This document defines key terms related to trees as data structures. It discusses different implementations of trees using links and arrays, and different traversal methods for trees including preorder, inorder, postorder, and levelorder traversal. It also provides examples of binary tree implementations using classes for tree nodes and the tree itself.
The document discusses decision trees, which classify data by recursively splitting it based on attribute values. It describes how decision trees work, including building the tree by selecting the attribute that best splits the data at each node. The ID3 algorithm and information gain are discussed for selecting the splitting attributes. Pruning techniques like subtree replacement and raising are covered for reducing overfitting. Issues like error propagation in decision trees are also summarized.
This document introduces binary trees and provides a series of practice problems of increasing difficulty related to binary trees. It begins with an introduction to binary tree structure and terminology. It then provides 14 binary tree problems with descriptions and hints for solving each problem. The problems involve tasks like counting nodes, finding minimum/maximum values, printing trees in different orders, checking for paths with a given sum, and printing all root-to-leaf paths. Sample solutions are provided in subsequent sections in C/C++ and Java languages.
This document discusses linear data structures and linked lists. It begins with an introduction to data structures and defines them as a way to organize and store data and relationships between data. It then discusses list abstract data types (ADTs) and their operations. The document focuses on linked list representations of lists and covers singly linked lists, doubly linked lists, and circular linked lists. It describes the nodes that make up each type of linked list and their operations like insertion, deletion, and searching. Finally, it provides an example of using linked lists to represent polynomial equations and operations like addition on polynomials.
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This document discusses various similarity measures that can be used to quantify the similarity between documents, queries, or a document and query in an information retrieval system. It describes classic measures like Dice coefficient, overlap coefficient, Jaccard coefficient, and cosine coefficient. It provides examples of calculating these measures and compares the relations between different measures. The document also discusses using term-document matrices and shows an example matrix.
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
This document discusses decision trees, which classify examples based on decisions made at internal nodes based on attribute values. It defines decision trees and how they classify examples by following branches from the root node to a terminal node. It describes how decision trees are constructed using a top-down, greedy search approach that recursively separates the training data into subsets based on the best attribute at each step until reaching a leaf node.
The document discusses decision trees, which classify data by recursively splitting it based on attribute values. It describes how decision trees work, including building the tree by selecting the attribute that best splits the data at each node. The ID3 algorithm and information gain are discussed for selecting the splitting attributes. Pruning techniques like subtree replacement and raising are covered for reducing overfitting. Issues like error propagation in decision trees are also summarized.
This document introduces binary trees and provides a series of practice problems of increasing difficulty related to binary trees. It begins with an introduction to binary tree structure and terminology. It then provides 14 binary tree problems with descriptions and hints for solving each problem. The problems involve tasks like counting nodes, finding minimum/maximum values, printing trees in different orders, checking for paths with a given sum, and printing all root-to-leaf paths. Sample solutions are provided in subsequent sections in C/C++ and Java languages.
This document discusses linear data structures and linked lists. It begins with an introduction to data structures and defines them as a way to organize and store data and relationships between data. It then discusses list abstract data types (ADTs) and their operations. The document focuses on linked list representations of lists and covers singly linked lists, doubly linked lists, and circular linked lists. It describes the nodes that make up each type of linked list and their operations like insertion, deletion, and searching. Finally, it provides an example of using linked lists to represent polynomial equations and operations like addition on polynomials.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
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call us at : 08263069601
This document discusses various similarity measures that can be used to quantify the similarity between documents, queries, or a document and query in an information retrieval system. It describes classic measures like Dice coefficient, overlap coefficient, Jaccard coefficient, and cosine coefficient. It provides examples of calculating these measures and compares the relations between different measures. The document also discusses using term-document matrices and shows an example matrix.
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
This document discusses decision trees, which classify examples based on decisions made at internal nodes based on attribute values. It defines decision trees and how they classify examples by following branches from the root node to a terminal node. It describes how decision trees are constructed using a top-down, greedy search approach that recursively separates the training data into subsets based on the best attribute at each step until reaching a leaf node.
The document discusses Real-Time Application Interface (RTAI), which adds real-time capabilities to the Linux kernel. RTAI runs below the Linux kernel and allows real-time tasks to run in parallel with Linux processes. It provides features like preemptable scheduling, interrupt handling, asynchronous I/O, and interprocess communication mechanisms like mailboxes and semaphores. RTAI defines an interface between itself and Linux to export hardware-related data and functions to allow real-time tasks to run alongside Linux processes.
This document discusses key concepts in digital communication including:
1) Sampling of analog signals is required to convert them to digital form, and the sampling frequency must be at least twice the bandwidth to avoid aliasing.
2) Quantization maps sampled analog values to digital codes, while pulse code modulation (PCM) combines sampling and quantization.
