DS Synopsis

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Submitted To- Submitted By- Miss Geetika Bhardwaj Manish Kumar Singh Lect. CSE & IT Dept. B.Tech CSE L.P.U C1801 Roll No: 06 Paper Review 2010 DATA STRUCTURE CSE 205 ROLE OF DATA STRUCTURE IN GRAPHIC IN ARTIFICAL INTELLIGENCE

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Submitted To- Submitted By-

Miss Geetika Bhardwaj Manish Kumar Singh

Lect. CSE & IT Dept. B.Tech CSE

L.P.U C1801

Roll No: 06

PaperReview

2010

DATASTRUCTURE

CSE 205

ROLE OF DATA STRUCTURE IN

GRAPHIC IN ARTIFICAL

INTELLIGENCE

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DataStructure

ROLE OF DATA STRUCTURE IN GRAPHIC IN ARTIFICAL INTELLIGENCE

Regd No. 10801843

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DataStructure

ROLE OF DATA STRUCTURE IN GRAPHIC IN ARTIFICAL INTELLIGENCE

  DATA STRUCTURE

In computer science, a data structure is a particular way of storing and organizing data in a

computer so that it can be used efficiently.

Different kinds of data structures are suited to different kinds of applications, and some are

highly specialized to specific tasks. For example, B-trees are particularly well-suited for 

implementation of databases, while compiler implementations usually use hash tables to look 

up identifiers.

Data structures are used in almost every program or software system. Specific data structures

are essential ingredients of many efficient algorithms, and make possible the management of 

huge amounts of data, such as large databases and internet indexing services. Some formal

design methods and programming languages emphasize data structures, rather than

algorithms, as the key organizing factor in software design.

  Basic principles

Data structures are generally based on the ability of a computer to fetch and store data at any

 place in its memory, specified by an address — a bit string that can be itself stored in

memory and manipulated by the program. Thus the record and array data structures are based

on computing the addresses of data items with arithmetic operations; while the linked data

structures are based on storing addresses of data items within the structure itself. Many data

structures use both principles, sometimes combined in non-trivial ways (as in XOR linking).

Role of Data Structure in Graphics

In recent years, methods from computational geometry have been widely adopted by the

computer graphics community. Many solutions draw their elegance and efficiency from the

mutually enriching combination of such geometrical data structures with computer graphics

algorithms. Our selection of data structure and algorithms consists of well known concepts,

which are both, powerful and easy to implement. However, we do not try to provide a survey

over any of the topics touched upon here. This would be far beyond the scope of this tutorial

does not provide a comprehensive overview of all techniques and algorithms from

computational geometry that might be of interest to computer graphics researchers and

developers. However, we do feel that the techniques we present here should be working

knowledge of anybody in this field. It include geometric data structure as quadtree and K-d

trees, BSP tree.

Artificial Intelligence

This branch of computer science aims to create synthetic systems which solve computational

 problems, reason and/or communicate like animals and humans do. This theoretical and

applied subfield requires a very rigorous and integrated expertise in multiple subject areas

such as applied mathematics, logic, semiotics, electrical engineering, philosophy of 

mind neurophysiology, and social intelligence which can be used to advance the field of 

intelligence research or be applied to other subject areas which require computational

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DataStructure

ROLE OF DATA STRUCTURE IN GRAPHIC IN ARTIFICAL INTELLIGENCE

understanding and modelling such as in finance or the physical sciences. It all started with

the grandfather of computer science and artificial intelligence, Alan Turing, who proposed

the Turing Test for the purpose of answering the ultimate question... "Can computers

think ?".

Machine Learning Computer visionImage

Processing

Pattern

Recognition

Cognitive Science Data MiningEvolutionary

Computation

Information

Retrieval

Knowledge

Representation

Natural

Language

Processing

RoboticsHuman–computer

interaction

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