The contents of the latest issue of:
International Journal of System Dynamics Applications (IJSDA)
Official Publication of the Information Resources Management Association
Volume 2, Issue 2, April – June 2013
Published: Quarterly in Print and Electronically
ISSN: 2160-9772, EISSN: 2160-9799
Published by IGI Publishing, Hershey, Pennsylvania, USA
www.igi-global.com/ijsda
Editor-in-Chief: Ahmad Taher Azar, Benha University, Egypt
EDITORIAL PREFACE
Special Issue on Soft Computing Techniques for Image Processing
Aboul Ella Hassanien, Department of Information Technology, Cairo University, Giza, Egypt,
& Scientific Research Group in Egypt (SRGE), Giza, Egypt
Ahmad Taher Azar, Faculty of Computers and Information, Benha University, Egypt
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/Files/Ancillary/011db502-710b-4587-8f6e8180722e3202_Pages%20from%202160-9772_2_2_Text.pdf
PAPER ONE
A PSO-Inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy
Yi Zhou (School of Software, Shanghai Jiao Tong University, Shanghai, China), Kai Xiao (School of
Software, Shanghai Jiao Tong University, Shanghai, China), Yiheng Wang (School of Software, Shanghai
Jiao Tong University, Shanghai, China), Alei Liang (School of Software, Shanghai Jiao Tong University,
Shanghai, China) and Aboul Ella Hassanien (Information Technology Department, Cairo University, Giza,
Egypt)
Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm
that coordinates a team of autonomous mobile robots to explore an unknown environment. The
proposed strategy is based on frontiers which are the regions on the boundary between open and
unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to
reduce the size of unknown region. Based on the Particle Swarm Optimization (PSO) model incorporated
in the algorithm, robots are navigated towards remote frontier after exploring the local area. The
exploration completes when there is no frontier cell in the environment. The experiments implemented
on both simulated and real robot scenarios show that the proposed algorithm is capable of completing
the exploration task. Compared to the conventional method of randomly selecting frontier, the
proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional
experimental results show the decreased coverage time when the number of robots increases, which
further suggests the validity, efficiency and scalability.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/a-pso-inspired-multi-robot-map-exploration-algorithm-using-frontierbased-strategy/78748
To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=78748
PAPER TWO
Reliable Face Recognition Using Artificial Neural Network
CShaimaa A. El-said (Department of Electronics and Communications, Zagazig University, Zagazig, Egypt)
Facial detection and recognition are among the most heavily researched fields of computer vision and
image processing. Most of the current face recognition techniques suffer when the noises affect the
global features or the local intensity pixels of the images under consideration. In the proposed Reliable
Face Recognition System (RFRS) system, for the first time, a combination of Gabor Filter (GF), Principal
component analysis (PCA) for efficient feature extraction, and ANN for classification is employed. This
demonstrates how to detect faces in noisy images by training the network several times on various
input; ideal and noisy images of faces. Applying GF before PCA reduces PCA sensitivity to noise, provides
a greater level of invariance, and trains the ANN on different sets of noisy images. The output of the
ANN is a vector whose length equal to the distinct subjects in Olivetti Research Laboratory (ORL). The
ANN is trained to output a 1 in the correct position of the output vector and to fill the rest of the output
vector with 0’s. Experimentation is carried out on RFRS by using ORL datasets. The experimental results
show that training the network on noisy input images of face greatly reduce its errors when it had to
classify or recognize noisy images. For noisy face images, the network did not make any errors for faces
with noise of mean 0.00 or 0.05, while the average recognition rate varies from 96.8% to 98%. When
noise of mean 0.10 is added to the images the network begins to make errors. For noiseless face images,
the proposed system achieves correct classification. Performance comparison between RFRS and other
face recognition techniques shows that for most of the cases, RFRS performs better than other
conventional techniques under different types of noises and it shows the high robustness of the
proposed algorithm.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/reliable-face-recognition-using-artificial-neural-network/78749
To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=78749
PAPER THREE
Image Color Transfer Approach by Analogy with Taylor Expansion
Hongbo Liu (School of Information, Dalian Maritime University, Dalian, China), Ye Ji (School of
Information, Dalian Maritime University, Dalian, China) and Aboul Ella Hassanien (Department of
Information Technology, Cairo University, Cairo, Egypt)
The Taylor expansion has shown in many fields to be an extremely powerful tool. In this paper, the
authors investigated image features and their relationships by analogy with Taylor expansion. The kind
of expansion could be helpful for analyzing image feature and engraftment, such as transferring color
between images. By analogy with Taylor expansion, the image color transfer algorithm is designed by
the first and second-order information. The luminance histogram represents the first-order information
of image, and the co-occurrence matrix represents the second-order information of image. Some results