3) Digital communication has advantages over analog like better noise immunity and use of error detection/correction.
The document discusses the seven layers of the Open Systems Interconnection (OSI) model, with a focus on the physical and data link layers. It describes the key responsibilities of the physical layer, including representing bits, synchronizing data transmission, defining transmission media and topology. It also outlines the major duties of the data link layer, such as framing data, implementing addressing, and handling errors.
RADIO FREQUENCY COMMUNICATION SYSTEMS, ANTENNA THEORY AND MICROWAVE DEVICESDr. Ghanshyam Singh
This document contains a tutorial on radio frequency communication systems, antenna theory, and microwave devices. It discusses topics such as electromagnetic spectrum, wavelength calculations, transmission line impedance and resistance, standing waves, digital versus analog communication, sampling, modulation techniques like PAM, PCM, FSK, BPSK, line encoding, error detection codes, channel capacity calculations, pulse propagation in transmission lines, incident and reflected waves, standing wave ratio calculations, and the Smith chart for impedance matching. Worked examples are provided to illustrate key concepts.
Conventional photomasks use a quartz plate coated with chrome and photolithography to transfer circuit designs onto the mask from a CAD file. Modern masks use precision lasers or electron beams to image designs requiring line widths smaller than 1 μm. The mask fabrication process involves creating a circuit layout, digitizing the data, generating patterns on a photomask or reticle, and using step-and-repeat or contact printing to produce a working mask plate for wafer exposure. X-ray masks require even finer features and use a silicon membrane with a patterned gold absorber layer.
This document presents a thesis on the equivalence between computational tree logic (CTL) and failure trace testing for verification of computing systems. It introduces CTL and failure trace testing, provides relevant background information on Kripke structures and labeled transition systems, and discusses previous works relating CTL and failure trace testing. The document then presents a theorem stating that for every failure trace test there exists an equivalent CTL formula and vice versa, which can be algorithmically converted between the two. It provides proofs of lemmas used to demonstrate the theorem.
The document provides an overview of computer networks and the OSI model. It discusses the seven layers of the OSI model from physical to application layer and the functions of each layer. It also covers topics like network goals, applications, architectures, services, and bandwidth.
The document discusses binary trees and various operations on them. It defines what a binary tree is composed of (nodes with values and pointers to left and right children). It describes tree traversals like preorder, inorder and postorder that output the nodes in different orders. It explains two common search strategies - depth-first search (DFS) and breadth-first search (BFS) - and provides examples of how they traverse a sample tree. It also briefly discusses operations like finding the minimum/maximum element, inserting a new element, and deleting an existing element from the binary search tree.
The document discusses various topics related to digital transmission including:
1. Digital-to-digital conversion techniques like line coding, block coding, and scrambling that are used to represent digital data with digital signals. Line coding is always needed while block coding and scrambling may or may not be needed.
2. Analog-to-digital conversion techniques like pulse code modulation (PCM) and delta modulation that are used to convert analog signals to digital data. PCM involves sampling, quantization, and encoding of analog signals.
3. Transmission modes including parallel transmission of multiple bits together and serial transmission of one bit at a time. Serial transmission can be asynchronous, synchronous, or isochronous depending
This document provides an overview of analog and digital data and signals. It discusses the key differences between analog and digital data, periodic and nonperiodic signals, and how signals can be represented in the time and frequency domains. It also covers topics like bandwidth, attenuation, distortion, and noise which can impair signals during transmission.
This document discusses binary trees as a data structure for storing hierarchical data. It defines key tree terminology like root, parent, child, and leaf nodes. It provides examples of how binary trees can be used to represent expressions and for file compression. It also introduces a BinaryTreeNode inner class to represent individual nodes, with constructors to initialize the data, left, and right references. Code is provided to build sample trees from these nodes and an example recursive size method is given.
Trees and Graphs in data structures and AlgorithmsBHARATH KUMAR
The document discusses tree data structures and binary search trees. It defines key concepts related to trees including root, parent, child, leaf nodes, internal nodes, height, depth, and traversal methods. It then describes properties of binary trees including full, complete, and perfect binary trees. Binary search trees are defined as having left and right subtrees containing only nodes with keys less than or greater than the parent node's key. Basic binary search tree operations like search, insert, and traversal methods are also outlined. Sample C code for traversing a binary tree using preorder, inorder, and postorder methods is provided.
The document discusses trees as a data structure. It begins by defining basic tree concepts such as nodes, branches, degrees of nodes, roots, leaves, internal nodes, parents, children, siblings, ancestors, descendants, paths, levels, heights, and subtrees. It then discusses binary trees specifically and their properties including traversal methods. Finally, it covers balanced binary search trees and techniques for maintaining balance such as rotations during insertions and deletions.
