•Descargar como PPTX, PDF•

0 recomendaciones•35 vistas

DFS (Depth First Search) is a graph traversal algorithm that uses a stack data structure. It starts at a root node and explores as far as possible along each branch before backtracking. DFS uses backtracking to traverse all unvisited nodes by popping nodes off the stack. It is faster than BFS but may cover more edges to reach a destination. DFS is suitable for decision trees as it explores all paths of a decision fully before moving on.

Denunciar

Compartir

Denunciar

Compartir

Topological Sort and BFS

Topological sorting is a linear ordering of the vertices in a directed acyclic graph (DAG) where for every edge from vertex u to vertex v, u comes before v in the ordering. The algorithm uses a depth-first search approach with a stack to iteratively push and pop vertices as their adjacent vertices are explored. Breadth-first search also traverses a graph by exploring the neighbor vertices, but uses a queue rather than a stack and marks visited vertices to avoid getting stuck in cycles. The document provides pseudocode for both algorithms with an example graph to demonstrate the process.

Bfs and dfs

The document discusses Breadth First Search (BFS) and Depth First Search (DFS) graph traversal algorithms. BFS uses a queue data structure and explores neighbor nodes level-wise, starting from a source node. Its time and space complexity is O(V+E) and O(V) respectively. DFS uses a stack and explores nodes by going deeper first until reaching a dead end, then backtracks. Its complexity is O(V+E) for adjacency lists and O(V^2) for adjacency matrices. Both algorithms are used for path finding, cycle detection and other graph applications.

Data structure note

This document discusses different graph traversal algorithms and spanning trees. It explains that breadth-first search (BFS) traverses a graph level-by-level using a queue, while depth-first search (DFS) traverses as far as possible from the root node using a stack. A spanning tree connects all nodes of a graph using the minimum number of edges without cycles. Minimum spanning trees for weighted graphs use algorithms like Kruskal's and Prim's that apply greedy approaches.

Daa chpater 12

The document provides information about graphs and graph algorithms. It defines graphs as mathematical structures representing pairwise relationships between objects using nodes and edges. It describes different types of graphs like undirected, directed, weighted, cyclic graphs and trees. It explains two common ways to represent graphs - adjacency matrix and adjacency list. It then describes three important graph traversal algorithms - Breadth First Search (BFS), Depth First Search (DFS), and topological sorting. BFS traverses the graph layer by layer. DFS uses backtracking to exhaustively search all nodes. Topological sorting outputs a linear ordering of vertices where edges only point forward.

Data structure

Breadth First Search (BFS) and Depth First Search (DFS) are two graph traversal algorithms. BFS uses a queue to visit all neighbor nodes at the present depth prior to moving to the next depth level, while DFS uses a stack to explore as far as possible along each branch before backtracking. The document provides pseudocode for BFS and DFS algorithms and explains their process through examples of traversing graphs from a starting node.

Graph traversals in Data Structures

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.

unit 5 stack & queue.ppt

The document discusses stacks and queues, which are linear data structures. It defines a stack as a first-in, last-out (FILO) structure where elements can only be inserted or removed from one end. A queue is defined as a first-in, first-out (FIFO) structure where elements can only be inserted at one end and removed from the other. The document then describes common stack and queue operations like push, pop, enqueue, dequeue and provides examples of their applications. It also discusses two common implementations of stacks and queues using arrays and linked lists.

Stack

A stack is a linear data structure in which an element can be inserted or deleted only at one end of the list.
A stack works on the principle of last in first out and is also known as a Last-In-First-Out (LIFO) list.
A bunch of books is one of the common examples of stack. A new book to be added to the bunch is placed at the top and a book to be removed is also taken off from the top.
Therefore, in order to take out the book at the bottom, all the books above it need to be removed from the bunch.

Topological Sort and BFS

Topological sorting is a linear ordering of the vertices in a directed acyclic graph (DAG) where for every edge from vertex u to vertex v, u comes before v in the ordering. The algorithm uses a depth-first search approach with a stack to iteratively push and pop vertices as their adjacent vertices are explored. Breadth-first search also traverses a graph by exploring the neighbor vertices, but uses a queue rather than a stack and marks visited vertices to avoid getting stuck in cycles. The document provides pseudocode for both algorithms with an example graph to demonstrate the process.

