Abstract: We propose that Virtual Learning Environments (VLEs) should be designed by taking into account the characteristics, the special needs and the specific operating rules of the academic institutions in which they are employed. In this context, we describe a VLE module that extends the support of the organization and delivery of course material by including administration activities related to the various stages of teaching. These include the co-ordination, collaboration and monitoring of the course material development process and institution-specific course material delivery modes. Our specialized module, which enhances VLE capabilities by Helping Educators and Learners through a Laboratory Assistance System, is willing to assist the Greek tertiary technological sector, which includes Technological Educational Institutes (T.E.I.).
Abstract: This paper presents a mathematical model and a
methodology to analyze the losses in transmission expansion
planning (TEP) under uncertainty in demand. The methodology is
based on discrete particle swarm optimization (DPSO). DPSO is a
useful and powerful stochastic evolutionary algorithm to solve the
large-scale, discrete and nonlinear optimization problems like TEP.
The effectiveness of the proposed idea is tested on an actual
transmission network of the Azerbaijan regional electric company,
Iran. The simulation results show that considering the losses even for
transmission expansion planning of a network with low load growth
is caused that operational costs decreases considerably and the
network satisfies the requirement of delivering electric power more
reliable to load centers.
Abstract: In this paper a new approach to face recognition is
presented that achieves double dimension reduction, making the
system computationally efficient with better recognition results and
out perform common DCT technique of face recognition. In pattern
recognition techniques, discriminative information of image
increases with increase in resolution to a certain extent, consequently
face recognition results change with change in face image resolution
and provide optimal results when arriving at a certain resolution
level. In the proposed model of face recognition, initially image
decimation algorithm is applied on face image for dimension
reduction to a certain resolution level which provides best
recognition results. Due to increased computational speed and feature
extraction potential of Discrete Cosine Transform (DCT), it is
applied on face image. A subset of coefficients of DCT from low to
mid frequencies that represent the face adequately and provides best
recognition results is retained. A tradeoff between decimation factor,
number of DCT coefficients retained and recognition rate with
minimum computation is obtained. Preprocessing of the image is
carried out to increase its robustness against variations in poses and
illumination level. This new model has been tested on different
databases which include ORL , Yale and EME color database.
Abstract: Finite impulse response (FIR) filters have the advantage of linear phase, guaranteed stability, fewer finite precision errors, and efficient implementation. In contrast, they have a major disadvantage of high order need (more coefficients) than IIR counterpart with comparable performance. The high order demand imposes more hardware requirements, arithmetic operations, area usage, and power consumption when designing and fabricating the filter. Therefore, minimizing or reducing these parameters, is a major goal or target in digital filter design task. This paper presents an algorithm proposed for modifying values and the number of non-zero coefficients used to represent the FIR digital pulse shaping filter response. With this algorithm, the FIR filter frequency and phase response can be represented with a minimum number of non-zero coefficients. Therefore, reducing the arithmetic complexity needed to get the filter output. Consequently, the system characteristic i.e. power consumption, area usage, and processing time are also reduced. The proposed algorithm is more powerful when integrated with multiplierless algorithms such as distributed arithmetic (DA) in designing high order digital FIR filters. Here the DA usage eliminates the need for multipliers when implementing the multiply and accumulate unit (MAC) and the proposed algorithm will reduce the number of adders and addition operations needed through the minimization of the non-zero values coefficients to get the filter output.
Abstract: Reliability is one of the most important quality attributes of software. Based on the approach of Reussner and the approach of Cheung, we proposed the reliability prediction model of component-based software architectures. Also, the value of the model is shown through the experimental evaluation on a web server system.
Abstract: In this paper, some practical solid transportation models are formulated considering per trip capacity of each type of conveyances with crisp and rough unit transportation costs. This is applicable for the system in which full vehicles, e.g. trucks, rail coaches are to be booked for transportation of products so that transportation cost is determined on the full of the conveyances. The models with unit transportation costs as rough variables are transformed into deterministic forms using rough chance constrained programming with the help of trust measure. Numerical examples are provided to illustrate the proposed models in crisp environment as well as with unit transportation costs as rough variables.
Abstract: The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.
Abstract: In order to perform on-line measuring and detection
of PD signals, a total solution composing of an HFCT, A/D
converter and a complete software package is proposed. The
software package includes compensation of HFCT contribution,
filtering and noise reduction using wavelet transform and soft
calibration routines. The results have shown good performance and
high accuracy.
Abstract: The evaluation of residual reliability of large sized
parallel computer interconnection systems is not practicable with
the existing methods. Under such conditions, one must go for
approximation techniques which provide the upper bound and lower
bound on this reliability. In this context, a new approximation method
for providing bounds on residual reliability is proposed here. The
proposed method is well supported by two algorithms for simulation
purpose. The bounds on residual reliability of three different categories
of interconnection topologies are efficiently found by using
the proposed method
Abstract: This paper aims to discuss the influence of resistance
characteristic on the high conductive concrete considering the changes
of voltage and environment. The high conductive concrete with
appropriate proportion is produced to the press-electrode method. The
curve of resistivity with the changes of voltage and environment is
plotted and the changes of resistivity are explored.
Abstract: In this paper, we propose an algorithm to compute
initial cluster centers for K-means clustering. Data in a cell is
partitioned using a cutting plane that divides cell in two smaller cells.
The plane is perpendicular to the data axis with the highest variance
and is designed to reduce the sum squared errors of the two cells as
much as possible, while at the same time keep the two cells far apart
as possible. Cells are partitioned one at a time until the number of
cells equals to the predefined number of clusters, K. The centers of
the K cells become the initial cluster centers for K-means. The
experimental results suggest that the proposed algorithm is effective,
converge to better clustering results than those of the random
initialization method. The research also indicated the proposed
algorithm would greatly improve the likelihood of every cluster
containing some data in it.