This document defines and provides examples of trees and binary trees. It begins by defining trees as hierarchical data structures with nodes and edges. It then provides definitions for terms like path, forest, ordered tree, height, and multiway tree. It specifically defines binary trees as having two children per node. The document gives examples and properties of binary trees, including full, complete, and binary search trees. It also explains linear and linked representations of binary trees and different traversal methods like preorder, postorder and inorder. Finally, it provides examples of insertion and deletion operations in binary search trees.
This document discusses various tree data structures and algorithms. It begins by defining a tree as a hierarchical data structure consisting of nodes connected by edges. It then discusses different tree terminology like root, child, parent, leaves, siblings, etc. It describes different types of trees like binary trees and general trees. It covers implementations of trees using arrays and linked lists. It discusses various tree traversal algorithms like inorder, preorder and postorder traversals. It describes applications of trees like representing directories in a file system and calculating the size of directories. It covers binary search trees and algorithms for operations on them like search, insert, find minimum and find maximum.
Here are the steps to construct the binary search tree from the given pre-order and in-order traversals:
1. The root node is the first element of pre-order traversal, which is 'a'.
2. Search for 'a' in in-order traversal. Elements before 'a' are in left subtree and elements after are in right subtree.
3. Recursively construct left subtree with pre-order elements 'b,c,d' and in-order elements 'c,d,b'.
4. Recursively construct right subtree with pre-order elements 'e,g,h,j' and in-order elements 'h,g,j,e'.
Tree and Binary search tree in data structure.
The complete explanation of working of trees and Binary Search Tree is given. It is discussed such a way that everyone can easily understand it. Trees have great role in the data structures.
Trees are hierarchical data structures that can represent relationships between data items. A tree consists of nodes connected by edges, with one root node at the top. Examples of trees include biological classification trees, file system directories, and HTML page elements.
Key terminology includes nodes, edges, the root node, leaf nodes, paths between nodes, parent and child relationships, subtrees, levels, heights, and depths. Binary trees have at most two child nodes per parent, and can be traversed in preorder, inorder, or postorder to visit the nodes.
The document discusses Real-Time Application Interface (RTAI), which adds real-time capabilities to the Linux kernel. RTAI runs below the Linux kernel and allows real-time tasks to run in parallel with Linux processes. It provides features like preemptable scheduling, interrupt handling, asynchronous I/O, and interprocess communication mechanisms like mailboxes and semaphores. RTAI defines an interface between itself and Linux to export hardware-related data and functions to allow real-time tasks to run alongside Linux processes.
This document discusses key concepts in digital communication including:
1) Sampling of analog signals is required to convert them to digital form, and the sampling frequency must be at least twice the bandwidth to avoid aliasing.
2) Quantization maps sampled analog values to digital codes, while pulse code modulation (PCM) combines sampling and quantization.
3) Digital communication has advantages over analog like better noise immunity and use of error detection/correction.
The document discusses the seven layers of the Open Systems Interconnection (OSI) model, with a focus on the physical and data link layers. It describes the key responsibilities of the physical layer, including representing bits, synchronizing data transmission, defining transmission media and topology. It also outlines the major duties of the data link layer, such as framing data, implementing addressing, and handling errors.
RADIO FREQUENCY COMMUNICATION SYSTEMS, ANTENNA THEORY AND MICROWAVE DEVICESDr. Ghanshyam Singh
This document contains a tutorial on radio frequency communication systems, antenna theory, and microwave devices. It discusses topics such as electromagnetic spectrum, wavelength calculations, transmission line impedance and resistance, standing waves, digital versus analog communication, sampling, modulation techniques like PAM, PCM, FSK, BPSK, line encoding, error detection codes, channel capacity calculations, pulse propagation in transmission lines, incident and reflected waves, standing wave ratio calculations, and the Smith chart for impedance matching. Worked examples are provided to illustrate key concepts.
Conventional photomasks use a quartz plate coated with chrome and photolithography to transfer circuit designs onto the mask from a CAD file. Modern masks use precision lasers or electron beams to image designs requiring line widths smaller than 1 μm. The mask fabrication process involves creating a circuit layout, digitizing the data, generating patterns on a photomask or reticle, and using step-and-repeat or contact printing to produce a working mask plate for wafer exposure. X-ray masks require even finer features and use a silicon membrane with a patterned gold absorber layer.
This document presents a thesis on the equivalence between computational tree logic (CTL) and failure trace testing for verification of computing systems. It introduces CTL and failure trace testing, provides relevant background information on Kripke structures and labeled transition systems, and discusses previous works relating CTL and failure trace testing. The document then presents a theorem stating that for every failure trace test there exists an equivalent CTL formula and vice versa, which can be algorithmically converted between the two. It provides proofs of lemmas used to demonstrate the theorem.
