Abstract: Web applications have become very complex and
crucial, especially when combined with areas such as CRM
(Customer Relationship Management) and BPR (Business Process
Reengineering), the scientific community has focused attention to
Web applications design, development, analysis, and testing, by
studying and proposing methodologies and tools. This paper
proposes an approach to automatic multi-dimensional concern
mining for Web Applications, based on concepts analysis, impact
analysis, and token-based concern identification. This approach lets
the user to analyse and traverse Web software relevant to a particular
concern (concept, goal, purpose, etc.) via multi-dimensional
separation of concerns, to document, understand and test Web
applications. This technique was developed in the context of WAAT
(Web Applications Analysis and Testing) project. A semi-automatic
tool to support this technique is currently under development.
Abstract: The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of
signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we
present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In
addition, this paper will include two main R peak detection methods
by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on
Dyadic Wavelet Transform DyWT.
Abstract: Canola is a specific edible type of rapeseed, developed
in the 1970s, which contains about 40 percent oil. This research was
carried out to determine the yield and some quality characteristics of
some winter canola cultivars during the 2010-2011 vegetation period
in Central Anatolia of Turkey. In this research; Oase, Dante,
Californium, Excalibur, Elvis, ES Hydromel, Licord, Orkan, Vectra,
Nelson, Champlain and NK Petrol winter canola varieties were used
as material. The field experiment was set up in a “Randomized
Complete Block Design” with three replications on 21 September
2010. In this research; seed yield, oil content, protein content, oil
yield and protein yield were examined.
As a result of this research; seed yield, oil content, oil yield and
protein yield (except protein content) were significant differences
between the cultivars. The highest seed yield (6348 kg ha-1) was
obtained from the NK Petrol, while the lowest seed yield (3949 kg
ha-1) was determined from the Champlain cultivar was obtained. The
highest oil content (46.73%) was observed from Oase and the lowest
value was obtained from Vectra (41.87%) cultivar. The highest oil
yield (2950 kg ha-1) was determined from NK Petrol while the least
value (1681 kg ha-1) was determined from Champlain cultivar. The
highest protein yield (1539.3 kg ha-1) was obtained from NK Petrol
and the lowest protein yield (976.5 kg ha-1) was obtained from
Champlain cultivar.
The main purpose of the cultivation of oil crops, to increase the
yield of oil per unit area. According the result of this research, NK
Petrol cultivar which ranks first with regard to both seed yield and oil
yield between cultivars as the most suitable winter canola cultivar of
local conditions.
Abstract: The new programming technologies allow for the
creation of components which can be automatically or manually
assembled to reach a new experience in knowledge understanding
and mastering or in getting skills for a specific knowledge area. The
project proposes an interactive framework that permits the creation,
combination and utilization of components that are specific to
mathematical training in high schools.
The main framework-s objectives are:
• authoring lessons by the teacher or the students; all they need
are simple operating skills for Equation Editor (or something
similar, or Latex); the rest are just drag & drop operations,
inserting data into a grid, or navigating through menus
• allowing sonorous presentations of mathematical texts and
solving hints (easier understood by the students)
• offering graphical representations of a mathematical function
edited in Equation
• storing of learning objects in a database
• storing of predefined lessons (efficient for expressions and
commands, the rest being calculations; allows a high
compression)
• viewing and/or modifying predefined lessons, according to the
curricula
The whole thing is focused on a mathematical expressions minicompiler,
storing the code that will be later used for different
purposes (tables, graphics, and optimisations).
Programming technologies used. A Visual C# .NET
implementation is proposed. New and innovative digital learning
objects for mathematics will be developed; they are capable to
interpret, contextualize and react depending on the architecture
where they are assembled.
Abstract: Religion revival including Islam in Kazakhstan represents reaction, first of all on internal social and political change, events after disintegration of the USSR. Process of revival of Kazakhstan Islam was accompanied as positive, so by negative tendencies. Old mosques were restored, were under construction new, Islamic schools and high schools were created, was widely studied religious the dogmatic person, the corresponding literature was published, expanded contacts with foreign Muslim brothers in the faith, the centers of the Arab-Muslim culture extended. At the same time in Kazakhstan, there are religious-political parties and movements, pursuing radical goals down to change the spiritual and cultural identity of Muslims of Kazakhstan by the forcible introduction of non-traditional religious and political, ethnic and cultural values.
Abstract: Optical Bursts Switching (OBS) is a relatively new
optical switching paradigm. Contention and burst loss in OBS
networks are major concerns. To resolve contentions, an interesting
alternative to discarding the entire data burst is to partially drop the
burst. Partial burst dropping is based on burst segmentation concept
that its implementation is constrained by some technical challenges,
besides the complexity added to the algorithms and protocols on both
edge and core nodes. In this paper, the burst segmentation concept is
investigated, and an implementation scheme is proposed and
evaluated. An appropriate dropping policy that effectively manages
the size of the segmented data bursts is presented. The dropping
policy is further supported by a new control packet format that
provides constant transmission overhead.
