Abstract: This study describes the preparation of a novel proton
conducting membranes based on bacterial cellulose (BC) modified by
grafting of 2-acrylamido-2-methyl-1 -propanesulfonic acid (AMPS)
through UV-induced graft polymerization. These AMPS-g-BC
membranes have been characterized by various techniques including
FTIR, SEM and TGA, to find their successful grafting of AMPS on
BC, surface morphology and thermal stability, respectively. Physical
properties of AMPS-g-BC membranes have been assessed in terms of
Lamda value( λ ), ion exchange capacity(IEC) and proton
conductivity. The relationship between degree of grafting and AMPS
concentration used for grafting has been determined by weight gain
method. An optimum proton conductivity equal to 2.89x10-2 S cm-1
and IEC value equal to 1.79 mmol g-1 have been obtained when 20
wt% AMPS concentration is used for grafting (i.e. the corresponding
membrane is notated as AMPS20-g-BC).
Abstract: This research aims at development of the Multiple
Intelligences Measurement of Elementary Students. The structural
accuracy test and normality establishment are based on the Multiple
Intelligences Theory of Gardner. This theory consists of eight aspects
namely linguistics, logic and mathematics, visual-spatial relations,
body and movement, music, human relations, self-realization/selfunderstanding
and nature. The sample used in this research consists
of elementary school students (aged between 5-11 years). The size of
the sample group was determined by Yamane Table. The group has
2,504 students. Multistage Sampling was used. Basic statistical
analysis and construct validity testing were done using confirmatory
factor analysis. The research can be summarized as follows; 1.
Multiple Intelligences Measurement consisting of 120 items is
content-accurate. Internal consistent reliability according to the
method of Kuder-Richardson of the whole Multiple Intelligences
Measurement equals .91. The difficulty of the measurement test is
between .39-.83. Discrimination is between .21-.85. 2). The Multiple
Intelligences Measurement has construct validity in a good range,
that is 8 components and all 120 test items have statistical
significance level at .01. Chi-square value equals 4357.7; p=.00 at the
degree of freedom of 244 and Goodness of Fit Index equals 1.00.
Adjusted Goodness of Fit Index equals .92. Comparative Fit Index
(CFI) equals .68. Root Mean Squared Residual (RMR) equals 0.064
and Root Mean Square Error of Approximation equals 0.82. 3). The
normality of the Multiple Intelligences Measurement is categorized
into 3 levels. Those with high intelligence are those with percentiles
of more than 78. Those with moderate/medium intelligence are those
with percentiles between 24 and 77.9. Those with low intelligence
are those with percentiles from 23.9 downwards.
Abstract: Human immunodeficiency virus infection and
acquired immunodeficiency syndrome is a global pandemic with
cases reporting from virtually every country and continues to be a
common infection in developing country like India.
Microalbuminuria is a manifestation of human immunodeficiency
virus associated nephropathy. Therefore, microalbuminuria may be
an early marker of human immunodeficiency virus associated
nephropathy, and screening for its presence may be beneficial. A
strikingly high prevalence of microalbuminuria among human
immunodeficiency virus infected patients has been described in
various studies. Risk factors for clinically significant proteinuria
include African - American race, higher human immunodeficiency
virus ribonucleic acid level and lower CD4 lymphocyte count. The
cardiovascular risk factors of increased systolic blood pressure and
increase fasting blood sugar level are strongly associated with
microalbuminuria in human immunodeficiency virus patient. These
results suggest that microalbuminuria may be a sign of current
endothelial dysfunction and micro-vascular disease and there is
substantial risk of future cardiovascular disease events. Positive
contributing factors include early kidney disease such as human
immunodeficiency virus associated nephropathy, a marker of end
organ damage related to co morbidities of diabetes or hypertension,
or more diffuse endothelial cells dysfunction. Nevertheless after
adjustment for non human immunodeficiency virus factors, human
immunodeficiency virus itself is a major risk factor. The presence of
human immunodeficiency virus infection is independent risk to
develop microalbuminuria in human immunodeficiency virus patient.