The document provides an overview of computer networks and the OSI model. It discusses the seven layers of the OSI model from physical to application layer and the functions of each layer. It also covers topics like network goals, applications, architectures, services, and bandwidth.
The document discusses binary trees and various operations on them. It defines what a binary tree is composed of (nodes with values and pointers to left and right children). It describes tree traversals like preorder, inorder and postorder that output the nodes in different orders. It explains two common search strategies - depth-first search (DFS) and breadth-first search (BFS) - and provides examples of how they traverse a sample tree. It also briefly discusses operations like finding the minimum/maximum element, inserting a new element, and deleting an existing element from the binary search tree.
The document discusses various topics related to digital transmission including:
1. Digital-to-digital conversion techniques like line coding, block coding, and scrambling that are used to represent digital data with digital signals. Line coding is always needed while block coding and scrambling may or may not be needed.
2. Analog-to-digital conversion techniques like pulse code modulation (PCM) and delta modulation that are used to convert analog signals to digital data. PCM involves sampling, quantization, and encoding of analog signals.
3. Transmission modes including parallel transmission of multiple bits together and serial transmission of one bit at a time. Serial transmission can be asynchronous, synchronous, or isochronous depending
This document provides an overview of analog and digital data and signals. It discusses the key differences between analog and digital data, periodic and nonperiodic signals, and how signals can be represented in the time and frequency domains. It also covers topics like bandwidth, attenuation, distortion, and noise which can impair signals during transmission.
This document discusses binary trees as a data structure for storing hierarchical data. It defines key tree terminology like root, parent, child, and leaf nodes. It provides examples of how binary trees can be used to represent expressions and for file compression. It also introduces a BinaryTreeNode inner class to represent individual nodes, with constructors to initialize the data, left, and right references. Code is provided to build sample trees from these nodes and an example recursive size method is given.
Trees and Graphs in data structures and AlgorithmsBHARATH KUMAR
The document discusses tree data structures and binary search trees. It defines key concepts related to trees including root, parent, child, leaf nodes, internal nodes, height, depth, and traversal methods. It then describes properties of binary trees including full, complete, and perfect binary trees. Binary search trees are defined as having left and right subtrees containing only nodes with keys less than or greater than the parent node's key. Basic binary search tree operations like search, insert, and traversal methods are also outlined. Sample C code for traversing a binary tree using preorder, inorder, and postorder methods is provided.
The document discusses trees as a data structure. It begins by defining basic tree concepts such as nodes, branches, degrees of nodes, roots, leaves, internal nodes, parents, children, siblings, ancestors, descendants, paths, levels, heights, and subtrees. It then discusses binary trees specifically and their properties including traversal methods. Finally, it covers balanced binary search trees and techniques for maintaining balance such as rotations during insertions and deletions.
This document defines and provides examples of trees and binary trees. It begins by defining trees as hierarchical data structures with nodes and edges. It then provides definitions for terms like path, forest, ordered tree, height, and multiway tree. It specifically defines binary trees as having two children per node. The document gives examples and properties of binary trees, including full, complete, and binary search trees. It also explains linear and linked representations of binary trees and different traversal methods like preorder, postorder and inorder. Finally, it provides examples of insertion and deletion operations in binary search trees.
This document discusses various tree data structures and algorithms. It begins by defining a tree as a hierarchical data structure consisting of nodes connected by edges. It then discusses different tree terminology like root, child, parent, leaves, siblings, etc. It describes different types of trees like binary trees and general trees. It covers implementations of trees using arrays and linked lists. It discusses various tree traversal algorithms like inorder, preorder and postorder traversals. It describes applications of trees like representing directories in a file system and calculating the size of directories. It covers binary search trees and algorithms for operations on them like search, insert, find minimum and find maximum.
Here are the steps to construct the binary search tree from the given pre-order and in-order traversals:
1. The root node is the first element of pre-order traversal, which is 'a'.
2. Search for 'a' in in-order traversal. Elements before 'a' are in left subtree and elements after are in right subtree.
3. Recursively construct left subtree with pre-order elements 'b,c,d' and in-order elements 'c,d,b'.
4. Recursively construct right subtree with pre-order elements 'e,g,h,j' and in-order elements 'h,g,j,e'.
Tree and Binary search tree in data structure.
The complete explanation of working of trees and Binary Search Tree is given. It is discussed such a way that everyone can easily understand it. Trees have great role in the data structures.
Trees are hierarchical data structures that can represent relationships between data items. A tree consists of nodes connected by edges, with one root node at the top. Examples of trees include biological classification trees, file system directories, and HTML page elements.
Key terminology includes nodes, edges, the root node, leaf nodes, paths between nodes, parent and child relationships, subtrees, levels, heights, and depths. Binary trees have at most two child nodes per parent, and can be traversed in preorder, inorder, or postorder to visit the nodes.
