Abstract: Beta-spline is built on G2 continuity which guarantees
smoothness of generated curves and surfaces using it. This curve is
preferred to be used in object design rather than reconstruction. This
study however, employs the Beta-spline in reconstructing a 3-
dimensional G2 image of the Stanford Rabbit. The original data
consists of multi-slice binary images of the rabbit. The result is then
compared with related works using other techniques.
Abstract: The aim for this research is to deliberately discuss
how and why the contexts of culture are the main significant factors
which need to be considered when conducting the international
business oversea. As a consequence of understanding these various
factors, the researcher would be able to infer some suggestions to the
international organizations. With this in mind, the results of the
understanding in a national culture environment can support the
organizations to settle its international strategies which may be useful
to develop the national export and import effectiveness. This data
collecting methods will be concentrated upon 5-10 interviews from
the senior members and business officers in the international
company in Thailand by e-mail interview and analyses the individual
manager’s viewpoint. As well as, focus on the questionnaires which
the respondents were selected randomly around 100 samples from
UK and Thailand, together with providing a functional sample size
and comparable to data. The results of the study question the role of
national culture, which contributed to in international business
effectiveness and emphasize the positive and negative aspects, as
well as suggestions to business investors are informed.
Abstract: This paper presents a single correlator RAKE receiver for direct sequence code division multiple access (DS-CDMA) systems. In conventional RAKE receivers, multiple correlators are used to despread the multipath signals and then to align and combine those signals in a later stage before making a bit decision. The simplified receiver structure presented here uses a single correlator and single code sequence generator to recover the multipaths. Modified Walsh- Hadamard codes are used here for data spreading that provides better uncorrelation properties for the multipath signals. The main advantage of this receiver structure is that it requires only a single correlator and a code generator in contrary to the conventional RAKE receiver concept with multiple correlators. It is shown in results that the proposed receiver achieves better bit error rates in comparison with the conventional one for more than one multipaths.
Abstract: The aim of this paper is to experimentally discover the workability coefficient of the Inconel 718 material by using a slide turning machining. Two different types of cutting inserts, one made of carbide and the other one made of ceramic, are being used. The purpose is to compare measured results and recommend the appropriate materials and cutting parameters for a machining of the Inconel 718. Furthermore, the durability of inserts with the chosen wear criterion is being compared for different cutting speeds. Machinability of these materials is a crucial characteristic as it allows us to shorten the technological cycle time and increase the machining productivity. And this is of great importance from an economic point of view.
Abstract: This paper addresses the problem of source separation
in images. We propose a FastICA algorithm employing a modified
Gaussian contrast function for the Blind Source Separation.
Experimental result shows that the proposed Modified Gaussian
FastICA is effectively used for Blind Source Separation to obtain
better quality images. In this paper, a comparative study has been
made with other popular existing algorithms. The peak signal to
noise ratio (PSNR) and improved signal to noise ratio (ISNR) are
used as metrics for evaluating the quality of images. The ICA metric
Amari error is also used to measure the quality of separation.
Abstract: The increasing development of wireless networks and
the widespread popularity of handheld devices such as Personal
Digital Assistants (PDAs), mobile phones and wireless tablets
represents an incredible opportunity to enable mobile devices as a
universal payment method, involving daily financial transactions.
Unfortunately, some issues hampering the widespread acceptance of
mobile payment such as accountability properties, privacy protection,
limitation of wireless network and mobile device. Recently, many
public-key cryptography based mobile payment protocol have been
proposed. However, limited capabilities of mobile devices and
wireless networks make these protocols are unsuitable for mobile
network. Moreover, these protocols were designed to preserve
traditional flow of payment data, which is vulnerable to attack and
increase the user-s risk. In this paper, we propose a private mobile
payment protocol which based on client centric model and by
employing symmetric key operations. The proposed mobile payment
protocol not only minimizes the computational operations and
communication passes between the engaging parties, but also
achieves a completely privacy protection for the payer. The future
work will concentrate on improving the verification solution to
support mobile user authentication and authorization for mobile
payment transactions.
Abstract: Knowledge is a key asset for any organisation to
sustain competitive advantages, but it is difficult to identify and
represent knowledge which is needed to perform activities in
business processes. The effective knowledge management and
support for relevant business activities definitely gives a huge impact
to the performance of the organisation as a whole. This is because
that knowledge have the functions of directing, coordinating and
controlling actions within business processes. The study has
introduced organisational morphology, a norm-based approach by
applying semiotic theories which emphasise on the representation of
knowledge in norms. This approach is concerned with the
identification of activities into three categories: substantive,
communication and control activities. All activities are directed by
norms; hence three types of norms exist; each is associated to a
category of activities. The paper describes the approach briefly and
illustrates the application of this approach through a case study of
academic activities in higher education institutions. The result of the
study shows that the approach provides an effective way to profile
business knowledge and the profile enables the understanding and
specification of business requirements of an organisation.
