Abstract: Most of the image watermarking methods, using the properties of the human visual system (HVS), have been proposed in literature. The component of the visual threshold is usually related to either the spatial contrast sensitivity function (CSF) or the visual masking. Especially on the contrast masking, most methods have not mention to the effect near to the edge region. Since the HVS is sensitive what happens on the edge area. This paper proposes ultrasound image watermarking using the visual threshold corresponding to the HVS in which the coefficients in a DCT-block have been classified based on the texture, edge, and plain area. This classification method enables not only useful for imperceptibility when the watermark is insert into an image but also achievable a robustness of watermark detection. A comparison of the proposed method with other methods has been carried out which shown that the proposed method robusts to blockwise memoryless manipulations, and also robust against noise addition.
Abstract: This paper presents a system for discovering
association rules from collections of unstructured documents called
EART (Extract Association Rules from Text). The EART system
treats texts only not images or figures. EART discovers association
rules amongst keywords labeling the collection of textual documents.
The main characteristic of EART is that the system integrates XML
technology (to transform unstructured documents into structured
documents) with Information Retrieval scheme (TF-IDF) and Data
Mining technique for association rules extraction. EART depends on
word feature to extract association rules. It consists of four phases:
structure phase, index phase, text mining phase and visualization
phase. Our work depends on the analysis of the keywords in the
extracted association rules through the co-occurrence of the keywords
in one sentence in the original text and the existing of the keywords
in one sentence without co-occurrence. Experiments applied on a
collection of scientific documents selected from MEDLINE that are
related to the outbreak of H5N1 avian influenza virus.
Abstract: Mobile learning (m-learning) is a new method in teaching and learning process which combines technology of mobile device with learning materials. It can enhance student's engagement in learning activities and facilitate them to access the learning materials at anytime and anywhere. In Kolej Poly-Tech Mara (KPTM), this method is seen as an important effort in teaching practice and to improve student learning performance. The aim of this paper is to discuss the development of m-learning application called Mobile EEF Learning System (MEEFLS) to be implemented for Electric and Electronic Fundamentals course using Flash, XML (Extensible Markup Language) and J2ME (Java 2 micro edition). System Development Life Cycle (SDLC) was used as an application development approach. It has three modules in this application such as notes or course material, exercises and video. MEELFS development is seen as a tool or a pilot test for m-learning in KPTM.
Abstract: The crossed cube is one of the most notable variations of hypercube, but some properties of the former are superior to those of the latter. For example, the diameter of the crossed cube is almost the half of that of the hypercube. In this paper, we focus on the problem embedding a Hamiltonian cycle through an arbitrary given edge in the crossed cube. We give necessary and sufficient condition for determining whether a given permutation with n elements over Zn generates a Hamiltonian cycle pattern of the crossed cube. Moreover, we obtain a lower bound for the number of different Hamiltonian cycles passing through a given edge in an n-dimensional crossed cube. Our work extends some recently obtained results.
Abstract: Bio-chips are used for experiments on genes and
contain various information such as genes, samples and so on. The
two-dimensional bio-chips, in which one axis represent genes and the
other represent samples, are widely being used these days. Instead of
experimenting with real genes which cost lots of money and much
time to get the results, bio-chips are being used for biological
experiments. And extracting data from the bio-chips with high
accuracy and finding out the patterns or useful information from such
data is very important. Bio-chip analysis systems extract data from
various kinds of bio-chips and mine the data in order to get useful
information. One of the commonly used methods to mine the data is
classification. The algorithm that is used to classify the data can be
various depending on the data types or number characteristics and so
on. Considering that bio-chip data is extremely large, an algorithm that
imitates the ecosystem such as the ant algorithm is suitable to use as an
algorithm for classification. This paper focuses on finding the
classification rules from the bio-chip data using the Ant Colony
algorithm which imitates the ecosystem. The developed system takes
in consideration the accuracy of the discovered rules when it applies it
to the bio-chip data in order to predict the classes.