This document provides information about tree data structures. It defines key tree terminology like root, parent, child, leaf node, internal node, subtree, and siblings. It also describes different types of trees like binary trees, binary search trees, threaded binary trees, and heaps. Common tree traversal algorithms like preorder, inorder, and postorder are explained. Priority queues and their representations using heaps are also discussed.
Trees are hierarchical data structures that organize data in a recursive structure. Binary trees have nodes with at most two children. Tree traversal algorithms visit each node exactly once in a defined order, such as depth-first or breadth-first. Threaded binary trees make inorder traversal faster using node pointers instead of recursion. Multiway trees can be represented as binary trees by making the leftmost child the left child of its corresponding binary tree node.
The document discusses different types of trees used in data structures and algorithms. It defines trees and their key terminology like root, edge, parent, child, leaf nodes, etc. It then describes different types of trees including general trees, binary trees, binary search trees, and their properties. It also covers tree traversal methods like preorder, inorder and postorder traversal and representations of binary trees using arrays and linked lists.
This document defines and explains various tree data structures, including binary trees and binary search trees. It begins by defining a tree as a non-linear hierarchical data structure and lists key tree terminology such as root, parent, child, leaf nodes, and levels. It then explains different types of binary trees like complete, full, skewed, and expression trees. Various tree traversal methods including preorder, inorder and postorder are also defined. Finally, it provides the definition of a binary search tree as a binary tree where all left descendants of a node are less than or equal to the node and all right descendants are greater than or equal.
The document discusses trees as a recursive data structure where each node can have multiple child nodes but only one parent node. It provides examples of using trees to represent hierarchies like a family tree or file system directory. Key terms like root, leaf, internal and edge nodes, depth, height, and traversal algorithms like depth-first search and breadth-first search are defined.
The document discusses various tree data structures and algorithms. It defines common tree terminology like nodes, roots, leaves. It describes different types of trees like binary trees, binary search trees, and their properties. It explains tree traversal algorithms like preorder, inorder and postorder. It also summarizes minimum spanning tree algorithms like Kruskal's and Prim's, and single source shortest path algorithms like Dijkstra's.
The document discusses binary trees and their implementation. It defines a binary tree as a tree where each node has at most two children. Binary trees are implemented by having each node store the data and pointers to its left and right children. The document also discusses traversing binary trees using preorder, postorder and inorder traversals and provides examples of using these traversals to list files in a directory and calculate their total size. Expression trees are given as an example of binary trees, where leaves are operands and internal nodes are operators. Algorithms are provided to construct expression trees from postfix notation.
This document defines and explains the key concepts of binary trees. It begins by defining a binary tree as a collection of nodes where each node contains data and pointers to its left and right children. It describes the root node and how trees can be empty or non-empty. It then explains terms like subtrees, successors, leaf nodes, siblings, levels, and degrees of nodes. The document also covers traversing binary trees using pre-order, in-order, and post-order algorithms and representing binary trees in memory using linked and sequential structures. It concludes with an example of using a binary tree to represent an algebraic expression.
Tree is a hierarchical data structure that stores information in a parent-child relationship. There are different types of trees including binary trees, binary search trees, and expression trees. Binary trees restrict each node to having zero, one, or two children while binary search trees further require that all left descendants of a node are less than the node and all right descendants are greater than or equal. Expression trees represent mathematical expressions with operators as internal nodes and operands as leaves.
This document provides instructions for installing and configuring the Squid proxy server on Linux. It discusses system requirements for disk performance and memory. It also covers downloading and installing Squid, important configuration notes, starting and stopping Squid, log files, configuring cache disks and directories, access control lists, authentication, and examples of restricting web access by time and to specific websites.
TCP wrappers and xinetd provide additional security layers for network services by controlling access at the application level. TCP wrappers work by checking the hosts.allow and hosts.deny files to determine if a client is allowed to connect to a wrapped service like sshd or xinetd. Xinetd is a super server that controls access and starts services like Telnet. It uses configuration files in /etc/xinetd.d to define access rules and settings for each managed service.
The document discusses Linux iptables firewall. Iptables is the default firewall package for Linux and runs inside the Linux kernel. It has three built-in tables (filter, nat, mangle) that are used to filter, alter, and inspect packets. Iptables uses built-in chains and user-defined rules to allow or deny traffic based on packet criteria like source/destination, protocol, interface etc. Common iptables commands and options are also explained.
The document discusses Linux file systems and permissions. It describes the Virtual File System (VFS) interface and how it interacts with filesystems, inodes, and open files. It then discusses the EXT2 filesystem in more detail, describing how inodes store file metadata and how hard and soft links work. It also covers common Linux permissions and how to manage users, groups, and permissions using commands like chmod, chown, useradd, and others.