Abstract: Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.
Abstract: The notion of Next Generation Network (NGN) is
based on the Network Convergence concept which refers to
integration of services (such as IT and communication services) over
IP layer. As the most popular implementation of Service Oriented
Architecture (SOA), Web Services technology is known to be the
base for service integration. In this paper, we present a platform to
deliver communication services as web services. We also implement
a sample service to show the simplicity of making composite web
and communication services using this platform. A Service Logic
Execution Environment (SLEE) is used to implement the
communication services. The proposed architecture is in agreement
with Service Oriented Architecture (SOA) and also can be integrated
to an Enterprise Service Bus to make a base for NGN Service
Delivery Platform (SDP).
Abstract: Artifact rejection plays a key role in many signal processing applications. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Our aim is to automatically remove the artifacts, in particular from the Electroencephalographic (EEG) recordings. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we try to enhance this technique proposing a new method based on the Renyi-s entropy. The performance of our method was tested and compared to the performance of the method in literature and the former proved to outperform the latter.
Abstract: HSDPA is a new feature which is introduced in
Release-5 specifications of the 3GPP WCDMA/UTRA standard to
realize higher speed data rate together with lower round-trip times.
Moreover, the HSDPA concept offers outstanding improvement of
packet throughput and also significantly reduces the packet call
transfer delay as compared to Release -99 DSCH. Till now the
HSDPA system uses turbo coding which is the best coding technique
to achieve the Shannon limit. However, the main drawbacks of turbo
coding are high decoding complexity and high latency which makes
it unsuitable for some applications like satellite communications,
since the transmission distance itself introduces latency due to
limited speed of light. Hence in this paper it is proposed to use LDPC
coding in place of Turbo coding for HSDPA system which decreases
the latency and decoding complexity. But LDPC coding increases the
Encoding complexity. Though the complexity of transmitter
increases at NodeB, the End user is at an advantage in terms of
receiver complexity and Bit- error rate. In this paper LDPC Encoder
is implemented using “sparse parity check matrix" H to generate a
codeword at Encoder and “Belief Propagation algorithm "for LDPC
decoding .Simulation results shows that in LDPC coding the BER
suddenly drops as the number of iterations increase with a small
increase in Eb/No. Which is not possible in Turbo coding. Also same
BER was achieved using less number of iterations and hence the
latency and receiver complexity has decreased for LDPC coding.
HSDPA increases the downlink data rate within a cell to a theoretical
maximum of 14Mbps, with 2Mbps on the uplink. The changes that
HSDPA enables includes better quality, more reliable and more
robust data services. In other words, while realistic data rates are
only a few Mbps, the actual quality and number of users achieved
will improve significantly.
Abstract: Generally, administrative systems in an academic
environment are disjoint and support independent queries. The
objective in this work is to semantically connect these independent
systems to provide support to queries run on the integrated platform.
The proposed framework, by enriching educational material in the
legacy systems, provides a value-added semantics layer where
activities such as annotation, query and reasoning can be carried out
to support management requirements. We discuss the development of
this ontology framework with a case study of UAE University
program administration to show how semantic web technologies can
be used by administration to develop student profiles for better
academic program management.
Abstract: This paper proposes a “soft systems" approach to
domain-driven design of computer-based information systems. We
propose a systemic framework combining techniques from Soft
Systems Methodology (SSM), the Unified Modelling Language
(UML), and an implementation pattern known as “Naked Objects".
We have used this framework in action research projects that have
involved the investigation and modelling of business processes using
object-oriented domain models and the implementation of software
systems based on those domain models. Within the proposed
framework, Soft Systems Methodology (SSM) is used as a guiding
methodology to explore the problem situation and to generate a
ubiquitous language (soft language) which can be used as the basis
for developing an object-oriented domain model. The domain model
is further developed using techniques based on the UML and is
implemented in software following the “Naked Objects"
implementation pattern. We argue that there are advantages from
combining and using techniques from different methodologies in this
way.
The proposed systemic framework is overviewed and justified as
multimethodologyusing Mingers multimethodology ideas.
This multimethodology approach is being evaluated through a
series of action research projects based on real-world case studies. A
Peer-Tutoring case study is presented here as a sample of the
framework evaluation process
Abstract: With the development of the Internet, E-commerce is
growing at an exponential rate, and lots of online stores are built up to
sell their goods online. A major factor influencing the successful
adoption of E-commerce is consumer-s trust. For new or unknown
Internet business, consumers- lack of trust has been cited as a major
barrier to its proliferation. As web sites provide key interface for
consumer use of E-Commerce, we investigate the design of web site to
build trust in E-Commerce from a design science approach. A
conceptual model is proposed in this paper to describe the ontology of
online transaction and human-computer interaction. Based on this
conceptual model, we provide a personalized webpage design
approach using Bayesian networks learning method. Experimental
evaluation are designed to show the effectiveness of web
personalization in improving consumer-s trust in new or unknown
online store.
Abstract: Digital watermarking in medical images can ensure
the authenticity and integrity of the image. This design paper reviews
some existing watermarking schemes and proposes a reversible
tamper detection and recovery watermarking scheme. Watermark
data from ROI (Region Of Interest) are stored in RONI (Region Of
Non Interest). The embedded watermark allows tampering detection
and tampered image recovery. The watermark is also reversible and
data compression technique was used to allow higher embedding
capacity.