Abstract: The ideal sinc filter, ignoring the noise statistics, is often
applied for generating an arbitrary sample of a bandlimited signal by
using the uniformly sampled data. In this article, an optimal interpolator is proposed; it reaches a minimum mean square error (MMSE)
at its output in the presence of noise. The resulting interpolator is
thus a Wiener filter, and both the optimal infinite impulse response
(IIR) and finite impulse response (FIR) filters are presented. The
mean square errors (MSE-s) for the interpolator of different length
impulse responses are obtained by computer simulations; it shows that
the MSE-s of the proposed interpolators with a reasonable length are
improved about 0.4 dB under flat power spectra in noisy environment with signal-to-noise power ratio (SNR) equal 10 dB. As expected,
the results also demonstrate the improvements for the MSE-s with various fractional delays of the optimal interpolator against the ideal
sinc filter under a fixed length impulse response.
Abstract: One of the biggest drawbacks of the wireless
environment is the limited bandwidth. However, the users sharing
this limited bandwidth have been increasing considerably. SDMA
technique which entails using directional antennas allows to increase
the capacity of a wireless network by separating users in the medium.
In this paper, it has been presented how the capacity can be enhanced
while the mean delay is reduced by using directional antennas in
wireless networks employing TDMA/FDD MAC. Computer
modeling and simulation of the wireless system studied are realized
using OPNET Modeler. Preliminary simulation results are presented
and the performance of the model using directional antennas is
evaluated and compared consistently with the one using
omnidirectional antennas.
Abstract: In the recent past, there has been an increasing interest
in applying evolutionary methods to Knowledge Discovery in
Databases (KDD) and a number of successful applications of Genetic
Algorithms (GA) and Genetic Programming (GP) to KDD have been
demonstrated. The most predominant representation of the
discovered knowledge is the standard Production Rules (PRs) in the
form If P Then D. The PRs, however, are unable to handle
exceptions and do not exhibit variable precision. The Censored
Production Rules (CPRs), an extension of PRs, were proposed by
Michalski & Winston that exhibit variable precision and supports an
efficient mechanism for handling exceptions. A CPR is an
augmented production rule of the form:
If P Then D Unless C, where C (Censor) is an exception to the rule.
Such rules are employed in situations, in which the conditional
statement 'If P Then D' holds frequently and the assertion C holds
rarely. By using a rule of this type we are free to ignore the exception
conditions, when the resources needed to establish its presence are
tight or there is simply no information available as to whether it
holds or not. Thus, the 'If P Then D' part of the CPR expresses
important information, while the Unless C part acts only as a switch
and changes the polarity of D to ~D.
This paper presents a classification algorithm based on evolutionary
approach that discovers comprehensible rules with exceptions in the
form of CPRs.
The proposed approach has flexible chromosome encoding, where
each chromosome corresponds to a CPR. Appropriate genetic
operators are suggested and a fitness function is proposed that
incorporates the basic constraints on CPRs. Experimental results are
presented to demonstrate the performance of the proposed algorithm.
Abstract: Open Agent System platform based on High Level
Architecture is firstly proposed to support the application involving
heterogeneous agents. The basic idea is to develop different wrappers
for different agent systems, which are wrapped as federates to join a
federation. The platform is based on High Level Architecture and the
advantages for this open standard are naturally inherited, such as
system interoperability and reuse. Especially, the federal architecture
allows different federates to be heterogeneous so as to support the
integration of different agent systems. Furthermore, both implicit
communication and explicit communication between agents can be
supported. Then, as the wrapper RTI_JADE an example, the
components are discussed. Finally, the performance of RTI_JADE is
analyzed. The results show that RTI_JADE works very efficiently.
Abstract: In this paper, we investigate a blind channel estimation method for Multi-carrier CDMA systems that use a subspace decomposition technique. This technique exploits the orthogonality property between the noise subspace and the received user codes to obtain channel of each user. In the past we used Singular Value Decomposition (SVD) technique but SVD have most computational complexity so in this paper use a new algorithm called URV Decomposition, which serve as an intermediary between the QR decomposition and SVD, replaced in SVD technique to track the noise space of the received data. Because of the URV decomposition has almost the same estimation performance as the SVD, but has less computational complexity.
