prim's algorithm to find the minimum spanning tree of a sample graph modeling the cable laying problem. It compares the time complexities of the two algorithms and discusses properties of minimum spanning trees.
Graphs In Data Structure
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.
A presentation on prim's and kruskal's algorithm
This slides are for a presentation on Prim's and Kruskal's algorithm. Where I have tried to explain how both the algorithms work, their similarities and their differences.
Spanning trees
PPT of Spanning trees
BY Shareb Ismaeel
DEPT. OF INFORMATION TECHNOLOGY
CENTRAL UNIVERSITY OF KASHMIR
Bellman ford Algorithm
The document describes the Bellman-Ford algorithm for finding the shortest paths in a graph. It begins by defining the shortest path problem and describing applications that can be modeled as shortest path problems, such as network routing. It then explains that Bellman-Ford can find single-source shortest paths in graphs with positive or negative edge weights, unlike Dijkstra's algorithm which only works for positive edges. The core of the algorithm uses relaxation to iteratively update the shortest path estimates over multiple rounds until convergence. Pseudocode is provided to demonstrate how the relaxation process is repeated for all edges |V|-1 times to find a shortest path from the source node to all other nodes.
Bellman Ford's Algorithm
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.
Peephole optimization techniques
The document discusses peephole optimization techniques. It defines peephole optimization as a type of optimization performed over a small set of instructions in generated code to recognize patterns that can be replaced by shorter or faster instructions. The goal is to improve performance, reduce memory footprint, and reduce code size. It describes machine-independent optimization as transformations that do not involve CPU registers or memory locations, while machine-dependent optimization targets the generated code according to the machine architecture. Common replacement rules used in peephole optimization include constant folding, strength reduction, null sequence elimination, combining operations, and using algebraic laws and special case instructions.
Vm migration techniques
This document discusses different techniques for virtual machine migration. It begins with an introduction to virtualization and how virtual machine migration involves copying a VM from one physical machine to another. There are three main categories of migration techniques: fault tolerant techniques which migrate VMs to prevent failures, load balancing techniques which distribute load across servers, and energy efficient techniques which optimize resource utilization to conserve energy. Live VM migration is described as migrating the entire OS and applications between physical machines without disrupting applications. The document also covers background details on virtual machine migration methods being either hot/live where the VM continues running, or cold/non-live where the VM status is lost during migration.
More Related Content
What's hot
SINGLE-SOURCE SHORTEST PATHS
The document discusses different single-source shortest path algorithms. It begins by defining shortest path and different variants of shortest path problems. It then describes Dijkstra's algorithm and Bellman-Ford algorithm for solving the single-source shortest paths problem, even in graphs with negative edge weights. Dijkstra's algorithm uses relaxation and a priority queue to efficiently solve the problem in graphs with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but requires multiple relaxation passes to converge. Pseudocode and examples are provided to illustrate the algorithms.
Minimum spanning tree
The document discusses minimum spanning tree algorithms for finding low-cost connections between nodes in a graph. It describes Kruskal's algorithm and Prim's algorithm, both greedy approaches. Kruskal's algorithm works by sorting edges by weight and sequentially adding edges that do not create cycles. Prim's algorithm starts from one node and sequentially connects the closest available node. Both algorithms run in O(ElogV) time, where E is the number of edges and V is the number of vertices. The document provides examples to illustrate the application of the algorithms.
Single source stortest path bellman ford and dijkstra
This document discusses algorithms for finding shortest paths in weighted graphs:
- Dijkstra's algorithm finds single-source shortest paths in graphs with non-negative edge weights using a greedy approach and priority queue. It runs in O(ElogV) time with a Fibonacci heap.
- Bellman-Ford algorithm can handle graphs with negative edge weights by relaxing all edges V-1 times to detect negative cycles. It runs in O(VE) time.
- Examples are provided to illustrate the relaxation process and execution of both algorithms on sample graphs. Key properties like handling of negative weights and cycles are also explained.
Minimum spanning Tree
Kruskal's algorithm finds a minimum spanning tree by growing it from a forest of trees. It works by repeatedly selecting the lowest cost edge that connects two different trees without forming a cycle and adding it to the spanning tree. This continues until only one tree remains, which is the minimum spanning tree. The algorithm considers edges in order of increasing weight, merging components with safe edges. It runs in O(ElogV) time by using a priority queue to track the lowest cost remaining edge.