The document discusses configuration of the Apache HTTP server. It describes how to start, stop and restart the server using the /sbin/service command. It explains how to configure the server by editing the main configuration file httpd.conf located at /etc/httpd/conf/httpd.conf. The document also discusses setting the default document root directory for web pages, setting file permissions, and describes several important configuration directives that can be set in the httpd.conf file to configure the server's listening ports, directories, users and other settings.
The document discusses the Domain Name System (DNS) and how it works. It explains that DNS associates domain names with IP addresses, allowing hosts to connect using names instead of hard-to-remember numbers. DNS uses a hierarchical system of servers, including root servers, TLD servers, and authoritative name servers that manage domain records and refer queries to other servers as needed to resolve domain names to IP addresses.
This document outlines a network administration course taught by Pham Van Tinh, consisting of 30 hours of theory and 60 hours of practice. The course covers topics such as Linux, shell scripts, routing, DHCP, DNS, file transfer protocols, remote access, web servers, email, firewalls, backups and more. Students will use Red Hat Linux manuals, exam guides, and Microsoft certification guides as literature.
DHCP is a protocol that automatically assigns IP addresses and other network configuration settings to clients. It allows administrators to change network settings centrally on the DHCP server rather than having to configure each client individually. The DHCP server stores lease information in /var/lib/dhcp/dhcpd.leases and is configured using /etc/dhcpd.conf which defines IP pools, default routes, DNS servers and other options. The DHCP relay agent forwards requests from clients without a local DHCP server to servers on other subnets.
This document summarizes basic Linux routing concepts including enabling IP forwarding, configuring routing tables, displaying routing and ARP tables, and examples of routing rules. Key points are:
1) IP forwarding can be enabled by editing /etc/sysctl.conf or /proc/sys/net/ipv4/ip_forward.
2) The routing table contains rules with destination, interface, and optional gateway to route packets.
3) Example commands demonstrate adding routing rules for different networks through specific interfaces.
Phase 1 involves reconnaissance where the hacker gathers information about the target without directly interacting with it. Phase 2 is scanning where the hacker scans the network to find specific information like open ports and operating systems. Phase 3 is gaining access where the hacker exploits a vulnerability to penetrate the system. Phase 4 is maintaining access where the hacker tries to retain ownership and may install backdoors. Phase 5 is covering tracks where the hacker hides evidence of the attack.
The document outlines a 15-module network security course taught by Phạm Văn Tính, PhD. The course covers ethical hacking theory over 45 hours and practice over 30 hours. Topics include footprinting, scanning, enumeration, system hacking, trojans, sniffers, denial of service attacks, social engineering, session hijacking, hacking web servers, SQL injection, wireless hacking, Linux hacking, and evading intrusion detection systems. The course material is based on CEH curriculums and references four literature sources on ethical hacking, Linux/Unix security, network security secrets and solutions, and web security.
Physical security involves preventing unauthorized access to computer systems and protecting data. It includes securing the company surroundings with fences, gates, and guards. Within premises, CCTV cameras, intruder alarms, and window/door bars provide security. Servers should be locked in enclosed rooms, and workstations in open areas need locks and CCTV monitoring. Access controls like smart cards, biometrics, and entry logs restrict access to sensitive areas. Wireless networks and other equipment also require security measures like encryption and locked storage to protect physical integrity of systems and data.
The document discusses denial of service (DoS) and distributed denial of service (DDoS) attacks. It defines DoS and DDoS attacks, describes different types of DoS attacks like SYN flooding and Smurf attacks. It also explains how botnets and tools are used to launch DDoS attacks, and discusses some common DDoS countermeasures like detection, mitigation and traceback.
The document discusses various techniques for hacking systems, including password cracking, privilege escalation, executing applications remotely, and using keyloggers and spyware. It provides an overview of tools that can perform functions like password cracking, sniffing network traffic, capturing credentials, escalating privileges, executing code remotely, and logging keystrokes covertly. Countermeasures to these techniques, like disabling LM hashes, changing passwords regularly, and using antivirus software, are also covered.
Session hijacking involves an attacker taking over an existing TCP connection between two machines by predicting sequence numbers and spoofing IP addresses. The document discusses the difference between spoofing and hijacking, the steps an attacker takes to hijack a session including predicting sequence numbers and killing the original connection, types of session hijacking techniques, and tools that can be used for session hijacking like Juggernaut, Hunt, IP Watcher, and T-Sight. It also provides countermeasures like using encryption, secure protocols, limiting connections, and educating employees.