Abstract: This paper discusses a new model of Islamic code of
ethics for directors. Several corporate scandals and local (example
Transmile and Megan Media) and overseas corporate (example
Parmalat and Enron) collapses show that the current corporate
governance and regulatory reform are unable to prevent these events
from recurring. Arguably, the code of ethics for directors is under
research and the current code of ethics only concentrates on binding
the work of the employee of the organization as a whole, without
specifically putting direct attention to the directors, the group of
people responsible for the performance of the company. This study
used a semi-structured interview survey of well-known Islamic
scholars such as the Mufti to develop the model. It is expected that
the outcome of the research is a comprehensive model of code of
ethics based on the Islamic principles that can be applied and used by
the company to construct a code of ethics for their directors.
Abstract: The software industry has been considered a critical
infrastructure for any nation. Several studies have indicated that
national competitiveness increasingly depends upon Information and
Communication Technology (ICT), and software is one of the major
components of ICT, important for both large and small enterprises.
Even though there has been strong growth in the software industry in
Thailand, the industry has faced many challenges and problems that
need to be resolved. For example, the amount of pirated software has
been rising, and Thailand still has a large gap in the digital divide.
Additionally, the adoption among SMEs has been slow. This paper
investigates various issues in the software industry in Thailand, using
information acquired through analysis of secondary sources,
observation, and focus groups. The results of this study can be used
as “lessons learned" for the development of the software industry in
any developing country.
Abstract: Active vibration control is an important problem in
structures. The objective of active vibration control is to reduce the vibrations of a system by automatic modification of the system-s
structural response. In this paper, the modeling and design of a fast
output sampling feedback controller for a smart flexible beam system embedded with shear sensors and actuators for SISO system using
Timoshenko beam theory is proposed. FEM theory, Timoshenko beam theory and the state space techniques are used to model the
aluminum cantilever beam. For the SISO case, the beam is divided into 5 finite elements and the control actuator is placed at finite
element position 1, whereas the sensor is varied from position 2 to 5, i.e., from the nearby fixed end to the free end. Controllers are
designed using FOS method and the performance of the designed FOS controller is evaluated for vibration control for 4 SISO models
of the same plant. The effect of placing the sensor at different locations on the beam is observed and the performance of the
controller is evaluated for vibration control. Some of the limitations of the Euler-Bernoulli theory such as the neglection of shear and
axial displacement are being considered here, thus giving rise to an accurate beam model. Embedded shear sensors and actuators have
been considered in this paper instead of the surface mounted sensors
and actuators for vibration suppression because of lot of advantages. In controlling the vibration modes, the first three dominant modes of
vibration of the system are considered.
Abstract: The perfect operation of common Active Filters is depended on accuracy of identification system distortion. Also, using a suitable method in current injection and reactive power compensation, leads to increased filter performance. Due to this fact, this paper presents a method based on predictive current control theory in shunt active filter applications. The harmonics of the load current is identified by using o–d–q reference frame on load current and eliminating the DC part of d–q components. Then, the rest of these components deliver to predictive current controller as a Threephase reference current by using Park inverse transformation. System is modeled in discreet time domain. The proposed method has been tested using MATLAB model for a nonlinear load (with Total Harmonic Distortion=20%). The simulation results indicate that the proposed filter leads to flowing a sinusoidal current (THD=0.15%) through the source. In addition, the results show that the filter tracks the reference current accurately.
Abstract: In this paper, based on the past project cost and time
performance, a model for forecasting project cost performance is
developed. This study presents a probabilistic project control concept
to assure an acceptable forecast of project cost performance. In this
concept project activities are classified into sub-groups entitled
control accounts. Then obtain the Stochastic S-Curve (SS-Curve), for
each sub-group and the project SS-Curve is obtained by summing
sub-groups- SS-Curves. In this model, project cost uncertainties are
considered through Beta distribution functions of the project
activities costs required to complete the project at every selected time
sections through project accomplishment, which are extracted from a
variety of sources. Based on this model, after a percentage of the
project progress, the project performance is measured via Earned
Value Management to adjust the primary cost probability distribution
functions. Then, accordingly the future project cost performance is
predicted by using the Monte-Carlo simulation method.
Abstract: Principle component analysis is often combined with
the state-of-art classification algorithms to recognize human faces.
However, principle component analysis can only capture these
features contributing to the global characteristics of data because it is a
global feature selection algorithm. It misses those features
contributing to the local characteristics of data because each principal
component only contains some levels of global characteristics of data.
In this study, we present a novel face recognition approach using
non-negative principal component analysis which is added with the
constraint of non-negative to improve data locality and contribute to
elucidating latent data structures. Experiments are performed on the
Cambridge ORL face database. We demonstrate the strong
performances of the algorithm in recognizing human faces in
comparison with PCA and NREMF approaches.