Shortest path algorithm
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
Prim's Algorithm on minimum spanning tree
Prim's algorithm is used to find the minimum spanning tree of a connected, undirected graph. It works by continuously adding edges to a growing tree that connects vertices. The algorithm maintains two lists - a closed list of vertices already included in the minimum spanning tree, and a priority queue of open vertices. It starts with a single vertex in the closed list. Then it selects the lowest cost edge that connects an open vertex to a closed one, adds it to the tree and updates the lists. This process repeats until all vertices are in the closed list and connected by edges in the minimum spanning tree. The algorithm runs in O(E log V) time when using a binary heap priority queue.
Shortest Path in Graph
The document discusses algorithms for finding shortest paths in graphs. It describes Dijkstra's algorithm and Bellman-Ford algorithm for solving the single-source shortest paths problem and Floyd-Warshall algorithm for solving the all-pairs shortest paths problem. Dijkstra's algorithm uses a priority queue to efficiently find shortest paths from a single source node to all others, assuming non-negative edge weights. Bellman-Ford handles graphs with negative edge weights but is slower. Floyd-Warshall finds shortest paths between all pairs of nodes in a graph.
Dijkstra s algorithm
Dijkstra's algorithm is a solution to the single-source shortest path problem in graph theory. It finds the shortest paths from a source vertex to all other vertices in a weighted graph where all edge weights are non-negative. The algorithm uses a greedy approach, maintaining a set of vertices whose final shortest path from the source vertex has already been determined.
Prim's algorithm
Cover the Prim's algorithm for Minimum Spanning Tree with the help of an example given in cormen book
Kruskal’s algorithm
The purpose of making this presentation is to explain Kruskal's Algorithm in a simple and attractive way. However, the content of this presentation is taken from more than one resources and merged at one place for the better understanding of students. It also contains an example which will definitely help students to understand it more quickly.
Dijkstra's algorithm presentation
The solution to the single-source shortest-path tree problem in graph theory. This slide was prepared for Design and Analysis of Algorithm Lab for B.Tech CSE 2nd Year 4th Semester.
Kruskal & Prim's Algorithm
This presentation summarizes Kruskal's algorithm and Prim's algorithm for finding minimum spanning trees in graphs. Kruskal's algorithm works by selecting the shortest edge in a network that does not create a cycle, repeating until all vertices are connected. Prim's algorithm starts with any vertex and selects the shortest edge connected to vertices already in the tree, repeating until all vertices are connected. Both algorithms are demonstrated on a example network to connect five villages to a market town, finding the minimum total edge length of 18 using either algorithm by connecting edges of length 2, 3, 4, 4, and 5.
Kruskal's algorithm
It is a algorithm used to find a minimum cost spanning tree for connected weighted undirected graph.This algorithm first appeared in Proceedings of the American Mathematical Society in 1956, and was written by Joseph Kruskal.
minimum spanning tree
- A minimum spanning tree (MST) connects all nodes in a graph with the minimum total edge weight while avoiding cycles.
- There are different algorithms that can find an MST, such as Kruskal's algorithm and Prim's algorithm, which were introduced in the document.
- Kruskal's algorithm works by sorting the edges by weight and building the MST by adding the shortest edges that do not create cycles. Prim's algorithm grows the MST from an initial node by always adding the shortest edge connected to the current MST.
Minimum spanning tree
This document discusses minimum spanning trees and algorithms to find them. It begins by introducing the problem of laying cable in a new neighborhood along certain paths. It then defines spanning trees and minimum spanning trees. Two algorithms are described - Prim's and Kruskal's. Prim's grows the minimum spanning tree by adding one edge at a time, while Kruskal's grows a forest by adding edges until a single tree remains. The document applies Prim's algorithm to find the minimum spanning tree of a sample graph modeling the cable laying problem. It compares the time complexities of the two algorithms and discusses properties of minimum spanning trees.
Graphs In Data Structure
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.
A presentation on prim's and kruskal's algorithm
This slides are for a presentation on Prim's and Kruskal's algorithm. Where I have tried to explain how both the algorithms work, their similarities and their differences.
Spanning trees
PPT of Spanning trees
BY Shareb Ismaeel
DEPT. OF INFORMATION TECHNOLOGY
CENTRAL UNIVERSITY OF KASHMIR
Bellman ford Algorithm
The document describes the Bellman-Ford algorithm for finding the shortest paths in a graph. It begins by defining the shortest path problem and describing applications that can be modeled as shortest path problems, such as network routing. It then explains that Bellman-Ford can find single-source shortest paths in graphs with positive or negative edge weights, unlike Dijkstra's algorithm which only works for positive edges. The core of the algorithm uses relaxation to iteratively update the shortest path estimates over multiple rounds until convergence. Pseudocode is provided to demonstrate how the relaxation process is repeated for all edges |V|-1 times to find a shortest path from the source node to all other nodes.