Bfs and dfs

The document discusses Breadth First Search (BFS) and Depth First Search (DFS) graph traversal algorithms. BFS uses a queue data structure and explores neighbor nodes level-wise, starting from a source node. Its time and space complexity is O(V+E) and O(V) respectively. DFS uses a stack and explores nodes by going deeper first until reaching a dead end, then backtracks. Its complexity is O(V+E) for adjacency lists and O(V^2) for adjacency matrices. Both algorithms are used for path finding, cycle detection and other graph applications.

Data structure note

This document discusses different graph traversal algorithms and spanning trees. It explains that breadth-first search (BFS) traverses a graph level-by-level using a queue, while depth-first search (DFS) traverses as far as possible from the root node using a stack. A spanning tree connects all nodes of a graph using the minimum number of edges without cycles. Minimum spanning trees for weighted graphs use algorithms like Kruskal's and Prim's that apply greedy approaches.

Daa chpater 12

The document provides information about graphs and graph algorithms. It defines graphs as mathematical structures representing pairwise relationships between objects using nodes and edges. It describes different types of graphs like undirected, directed, weighted, cyclic graphs and trees. It explains two common ways to represent graphs - adjacency matrix and adjacency list. It then describes three important graph traversal algorithms - Breadth First Search (BFS), Depth First Search (DFS), and topological sorting. BFS traverses the graph layer by layer. DFS uses backtracking to exhaustively search all nodes. Topological sorting outputs a linear ordering of vertices where edges only point forward.

Data structure

Breadth First Search (BFS) and Depth First Search (DFS) are two graph traversal algorithms. BFS uses a queue to visit all neighbor nodes at the present depth prior to moving to the next depth level, while DFS uses a stack to explore as far as possible along each branch before backtracking. The document provides pseudocode for BFS and DFS algorithms and explains their process through examples of traversing graphs from a starting node.

Graph traversals in Data Structures

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.

unit 5 stack & queue.ppt

The document discusses stacks and queues, which are linear data structures. It defines a stack as a first-in, last-out (FILO) structure where elements can only be inserted or removed from one end. A queue is defined as a first-in, first-out (FIFO) structure where elements can only be inserted at one end and removed from the other. The document then describes common stack and queue operations like push, pop, enqueue, dequeue and provides examples of their applications. It also discusses two common implementations of stacks and queues using arrays and linked lists.

Stack

A stack is a linear data structure in which an element can be inserted or deleted only at one end of the list.
A stack works on the principle of last in first out and is also known as a Last-In-First-Out (LIFO) list.
A bunch of books is one of the common examples of stack. A new book to be added to the bunch is placed at the top and a book to be removed is also taken off from the top.
Therefore, in order to take out the book at the bottom, all the books above it need to be removed from the bunch.

linked list using c

The document discusses linked lists and their advantages over arrays. It defines a linked list as a linear data structure composed of nodes, where each node contains data and a pointer to the next node. The key points are:
- Linked lists have dynamic sizes while arrays are fixed. Inserting and deleting from linked lists is more efficient as it doesn't require shifting elements.
- Linked lists don't allow random access so operations like sorting are less efficient, while arrays don't waste space if fully filled.
- The document then describes the basic operations on a singly linked list like creation, insertion, deletion and searching and provides algorithms to implement these operations.

Depth-First Search

Depth-first search (DFS) is an algorithm for traversing tree and graph data structures. It starts at the root node and explores as far as possible along each branch before backtracking. It continues visiting neighbors in a recursive pattern, recursively visiting all unvisited neighbors of each visited vertex before backtracking. The DFS algorithm works by starting with any vertex on a stack, visiting and removing it from the stack while adding its unvisited neighbors to the top of the stack, repeating until the stack is empty.

