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The document provides an overview of processes, threads, jobs and programs in Windows XP. It discusses the process and thread architecture, including fields in the executive process and thread blocks. It also summarizes thread scheduling priorities and quanta, and registry and memory improvements in Windows XP like boot time optimizations and caching of registry keys. The document also summarizes the file system architecture, including FAT16, FAT32 and NTFS, and the master file table. It concludes with a brief overview of various inter-process communication methods in Windows XP like DDE, OLE, named pipes and Windows sockets.
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Windows xp
this ppt includes these following windows xp ,operating system ,xp interface ,history ,concurrency control ,process schedulling ,thread management in windows xp ,memory management in windows xp ,multiprogramming ,problems related to concurrency ,input output management ,i/o management
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The Windows XP architecture consists of several main system components including the kernel, executive, and subsystems. The kernel handles low-level tasks like thread scheduling, interrupts, and power management. The executive layer includes the object manager, virtual memory manager, process manager, I/O manager, and plug-and-play manager. The object manager tracks object usage. The virtual memory manager uses 4KB pages and a two-step allocation process. The process manager handles processes and threads. The I/O manager controls file systems, devices, and network drivers. The plug-and-play manager loads drivers and manages hardware resources.
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Windows xp
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For Complete Learning: http://www.thelearnet.com/
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Windows 2.0 was a 16-bit graphical user interface released by Microsoft in October 1987 that allowed overlapping application windows. It introduced more keyboard shortcuts and terms like "Minimize" and "Maximize." Subsequent versions of Windows 2.0 improved support for Intel processors, memory management, and number of supported printers. Windows 2.11, the final version, added driver updates before being superseded by Windows 3.0 in May 1990.
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operating system structure
The document discusses various components and services provided by operating systems. It describes system components like process management, memory management, file management, I/O management, and secondary storage management. It also discusses operating system services like program execution, I/O operations, file manipulation, communications, error detection, resource allocation, and accounting. The document explains system calls that provide an interface between processes and the operating system and covers types of system calls for process control, file management, device management, and information maintenance. It also briefly discusses system programs, communication models, and execution of programs in MS-DOS and UNIX operating systems.
Introduction of operating system
The document discusses operating systems, including what they are, their structure and functions. It defines an operating system as software that enables computer programs to run by managing hardware resources. The key points covered are:
- Operating systems control and allocate memory, prioritize tasks, manage files and I/O devices.
- They consist of four layers - hardware, OS software, system programs and application programs.
- Types of OS include batch, multiprogramming, time-sharing, multi-user and real-time systems.
- Their main components are process management, memory management, file management and I/O management.
- OS provide services like resource allocation, protection and user interfaces to facilitate programming.
Os
This document summarizes key aspects of disk structure, disk scheduling, disk management, swap space management, and Windows 2000. It discusses how disks are logically structured and addressed, factors that impact disk access time like seek time and rotational latency. It covers disk formatting, partitioning, boot blocks, handling bad blocks. It explains how operating systems manage swap space, where it is located, and examples of swap space use. It provides an overview of Windows 2000, describing its design, components like the kernel, virtual memory manager, I/O manager, and environmental subsystems.
CS403: Operating System : Unit I _merged.pdf
This document describes the syllabus for a course on operating systems. It includes 5 units that will cover topics like process management, CPU scheduling, deadlocks, memory management, file systems, and system calls. The course objectives are to introduce operating system concepts and design issues. Students will learn how to control access to computers and files, recognize user problems, and understand how programming languages, operating systems, and architectures interact.
Os
The document discusses operating systems and processes. It defines an operating system as software that controls hardware and manages resources. A process is a program in execution that has a unique ID and state. Processes go through various states like running, ready, blocked/waiting, and terminated. Threads are lightweight processes that can be scheduled independently and share resources within a process. User-level threads are managed in libraries while kernel-level threads are managed by the operating system kernel.