Bellman Ford's Algorithm
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.
What's hot (20)
Single source stortest path bellman ford and dijkstra
Single source stortest path bellman ford and dijkstra
More from garishma bhatia
Peephole optimization techniques
The document discusses peephole optimization techniques. It defines peephole optimization as a type of optimization performed over a small set of instructions in generated code to recognize patterns that can be replaced by shorter or faster instructions. The goal is to improve performance, reduce memory footprint, and reduce code size. It describes machine-independent optimization as transformations that do not involve CPU registers or memory locations, while machine-dependent optimization targets the generated code according to the machine architecture. Common replacement rules used in peephole optimization include constant folding, strength reduction, null sequence elimination, combining operations, and using algebraic laws and special case instructions.
Vm migration techniques
This document discusses different techniques for virtual machine migration. It begins with an introduction to virtualization and how virtual machine migration involves copying a VM from one physical machine to another. There are three main categories of migration techniques: fault tolerant techniques which migrate VMs to prevent failures, load balancing techniques which distribute load across servers, and energy efficient techniques which optimize resource utilization to conserve energy. Live VM migration is described as migrating the entire OS and applications between physical machines without disrupting applications. The document also covers background details on virtual machine migration methods being either hot/live where the VM continues running, or cold/non-live where the VM status is lost during migration.
Denial of service
This document discusses denial of service (DoS) and distributed denial of service (DDoS) attacks. It defines DoS attacks as attempts to render a system unusable or slow it down for legitimate users by overloading its resources. DDoS attacks multiply the effectiveness of DoS by using multiple compromised computers to launch attacks simultaneously. Common DoS attack types like SYN floods, Smurf attacks, and ping of death are described. The rise of botnets, which are networks of compromised computers controlled remotely, enabled more powerful DDoS attacks. Mitigation strategies include load balancing, throttling traffic, and using honeypots to gather attacker information.
Iot + cloud
This document discusses Internet of Things (IoT) and cloud computing. It defines IoT as connecting physical objects to the internet and collecting data from sensors. The cloud provides shared computing resources over the internet. The document outlines an IoT architecture with things, gateways, and a network/cloud. Gateways interface devices like sensors to the cloud using protocols like RS-485 and WiFi. The cloud provides services like software, platforms and infrastructure on a pay-per-use basis.
WORKFLOW OF THE PROCESS IN SPM
The document outlines the various workflows that make up the software development process, including management, environment, requirements, design, implementation, assessment, and deployment workflows. It describes the key activities for each workflow, such as controlling the process, evolving requirements and design artifacts, programming components, assessing product quality, and transitioning the product to users. The document also notes that iterations consist of sequential activities that vary depending on where an iteration falls in the development cycle.
MULTILEVEL QUEUE SCHEDULING
Multilevel queue scheduling divides processes into groups based on properties like type and resource usage. These groups are placed into multiple queues, each with a different priority level. Higher priority queues like the system process queue use scheduling algorithms like FCFS, while lower priority queues like the student process queue use priority scheduling. This approach allows different scheduling optimizations based on the process type compared to a single queue approach. A disadvantage is the potential for lower priority processes to starve if higher priority processes monopolize resources.
Constructor overloading & method overloading
This document discusses constructor overloading in Java. It defines constructors as special methods that initialize objects. There are two types of constructors: default (no-argument) constructors and parameterized constructors. Constructor overloading allows a class to have multiple constructors that differ in their parameter lists. This allows constructors to perform different initialization tasks depending on the arguments passed. The document provides examples of default, parameterized, and overloaded constructors.
MULTIPLE ACCESS PROTOCOL COMPUTER NETWORKS
Multiple access protocols can be categorized as either random access or controlled access. Random access protocols like Aloha and Slotted Aloha use a probabilistic approach where nodes transmit randomly, while controlled access protocols like TDMA use predetermined allocation of transmission time slots. Carrier sense multiple access (CSMA) protocols like CSMA/CD used in Ethernet require nodes to first listen for activity on the channel before transmitting to avoid collisions.