This document provides an overview of network sniffing including definitions, vulnerable protocols, types of sniffing attacks, tools used for sniffing, and countermeasures. It discusses passive and active sniffing, ARP spoofing, MAC flooding, DNS poisoning techniques, and popular sniffing tools like Wireshark, Arpspoof, and Dsniff. It also outlines methods for detecting sniffing activity on a network such as monitoring for changed MAC addresses and unusual packets, as well as recommendations for implementing countermeasures like encryption, static ARP tables, port security, and intrusion detection systems.
The document discusses techniques for enumerating information from systems during the hacking process. It describes establishing null sessions to extract user names, shares, and other details without authentication. Tools like DumpSec, Netview, Nbtstat, GetAcct, and PS Tools are also covered as ways to enumerate users, groups, shares, permissions, and more from Windows and UNIX systems. The document also provides countermeasures like restricting null sessions and the anonymous user to protect against enumeration attacks.
The document discusses scanning techniques used during penetration testing and hacking. It defines different types of scanning like port scanning, network scanning, and vulnerability scanning. It describes tools like Nmap that can be used to perform these scans and examines techniques like SYN scanning, XMAS scanning, NULL scanning, and IDLE scanning. The document also discusses using proxies and anonymizers to hide one's location while scanning and ways to document results like creating network diagrams of vulnerable systems.
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During the budget session of 2024-25, the finance minister, Nirmala Sitharaman, introduced the “solar Rooftop scheme,” also known as “PM Surya Ghar Muft Bijli Yojana.” It is a subsidy offered to those who wish to put up solar panels in their homes using domestic power systems. Additionally, adopting photovoltaic technology at home allows you to lower your monthly electricity expenses. Today in this blog we will talk all about what is the PM Surya Ghar Muft Bijli Yojana. How does it work? Who is eligible for this yojana and all the other things related to this scheme?
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78. FIGURE 12.12 UML description of the Postfix example
Editor's Notes
This chapter begins our exploration of non-linear data structures and collections. We will discuss the use and implementation of trees, definitions of terms associated with trees, and the analysis of tree implementations of collections we have already discussed. Chapter Objectives Define trees as data structures Define the terms associated with trees Discuss the possible implementations of trees Discuss the analysis of tree implementations of collections
A Tree is a non-linear structure defined by the concept that each node in the tree, other than the first node or root node, has exactly one parent. After our earlier discussions, one might immediately ask whether a Tree is a data structure or a collection. In general, a tree is a data structure used to implement collections. Unlike lists where we were able to abstract a set of operations that were common for all lists and then define three specific list collections, it is virtually impossible to define a common set of operations for all trees. For trees, the operations are dependent upon the type of tree and it’s use. Building upon our earlier definitions, a tree can be said to be an abstract data structure because it is a data structure that is not inherently defined within a programming language. Even once we know the specific type and structure of a tree, in most cases, as we will discuss in the next several chapters, a tree is still a data structure used to implement other types of collections. In chapter 17 we will discuss a collection called a hierarchy that is a special kind of tree.
In order to discuss trees, we must first have a common vocabulary. We have already introduced a couple of terms: node which refers to a location in the tree where an element is stored, and root which refers to the node at the base of the tree or the one node in the tree that does not have a parent. Trees, in this context, are generally viewed upside down with the root at the top.
Each node of the tree points to the nodes that are directly beneath it in the tree. These nodes are referred to as its children . A child of a child is then called a grandchild , a child of a grandchild called a great-grandchild , and so on. A node that does not have children is called a leaf . A node that is not the root and has at least one child is called an internal node .
Any node below another node and on a path from that node is called a descendant of that node. Any node above another node with a connecting path to that node is called an ancestor of that node. All children of the same node are called siblings .
Each node of the tree is at a specific level or depth within the tree. The level of a node is the length of the path from the root to the node. This pathlength is determined by counting the number of links that must be followed to get from the root to the node. The root is considered to be level 0, the children of the root are at level 1, the grandchildren of the root are at level 2, and so on.
The height or order of a tree is the length of the longest path from the root to a leaf. Thus the height or order of the tree in Figure 10.3 is 3. The path from the root (A) to leaf (F) is of length 3. The path from the root (A) to leaf (C) is of length 1.
A tree is considered to be balanced if all of the leaves of the tree are at roughly the same depth. While the use of the term “roughly” may not be intellectually satisfying, the actual definition is dependent upon the algorithm being used. Some algorithms define balanced as all of the leaves being at level h or h-1 where h is the height of the tree. Other algorithms use much less restrictive definitions.
The concept of a complete tree is related to the balance of a tree. A tree is considered complete if it is balanced and all of the leaves at level h are on the left side of the tree. While a seemingly arbitrary concept, as we will discuss in later chapters, this definition has implications for how the tree is stored in certain implementations.