BFS (Breadth First Search) Tree Traversal

1) Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph) and explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level.
2) BFS uses a queue to keep track of nodes to visit at each level, processing nodes level-by-level from the queue in a FIFO order. It marks visited nodes to avoid processing them again.
3) The time complexity of BFS is O(V+E) where V is the number of vertices and E is the number of edges. BFS can be used to

graphtraversals.pdf

This document discusses graph traversal techniques for searching graphs. It describes two common techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue data structure to visit all adjacent vertices of the starting vertex before moving to the next level, producing a spanning tree. DFS uses a stack, visiting all vertices reachable from the starting point before backtracking, also producing a spanning tree. The document outlines the step-by-step process for implementing BFS and DFS on a graph.

data structures and applications power p

A linked list is a linear data structure where nodes are linked using pointers. Each node contains a data field for storing elements and a pointer field for linking to the next node. There are different types of linked lists including singly linked lists where each node has a pointer to the next node, doubly linked lists where each node has a pointer to both the next and previous nodes, and circular linked lists where the last node is linked to the first node. Common operations on linked lists include insertion, deletion, traversal, searching, and sorting of nodes. Linked lists have advantages over arrays for dynamic memory allocation and efficient insertion/deletion but have disadvantages for random access and memory usage.

Linkedlist

Linked lists are a linear data structure composed of nodes where each node contains data and a link to the next node. There are several types of linked lists including single and double linked lists. Basic operations on linked lists include creation, insertion, deletion, traversal, and searching. Insertion can be done at the beginning, end, or after a particular node. Deletion can remove the first, last, or intermediate nodes. Linked lists allow dynamic memory allocation and efficient insertion/deletion compared to arrays. However, linked lists use more storage space than arrays. Linked lists are used in many data structures and applications like polynomial manipulation and representing trees, stacks and queues.

DSA chapter 4.pptxhdjaaaaaadjhsssssssssssssssssssssssssss

This document discusses linked lists and provides details about singly linked lists and doubly linked lists. It defines linked lists as data structures made up of nodes that are linked together via pointers. Singly linked lists have pointers to the next node only, while doubly linked lists have pointers to both the next and previous nodes. The document outlines common operations for each like insertion, deletion, and display and provides pseudocode for implementing these operations.

Linked list and its operations - Traversal

The document discusses linked lists and operations performed on singly linked lists. It defines a linked list as a data structure containing nodes that point to the next node in the list. Singly linked lists contain nodes with a data field and pointer to the next node. Common operations on singly linked lists include traversing the list, inserting and deleting nodes from different positions, searching for a node, sorting list elements, and merging two linked lists.

Depth first traversal(data structure algorithms)

The document discusses depth-first search (DFS) algorithms for graphs. It explains that DFS traverses a graph in a depthward motion using a stack. It explores all adjacent unvisited nodes, marks them as visited, and pushes them onto the stack before backtracking. The document provides pseudocode for DFS and an example of applying it to a graph. It also discusses uses of DFS in finding connected components, biconnectivity, and strongly connected components in graphs.

Dounly linked list

Introduction to doubly linked list, operations performed on it and algorithms to perform this operations..

Trees

The document discusses trees and binary trees. It defines key tree terminology like nodes, edges, root, leaf, etc. It explains properties of binary trees including full, complete, and binary search trees. It describes techniques for traversing binary trees including preorder, inorder and postorder traversal using recursion and stacks. Searching and insertion operations in binary search trees are also summarized.

Casting-Defect-inSlab continuous casting.pdf

Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting

Modelagem de um CSTR com reação endotermica.pdf

Modelagem em função de transferencia. CSTR não-linear.

basic-wireline-operations-course-mahmoud-f-radwan.pdf

Basics on wireline techniques

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.

TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM

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

Literature Review Basics and Understanding Reference Management.pptx

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 monitoring

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.

CSM Cloud Service Management Presentarion

Usefull PPT

Question paper of renewable energy sources

question papers

KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions

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.

linked list using c

The document discusses linked lists and their advantages over arrays. It defines a linked list as a linear data structure composed of nodes, where each node contains data and a pointer to the next node. The key points are:
- Linked lists have dynamic sizes while arrays are fixed. Inserting and deleting from linked lists is more efficient as it doesn't require shifting elements.
- Linked lists don't allow random access so operations like sorting are less efficient, while arrays don't waste space if fully filled.
- The document then describes the basic operations on a singly linked list like creation, insertion, deletion and searching and provides algorithms to implement these operations.

Depth-First Search

Depth-first search (DFS) is an algorithm for traversing tree and graph data structures. It starts at the root node and explores as far as possible along each branch before backtracking. It continues visiting neighbors in a recursive pattern, recursively visiting all unvisited neighbors of each visited vertex before backtracking. The DFS algorithm works by starting with any vertex on a stack, visiting and removing it from the stack while adding its unvisited neighbors to the top of the stack, repeating until the stack is empty.

