correlations can be exploited to predict the value of a pixel from its respective neighbors. A transformation is‚ therefore‚ defined to map this spatial (correlated) data into transformed (uncorrelated) coefficients. Clearly‚ the transformation should utilize the fact that the information content of an individual pixel is relatively small i.e.‚ to a large extent visual contribution of a pixel can be predicted using its neighbors. A typical image/video transmission system is outlined in Figure 1. The
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A System for Video Surveillance and Monitoring Robert T. Collins‚ Alan J. Lipton‚ Takeo Kanade‚ Hironobu Fujiyoshi‚ David Duggins‚ Yanghai Tsin‚ David Tolliver‚ Nobuyoshi Enomoto‚ Osamu Hasegawa‚ Peter Burt1 and Lambert Wixson1 CMU-RI-TR-00-12 The Robotics Institute‚ Carnegie Mellon University‚ Pittsburgh PA 1 The Sarnoff Corporation‚ Princeton‚ NJ Abstract Under the three-year Video Surveillance and Monitoring (VSAM) project (1997–1999)‚ the Robotics Institute at Carnegie Mellon University
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software refactoring that dynamically adapts and suggests refactorings to developers based on their feedback and introduced code changes. Our approach starts by finding upfront a set of refactoring solutions using a multi-objective evolutionary algorithm NSGA-II‚ proposed by Deb [7]‚ to improve software quality‚ reduce the number of refactorings and increase semantic coherence. The output of NSGA-II is a set of non-dominated refactoring solutions that find a good trade-off between these three
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pattern recognition: a recurrent neural network approach‚ in: Proceedings of the International Joint Conference on Neural Networks‚ San Diego‚ CA‚ 1990‚ pp. 215 –221. K.-j. Kim / Neurocomputing 55 (2003) 307 – 319 319 [12] K. Kim‚ I. Han‚ Genetic algorithms approach to feature discretization in artiÿcial neural networks for the prediction of stock price index‚ Expert Syst. Appl. 19 (2) (2000) 125–132. [13] T. Kimoto‚ K. Asakawa‚ M. Yoda‚ M. Takeoka‚ Stock market prediction system with modular neural
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The main purpose for performing testing stage is to discover defects or the negative shade by testing each and every individual program components. Further‚ these components can be functions‚ objects or modules. On the due course of system testing‚ these components are fused together to create a complete system. At this level of stage‚ testing should preliminarily focus on establishing and even by showing that the system reaches its target of its functional requirements‚ and contrarily should not
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Vector Routing Share the most you know about the entire autonomous system Share with all your direct neighbors‚ and them only Share periodically‚ e.g. every 30 seconds Destination 163.5.0.0 197.5.13.0 189.45.0.0 Hop Count 7 5 4 Next Hop 172.6.23.4 176.3.6.17 200.5.1.6 Other Info 5 ©The McGraw-Hill Companies‚ Inc.‚ 2000 5 © Adapted for use at JMU by Mohamed Aboutabl‚ 2003 RIP Updating Algorithm Receive: a response RIP message 1. Add one to the hop count for each advertised destination 2. Repeat
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Auditing: A Journal of Practice & Theory Vol. 30‚ No. 2 May 2011 pp. 19–50 American Accounting Association DOI: 10.2308/ajpt-50009 Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms Johan Perols SUMMARY: This study compares the performance of six popular statistical and machine learning models in detecting financial statement fraud under different assumptions of misclassification costs and ratios of fraud firms to nonfraud firms. The results show
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T H O M A S H. C O R M E N C H A R L E S E. L E I S E R S O N R O N A L D L. R I V E S T C L I F F O R D STEIN INTRODUCTION TO ALGORITHMS T H I R D E D I T I O N Introduction to Algorithms Third Edition Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Clifford Stein Introduction to Algorithms Third Edition The MIT Press Cambridge‚ Massachusetts London‚ England c 2009 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced
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Remote sensed images are not maps.by Mather and Koch Contents 1. Introduction----------------------------------------------------------------------------------1 2. Function of image preprocessing and its importance for image Analysis-----------1-10 2.1 Radio Metric correction Method--------------------------------------------------- 2-6 2.1.1 2.1.2 Cosmetic correction--------------------------------------------------------- 3-4 Atmospheric correction-----------------------------------------------------
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WIRELESS FINGERPRINT BASED STUDENT ATTENDANCE SYSTEM A thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Technology in Electrical Engineering by Debidutt Acharya(10602015) and Arun Kumar Mishra(10602061) Under the guidance of Prof. Susmita Das Department of Electrical Engineering National Institute of Technology Rourkela-769008 2010 WIRELESS FINGERPRINT BASED STUDENT ATTENDANCE SYSTEM A thesis submitted in partial fulfillment of the requirements
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