Cardiovascular risk factors appeared to be stronger predictors of
microalbuminuria than markers of human immunodeficiency virus
severity person with human immunodeficiency virus infection and
microalbuminuria therefore appear to potentially bear the burden of
two separate damage related to known vascular end organ damage
related to know vascular risk factors, and human immunodeficiency
virus specific processes such as the direct viral infection of kidney
cells.The higher prevalence of microalbuminuria among the human
immunodeficiency virus infected could be harbinger of future
increased risks of both kidney and cardiovascular disease. Further
study defining the prognostic significance of microalbuminuria
among human immunodeficiency virus infected persons will be
essential. Microalbuminuria seems to be a predictor of cardiovascular
disease in diabetic and non diabetic subjects, hence it can also be
used for early detection of micro vascular disease in human
immunodeficiency virus positive patients, thus can help to diagnose
the disease at the earliest.
Abstract: Using activity theory, organisational theory and
didactics as theoretical foundations, a comprehensive model of the
organisational dimensions relevant for learning and knowledge
transfer will be developed. In a second step, a Learning Assessment
Guideline will be elaborated. This guideline will be designed to
permit a targeted analysis of organisations to identify the status quo
in those areas crucial to the implementation of learning and
knowledge transfer. In addition, this self-analysis tool will enable
learning managers to select adequate didactic models for e- and
blended learning. As part of the European Integrated Project
"Process-oriented Learning and Information Exchange" (PROLIX),
this model of organisational prerequisites for learning and knowledge
transfer will be empirically tested in four profit and non-profit
organisations in Great Britain, Germany and France (to be finalized
in autumn 2006). The findings concern not only the capability of the
model of organisational dimensions, but also the predominant
perceptions of and obstacles to learning in organisations.
Abstract: Self-efficacy, self-reliance, and motivation were
examined in a quasi-experimental study with 178 sophomore
university students. Participants used an interactive cardiovascular
anatomy and physiology CD-ROM, and completed a 15-item
questionnaire. Reliability of the questionnaire was established using
Cronbach-s alpha. Post-tests and course grades were examined using
a t-test, demonstrating no significance. Results of an item-to-item
analysis of the questionnaire showed overall satisfaction with the
teaching methodology and varied results for self-efficacy, selfreliance,
and motivation. Kendall-s Tau was calculated for all items
in the questionnaire.
Abstract: To understand complex living system an effort has
made by mechanical engineers and dentists to deliver prompt
products and services to patients concerned about their aesthetic look.
Since two decades various bracket systems have designed involving
techniques like milling, injection molding which are technically not
flexible for the customized dental product development. The aim of
this paper to design, develop a customized system which is
economical and mainly emphasizes the expertise design and
integration of engineering and dental fields. A custom made selfadjustable
lingual bracket and customized implants are designed and
developed using computer aided design (CAD) and rapid prototyping
technology (RPT) to improve the smiles and to overcome the
difficulties associated with conventional ones. Lengthy orthodontic
treatment usually not accepted by the patients because the patient
compliance is lost. Patient-s compliance can be improved by
facilitating faster tooth movements by designing a localized dental
vibrator using advanced engineering principles.
Abstract: Because of increasing demands for security in today-s
society and also due to paying much more attention to machine
vision, biometric researches, pattern recognition and data retrieval in
color images, face detection has got more application. In this article
we present a scientific approach for modeling human skin color, and
also offer an algorithm that tries to detect faces within color images
by combination of skin features and determined threshold in the
model. Proposed model is based on statistical data in different color
spaces. Offered algorithm, using some specified color threshold, first,
divides image pixels into two groups: skin pixel group and non-skin
pixel group and then based on some geometric features of face
decides which area belongs to face.
Two main results that we received from this research are as follow:
first, proposed model can be applied easily on different databases and
color spaces to establish proper threshold. Second, our algorithm can
adapt itself with runtime condition and its results demonstrate
desirable progress in comparison with similar cases.
Abstract: This paper studies the dependability of componentbased
applications, especially embedded ones, from the diagnosis
point of view. The principle of the diagnosis technique is to
implement inter-component tests in order to detect and locate the
faulty components without redundancy. The proposed approach for
diagnosing faulty components consists of two main aspects. The first
one concerns the execution of the inter-component tests which
requires integrating test functionality within a component. This is the
subject of this paper. The second one is the diagnosis process itself
which consists of the analysis of inter-component test results to
determine the fault-state of the whole system. Advantage of this
diagnosis method when compared to classical redundancy faulttolerant
techniques are application autonomy, cost-effectiveness and
better usage of system resources. Such advantage is very important
for many systems and especially for embedded ones.