BLOCKCHAIN TECHNOLOGY
Blockchain is a decentralized database that maintains a continuously growing list of records called blocks. Each block contains transaction data and a link to the previous block. The first block is special as it has no preceding block. Blocks are linked through cryptography, forming a chain. Miners process transactions by solving complex math problems to find new blocks and receive rewards. The first application was Bitcoin, solving the double spending problem without a central authority. Other applications include Ethereum, Litecoin, Ripple, and SolarCoin. Key variables that impact the blockchain include block size, header size, and transaction size.
Knapsack problem
The document discusses different types of knapsack problems. It provides an example of a 0/1 knapsack problem where items must either be fully included or excluded from a knapsack with limited capacity. Brute force and greedy algorithms are presented as approaches to solve such problems. The document also briefly introduces fractional knapsack problems and provides pseudocode for a greedy algorithm solution.
Data types IN JAVA
This document discusses Java's primitive and non-primitive data types. It describes the 8 primitive types in Java - byte, short, int, long, char, float, double, boolean - including their size, range of values, and usage. Non-primitive types include classes, interfaces, and arrays. The numeric primitive types are further broken down into integer (byte, short, int, long) and floating point (float, double) categories.
Data Algorithms And Analysis
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. It works by starting with a single vertex, and at each step adding the edge with the minimum weight that connects the growing spanning tree to another vertex not yet included in the tree, as long as this does not create a cycle. The time complexity of Prim's algorithm is O(V log V + E).
VEHICLE REGISTRATION PROJECT
The document describes drawing an ER diagram for a Vehicle Registration System. It involves installing Star UML software, then using it to draw the ER diagram with entities such as Vehicles, Owners, and a relationship between them. The objective is to model the data requirements and relationships for a system to manage vehicle registrations.
WIFI TECH
Wi-Fi is a wireless networking technology that uses radio waves to provide wireless internet access to devices within a 150-300 foot range. It was invented in the 1990s in Australia by Dr. John O'Sullivan. A Wi-Fi network uses a router connected to a modem to connect to the internet and broadcast a radio signal to nearby devices, allowing them wireless access to browse and use the internet without wires. Wi-Fi provides advantages over wired connections like mobility and easy installation. However, it also has limitations like a smaller range and potential security risks compared to wired networks.
MATCHING GRAPH THEORY
This document discusses different types of matchings in graphs. A matching is a set of edges without common vertices. A maximum matching contains the largest possible number of edges. A maximal matching is one where no additional edges can be added without violating the matching property. A perfect matching is where every vertex is incident to exactly one edge, making it both maximum and maximal. The document provides definitions and properties of different matchings in graphs.
Garishma se
A data flow diagram (DFD) is a graphical representation of the flow of data through a system. It uses symbols like rectangles, circles, and arrows to show data inputs, outputs, storage points, and the routes between each destination. A DFD shows what data will be input and output from the system and where data will be stored. It also shows external entities, processes, data stores, and data flows. There are three levels of a DFD - level 0 provides an overview, level 1 provides more detail, and level 2 provides further detail into parts of level 1. A DFD is used to create an overview of a system without detail and can also visualize data processing.
Garishma xcs
R is a programming language developed from S and created by Ross and Robert. It can be used for developing static software, visualizing and reporting data, and has a command line interface. R is commonly used in projects involving banking, political campaigns, startups, and more. Packages extend R's functionality by providing additional functions, data, help files, and graphics capabilities.
Garishma maTHS
This document discusses relations and their properties. It defines a relation as a subset of A x B where A and B are sets. Relations can exist between a set and itself. Common examples of relations are subsets representing inequalities or functions. The properties of relations that are discussed include reflexivity, symmetry, antisymmetry, and transitivity. Graphs can be interpreted as relations based on their edge sets. Relations are useful in theoretical computer science, game theory, and logic.
Garishma ec
The document discusses complementary metal-oxide-semiconductor (CMOS) circuits. CMOS circuits contain both n-channel and p-channel transistors arranged so that all PMOS transistors receive input from the voltage source or another PMOS, and all NMOS transistors receive input from ground or another NMOS. The basic CMOS gates are NAND and NOR gates, from which AND and OR gates can be derived by adding an inverter to the output. CMOS is commonly used in microprocessors, microcontrollers, static RAM, analog circuits, and data converters.
More from garishma bhatia (19)
Recently uploaded
SELENIUM CONF -PALLAVI SHARMA - 2024.pdf
Begin your journey to contribute to Selenium - A Talk at the Selenium Conference 2024
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...Paris Salesforce Developer Group
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
【主营项目】
一.毕业证【微bwp0011】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Object Oriented Analysis and Design - OOAD
This ppt gives detailed description of Object Oriented Analysis and design.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Accident detection system project report.pdf
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
原版一模一样【微信：741003700 】【(爱大毕业证书)爱荷华大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
OOPS_Lab_Manual - programs using C++ programming language
This manual contains programs on object oriented programming concepts using C++ language.