Os
The document discusses operating systems and processes. It defines an operating system as software that controls hardware and manages resources. A process is a program in execution that has a unique ID and state. Processes go through various states like running, ready, blocked/waiting, and terminated. Threads are lightweight processes that can be scheduled independently and share resources within a process. User-level threads are managed in libraries while kernel-level threads are managed by the operating system kernel.
Windows
This document discusses the key components of Windows threads. It explains that a Windows thread consists of three main components: the thread kernel object, stack, and Thread Environment Block (TEB). The thread kernel object contains context and statistical information about the thread. A thread has both a user-mode and kernel-mode stack. The TEB stores thread-specific data like exception handling information. The document also discusses thread states, scheduler queues, and how the OS schedules threads for execution every 20 milliseconds by examining threads in the ready queue.
Visual comparison of Unix-like systems & Virtualisation
This is a presentation on SFD 2010 of Dalian China by Mark Smith who is lead, Unix & Open Source Professions.
operating system1.pdf
This document provides an introduction to operating systems (OS). It discusses key topics like what an OS is, its main functions, popular OS types, and basics of UNIX/Windows.
The main functions of an OS are process management, memory management, file management, and I/O (input/output) device management. Process management involves creating, running, and terminating processes. Memory management involves allocating and managing RAM and virtual memory. File management involves file and directory creation/deletion and mapping files to storage. I/O management involves interfacing between the CPU and devices using device drivers and controllers.
When a computer boots up, the BIOS runs initial checks and loads the master boot record from the hard drive
Introduction and fundamentals of Operating System.ppt
The document provides an introduction to operating systems. It discusses key topics such as what an operating system is, its main functions, popular types of operating systems, and the history and evolution of operating systems. Specifically, it covers how operating systems manage processes, memory, files, devices, security, and the user interface. It also discusses different types of operating systems including batch, multiprogramming, time-sharing, multiprocessing, distributed, network, and real-time operating systems.
introduce computer .pptx
The document provides an overview of operating systems and computer systems. It discusses the CPU and its parts, types of operating systems like mainframe OS and real-time OS. It also summarizes the key functions of an operating system such as memory management, file management, and I/O device handling. Special functions like time sharing, batch processing, multi-tasking, and spooling are explained. Important concepts covered include memory hierarchy, fetch-execute cycle, and computer system components like hardware, software, users, and data.
File system
The document discusses file systems and how they organize files on storage devices. It covers key concepts like directories, file allocation tables (FAT), file attributes, and different file systems like FAT, FAT32, NTFS, and Ext3. It compares the capabilities and limitations of different file systems and how they are used depending on factors like hardware, software, security needs and disk size.
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Visual comparison of Unix-like systems & Virtualisation
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Introduction and fundamentals of Operating System.ppt
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More from Dr. C.V. Suresh Babu
Data analytics with R
This presentation discusses the following topics:
Basic features of R
Exploring R GUI
Data Frames & Lists
Handling Data in R Workspace
Reading Data Sets & Exporting Data from R
Manipulating & Processing Data in R
Association rules
Association rule mining is used to find relationships between items in transaction data. It identifies rules that can predict the occurrence of an item based on other items purchased together frequently. Some key metrics used to evaluate rules include support, which measures how frequently an itemset occurs; confidence, which measures how often items in the predicted set occur given items in the predictor set; and lift, which compares the confidence to expected confidence if items were independent. An example association rule evaluated is {Milk, Diaper} -> {Beer} with support of 0.4, confidence of 0.67, and lift of 1.11.
Clustering
This document discusses clustering, which is the task of grouping data points into clusters so that points within the same cluster are more similar to each other than points in other clusters. It describes different types of clustering methods, including density-based, hierarchical, partitioning, and grid-based methods. It provides examples of specific clustering algorithms like K-means, DBSCAN, and discusses applications of clustering in fields like marketing, biology, libraries, insurance, city planning, and earthquake studies.
Classification
Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.
Blue property assumptions.