Abstract: Climate change has profound consequences for the agriculture of south-eastern Australia and its climate-induced water shortage in the Murray-Darling Basin. Post Keynesian Economics (PKE) macro-dynamics, along with Kaleckian investment and growth theory, are used to develop an ecological-economic system dynamics model of this complex nonlinear river basin system. The Murray- Darling Basin Simulation Model (MDB-SM) uses the principles of PKE to incorporate the fundamental uncertainty of economic behaviors of farmers regarding the investments they make and the climate change they face, particularly as regards water ecosystem services. MDB-SM provides a framework for macroeconomic policies, especially for long-term fiscal policy and for policy directed at the sustainability of agricultural water, as measured by socio-economic well-being considerations, which include sustainable consumption and investment in the river basin. The model can also reproduce other ecological and economic aspects and, for certain parameters and initial values, exhibit endogenous business cycles and ecological sustainability with realistic characteristics. Most importantly, MDBSM provides a platform for the analysis of alternative economic policy scenarios. These results reveal the importance of understanding water ecosystem adaptation under climate change by integrating a PKE macroeconomic analytical framework with the system dynamics modelling approach. Once parameterised and supplied with historical initial values, MDB-SM should prove to be a practical tool to provide alternative long-term policy simulations of agricultural water and socio-economic well-being.
Abstract: This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together using the new triangular averaging based blending algorithm. The quality of the resultant image is tested for photometric inconsistencies and geometric misalignments. This method cannot correct rotational, scale and perspective artifacts.
Abstract: The daily increase of organic waste materials resulting
from different activities in the country is one of the main factors for
the pollution of environment. Today, with regard to the low level of
the output of using traditional methods, the high cost of disposal
waste materials and environmental pollutions, the use of modern
methods such as anaerobic digestion for the production of biogas has
been prevailing. The collected biogas from the process of anaerobic
digestion, as a renewable energy source similar to natural gas but
with a less methane and heating value is usable. Today, with the help
of technologies of filtration and proper preparation, access to biogas
with features fully similar to natural gas has become possible. At
present biogas is one of the main sources of supplying electrical and
thermal energy and also an appropriate option to be used in four
stroke engine, diesel engine, sterling engine, gas turbine, gas micro
turbine and fuel cell to produce electricity. The use of biogas for
different reasons which returns to socio-economic and environmental
advantages has been noticed in CHP for the production of energy in
the world. The production of biogas from the technology of anaerobic
digestion and its application in CHP power plants in Iran can not only
supply part of the energy demands in the country, but it can
materialize moving in line with the sustainable development. In this
article, the necessity of the development of CHP plants with biogas
fuels in the country will be dealt based on studies performed from the
economic, environmental and social aspects. Also to prove the
importance of the establishment of these kinds of power plants from
the economic point of view, necessary calculations has been done as
a case study for a CHP power plant with a biogas fuel.
Abstract: This research work proposed a study of fruit bruise detection by means of a biospeckle method, selecting the papaya fruit (Carica papaya) as testing body. Papaya is recognized as a fruit of outstanding nutritional qualities, showing high vitamin A content, calcium, carbohydrates, exhibiting high popularity all over the world, considering consumption and acceptability. The commercialization of papaya faces special problems which are associated to bruise generation during harvesting, packing and transportation. Papaya is classified as climacteric fruit, permitting to be harvested before the maturation is completed. However, by one side bruise generation is partially controlled once the fruit flesh exhibits high mechanical firmness. By the other side, mechanical loads can set a future bruise at that maturation stage, when it can not be detected yet by conventional methods. Mechanical damages of fruit skin leave an entrance door to microorganisms and pathogens, which will cause severe losses of quality attributes. Traditional techniques of fruit quality inspection include total soluble solids determination, mechanical firmness tests, visual inspections, which would hardly meet required conditions for a fully automated process. However, the pertinent literature reveals a new method named biospeckle which is based on the laser reflectance and interference phenomenon. The laser biospeckle or dynamic speckle is quantified by means of the Moment of Inertia, named after its mechanical counterpart due to similarity between the defining formulae. Biospeckle techniques are able to quantify biological activities of living tissues, which has been applied to seed viability analysis, vegetable senescence and similar topics. Since the biospeckle techniques can monitor tissue physiology, it could also detect changes in the fruit caused by mechanical damages. The proposed technique holds non invasive character, being able to generate numerical results consistent with an adequate automation. The experimental tests associated to this research work included the selection of papaya fruit at different maturation stages which were submitted to artificial mechanical bruising tests. Damages were visually compared with the frequency maps yielded by the biospeckle technique. Results were considered in close agreement.
Abstract: K-Means (KM) is considered one of the major
algorithms widely used in clustering. However, it still has some
problems, and one of them is in its initialization step where it is
normally done randomly. Another problem for KM is that it
converges to local minima. Genetic algorithms are one of the
evolutionary algorithms inspired from nature and utilized in the field
of clustering. In this paper, we propose two algorithms to solve the
initialization problem, Genetic Algorithm Initializes KM (GAIK) and
KM Initializes Genetic Algorithm (KIGA). To show the effectiveness
and efficiency of our algorithms, a comparative study was done
among GAIK, KIGA, Genetic-based Clustering Algorithm (GCA),
and FCM [19].