Abstract: A trend in agent community or enterprises is that they are shifting from closed to open architectures composed of a large number of autonomous agents. One of its implications could be that interface agent framework is getting more important in multi-agent system (MAS); so that systems constructed for different application domains could share a common understanding in human computer interface (HCI) methods, as well as human-agent and agent-agent interfaces. However, interface agent framework usually receives less attention than other aspects of MAS. In this paper, we will propose an interface web agent framework which is based on our former project called WAF and a Distributed HCI template. A group of new functionalities and implications will be discussed, such as web agent presentation, off-line agent reference, reconfigurable activation map of agents, etc. Their enabling techniques and current standards (e.g. existing ontological framework) are also suggested and shown by examples from our own implementation in WAF.
Abstract: This paper discusses EM algorithm and Bootstrap
approach combination applied for the improvement of the satellite
image fusion process. This novel satellite image fusion method based
on estimation theory EM algorithm and reinforced by Bootstrap
approach was successfully implemented and tested. The sensor
images are firstly split by a Bayesian segmentation method to
determine a joint region map for the fused image. Then, we use the
EM algorithm in conjunction with the Bootstrap approach to develop
the bootstrap EM fusion algorithm, hence producing the fused
targeted image. We proposed in this research to estimate the
statistical parameters from some iterative equations of the EM
algorithm relying on a reference of representative Bootstrap samples
of images. Sizes of those samples are determined from a new
criterion called 'hybrid criterion'. Consequently, the obtained results
of our work show that using the Bootstrap EM (BEM) in image
fusion improve performances of estimated parameters which involve
amelioration of the fused image quality; and reduce the computing
time during the fusion process.
Abstract: Polynomial bases and normal bases are both used for
elliptic curve cryptosystems, but field arithmetic operations such as
multiplication, inversion and doubling for each basis are implemented
by different methods. In general, it is said that normal bases, especially
optimal normal bases (ONB) which are special cases on normal bases,
are efficient for the implementation in hardware in comparison with
polynomial bases. However there seems to be more examined by
implementing and analyzing these systems under similar condition. In
this paper, we designed field arithmetic operators for each basis over
GF(2233), which field has a polynomial basis recommended by SEC2
and a type-II ONB both, and analyzed these implementation results.
And, in addition, we predicted the efficiency of two elliptic curve
cryptosystems using these field arithmetic operators.
Abstract: The main aim of this paper is to present the research
findings on the solution of centralized Web-Services for students by
adopting a framework and a prototype for Service Oriented
Architecture (SOA) Web-Services. The current situation of students-
Web-based application services has been identified and proposed an
effective SOA to increase the operational efficiency of Web-Services
for them it was necessary to identify the challenges in delivering a
SOA technology to increase operational efficiency of Web-Services.
Moreover, the SOA is an emerging concept, used for delivering
efficient student SOA Web-Services. Therefore, service reusability
from SOA Web-Services is provided and logically divided services
into smaller services to increase reusability and modularity. In this
case each service is a modular unit by itself and interoperability
services.
Abstract: This study created new graphical icons and operating
functions in a CAD/CAM software system by analyzing icons in some
of the popular systems, such as AutoCAD, AlphaCAM, Mastercam
and the 1st edition of LiteCAM. These software systems all focused on
geometric design and editing, thus how to transmit messages
intuitively from icon itself to users is an important function of
graphical icons. The primary purpose of this study is to design
innovative icons and commands for new software.
This study employed the TRIZ method, an innovative design
method, to generate new concepts systematically. Through literature
review, it then investigated and analyzed the relationship between
TRIZ and idea development. Contradiction Matrix and 40 Principles
were used to develop an assisting tool suitable for icon design in
software development. We first gathered icon samples from the
selected CAD/CAM systems. Then grouped these icons by
meaningful functions, and compared useful and harmful properties.
Finally, we developed new icons for new software systems in order to
avoid intellectual property problem.
Abstract: Apart from geometry, functionality is one of the most
significant hallmarks of a product. The functionality of a product can
be considered as the fundamental justification for a product
existence. Therefore a functional analysis including a complete and
reliable descriptor has a high potential to improve product
development process in various fields especially in knowledge-based
design. One of the important applications of the functional analysis
and indexing is in retrieval and design reuse concept. More than 75%
of design activity for a new product development contains reusing
earlier and existing design know-how. Thus, analysis and
categorization of product functions concluded by functional
indexing, influences directly in design optimization. This paper
elucidates and evaluates major classes for functional analysis by
discussing their major methods. Moreover it is finalized by
presenting a noble hybrid approach for functional analysis.
