Abstract: The major purpose of this study is to use network and multimedia technologies to build a game-based learning system for junior high school students to apply in learning “World Geography" through the “role-playing" game approaches. This study first investigated the motivation and habits of junior high school students to use the Internet and online games, and then designed a game-based learning system according to situated and game-based learning theories. A teaching experiment was conducted to analyze the learning effectiveness of students on the game-based learning system and the major factors affecting their learning. A questionnaire survey was used to understand the students- attitudes towards game-based learning. The results showed that the game-based learning system can enhance students- learning, but the gender of students and their habits in using the Internet have no significant impact on learning. Game experience has a significant impact on students- learning, and the higher the experience value the better the effectiveness of their learning. The results of questionnaire survey also revealed that the system can increase students- motivation and interest in learning "World Geography".
Abstract: The production of ethyl tert-butyl ether (ETBE) was
simulated through Aspen Plus. The objective of this work was to use
the simulation results to be an alternative platform for ETBE
production from naphtha cracking wastes for the industry to develop.
ETBE is produced from isobutylene which is one of the wastes in
naphtha cracking process. The content of isobutylene in the waste is
less than 30% weight. The main part of this work was to propose a
process to save the environment and to increase the product value by
converting a great majority of the wastes into ETBE. Various
processes were considered to determine the optimal production of
ETBE. The proposed process increased ETBE production yield by
100% from conventional process with the purity of 96% weight. The
results showed a great promise for developing this proposed process
in an industrial scale.
Abstract: Our study proposes an alternative method in building
Fuzzy Rule-Based System (FRB) from Support Vector Machine
(SVM). The first set of fuzzy IF-THEN rules is obtained through
an equivalence of the SVM decision network and the zero-ordered
Sugeno FRB type of the Adaptive Network Fuzzy Inference System
(ANFIS). The second set of rules is generated by combining the
first set based on strength of firing signals of support vectors using
Gaussian kernel. The final set of rules is then obtained from the
second set through input scatter partitioning. A distinctive advantage
of our method is the guarantee that the number of final fuzzy IFTHEN
rules is not more than the number of support vectors in the
trained SVM. The final FRB system obtained is capable of performing
classification with results comparable to its SVM counterpart, but it
has an advantage over the black-boxed SVM in that it may reveal
human comprehensible patterns.
Abstract: This work presents a new phonetic transcription system based on a tree of hierarchical pronunciation rules expressed as context-specific grapheme-phoneme correspondences. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context levels, eventually representing a minimum set of exhaustive rules that pronounce without errors all the words in the training dictionary and that can be applied to out-of-vocabulary words. The proposed approach improves upon existing rule-tree-based techniques in that it makes use of graphemes, rather than letters, as elementary orthographic units. A new linear algorithm for the segmentation of a word in graphemes is introduced to enable outof- vocabulary grapheme-based phonetic transcription. Exhaustive rule trees provide a canonical representation of the pronunciation rules of a language that can be used not only to pronounce out-of-vocabulary words, but also to analyze and compare the pronunciation rules inferred from different dictionaries. The proposed approach has been implemented in C and tested on Oxford British English and Basic English. Experimental results show that grapheme-based rule trees represent phonetically sound rules and provide better performance than letter-based rule trees.
Abstract: A transient finite element model has been developed
to study the heat transfer and fluid flow during spot Gas Tungsten
Arc Welding (GTAW) on stainless steel. Temperature field, fluid
velocity and electromagnetic fields are computed inside the cathode,
arc-plasma and anode using a unified MHD formulation. The
developed model is then used to study the influence of different
helium-argon gas mixtures on both the energy transferred to the
workpiece and the time evolution of the weld pool dimensions. It is
found that the addition of helium to argon increases the heat flux
density on the weld axis by a factor that can reach 6.5. This induces
an increase in the weld pool depth by a factor of 3. It is also found
that the addition of only 10% of argon to helium decreases
considerably the weld pool depth, which is due to the electrical
conductivity of the mixture that increases significantly when argon is
added to helium.
Abstract: The objective of the presented work is to implement the Kalman Filter into an application that reduces the influence of the environmental changes over the robot expected to navigate over a terrain of varying friction properties. The Discrete Kalman Filter is used to estimate the robot position, project the estimated current state ahead at time through time update and adjust the projected estimated state by an actual measurement at that time via the measurement update using the data coming from the infrared sensors, ultrasonic sensors and the visual sensor respectively. The navigation test has been performed in a real world environment and has been found to be robust.