BFS (Breadth First Search) Tree Traversal

1) Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph) and explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level.
2) BFS uses a queue to keep track of nodes to visit at each level, processing nodes level-by-level from the queue in a FIFO order. It marks visited nodes to avoid processing them again.
3) The time complexity of BFS is O(V+E) where V is the number of vertices and E is the number of edges. BFS can be used to

graphtraversals.pdf

This document discusses graph traversal techniques for searching graphs. It describes two common techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue data structure to visit all adjacent vertices of the starting vertex before moving to the next level, producing a spanning tree. DFS uses a stack, visiting all vertices reachable from the starting point before backtracking, also producing a spanning tree. The document outlines the step-by-step process for implementing BFS and DFS on a graph.

data structures and applications power p

A linked list is a linear data structure where nodes are linked using pointers. Each node contains a data field for storing elements and a pointer field for linking to the next node. There are different types of linked lists including singly linked lists where each node has a pointer to the next node, doubly linked lists where each node has a pointer to both the next and previous nodes, and circular linked lists where the last node is linked to the first node. Common operations on linked lists include insertion, deletion, traversal, searching, and sorting of nodes. Linked lists have advantages over arrays for dynamic memory allocation and efficient insertion/deletion but have disadvantages for random access and memory usage.

Linkedlist

Linked lists are a linear data structure composed of nodes where each node contains data and a link to the next node. There are several types of linked lists including single and double linked lists. Basic operations on linked lists include creation, insertion, deletion, traversal, and searching. Insertion can be done at the beginning, end, or after a particular node. Deletion can remove the first, last, or intermediate nodes. Linked lists allow dynamic memory allocation and efficient insertion/deletion compared to arrays. However, linked lists use more storage space than arrays. Linked lists are used in many data structures and applications like polynomial manipulation and representing trees, stacks and queues.

DSA chapter 4.pptxhdjaaaaaadjhsssssssssssssssssssssssssss

This document discusses linked lists and provides details about singly linked lists and doubly linked lists. It defines linked lists as data structures made up of nodes that are linked together via pointers. Singly linked lists have pointers to the next node only, while doubly linked lists have pointers to both the next and previous nodes. The document outlines common operations for each like insertion, deletion, and display and provides pseudocode for implementing these operations.

Linked list and its operations - Traversal

The document discusses linked lists and operations performed on singly linked lists. It defines a linked list as a data structure containing nodes that point to the next node in the list. Singly linked lists contain nodes with a data field and pointer to the next node. Common operations on singly linked lists include traversing the list, inserting and deleting nodes from different positions, searching for a node, sorting list elements, and merging two linked lists.

Depth first traversal(data structure algorithms)

The document discusses depth-first search (DFS) algorithms for graphs. It explains that DFS traverses a graph in a depthward motion using a stack. It explores all adjacent unvisited nodes, marks them as visited, and pushes them onto the stack before backtracking. The document provides pseudocode for DFS and an example of applying it to a graph. It also discusses uses of DFS in finding connected components, biconnectivity, and strongly connected components in graphs.

Dounly linked list

Introduction to doubly linked list, operations performed on it and algorithms to perform this operations..

Trees

The document discusses trees and binary trees. It defines key tree terminology like nodes, edges, root, leaf, etc. It explains properties of binary trees including full, complete, and binary search trees. It describes techniques for traversing binary trees including preorder, inorder and postorder traversal using recursion and stacks. Searching and insertion operations in binary search trees are also summarized.

linked list using c

linked list using c

Depth-First Search

Depth-First Search

BFS (Breadth First Search) Tree Traversal

BFS (Breadth First Search) Tree Traversal

graphtraversals.pdf

graphtraversals.pdf

data structures and applications power p

data structures and applications power p

Linkedlist

Linkedlist

DSA chapter 4.pptxhdjaaaaaadjhsssssssssssssssssssssssssss

DSA chapter 4.pptxhdjaaaaaadjhsssssssssssssssssssssssssss

Linked list and its operations - Traversal

Linked list and its operations - Traversal

Depth first traversal(data structure algorithms)

Depth first traversal(data structure algorithms)

Dounly linked list

Dounly linked list

Trees

Trees

Casting-Defect-inSlab continuous casting.pdf

Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting

Modelagem de um CSTR com reação endotermica.pdf

Modelagem em função de transferencia. CSTR não-linear.

basic-wireline-operations-course-mahmoud-f-radwan.pdf

Basics on wireline techniques

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.

TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM

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

Literature Review Basics and Understanding Reference Management.pptx

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 monitoring

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.

CSM Cloud Service Management Presentarion

Usefull PPT

Question paper of renewable energy sources

question papers

KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions

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.

Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor

Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, SerbiaInternational Conference on NLP, Artificial Intelligence, Machine Learning an...

International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.

Eric Nizeyimana's document 2006 from gicumbi to ttc nyamata handball play

this notes have been created
by Eric36 at nyamata ttc
after readings
pe book for y2sme .

Engine Lubrication performance System.pdf

This is the best summary for oil in engine

Engineering Drawings Lecture Detail Drawings 2014.pdf

Paul Briozzo engineering drawings lecture

ISPM 15 Heat Treated Wood Stamps and why your shipping must have one

For International shipping and maritime laws all wood must contain the ISPM 15 Stamp. Here is how and why.

The Python for beginners. This is an advance computer language.

Python language is very important language at this time. we can easily understand this language by these notes.

Understanding Inductive Bias in Machine Learning

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.

Casting-Defect-inSlab continuous casting.pdf

Casting-Defect-inSlab continuous casting.pdf

Modelagem de um CSTR com reação endotermica.pdf

Modelagem de um CSTR com reação endotermica.pdf

basic-wireline-operations-course-mahmoud-f-radwan.pdf

basic-wireline-operations-course-mahmoud-f-radwan.pdf

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM

TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM

Literature Review Basics and Understanding Reference Management.pptx

Literature Review Basics and Understanding Reference Management.pptx

Embedded machine learning-based road conditions and driving behavior monitoring

Embedded machine learning-based road conditions and driving behavior monitoring

CSM Cloud Service Management Presentarion

CSM Cloud Service Management Presentarion

Question paper of renewable energy sources

Question paper of renewable energy sources

KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions

KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions

Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...

Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...

International Conference on NLP, Artificial Intelligence, Machine Learning an...

International Conference on NLP, Artificial Intelligence, Machine Learning an...

Eric Nizeyimana's document 2006 from gicumbi to ttc nyamata handball play

Eric Nizeyimana's document 2006 from gicumbi to ttc nyamata handball play

Engine Lubrication performance System.pdf

Engine Lubrication performance System.pdf

Engineering Drawings Lecture Detail Drawings 2014.pdf

Engineering Drawings Lecture Detail Drawings 2014.pdf

ISPM 15 Heat Treated Wood Stamps and why your shipping must have one

ISPM 15 Heat Treated Wood Stamps and why your shipping must have one

The Python for beginners. This is an advance computer language.

The Python for beginners. This is an advance computer language.

