................................................................................................... 4 MATLAB ....................................................................................................................... 4 What does MATLAB stand for? ......................................................................................................4 Getting acquainted with MATLAB environment .............................................................................4 General functions/commands
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SECURITY MANAGEMENT AGAINST CLONING MOBILE PHONES Mirela Sechi Moretti Annoni Notare1 Azzedine Boukerche2 Fernando A. S. Cruz1 Bernardo G. Riso1 Carlos B. Westphall1 1 Network and Management Laboratory Federal University of Santa Catarina (UFSC) {mirela‚ cruz‚ riso‚ westphal}@lrg.ufsc.br 2 Department of Computer Sciences University of North Texas boukerche@silo.csci.unt.edu Abstract: This work presents the development of a distributed security management system for telecommunication networks
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Math Works‚ MATLAB allows easy matrix manipulation‚ plotting of functions and data‚ implementation of algorithms‚ creation of the user interface‚ and interfacing with programs in other languages. Although it is numeric only‚ an optional toolbox uses the MuPad symbolic engine‚ allowing access to computer algebra capabilities. Functions for integrating MATLAB based algorithms with external applications and languages‚ such as C‚ C++‚ FORTRAN‚ Java‚ COM‚ and Microsoft Excel. In this way‚ MATLAB can be used
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J.K.‚ Lowe‚ A.E. and Stachurski‚ Z.H. (2001) ‘Application of nano-indentation‚ nano-scratch and single fiber tests in investigation of interphases in composite materials’‚ Micron 32‚ 765-775. McCarthy‚ C.T. and Vaughan‚ T.J. (2011) ‘COMM Toolbox: A Matlab toolbox for micromechanical analysis of composite materials’‚ Journal of Composite Materials‚ 1-15. Hardiman‚ M.‚ Vaughan‚ T.J. and McCarthy‚ C.T. (2012) ‘The effect of fibre constraint in the nanoindentation of fibrous composite microstructures:
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tissues as well. In this paper‚ we will develop a technique of SEGMENTATION of a brain tumor by using segmentation in conjunction with morphological operations. In our projects the MRI image of brain tumor is given as input to MATLAB (image processing toolbox) coding. It will extract the tumor portion and calculate its area within a small duration automatically. Here the quality of the detection is improved. 1.2 IMAGE : Images may be two-dimensional‚ such as a photograph‚ screen
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5023/EE4183 Wireless Information Systems Laboratory Lab #11 Software Defined Radio (SDR) I. Experimental Procedure Equipment List Universal Software Radio Peripheral (USRP) with Flex900 Radio Spectrum analyzer (Agilent E4411 or equivalent) PC with Matlab/Simulink R2011B CAT 6 Ethernet cable SMA(male)-to-BNC Adapter (for spectrum analyzer) 915 MHz Antenna (qty 2) A. INSTRUMENT CONNECTIONS: Connect the antenna to the SMA connector labeled RF1 port on the front of the USRP. Connect another antenna to
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chapter ONE introduction 1.0 Background A significant problem in communications is the generation of echoes. The echoes arise for a number of reasons‚ with the primary reason being an impedance mismatch. The impedance mismatch occurs when the two-wire network meets the four-wire network‚ this interface is known as the hybrid. This impedance mismatch causes some of the signal energy to be returned to the source as an echo [1]. This can be seen in Figure 1. (All figures appear at the end
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Abstract In this project our main objective is to detect the tumor and calculate its area based on classifying the images. Death rate among people has been increased due to diseases like brain tumor. The brain tumor that can be identified by using image processing. The proposed system enables automatic detection of brain tumor through MRI. So our main objective is to study‚ analyze and enhance MRI Image from the existing algothrim. Certain traditional approach requires manually extracting the
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RECOGNIZING EMOTIONS FROM STUDENT SPEECH IN TUTORING DIALOGUES Diane Litman Kate Forbes University of Pittsburgh Learning Research and Development Ctr. Pittsburgh PA‚ 15260‚ USA University of Pittsburgh Department of Computer Science Learning Research and Development Ctr. Pittsburgh PA‚ 15260‚ USA ABSTRACT We investigate the automatic classification of student emotional states in a corpus of human-human spoken tutoring dialogues. We first annotated student turns in this corpus for negative
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application of video object tracking‚ where aircrafts are more or less continuously visible on radar‚ but in case the transponders are absent‚ the identity is only revealed when the pilot reports by radio. The objective of this project is to develop MATLAB programs and SIMULINK models for tracking single and multiple objects. We present various algorithms for tracking objects and determining the velocity of moving object in sequence of frames. Using the segmentation technique we can locate a target
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