Abstract: This project focuses on the development of a line
follower algorithm for a Two Wheels Balancing Robot. In this
project, ATMEGA32 is chosen as the brain board controller to react
towards the data received from Balance Processor Chip on the
balance board to monitor the changes of the environment through
two infra-red distance sensor to solve the inclination angle problem.
Hence, the system will immediately restore to the set point (balance
position) through the implementation of internal PID algorithms at
the balance board. Application of infra-red light sensors with the PID
control is vital, in order to develop a smooth line follower robot. As a
result of combination between line follower program and internal self
balancing algorithms, we are able to develop a dynamically
stabilized balancing robot with line follower function.
Abstract: The stone is a constituent part of the geological
structure of the Territory, introducing himself as a subject that has always interconnected human and environment in the development of a discourse of meanings and symbols that reflect elements realized in
different cultures and experiences.
This action meant that the first settlements and their areas of influence gained importance in the field of humanization and spatial
organization of the territory, not only for the appropriation that its
inhabitants did, but mainly because the community regardless of their
economic or social condition, used it as living space and cultural integration.
These factors become decisive in the characterization of the
landscape area in the northwest of Portugal, because the stone is a
material that appears not only in the natural landscape, but is also a strong element in humanized landscape, becoming this relation the
main characterization of the study area.
Abstract: In this paper, we present a novel technique called Self-Learning Expert System (SLES). Unlike Expert System, where there is a need for an expert to impart experiences and knowledge to create the knowledge base, this technique tries to acquire the experience and knowledge automatically. To display this technique at work, a simulation of a mobile robot navigating through an environment with obstacles is employed using visual basic. The mobile robot will move through this area without colliding with any obstacle and save the path that it took. If the mobile robot has to go through a similar environment again, then it will apply this experience to help it move through quicker without having to check for collision.
Abstract: The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.
Abstract: Artificial Immune System is adopted as a Heuristic
Algorithm to solve the combinatorial problems for decades.
Nevertheless, many of these applications took advantage of the benefit
for applications but seldom proposed approaches for enhancing the
efficiency. In this paper, we continue the previous research to develop
a Self-evolving Artificial Immune System II via coordinating the T
and B cell in Immune System and built a block-based artificial
chromosome for speeding up the computation time and better
performance for different complexities of problems. Through the
design of Plasma cell and clonal selection which are relative the
function of the Immune Response. The Immune Response will help
the AIS have the global and local searching ability and preventing
trapped in local optima. From the experimental result, the significant
performance validates the SEAIS II is effective when solving the
permutation flows-hop problems.
Abstract: Cosmic showers, during the transit through space, produce
sub - products as a result of interactions with the intergalactic
or interstellar medium which after entering earth generate secondary
particles called Extensive Air Shower (EAS). Detection and analysis
of High Energy Particle Showers involve a plethora of theoretical and
experimental works with a host of constraints resulting in inaccuracies
in measurements. Therefore, there exist a necessity to develop a
readily available system based on soft-computational approaches
which can be used for EAS analysis. This is due to the fact that soft
computational tools such as Artificial Neural Network (ANN)s can be
trained as classifiers to adapt and learn the surrounding variations. But
single classifiers fail to reach optimality of decision making in many
situations for which Multiple Classifier System (MCS) are preferred
to enhance the ability of the system to make decisions adjusting
to finer variations. This work describes the formation of an MCS
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN) with data inputs
from correlation mapping Self Organizing Map (SOM) blocks and
the output optimized by another SOM. The results show that the setup
can be adopted for real time practical applications for prediction
of primary energy and location of EAS from density values captured
using detectors in a circular grid.
Abstract: Since communications between tag and reader in RFID
system are by radio, anyone can access the tag and obtain its any
information. And a tag always replies with the same ID so that it is
hard to distinguish between a real and a fake tag. Thus, there are many
security problems in today-s RFID System. Firstly, unauthorized
reader can easily read the ID information of any Tag. Secondly,
Adversary can easily cheat the legitimate reader using the collected
Tag ID information, such as the any legitimate Tag. These security
problems can be typically solved by encryption of messages
transmitted between Tag and Reader and by authentication for Tag.