Abstract: This paper presented a modified efficient inductive
powering link based on ASK modulator and proposed efficient class-
E power amplifier. The design presents the external part which is
located outside the body to transfer power and data to the implanted
devices such as implanted Microsystems to stimulate and monitoring
the nerves and muscles. The system operated with low band
frequency 10MHZ according to industrial- scientific – medical (ISM)
band to avoid the tissue heating. For external part, the modulation
index is 11.1% and the modulation rate 7.2% with data rate 1 Mbit/s
assuming Tbit = 1us. The system has been designed using 0.35-μm
fabricated CMOS technology. The mathematical model is given and
the design is simulated using OrCAD P Spice 16.2 software tool and
for real-time simulation, the electronic workbench MULISIM 11 has
been used.
Abstract: Scheduling of diversified service requests in
distributed computing is a critical design issue. Cloud is a type of
parallel and distributed system consisting of a collection of
interconnected and virtual computers. It is not only the clusters and
grid but also it comprises of next generation data centers. The paper
proposes an initial heuristic algorithm to apply modified ant colony
optimization approach for the diversified service allocation and
scheduling mechanism in cloud paradigm. The proposed optimization
method is aimed to minimize the scheduling throughput to service all
the diversified requests according to the different resource allocator
available under cloud computing environment.
Abstract: This paper presents a new circuit arrangement for a
current-mode Wheatstone bridge that is suitable for low-voltage
integrated circuits implementation. Compared to the other proposed
circuits, this circuit features severe reduction of the elements number,
low supply voltage (1V) and low power consumption (
Abstract: Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.
Abstract: This paper suggests a rethinking of the existing
research about Genetically Modified (GM) food. Since the first batch
of GM food was commercialised in the UK market, GM food rapidly
received and lost media attention in the UK. Disagreement on GM
food policy between the US and the EU has also drawn scholarly
attention to this issue. Much research has been carried out intending to
understand people-s views about GM food and the shaping of these
views. This paper was based on the data collected in twenty-nine
semi-structured interviews, which were examined through Erving
Goffman-s idea of self-presentation in interactions to suggest that the
existing studies investigating “consumer attitudes" towards GM food
have only considered the “front stage" in the dramaturgic metaphor.
This paper suggests that the ways in which people choose to present
themselves when participating these studies should be taken into
account during the data analysis.
Abstract: In the present work, we propose a new technique to
enhance the learning capabilities and reduce the computation
intensity of a competitive learning multi-layered neural network
using the K-means clustering algorithm. The proposed model use
multi-layered network architecture with a back propagation learning
mechanism. The K-means algorithm is first applied to the training
dataset to reduce the amount of samples to be presented to the neural
network, by automatically selecting an optimal set of samples. The
obtained results demonstrate that the proposed technique performs
exceptionally in terms of both accuracy and computation time when
applied to the KDD99 dataset compared to a standard learning
schema that use the full dataset.
Abstract: Decrease in hardware costs and advances in computer
networking technologies have led to increased interest in the use of
large-scale parallel and distributed computing systems. One of the
biggest issues in such systems is the development of effective
techniques/algorithms for the distribution of the processes/load of a
parallel program on multiple hosts to achieve goal(s) such as
minimizing execution time, minimizing communication delays,
maximizing resource utilization and maximizing throughput.
Substantive research using queuing analysis and assuming job
arrivals following a Poisson pattern, have shown that in a multi-host
system the probability of one of the hosts being idle while other host
has multiple jobs queued up can be very high. Such imbalances in
system load suggest that performance can be improved by either
transferring jobs from the currently heavily loaded hosts to the lightly
loaded ones or distributing load evenly/fairly among the hosts .The
algorithms known as load balancing algorithms, helps to achieve the
above said goal(s). These algorithms come into two basic categories -
static and dynamic. Whereas static load balancing algorithms (SLB)
take decisions regarding assignment of tasks to processors based on
the average estimated values of process execution times and
communication delays at compile time, Dynamic load balancing
algorithms (DLB) are adaptive to changing situations and take
decisions at run time.
The objective of this paper work is to identify qualitative
parameters for the comparison of above said algorithms. In future this
work can be extended to develop an experimental environment to
study these Load balancing algorithms based on comparative
parameters quantitatively.
Abstract: This paper simulates the ad-hoc mesh network in rural areas, where such networks receive great attention due to their cost, since installing the infrastructure for regular networks in these areas is not possible due to the high cost. The distance between the communicating nodes is the most obstacles that the ad-hoc mesh network will face. For example, in Terranet technology, two nodes can communicate if they are only one kilometer far from each other. However, if the distance between them is more than one kilometer, then each node in the ad-hoc mesh networks has to act as a router that forwards the data it receives to other nodes. In this paper, we try to find the critical number of nodes which makes the network fully connected in a particular area, and then propose a method to enhance the intermediate node to accept to be a router to forward the data from the sender to the receiver. Much work was done on technological changes on peer to peer networks, but the focus of this paper will be on another feature which is to find the minimum number of nodes needed for a particular area to be fully connected and then to enhance the users to switch on their phones and accept to work as a router for other nodes. Our method raises the successful calls to 81.5% out of 100% attempt calls.
