Abstract:

This paper presents a data clustering method named BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrate that these especially suitable for very large data bases . BIRCH icrementally and dynamically clusters incoming multy dimensional metric data points to trie to produce the best qulity clustering with available recourses (i.e., available memory and time constraints).

BIRCH can typically find a good clustering with a single-scan of the data and improve the quality further with few additional ....etc

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ABSTRACT
Holographic Versatile Disc (HVD) is an optical disc technology still in the research stage which would greatly increase storage over Blue-ray Disc and HP DVD optical disc systems. It employs a technique known as collinear holography, whereby two lasers, one red and one blue-green, are collimated in a single beam. The blue-green laser reads data encoded as laser interference fringes from a holographic layer near the top of the disc while the red laser is used as the reference beam and to read servo information from ....etc

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GSM Based Temperature Monitoring System

ABSTRACT
In recent years industrial automation and control systems are an integral part of an industry and hence the project temperature monitoring system is an important system in this state of affairs.
TMS aims at monitoring the temperature statistics in a factory, room etc, and controlling peripheral systems. It uses embedded technology from Intel Corporation and built for application in highly sensitive and critical systems.
Through the system we try to bring down the overhe ....etc

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Packet classification algorithms


Introduction
The process of categorizing packets into flows in an internet router is called packet classification.Packet classification is needed for non best-effort services, such as firewalls and quality of service, service s that require the capability to distinguish and isolate traffic in different flows for suitable processing. Packet classification can be done on a single field or multiple fields. There are a variety of algorithms proposed for packet classification. Th ....etc

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1. Shifting Inference Control to User Side: Architecture and Protocol
2. NCTUns Network Simulator for Advanced Wireless Vehicular Network Researches
3. How Developers Experience and Ability Influence Web Application Comprehension Tasks Supported by UML Stereotypes A Series of Four Experiments
4. Efficient and secure content processing and distribution by corporate intermediaries
5. A Video Coding Scheme Based on Joint Spatiotemporal and Adaptive Prediction
6. Controlled WiFi Sharing in Cities A Decentralized Approach Relying on Indirect Re ....etc

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Sivaramaiah.B.V

M. Sc. (Engg.) in Computer Science and Networking
Module Leader: N.D.Gangadhar

Contents

Introduction to Trees
Succinct Trees
Representation of Trees
LOUDS Representation
Balanced Parentheses
DOUDS Representation

Introduction to Trees
Trees are the most fundamental objects which stores a large amount of data.
Standard representation of the binary trees on n nodes using pointers take O(n log n) bits of space.
The extra space 2n is needed in order to sup ....etc

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creating evolving user behavior profiles automatically ppt

ABSTRACT
Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find ou ....etc

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Packet Classi cation Algorithms


Contents
1 Introduction 5
2 Requirement of packet classi cation 7
3 Speci cations of classi cation 8
4 Classi cation algorithms 9
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2 Basic Data Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2.1 Linear Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2.2 Hierarchical Tries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....etc

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1. Hybrid BinaryTernary Number System for Elliptic Curve Cryptosystems
2. Gait Feature Subset Selection by Mutual Information
3. Target Association Rules: A New Behavioral Patterns for Point of Coverage Wireless Sensor Networks
4. Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks
5. Signature Tree Generation for Polymorphic Worms
6. A Native APIs Protection Mechanism in the Kernel Mode against Malicious Code
7. Exploiting Heterogeneity in PP Video Streaming
8. Evaluating Elastic Buffer and Wormhole Fl ....etc

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Sivaramaiah.B.V

M. Sc. (Engg.) in Computer Science and Networking
Module Leader: N.D.Gangadhar

Contents

Introduction to Trees
Succinct Trees
Representation of Trees
LOUDS Representation
Balanced Parentheses
DOUDS Representation

Introduction to Trees
Trees are the most fundamental objects which stores a large amount of data.
Standard representation of the binary trees on n nodes using pointers take O(n log n) bits of space.
The extra space 2n is needed in order to sup ....etc

[:=Read Full Message Here=:]

1. Graphbased Recommendation on Social Networks
2. Adaptive Multiagent System For Traffic Monitoring
3. Architectural Risk Analysis of Software Systems Based on Security Patterns
4. Providing Architectural Languages and Tools Interoperability through Model Transformation Technologies
5. SAMI: The SLA Management Instance
6. The CoQUOS Approach to Continuous Queries in Unstructured Overlays
7. Introduction to the DomainDriven Data Mining Special Section
8. Beyond Redundancies A MetricInvariant Method for Unsupervised Feature Selection
9. ....etc

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