Abstract: Among other factors that characterize satellite communication
channels is their high bit error rate. We present a system for
still image transmission over noisy satellite channels. The system
couples image compression together with error control codes to
improve the received image quality while maintaining its bandwidth
requirements. The proposed system is tested using a high resolution
satellite imagery simulated over the Rician fading channel. Evaluation
results show improvement in overall system including image quality
and bandwidth requirements compared to similar systems with different
coding schemes.
Abstract: During more than a decade, many proposals and standards have been designed to deal with the mobility issues; however, there are still some serious limitations in basing solutions on them. In this paper we discuss the possibility of handling mobility at the application layer. We do this while revisiting the conventional implementation of the Two Phase Commit (2PC) protocol which is a fundamental asset of transactional technology for ensuring the consistent commitment of distributed transactions. The solution is based on an execution framework providing an efficient extension that is aware of the mobility and preserves the 2PC principle.
Abstract: In this article, we introduce a new approach for
analyzing UML designs to detect the inconsistencies between
multiple state diagrams and sequence diagrams. The Super State
Analysis (SSA) identifies the inconsistencies in super states, single
step transitions, and sequences. Because SSA considers multiple
UML state diagrams, it discovers inconsistencies that cannot be
discovered when considering only a single UML state diagram. We
have introduced a transition set that captures relationship information
that is not specifiable in UML diagrams. The SSA model uses the
transition set to link transitions of multiple state diagrams together.
The analysis generates three different sets automatically. These sets
are compared to the provided sets to detect the inconsistencies. SSA
identifies five types of inconsistencies: impossible super states,
unreachable super states, illegal transitions, missing transitions, and
illegal sequences.
Abstract: Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the KDD process that aims at selecting and applying a particular data mining algorithm to extract an interesting and useful knowledge. It is highly expected that data mining methods will find interesting patterns according to some measures, from databases. It is of vital importance to define good measures of interestingness that would allow the system to discover only the useful patterns. Measures of interestingness are divided into objective and subjective measures. Objective measures are those that depend only on the structure of a pattern and which can be quantified by using statistical methods. While, subjective measures depend only on the subjectivity and understandability of the user who examine the patterns. These subjective measures are further divided into actionable, unexpected and novel. The key issues that faces data mining community is how to make actions on the basis of discovered knowledge. For a pattern to be actionable, the user subjectivity is captured by providing his/her background knowledge about domain. Here, we consider the actionability of the discovered knowledge as a measure of interestingness and raise important issues which need to be addressed to discover actionable knowledge.
Abstract: This paper presents the study of a variable speed wind
energy conversion system based on a Doubly Fed Induction Generator
(DFIG) based on a sliding mode control applied to achieve control of
active and reactive powers exchanged between the stator of the DFIG
and the grid to ensure a Maximum Power Point Tracking (MPPT) of
a wind energy conversion system. The proposed control algorithm is
applied to a DFIG whose stator is directly connected to the grid and
the rotor is connected to the PWM converter. To extract a maximum
of power, the rotor side converter is controlled by using a stator
flux-oriented strategy. The created decoupling control between active
and reactive stator power allows keeping the power factor close to
unity. Simulation results show that the wind turbine can operate at
its optimum energy for a wide range of wind speed.
Abstract: Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: In present days the area of data migration is very topical. Current tools for data migration in the area of relational database have several disadvantages that are presented in this paper. We propose a methodology for data migration of the database tables and their data between various types of relational database systems (RDBMS). The proposed methodology contains an expert system. The expert system contains a knowledge base that is composed of IFTHEN rules and based on the input data suggests appropriate data types of columns of database tables. The proposed tool, which contains an expert system, also includes the possibility of optimizing the data types in the target RDBMS database tables based on processed data of the source RDBMS database tables. The proposed expert system is shown on data migration of selected database of the source RDBMS to the target RDBMS.
Abstract: In this paper, we propose an approach of unsupervised
segmentation with fuzzy connectedness. Valid seeds are first specified
by an unsupervised method based on scale space theory. A region is
then extracted for each seed with a relative object extraction method of
fuzzy connectedness. Afterwards, regions are merged according to the
values between them of an introduced measure. Some theorems and
propositions are also provided to show the reasonableness of the
measure for doing mergence. Experiment results on a synthetic image,
a color image and a large amount of MR images of our method are
reported.