The document discusses the assumptions and properties of ordinary least squares (OLS) estimators in linear regression analysis. It notes that OLS estimators are best linear unbiased estimators (BLUE) if the assumptions of the linear regression model are met. Specifically, it assumes errors have zero mean and constant variance, are uncorrelated, and are normally distributed. Violation of the assumption of constant variance is known as heteroscedasticity. The document outlines how heteroscedasticity impacts the properties of OLS estimators and their use in applications like econometrics.
Introduction to regression
This document provides an introduction to regression analysis. It discusses that regression analysis investigates the relationship between dependent and independent variables to model and analyze data. The document outlines different types of regressions including linear, polynomial, stepwise, ridge, lasso, and elastic net regressions. It explains that regression analysis is used for predictive modeling, forecasting, and determining the impact of variables. The benefits of regression analysis are that it indicates significant relationships and the strength of impact between variables.
DART
This presentation briefly discusses about DART (Diagnostic Assistance Reference Tool) in Expert Systems
Mycin
MYCIN was an early expert system developed at Stanford University in 1972 to assist physicians in diagnosing and selecting treatment for bacterial and blood infections. It used over 600 production rules encoding the clinical decision criteria of infectious disease experts to diagnose patients based on reported symptoms and test results. While it could not replace human diagnosis due to computing limitations at the time, MYCIN demonstrated that expert knowledge could be represented computationally and established a foundation for more advanced machine learning and knowledge base systems.
Expert systems
The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
Dempster shafer theory
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
Bayes network
A Bayesian network is a probabilistic graphical model that represents conditional dependencies among random variables using a directed acyclic graph. It consists of nodes representing variables and directed edges representing causal relationships. Each node contains a conditional probability table that quantifies the effect of its parent nodes on that variable. Bayesian networks can be used to calculate the probability of events occurring based on the network structure and conditional probability tables, such as computing the probability of an alarm sounding given that no burglary or earthquake occurred but two neighbors called.
Bayes' theorem
This presentation gives a brief introduction about Bayes' Theorem and its applications in Artificial intelligence
Knowledge based agents
This document discusses knowledge-based agents in artificial intelligence. It defines knowledge-based agents as agents that maintain an internal state of knowledge, reason over that knowledge, update their knowledge based on observations, and take actions. Knowledge-based agents have two main components: a knowledge base that stores facts about the world, and an inference system that applies logical rules to deduce new information from the knowledge base. The document also describes the architecture of knowledge-based agents and different approaches to designing them.
Rule based system
A rule-based system uses predefined rules to make logical deductions and choices to perform automated actions. It consists of a database of rules representing knowledge, a database of facts as inputs, and an inference engine that controls the process of deriving conclusions by applying rules to facts. A rule-based system mimics human decision making by applying rules in an "if-then" format to incoming data to perform actions, but unlike AI it does not learn or adapt on its own.
Formal Logic in AI
This document discusses formal logic and its applications in AI and machine learning. It begins by explaining why logic is useful in complex domains or with little data. It then describes logic-based approaches to AI that use symbolic reasoning as an alternative to machine learning. The document proceeds to explain propositional logic and first-order logic, noting how first-order logic improves on propositional logic by allowing variables. It also mentions other logics and their applications in areas like automated discovery, inductive programming, and verification of computer systems and machine learning models.
Production based system
The document discusses production systems, which are rule-based systems used in artificial intelligence to model intelligent behavior. A production system consists of a global database, set of production rules, and control system. The rules fire to modify the database based on conditions. Different control strategies are used to determine which rules fire. Production systems are modular and allow knowledge representation as condition-action rules. Examples of applications in problem solving are provided.
Game playing in AI
The document discusses game playing in artificial intelligence. It describes how general game playing (GGP) involves designing AI that can play multiple games by learning the rules, rather than being programmed for a specific game. The document outlines how the minimax algorithm is commonly used for game playing, involving move generation and static evaluation functions to search game trees and determine the best move by maximizing or minimizing values at each level.
Diagnosis test of diabetics and hypertension by AI
A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
More from Dr. C.V. Suresh Babu (20)
Diagnosis test of diabetics and hypertension by AI
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A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “impact of artificial intelligence in covid19 diagnosis”
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
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GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
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Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
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Windows xp
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