Abstract: One of the determinants of a firm-s prosperity is the
customers- perceived service quality and satisfaction. While service
quality is wide in scope, and consists of various dimensions, there
may be differences in the relative importance of these dimensions in
affecting customers- overall satisfaction of service quality.
Identifying the relative rank of different dimensions of service quality
is very important in that it can help managers to find out which
service dimensions have a greater effect on customers- overall
satisfaction. Such an insight will consequently lead to more effective
resource allocation which will finally end in higher levels of
customer satisfaction. This issue – despite its criticality- has not
received enough attention so far. Therefore, using a sample of 240
bank customers in Iran, an artificial neural network is developed to
address this gap in the literature. As customers- evaluation of service
quality is a subjective process, artificial neural networks –as a brain
metaphor- may appear to have a potentiality to model such a
complicated process. Proposing a neural network which is able to
predict the customers- overall satisfaction of service quality with a
promising level of accuracy is the first contribution of this study. In
addition, prioritizing the service quality dimensions in affecting
customers- overall satisfaction –by using sensitivity analysis of
neural network- is the second important finding of this paper.
Abstract: A multi-agent system is developed here to predict
monthly details of the upcoming peak of the 24th solar magnetic
cycle. While studies typically predict the timing and magnitude of
cycle peaks using annual data, this one utilizes the unsmoothed
monthly sunspot number instead. Monthly numbers display more
pronounced fluctuations during periods of strong solar magnetic
activity than the annual sunspot numbers. Because strong magnetic
activities may cause significant economic damages, predicting
monthly variations should provide different and perhaps helpful
information for decision-making purposes. The multi-agent system
developed here operates in two stages. In the first, it produces twelve
predictions of the monthly numbers. In the second, it uses those
predictions to deliver a final forecast. Acting as expert agents, genetic
programming and neural networks produce the twelve fits and
forecasts as well as the final forecast. According to the results
obtained, the next peak is predicted to be 156 and is expected to
occur in October 2011- with an average of 136 for that year.
Abstract: In the current economy of increasing global
competition, many organizations are attempting to use knowledge as
one of the means to gain sustainable competitive advantage. Besides
large organizations, the success of SMEs can be linked to how well
they manage their knowledge. Despite the profusion of research
about knowledge management within large organizations, fewer
studies tried to analyze KM in SMEs.
This research proposes a new framework showing the determinant
role of organizational dimensions onto KM approaches. The paper
and its propositions are based on a literature review and analysis.
In this research, personalization versus codification,
individualization versus institutionalization and IT-based versus non
IT-based are highlighted as three distinct dimensions of knowledge
management approaches.
The study contributes to research by providing a more nuanced
classification of KM approaches and provides guidance to managers
about the types of KM approaches that should be adopted based on
the size, geographical dispersion and task nature of SMEs.
To the author-s knowledge, the paper is the first of its kind to
examine if there are suitable configurations of KM approaches for
SMEs with different dimensions. It gives valuable information, which
hopefully will help SME sector to accomplish KM.
Abstract: In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.
To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.
Abstract: The information revealed by derivatives can help to
better characterize digital near-end crosstalk signatures with the
ultimate goal of identifying the specific aggressor signal.
Unfortunately, derivatives tend to be very sensitive to even low
levels of noise. In this work we approximated the derivatives of both
quiet and noisy digital signals using a wavelet-based technique. The
results are presented for Gaussian digital edges, IBIS Model digital
edges, and digital edges in oscilloscope data captured from an actual
printed circuit board. Tradeoffs between accuracy and noise
immunity are presented. The results show that the wavelet technique
can produce first derivative approximations that are accurate to
within 5% or better, even under noisy conditions. The wavelet
technique can be used to calculate the derivative of a digital signal
edge when conventional methods fail.
Abstract: Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.
Abstract: Scouring around a bridge pier is a complex
phenomenon. More laboratory experiments are required to
understand the scour mechanism. This paper focused on time
development of local scour around piers and piles in semi integral
bridges. Laboratory data collected at Hydraulics Laboratory,
University of Malaya was analyzed for this purpose. Tests were
performed with two different uniform sediment sizes and five ranges
of flow velocities. Fine and coarse sediments were tested in the
flume. Results showed that scour depths for both pier and piles
increased with time up to certain levels and after that they became
almost constant. It had been found that scour depths increased when
discharges increased. Coarser sediment also produced lesser scouring
at the piers and combined piles.