While it is not possible to discuss the details of an implementation of a tree without defining the type of tree and its use, we can look at general strategies for implementing trees. The most obvious implementation of tree is a linked structure. Each node could be defined as a TreeNode class, as we did with the LinearNode class for linked lists. Each node would contain a pointer to the element to be stored in that node as well as pointers for each of the possible children of the node. Depending on the implementation, it may also be useful to store a pointer in each node to its parent. The strategies for array implementations of a tree may be less obvious. There are two principle approaches: a computational strategy and a simulated link strategy.
While it is not possible to discuss the details of an implementation of a tree without defining the type of tree and its use, we can look at general strategies for implementing trees. The most obvious implementation of tree is a linked structure. Each node could be defined as a TreeNode class, as we did with the LinearNode class for linked lists. Each node would contain a pointer to the element to be stored in that node as well as pointers for each of the possible children of the node. Depending on the implementation, it may also be useful to store a pointer in each node to its parent. The strategies for array implementations of a tree may be less obvious. There are two principle approaches: a computational strategy and a simulated link strategy.
For certain types of trees, specifically binary trees, a computational strategy can be used for storing a tree using an array. For any element stored in position n of the array, that elements left child will be stored in position (2*n) and that elements right child will be stored in position (2*n+1). This strategy is very effective and can be managed in terms of capacity in much the same way that we managed capacity for the array implementations of lists, queues, and stacks. However, despite the conceptual elegance of this solution, it is not without drawbacks. For example, if the tree that we are storing is not complete or relatively complete, we may be wasting large amounts of memory allocated in the array for positions of the tree that do not contain data.
For certain types of trees, specifically binary trees, a computational strategy can be used for storing a tree using an array. For any element stored in position n of the array, that elements left child will be stored in position (2*n) and that elements right child will be stored in position (2*n+1). This strategy is very effective and can be managed in terms of capacity in much the same way that we managed capacity for the array implementations of lists, queues, and stacks. However, despite the conceptual elegance of this solution, it is not without drawbacks. For example, if the tree that we are storing is not complete or relatively complete, we may be wasting large amounts of memory allocated in the array for positions of the tree that do not contain data.
A second possible array implementation of trees is modeled after the way operating systems manage memory. Instead of assigning elements of the tree to array position by location in the tree, array positions are allocated contiguously on a first come first served basis. Each element of the array will be a node class similar to the TreeNode class that we discussed earlier. However, instead of storing object reference variables as pointers to its children (and perhaps its parent), each node would store the array index of each child (and perhaps its parent). This approach allows elements to be stored contiguously in the array so that space is not wasted. However, this approach increases the overhead for deleting elements in the tree since either remaining elements will have to be shifted to maintain contiguity or a freelist will have to be maintained.
A second possible array implementation of trees is modeled after the way operating systems manage memory. Instead of assigning elements of the tree to array position by location in the tree, array positions are allocated contiguously on a first come first served basis. Each element of the array will be a node class similar to the TreeNode class that we discussed earlier. However, instead of storing object reference variables as pointers to its children (and perhaps its parent), each node would store the array index of each child (and perhaps its parent). This approach allows elements to be stored contiguously in the array so that space is not wasted. However, this approach increases the overhead for deleting elements in the tree since either remaining elements will have to be shifted to maintain contiguity or a freelist will have to be maintained. This same strategy can also be used when tree structures need to be stored directly on disk using a direct io approach. In this case, rather than using an array index as a pointer, each node will store the relative position in the file of its children so that an offset can be calculated given the base address of the file.
As we discussed earlier, trees are a useful and efficient way to implement other collections. Let’s take an ordered list for example. In our analysis of list implementation in Chapter 9, we described the find operation as expected case n/2 or O(n). However, if we implemented an ordered list using a balanced binary search tree , a binary tree with the added property that the left child is always less than the parent which is always less than the right child, then we could improve the efficiency of the find operation to O(log n). This is due to the fact that the height or order of such a tree will always be log2n where n is the number of elements in the tree. This is very similar to our discussion of binary search in Chapter 5. In fact, for any balanced N-ary tree with n elements, the tree’s height will be logNn. With the added ordering property of a binary searched tree, you are guaranteed to at worst search one path from the root to a leaf.
As we discussed earlier, trees are a useful and efficient way to implement other collections. Let’s take an ordered list for example. In our analysis of list implementation in Chapter 9, we described the find operation as expected case n/2 or O(n). However, if we implemented an ordered list using a balanced binary search tree , a binary tree with the added property that the left child is always less than the parent which is always less than the right child, then we could improve the efficiency of the find operation to O(log n). This is due to the fact that the height or order of such a tree will always be log 2 n where n is the number of elements in the tree. This is very similar to our discussion of binary search in Chapter 5. In fact, for any balanced N-ary tree with n elements, the tree’s height will be log N n. With the added ordering property of a binary searched tree, you are guaranteed to at worst search one path from the root to a leaf.