Abstract: Internet Protocol version 4 (IPv4) address is decreasing and a rapid transition method to the next generation IP address (IPv6) should be established. This study aims to evaluate and select the best performance of the IPv6 address network transitionmechanisms, such as IPv4/IPv6 dual stack, transport Relay Translation (TRT) and Reverse Proxy with additional features. It is also aim to prove that faster access can be done while ensuring optimal usage of available resources used during the test and actual implementation. This study used two test methods such asInternet Control Message Protocol (ICMP)ping and ApacheBenchmark (AB) methodsto evaluate the performance.Performance metrics for this study include aspects ofaverageaccessin one second,time takenfor singleaccess,thedata transfer speed and the costof additional requirements.Reverse Proxy with Caching featureis the most efficientmechanism because of it simpler configurationandthe best performerfrom the test conducted.
Abstract: Due to new distributed database applications such as
huge deductive database systems, the search complexity is constantly
increasing and we need better algorithms to speedup traditional
relational database queries. An optimal dynamic programming
method for such high dimensional queries has the big disadvantage of
its exponential order and thus we are interested in semi-optimal but
faster approaches. In this work we present a multi-agent based
mechanism to meet this demand and also compare the result with
some commonly used query optimization algorithms.
Abstract: This paper is concerned with the production of an Arabic word semantic similarity benchmark dataset. It is the first of its kind for Arabic which was particularly developed to assess the accuracy of word semantic similarity measurements. Semantic similarity is an essential component to numerous applications in fields such as natural language processing, artificial intelligence, linguistics, and psychology. Most of the reported work has been done for English. To the best of our knowledge, there is no word similarity measure developed specifically for Arabic. In this paper, an Arabic benchmark dataset of 70 word pairs is presented. New methods and best possible available techniques have been used in this study to produce the Arabic dataset. This includes selecting and creating materials, collecting human ratings from a representative sample of participants, and calculating the overall ratings. This dataset will make a substantial contribution to future work in the field of Arabic WSS and hopefully it will be considered as a reference basis from which to evaluate and compare different methodologies in the field.
Abstract: Image interpolation is a common problem in imaging applications. However, most interpolation algorithms in existence suffer visually to some extent the effects of blurred edges and jagged artifacts in the image. This paper presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to enhance edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts (''jaggies'') along the tangent directions. In order to preserve image features such as edges, angles and textures, the nonlinear diffusion coefficients are locally adjusted according to the first and second order directional derivatives of the image. Experimental results on synthetic images and nature images demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.
Abstract: The performance of sensor-less controlled induction
motor drive depends on the accuracy of the estimated speed.
Conventional estimation techniques being mathematically complex
require more execution time resulting in poor dynamic response. The
nonlinear mapping capability and powerful learning algorithms of
neural network provides a promising alternative for on-line speed
estimation. The on-line speed estimator requires the NN model to be
accurate, simpler in design, structurally compact and computationally
less complex to ensure faster execution and effective control in real
time implementation. This in turn to a large extent depends on the
type of Neural Architecture. This paper investigates three types of
neural architectures for on-line speed estimation and their
performance is compared in terms of accuracy, structural
compactness, computational complexity and execution time. The
suitable neural architecture for on-line speed estimation is identified
and the promising results obtained are presented.
Abstract: Edge detection is usually the first step in medical
image processing. However, the difficulty increases when a
conventional kernel-based edge detector is applied to ultrasonic
images with a textural pattern and speckle noise. We designed an
adaptive diffusion filter to remove speckle noise while preserving the
initial edges detected by using a Sobel edge detector. We also propose
a genetic algorithm for edge selection to form complete boundaries of
the detected entities. We designed two fitness functions to evaluate
whether a criterion with a complex edge configuration can render a
better result than a simple criterion such as the strength of gradient.
The edges obtained by using a complex fitness function are thicker and
more fragmented than those obtained by using a simple fitness
function, suggesting that a complex edge selecting scheme is not
necessary for good edge detection in medical ultrasonic images;
instead, a proper noise-smoothing filter is the key.
Abstract: Fractal analyses of successive event of explosion
earthquake and harmonic tremor recorded at Semeru volcano were
carried out to investigate the dynamical system regarding to their
generating mechanism. The explosive eruptions accompanied by
explosion earthquakes and following volcanic tremor which are
generated by continuous emission of volcanic ash. The fractal
dimension of successive event of explosion and harmonic tremor was
estimated by Critical Exponent Method (CEM). It was found that the
method yield a higher fractal dimension of explosion earthquakes and
gradually decrease during the occurrence of harmonic tremor, and can
be considerably as correlated complexity of the source mechanism
from the variance of fractal dimension.
Abstract: Imitation learning is considered to be an effective way of teaching humanoid robots and action recognition is the key step to imitation learning. In this paper an online algorithm to recognize
parametric actions with object context is presented. Objects are key instruments in understanding an action when there is uncertainty.
Ambiguities arising in similar actions can be resolved with objectn context. We classify actions according to the changes they make to
the object space. Actions that produce the same state change in the object movement space are classified to belong to the same class. This allow us to define several classes of actions where members of
each class are connected with a semantic interpretation.