Abstract: In this work we study analytically and numerically the
performance of the mean heave motion of an OWC coupled with the
governing equation of the spreading ocean waves due to the wide
variation in an open parabolic channel with constant depth. This
paper considers that the ocean wave propagation is under the
assumption of a shallow flow condition. In order to verify the effect
of the waves in the OWC firstly we establish the analytical model in
a non-dimensional form based on the energy equation. The proposed
wave-power system has to aims: one is to perturb the ocean waves as
a consequence of the channel shape in order to concentrate the
maximum ocean wave amplitude in the neighborhood of the OWC
and the second is to determine the pressure and volume oscillation of
air inside the compression chamber.
Abstract: The task of face recognition has been actively
researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an
overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented.
Description and limitations of face databases which are used to test
the performance of these face recognition algorithms are given. A
brief summary of the face recognition vendor test (FRVT) 2002, a
large scale evaluation of automatic face recognition technology, and
its conclusions are also given. Finally, we give a summary of the research results.
Abstract: We review a knowledge extractor model in
constructing 3G Killer Applications. The success of 3G is essential
for Government as it became part of Telecommunications National
Strategy. The 3G wireless technologies may reach larger area and
increase country-s ICT penetration. In order to understand future
customers needs, the operators require proper information
(knowledge) lying inside. Our work approached future customers as
complex system where the complex knowledge may expose regular
behavior. The hidden information from 3G future customers is
revealed by using fractal-based questionnaires. Afterward, further
statistical analysis is used to match the results with operator-s
strategic plan. The developments of 3G applications also consider its
saturation time and further improvement of the application.
Abstract: This research project is developed in order to study
managerial styles of modern Thai executives. The thorough
understanding will lead to continuous improvement and efficient
performance of Thai business organizations. Regarding managerial
skills, Thai executives focus heavily upon human skills. Also, the
negotiator roles are most emphasis in their management. In addition,
Thai executives pay most attention to the fundamental management
principles including Harmony and Unity of Direction of the
organizations. Moreover, the management techniques, consisting of
Team work and Career Planning are of their main concern. Finally,
Thai executives wish to enhance their firms- image and employees-
morale through conducting the ethical and socially responsible
activities. The major tactic deployed to stimulate employees- ethical
behaviors and mindset is Code of Ethics development.
Abstract: Maize and Indian mustard are significant crops in
semi-arid climate zones of India. Improved water management
requires precise scheduling of irrigation, which in turn requires an
accurate computation of daily crop evapotranspiration (ETc). Daily
crop evapotranspiration comes as a product of reference
evapotranspiration (ET0) and the growth stage specific crop
coefficients modified for daily variation. The first objective of
present study is to develop crop coefficients Kc for Maize and Indian
mustard. The estimated values of Kc for maize at the four crop
growth stages (initial, development, mid-season, and late season) are
0.55, 1.08, 1.25, and 0.75, respectively, and for Indian mustard the Kc
values at the four growth stages are 0.3, 0.6, 1.12, and 0.35,
respectively. The second objective of the study is to compute daily
crop evapotranspiration from ET0 and crop coefficients. Average
daily ETc of maize varied from about 2.5 mm/d in the early growing
period to > 6.5 mm/d at mid season. The peak ETc of maize is 8.3
mm/d and it occurred 64 days after sowing at the reproductive growth
stage when leaf area index was 4.54. In the case of Indian mustard,
average ETc is 1 mm/d at the initial stage, >1.8 mm/d at mid season
and achieves a peak value of 2.12 mm/d on 56 days after sowing.
Improved schedules of irrigation have been simulated based on daily
crop evapo-transpiration and field measured data. Simulation shows a
close match between modeled and field moisture status prevalent
during crop season.
Abstract: Renewed interest in propeller propulsion on aircraft
configurations combined with higher propeller loads lead to the question how the effects of the propulsion on model support disturbances
should be accounted for. In this paper, the determination of engine power effects on support interference of sting-mounted models is
demonstrated by a measurement on a four-engine turboprop aircraft.
CFD results on a more generic model are presented in order to clarify
the possible mechanism behind engine power effects on support
interference. The engine slipstream induces a local change in angle
of sideslip at the model sting thereby influencing the sting near-field and far-field effects. Whether or not the net result of these changes
in the disturbance pattern leads to a significant engine power effect depends on the configuration of the wind tunnel model and the test
setup.
Abstract: This paper proposes the method combining artificial
neural network (ANN) with particle swarm optimization (PSO) to
implement the maximum power point tracking (MPPT) by controlling
the rotor speed of the wind generator. First, the measurements of wind
speed, rotor speed of wind power generator and output power of wind
power generator are applied to train artificial neural network and to
estimate the wind speed. Second, the method mentioned above is
applied to estimate and control the optimal rotor speed of the wind
turbine so as to output the maximum power. Finally, the result reveals
that the control system discussed in this paper extracts the maximum
output power of wind generator within the short duration even in the
conditions of wind speed and load impedance variation.