illustrate the proposed algorithm is effective. In this study, each polynomial in the Taylor analogy
expansion of images is considered as one of image features which help in re-understanding images and
its features. By using the proposed technique, the features of image, such as color, texture, dimension,
time series, would be not isolated but mutual relational based on image expansion.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/image-color-transfer-approach-by-analogy-with-taylorexpansion/78750
To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=78750
PAPER FOUR
Blind Deconvolution of Sources in Fourier Space Based on Generalized Laplace Distribution
M. El-Sayed Waheed (Department of Computer Science, Suez Canal University, Ismailia, Egypt),
Mohamed-H Mousa (Department of Computer Science, Suez Canal University, Ismailia, Egypt) and
Mohamed-K Hussein (Department of Computer Science, Suez Canal University, Ismailia, Egypt)
An approach to multi-channel blind de-convolution is developed, which uses an adaptive filter that
performs blind source separation in the Fourier space. The approach keeps (during the learning process)
the same permutation and provides appropriate scaling of components for all frequency bins in the
frequency space. Experiments indicate that Generalized Laplace Distribution can be used effectively to
blind de-convolution of convolution mixtures of sources in Fourier space compared to the conventional
Laplacian and Gaussian function.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/blind-deconvolution-of-sources-in-fourier-space-based-ongeneralized-laplace-distribution/78751
To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=78751
PAPER FIVE
A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images
Onsy A. Abdel Alim (Department of Electrical Engineering, Beirut University, Beirut, Lebanon), Amin
Shoukry (Computer Department, Egyptian Japanese University of Science and Technology (EJUST),
Alexandria, Egypt), Neamat A. Elboughdadly (Department of Electrical Engineering, Faculty of
Engineering, Alexandria University, Alexandria, Egypt) and Gehan Abouelseoud (Department of
Engineering, Alexandria University, Alexandria, Egypt)
In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The
proposed module takes advantage of both the generalization capability of neural networks and the
possibility of manipulating 3-D images to generate views at different poses of the object that is to be
recognized. This allows the construction of a robust 3-D object recognition module that can find use in
various applications including military, biomedical and mine detection applications. The paper proposes
an efficient training procedure and decision making strategy for the suggested neural network. Sample
results of testing the module on 3-D images of several objects are also included along with an insightful
discussion of the implications of the results.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/a-probabilistic-neural-network-based-module-for-recognition-ofobjects-from-their-3-d-images/78752
To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=78752
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For full copies of the above articles, check for this issue of the International Journal of System Dynamics
Applications (IJSDA) in your institution's library. This journal is also included in the IGI Global aggregated
"InfoSci-Journals" database: http://www.igi-global.com/EResources/InfoSciJournals.aspx.
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CALL FOR PAPERS
Mission of IJSDA:
The mission of the International Journal of System Dynamics Applications (IJSDA) is to provide a
deeper understanding of the dynamic behavior of complex systems. The journal promotes the
development of System Dynamics (SD) and the interchange of learning and research in related fields. As
limited applications related to systems engineering and non-linear control systems and their dynamics
exist in the literature, the journal provides this research from an engineering perspective. The journal
publishes advances in mathematical modeling, computer simulations of dynamic feedback systems, and
methods and applications of systems thinking approaches to promote this mission.
Coverage of IJSDA:
Topics to be discussed in this journal include (but are not limited to) the following:
Complex nonlinear dynamics and deterministic chaos
Complexity/Agent-Based Modeling
Control of chaotic systems
Corporate planning and policy design based on information feedback and circular causality
Decision support systems
Discrete event dynamic systems
Dynamics decision making
Economic dynamics
Energy and environmental dynamics
Generic structures (dynamic feedback systems that support particular widely applicable
behavioural insights)
Healthcare dynamics
Mathematical modeling and computer simulation of dynamic feedback systems
Model calibration and validation
Modelling and analysis of engineering systems
Modelling physiological systems
Nonlinear and linear system identification
Nonlinear system control
Non-smooth dynamical systems with impacts or discontinuities
Operations management and supply chains
Optimal control and applications
Psychology and social dynamics
Qualitative system dynamics
Significant contributions to system dynamics teaching
Soft computing (artificial intelligence, neural networks, fuzzy logic, genetic algorithms, etc.)
Static and dynamic systems modeling
System thinking
Various control techniques
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Interested authors should consult the journal's manuscript submission guidelines at www.igiglobal.com/ijsda.
All inquiries and submissions should be sent to: Ahmad Taher Azar, E-mail: ahmad_t_azar@ieee.org.