Abstract: Soil stabilization has been widely used to improve
soil strength and durability or to prevent erosion and dust generation.
Generally to reduce problems of clayey soils in engineering work and
to stabilize these soils additional materials are used. The most
common materials are lime, fly ash and cement. Using this materials,
although improve soil property , but in some cases due to financial
problems and the need to use special equipment are limited .One of
the best methods for stabilization clayey soils is neutralization the
clay particles. For this purpose we can use ion exchange materials.
Ion exchange solution like CBR plus can be used for soil
stabilization. One of the most important things in using CBR plus is
determination the amount of this solution for various soils with
different properties. In this study a laboratory experiment is conduct
to evaluate the ion exchange capacity of three soils with various
plasticity index (PI) to determine amount or required CBR plus
solution for soil stabilization.
Abstract: This work deals with unsupervised image deblurring.
We present a new deblurring procedure on images provided by lowresolution
synthetic aperture radar (SAR) or simply by multimedia in
presence of multiplicative (speckle) or additive noise, respectively.
The method we propose is defined as a two-step process. First, we
use an original technique for noise reduction in wavelet domain.
Then, the learning of a Kohonen self-organizing map (SOM) is
performed directly on the denoised image to take out it the blur. This
technique has been successfully applied to real SAR images, and the
simulation results are presented to demonstrate the effectiveness of
the proposed algorithms.
Abstract: Diagnosis can be achieved by building a model of a
certain organ under surveillance and comparing it with the real time
physiological measurements taken from the patient. This paper deals
with the presentation of the benefits of using Data Mining techniques
in the computer-aided diagnosis (CAD), focusing on the cancer
detection, in order to help doctors to make optimal decisions quickly
and accurately. In the field of the noninvasive diagnosis techniques,
the endoscopic ultrasound elastography (EUSE) is a recent elasticity
imaging technique, allowing characterizing the difference between
malignant and benign tumors. Digitalizing and summarizing the main
EUSE sample movies features in a vector form concern with the use
of the exploratory data analysis (EDA). Neural networks are then
trained on the corresponding EUSE sample movies vector input in
such a way that these intelligent systems are able to offer a very
precise and objective diagnosis, discriminating between benign and
malignant tumors. A concrete application of these Data Mining
techniques illustrates the suitability and the reliability of this
methodology in CAD.
Abstract: Tourism and coastal lines are the business sectors
since centuries especially in the European Nations and Albania is one
such spots. However, in recent decades tourism is experienced as
vulnerability of the surrounding ecological conditions of air, soil,
water, land and the communities that are dependant and sharing the
ecosystem among flora and fauna. Experts opine that apart from the
maintenance of near-originality of ecological biodiversity the tourism
rather known as ecotourism an indigenous socio-cultural
maintenance of indigenous/traditional knowledge of the local people
must be well cared in order to sustain on sustainable grounds. As a
general tendency, growth of tourism has been affected by the deterioration in the economic conditions on one aspect and unsustainable ecological areas affected since human interventions
earlier to this has negative impact on futuristic tourist spots. However, tourism in Albania as of now is 11% of GDP and coastal regions accounting to 2-4%. An amicable Mediterranean
climate with 300 sunny days similar parameters of Greece and Spain
throws up sustainable ecotourism in future decades provided public services namely, transportation, road safety, lodging, food
availability, recreational regiments, banking accessibility are as per
the World Tourism Organizations- protocols. Thus as of Albanian
situation, classification of ecotourism activities to safe-guard the localities with its maintenance of ecological land, water and climate
has become a paramount importance with a wanting and satisfactory options through harnessing human energy for profit and fitness of
ecological flora and fauna. A check on anthropogenic wastes and
their safer utilizations inclusive of agricultural and industrial
operations in line with Lalzi Bay Coastal Line are of utmost importance for the reason that the Adriatic Sea Coast is the one long
stretch of Albanian Lifeline. The present work is based on the methodology of the sustainable management of the same issue.
Abstract: In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.
Abstract: The quest of providing more secure identification
system has led to a rise in developing biometric systems. Dorsal
hand vein pattern is an emerging biometric which has attracted the
attention of many researchers, of late. Different approaches have
been used to extract the vein pattern and match them. In this work,
Principle Component Analysis (PCA) which is a method that has
been successfully applied on human faces and hand geometry is
applied on the dorsal hand vein pattern. PCA has been used to obtain
eigenveins which is a low dimensional representation of vein pattern
features. Low cost CCD cameras were used to obtain the vein
images. The extraction of the vein pattern was obtained by applying
morphology. We have applied noise reduction filters to enhance the
vein patterns. The system has been successfully tested on a database
of 200 images using a threshold value of 0.9. The results obtained are
encouraging.