Butterfly Valves Manufacturer (LBF Series).pdf
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
FULL STACK PROGRAMMING - Both Front End and Back End
This ppt gives details about Full Stack Programming and its basics.
This study Examines the Effectiveness of Talent Procurement through the Imple...
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Determination of Equivalent Circuit parameters and performance characteristic...
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Beckhoff Programmable Logic Control Overview Presentation
This presentation is to describe the overview of PLC Beckhoff for beginners
Properties of Fluids, Fluid Statics, Pressure Measurement
Properties of Fluids: Density, viscosity, surface tension, compressibility, and specific gravity define fluid behavior.
Fluid Statics: Studies pressure, hydrostatic pressure, buoyancy, and fluid forces on surfaces.
Pressure at a Point: In a static fluid, the pressure at any point is the same in all directions. This is known as Pascal's principle. The pressure increases with depth due to the weight of the fluid above.
Hydrostatic Pressure: The pressure exerted by a fluid at rest due to the force of gravity. It can be calculated using the formula P=ρghP=ρgh, where PP is the pressure, ρρ is the fluid density, gg is the acceleration due to gravity, and hh is the height of the fluid column above the point in question.
Buoyancy: The upward force exerted by a fluid on a submerged or partially submerged object. This force is equal to the weight of the fluid displaced by the object, as described by Archimedes' principle. Buoyancy explains why objects float or sink in fluids.
Fluid Pressure on Surfaces: The analysis of pressure forces on surfaces submerged in fluids. This includes calculating the total force and the center of pressure, which is the point where the resultant pressure force acts.
Pressure Measurement: Manometers, barometers, pressure gauges, and differential pressure transducers measure fluid pressure.
Ericsson LTE Throughput Troubleshooting Techniques.ppt
Ericsson LTE Throughput Troubleshooting Techniques
Recently uploaded (20)
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...
OOPS_Lab_Manual - programs using C++ programming language
OOPS_Lab_Manual - programs using C++ programming language
309475979-Creativity-Innovation-notes-IV-Sem-2016-pdf.pdf
309475979-Creativity-Innovation-notes-IV-Sem-2016-pdf.pdf
FULL STACK PROGRAMMING - Both Front End and Back End
FULL STACK PROGRAMMING - Both Front End and Back End
This study Examines the Effectiveness of Talent Procurement through the Imple...
This study Examines the Effectiveness of Talent Procurement through the Imple...
Determination of Equivalent Circuit parameters and performance characteristic...
Determination of Equivalent Circuit parameters and performance characteristic...
Beckhoff Programmable Logic Control Overview Presentation
Beckhoff Programmable Logic Control Overview Presentation
Properties of Fluids, Fluid Statics, Pressure Measurement
Properties of Fluids, Fluid Statics, Pressure Measurement
Ericsson LTE Throughput Troubleshooting Techniques.ppt
Ericsson LTE Throughput Troubleshooting Techniques.ppt
PRIM'S ALGORITHM
- 1. Prim’s Algorithm By : Garishma Bhatia
- 2. Content Layout List • Minimal Spanning Tree • Prim’s Algorithm • Example • Algorithm • Analysis • Complexity • References
- 3. What is Minimum Spanning Tree? • A spanning tree is a subset of Graph G, which has all the vertices covered with minimum possible number of edges. • Hence, a spanning tree does not have cycles and it cannot be disconnected.
- 4. Prim’s Algorithm • Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. • The algorithm was developed in 1930 by Czech mathematician Vojtěch Jarník. • But later rediscovered and republished by computer scientists Robert C. Prim in 1957 and Edsger W. Dijkstra in 1959. • Therefore, it is also sometimes called the DJP algorithm, the Prim– Dijkstra algorithm
- 5. Two Content Layout with Table • First bullet point here • Second bullet point here • Third bullet point here Class Group A Group B Class 1 82 95 Class 2 76 88 Class 3 84 90
- 6. Two Content Layout with SmartArt Group A • Task 1 • Task 2 Group B • Task 1 • Task 2 Group C • Task 1 • First bullet point here • Second bullet point here • Third bullet point here
- 7. Add a Slide Title - 1
- 8. Add a Slide Title - 2
- 9. Add a Slide Title - 3
- 11. Add a Slide Title - 4
- 12. Add a Slide Title - 5