Abstract: Internet is nowadays included to all National Curriculums of the elementary school. A comparative study of their
goals leads to the conclusion that a complete curriculum should aim to student-s acquisition of the abilities to navigate and search for
information and additionally to emphasize on the evaluation of the information provided by the World Wide Web. In a constructivistic knowledge framework the design of a course has to take under
consideration the conceptual representations of students. The following paper presents the conceptual representation of students of eleven years old, attending the Sixth Grade of Greek Elementary School about World Wide Web and their use in the design and
implementation of an innovative course.
Abstract: Back-to-back static synchronous compensator (BtBSTATCOM) consists of two back-to-back voltage-source converters (VSC) with a common DC link in a substation. This configuration extends the capabilities of conventional STATCOM that bidirectional active power transfer from one bus to another is possible. In this paper, VSCs are designed in quasi multi-pulse form in which GTOs are triggered only once per cycle in PSCAD/EMTDC. The design details of VSCs as well as gate switching circuits and controllers are fully represented. Regulation modes of BtBSTATCOM are verified and tested on a multi-machine power system through different simulation cases. The results presented in the form of typical time responses show that practical PI controllers are almost robust and stable in case of start-up, set-point change, and line faults.
Abstract: Improving performance measures in the construction
processes has been a major concern for managers and decision
makers in the industry. They seek for ways to recognize the key
factors which have the largest effect on the process. Identifying such
factors can guide them to focus on the right parts of the process in
order to gain the best possible result. In the present study design of
experiment (DOE) has been applied to a computer simulation model
of brick laying process to determine significant factors while
productivity has been chosen as the response of the experiment. To
this end, four controllable factors and their interaction have been
experimented and the best factor level has been calculated for each
one. The results indicate that three factors, namely, labor of brick,
labor of mortar and inter arrival time of mortar along with interaction
of labor of brick and labor of mortar are significant.
Abstract: The paper presents an optimization study based on
genetic algorithms (GA-s) for a radio-frequency applicator used in
heating dielectric band products. The weakly coupled electro-thermal
problem is analyzed using 2D-FEM. The design variables in the
optimization process are: the voltage of a supplementary “guard"
electrode and six geometric parameters of the applicator. Two
objective functions are used: temperature uniformity and total active
power absorbed by the dielectric. Both mono-objective and multiobjective
formulations are implemented in GA optimization.
Abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.
Abstract: Rotation or tilt present in an image capture by digital
means can be detected and corrected using Artificial Neural Network
(ANN) for application with a Face Recognition System (FRS). Principal
Component Analysis (PCA) features of faces at different angles
are used to train an ANN which detects the rotation for an input image
and corrected using a set of operations implemented using another
system based on ANN. The work also deals with the recognition
of human faces with features from the foreheads, eyes, nose and
mouths as decision support entities of the system configured using
a Generalized Feed Forward Artificial Neural Network (GFFANN).
These features are combined to provide a reinforced decision for
verification of a person-s identity despite illumination variations. The
complete system performing facial image rotation detection, correction
and recognition using re-enforced decision support provides a
success rate in the higher 90s.
Abstract: The objective this study was to characterize and
develop anthropomorphic liver phantoms in tomography hepatic
procedures for quality control and improvement professionals in
nuclear medicine. For the conformation of the anthropomorphic
phantom was used in plaster and acrylic. We constructed three
phantoms representing processes with liver cirrhosis. The phantoms
were filled with 99mTc diluted with water to obtain the scintigraphic
images. Tomography images were analyzed anterior and posterior
phantom representing a body with a greater degree cirrhotic. It was
noted that the phantoms allow the acquisition of images similar to
real liver with cirrhosis. Simulations of hemangiomas may contribute
to continued professional education of nuclear medicine, on the
question of image acquisition, allowing of the study parameters such
of the matrix, energy window and count statistics.
Abstract: In this paper, we investigated vector control of an induction machine taking into account discretization problems of the command. In the purpose to show how to include in a discrete model of this current control and with rotor time constant update. The results of simulation obtained are very satisfaisant. That was possible thanks to the good choice of the values of the parameters of the regulators used which shows, the founded good of the method used, for the choice of the parameters of the discrete regulators. The simulation results are presented at the end of this paper.
Abstract: Shot boundary detection is a fundamental step for the organization of large video data. In this paper, we propose a new method for video gradual shots detection and classification, using advantages of fractal analysis and AIS-based classifier. Proposed features are “vertical intercept" and “fractal dimension" of each frame of videos which are computed using Fourier transform coefficients. We also used a classifier based on Clonal Selection Algorithm. We have carried out our solution and assessed it according to the TRECVID2006 benchmark dataset.