In this paper, to solve these security problems on RFID system, we
propose the Tag Authentication Scheme based on self shrinking
generator (SSG). SSG Algorithm using in our scheme is proposed by
W.Meier and O.Staffelbach in EUROCRYPT-94. This Algorithm is
organized that only one LFSR and selection logic in order to generate
random stream. Thus it is optimized to implement the hardware logic
on devices with extremely limited resource, and the output generating
from SSG at each time do role as random stream so that it is allow our
to design the light-weight authentication scheme with security against
some network attacks. Therefore, we propose the novel tag
authentication scheme which use SSG to encrypt the Tag-ID
transmitted from tag to reader and achieve authentication of tag.
Abstract: The design of technological procedures for
manufacturing certain products demands the definition and
optimization of technological process parameters. Their
determination depends on the model of the process itself and its
complexity. Certain processes do not have an adequate mathematical
model, thus they are modeled using heuristic methods. First part of
this paper presents a state of the art of using soft computing
techniques in manufacturing processes from the perspective of
applicability in modern CAx systems. Methods of artificial
intelligence which can be used for this purpose are analyzed. The
second part of this paper shows some of the developed models of
certain processes, as well as their applicability in the actual
calculation of parameters of some technological processes within the
design system from the viewpoint of productivity.
Abstract: In this paper the problem of estimating the time delay
between two spatially separated noisy sinusoidal signals by system
identification modeling is addressed. The system is assumed to be
perturbed by both input and output additive white Gaussian noise. The
presence of input noise introduces bias in the time delay estimates.
Normally the solution requires a priori knowledge of the input-output
noise variance ratio. We utilize the cascade of a self-tuned filter with
the time delay estimator, thus making the delay estimates robust to
input noise. Simulation results are presented to confirm the superiority
of the proposed approach at low input signal-to-noise ratios.
Abstract: A cognitive collaborative reinforcement learning
algorithm (CCRL) that incorporates an advisor into the learning
process is developed to improve supervised learning. An autonomous
learner is enabled with a self awareness cognitive skill to decide
when to solicit instructions from the advisor. The learner can also
assess the value of advice, and accept or reject it. The method is
evaluated for robotic motion planning using simulation. Tests are
conducted for advisors with skill levels from expert to novice. The
CCRL algorithm and a combined method integrating its logic with
Clouse-s Introspection Approach, outperformed a base-line fully
autonomous learner, and demonstrated robust performance when
dealing with various advisor skill levels, learning to accept advice
received from an expert, while rejecting that of less skilled
collaborators. Although the CCRL algorithm is based on RL, it fits
other machine learning methods, since advisor-s actions are only
added to the outer layer.
Abstract: The SOM has several beneficial features which make
it a useful method for data mining. One of the most important
features is the ability to preserve the topology in the projection.
There are several measures that can be used to quantify the goodness
of the map in order to obtain the optimal projection, including the
average quantization error and many topological errors. Many
researches have studied how the topology preservation should be
measured. One option consists of using the topographic error which
considers the ratio of data vectors for which the first and second best
BMUs are not adjacent. In this work we present a study of the
behaviour of the topographic error in different kinds of maps. We
have found that this error devaluates the rectangular maps and we
have studied the reasons why this happens. Finally, we suggest a new
topological error to improve the deficiency of the topographic error.
Abstract: The IDR(s) method based on an extended IDR theorem was proposed by Sonneveld and van Gijzen. The original IDR(s) method has excellent property compared with the conventional iterative methods in terms of efficiency and small amount of memory. IDR(s) method, however, has unexpected property that relative residual 2-norm stagnates at the level of less than 10-12. In this paper, an effective strategy for stagnation detection, stagnation avoidance using adaptively information of parameter s and improvement of convergence rate itself of IDR(s) method are proposed in order to gain high accuracy of the approximated solution of IDR(s) method. Through numerical experiments, effectiveness of adaptive tuning IDR(s) method is verified and demonstrated.