MATLAB Applications for the Practical Engineer

655 Pages·2014·70.12 MB·English

by  Bennett K.

Applications for the
MATLAB
Practical Engineer

Edited by Kelly Bennett
MATLAB Applications for the Practical Engineer

E Ddi3tepdZ by4 iK &elly b Bhegnvneldtt, Dennixxx & rosea (for softarchive)

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Copyright © 2014
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Published September 08, 2014

ISBN-10 953511719X
ISBN-13 978-9535117193

Contents

P reface

Chapter 1 Knowledge Discovery and Information Extraction on the Open

Internet Using MATLAB and Amazon Web Services (AWS)
by Kelly Bennett and James Robertson
C hapter 2 A MATLAB-based Microscope
by Milton P. Macedo
Chapter 3 Obstacle Avoidance Task for a Wheeled Mobile Robot –

A Matlab-Simulink-Based Didactic Application
by R. Silva-Ortigoza, C . Mÿrquez-Sÿnchez, F. Carrizosa-Corral,
V. M. Hernÿndez-Guzmÿn, J. R. Garcÿa-Sÿnchez, H. Taud,
M. Marcia no- Melchor and J. A. ÿlvarez-Cedillo
Chapter 4 Dual Heuristic Neural Programming Controller for
Synchronous Generator

by Mato Miskovic, Ivan Miskovic and Marija Mirosevic
Chapter 5 Design of Fractionation Columns
by Hassan Al-Haj Ibrahim
Chapter 6 Remotely Train Control with the Aid of PIC32 Microcontroller

by Mostefa Ghassoul

Chapter 7 Integration of MATLAB and ANSYS for Advanced Analysis of
Vehicle S tructures
by A. Gauchÿa, B.L. Boada, M.J.L. Boada and V. Dÿaz

Chapter 8 Modelling and Analysis of Higher Phase Order (HPO) Squirrel

Cage Induction Machine
by A.A. Jimoh, E.K. Appiah and A.S.O. Ogunjuyigbe
Chapter 9 Atmospheric Propagation Model for S atellite Communications
by Ali Mohammed Al-Saegh, A. Sali, J. S. Mandeep, Alyani Ismail,

Abdulmajeed H.J. Al-Jumaily and Chandima Gomes

Chapter 10 Stateflow® Aided Modelling and Simulation of Friction in a
Planar Pi ezoelectric Actuator
by G. M’boungui, A.A. Jimoh, B. Semail and F. Giraud

Chapter 11 Modelling and Simulation Based Matlab/Simulink of a
Strap-Down Inertial Navigation System’ Errors due to
the Inertial Sensors
by Teodor Lucian Grigorie and Ruxandra Mihaela Botez
VI Contents

Chapter 12 Tool of the Complete Optimal Control for Variable Speed
Electrical Drives
by Marian Gaiceanu
Chapter 13 Modeling of Control Systems
by Roger Chiu, Francisco J. Casillas, Didier Lÿpez-Mancilla,

Francisco G. Peÿa-Lecona, Miguel Mora-Gonzÿlez and
Jesÿs Muÿoz Maciel
Chapter 14 Advanced Decimator Modeli ng with a HDL Conversion in Mind
by Drago Strle and Duÿan Raic

Chapter 15 Code Generation From MATLAB – Simulink Models
by Tiago da Silva Almeida, Ian Andrew Grout and
Alexandre Cÿsar Rodrigues da Silva
Chapter 16 DC/DC Boost-Boost power converter as a didactic system:
Experimental results with Matlab/Simulink via current
and voltage probes
by R. Silva-Ortigoza, M. Antonio-Cruz, M. Marcelino-Aranda,
G. Saldaÿa-Gonzÿlez, C. A. Merlo-Zapata and P. Pÿrez-Romero
Chapter 17 Using Matlab and Simulink for High – Level Modeling
in Biosystems
by Cristina-Maria Dabu
Chapter 18 Eigenvalue Analysis in Mode Domain Considering a Three-Phase
System with two Ground Wires
by R. C. Monzani, A. J. Prado, L.S. Lessa and L. F. Bovolato
Chapter 19 Analysis of Balancing of Unbalanced Rotors and Long Shafts
using GUI MATLAB
by Viliam Fedÿk, Pavel Zÿskalickyÿ and Zoltÿn Gelvanic
Chapter 20 Analysis of Robotic System Motion in SimMechanics and
MATLAB GUI Environment
by Viliam Fedÿk, Frantiÿek Durovskyÿ and Rÿbert ÿveges
Chapter 21 Vibration Analysis of Laminated Composite Variable Thickness
Plate Using Finite Strip Transition Matrix Technique and
MATLAB Verifications
by Wael A. Al-Tabey
Chapter 22 Image Processing with MATLAB and GPU
by Antonios Georgantzoglou, Joakim da Silva and Rajesh Jena

Preface

Being an automotive enthusiast and hobbyist, there is a saying
around the race track that speed isnt everything, its just most
everything. Similar sentiments can be said about MATLAB.
MATLAB isnt everything to a practicing engineer, its just most
everything.
MATLAB is an indispensable asset for scientists, researchers,
and engineers.
The richness of the MATLAB computational environment combined with
an integrated development environment (IDE) and straightforward
interface, toolkits, and simulation and modeling capabilities,

creates a research and development tool that has no equal.
From quick code prototyping to full blown deployable applications,
MATLAB stands as a de facto development language and environment
serving the technical needs of a wide range of users.
As a collection of diverse applications, each book chapter presents
a novel application and use of MATLAB for a specific result.

Chapter 1
Knowledge Discovery and Information Extraction on the
Open Internet Using MATLAB and Amazon Web Services
(AWS)
Kelly Bennett and James Robertson
Additional information is available at the end of the chapter
http://dx.doi.org/10.5772/58895
1. Introduction
The popularity of social networking has allowed access to staggering amounts of unique data,
which has created new possibilities for data analysis and exploitation. Such data has proven
useful in marketing, decision making, destabilizing terrorist networks, behavior evolution,
and determining future social trends [1].
Increased usage of social networking sites has also been observed during events related to
natural disasters; significant political, sporting, and social events; and other crises. Twitter
users provide status updates through tweets. Since tweets are typically short and always less
than 140 characters, they may need to undergo additional analysis to provide contextual clues.
Applying traditional natural language processing algorithms on such data is challenging.
Before making use of this data, it must first be extracted, processed and analyzed appropriately
using certain algorithms and theories.
According to a 2011 study from the International Data Corporation (IDC)–a marketing firm
specializing in information technology and other consumer technologies–unstructured data,
that is, data that does not have a pre-defined data model or is not organized in a pre-defined
manner, is growing at a faster rate than structured data. Within the next decade, unstructured
data will account for 90 percent of all data created.
A large driving factor in the increase in unstructured data is social networking data, such as
tweets. It is estimated that more than 80 percent of all potentially useful data is unstructured [2].