Understanding Inductive Bias in Machine Learning

Understanding Inductive Bias in Machine Learning

- 1. What is DFS? DFS stands for Depth First Search. In DFS traversal, the stack data structure is used, which works on the LIFO (Last In First Out) principle. In DFS, traversing can be started from any node, or we can say that any node can be considered as a root node until the root node is not mentioned in the problem. In the case of BFS, the element which is deleted from the Queue, the adjacent nodes of the deleted node are added to the Queue. In contrast, in DFS, the element which is removed from the stack, then only one adjacent node of a deleted node is added in the stack.
- 2. Let's consider the below graph for the Depth First Search traversal Consider node 0 as a root node.
- 3. First, we insert the element 0 in the stack as shown below:
- 4. The node 0 has two adjacent nodes, i.e., 1 and 3. Now we can take only one adjacent node, either 1 or 3, for traversing. Suppose we consider node 1; therefore, 1 is inserted in a stack and gets printed as shown below:
- 5. Now we will look at the adjacent vertices of node 1. The unvisited adjacent vertices of node 1 are 3, 2, 5 and 6. We can consider any of these four vertices. Suppose we take node 3 and insert it in the stack as shown below:
- 6. Consider the unvisited adjacent vertices of node 3. The unvisited adjacent vertices of node 3 are 2 and 4. We can take either of the vertices, i.e., 2 or 4. Suppose we take vertex 2 and insert it in the stack as shown below:
- 7. The unvisited adjacent vertices of node 2 are 5 and 4. We can choose either of the vertices, i.e., 5 or 4. Suppose we take vertex 4 and insert in the stack as shown below:
- 8. Now we will consider the unvisited adjacent vertices of node 4. The unvisited adjacent vertex of node 4 is node 6. Therefore, element 6 is inserted into the stack as shown below:
- 9. After inserting element 6 in the stack, we will look at the unvisited adjacent vertices of node 6. As there is no unvisited adjacent vertices of node 6, so we cannot move beyond node 6. In this case, we will perform backtracking. The topmost element, i.e., 6 would be popped out from the stack as shown below:
- 11. The topmost element in the stack is 4. Since there are no unvisited adjacent vertices left of node 4; therefore, node 4 is popped out from the stack as shown below:
- 12. The next topmost element in the stack is 2. Now, we will look at the unvisited adjacent vertices of node 2. Since only one unvisited node, i.e., 5 is left, so node 5 would be pushed into the stack above 2 and gets printed as shown below:
- 13. Now we will check the adjacent vertices of node 5, which are still unvisited. Since there is no vertex left to be visited, so we pop the element 5 from the stack as shown below:
- 14. We cannot move further 5, so we need to perform backtracking. In backtracking, the topmost element would be popped out from the stack. The topmost element is 5 that would be popped out from the stack, and we move back to node 2 as shown below:
- 15. Now we will check the unvisited adjacent vertices of node 2. As there is no adjacent vertex left to be visited, so we perform backtracking. In backtracking, the topmost element, i.e., 2 would be popped out from the stack, and we move back to the node 3 as shown below:
- 16. Now we will check the unvisited adjacent vertices of node 3. As there is no adjacent vertex left to be visited, so we perform backtracking. In backtracking, the topmost element, i.e., 3 would be popped out from the stack and we move back to node 1 as shown below:
- 17. After popping out element 3, we will check the unvisited adjacent vertices of node 1. Since there is no vertex left to be visited; therefore, the backtracking will be performed. In backtracking, the topmost element, i.e., 1 would be popped out from the stack, and we move back to node 0 as shown below:
- 18. We will check the adjacent vertices of node 0, which are still unvisited. As there is no adjacent vertex left to be visited, so we perform backtracking. In this, only one element, i.e., 0 left in the stack, would be popped out from the stack as shown below: As we can observe in the above figure that the stack is empty. So, we have to stop the DFS traversal here, and the elements which are printed is the result of the DFS traversal.
- 19. Differences between BFS and DFS The following are the differences between the BFS and DFS: BFS DFS Full form BFS stands for Breadth First Search. DFS stands for Depth First Search. Technique It a vertex-based technique to find the shortest path in a graph. It is an edge-based technique because the vertices along the edge are explored first from the starting to the end node.
- 20. Definition BFS is a traversal technique in which all the nodes of the same level are explored first, and then we move to the next level. DFS is also a traversal technique in which traversal is started from the root node and explore the nodes as far as possible until we reach the node that has no unvisited adjacent nodes. Data Structure Queue data structure is used for the BFS traversal. Stack data structure is used for the BFS traversal.
- 21. Backtracking BFS does not use the backtracking concept. DFS uses backtracking to traverse all the unvisited nodes. Number of edges BFS finds the shortest path having a minimum number of edges to traverse from the source to the destination vertex. In DFS, a greater number of edges are required to traverse from the source vertex to the destination vertex. Optimality BFS traversal is optimal for those vertices which are to be searched closer to the source vertex. DFS traversal is optimal for those graphs in which solutions are away from the source vertex.
- 22. Speed BFS is slower than DFS. DFS is faster than BFS. Suitability for decision tree It is not suitable for the decision tree because it requires exploring all the neighboring nodes first. It is suitable for the decision tree. Based on the decision, it explores all the paths. When the goal is found, it stops its traversal. Memory efficient It is not memory efficient as it requires more memory than DFS. It is memory efficient as it requires less memory than BFS.