Abstract: We report on the development of a model to
understand why the range of experience with respect to HIV
infection is so diverse, especially with respect to the latency period.
To investigate this, an agent-based approach is used to extract highlevel
behaviour which cannot be described analytically from the set
of interaction rules at the cellular level. A network of independent
matrices mimics the chain of lymph nodes. Dealing with massively
multi-agent systems requires major computational effort. However,
parallelisation methods are a natural consequence and advantage of
the multi-agent approach and, using the MPI library, are here
implemented, tested and optimized. Our current focus is on the
various implementations of the data transfer across the network.
Three communications strategies are proposed and tested, showing
that the most efficient approach is communication based on the
natural lymph-network connectivity.
Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
Abstract: Urban road network traffic has become one of the
most studied research topics in the last decades. This is mainly due to
the enlargement of the cities and the growing number of motor
vehicles traveling in this road network. One of the most sensitive
problems is to verify if the network is congestion-free. Another
related problem is the automatic reconfiguration of the network
without building new roads to alleviate congestions. These problems
require an accurate model of the traffic to determine the steady state
of the system. An alternative is to simulate the traffic to see if there
are congestions and when and where they occur. One key issue is to
find an adequate model for road intersections. Once the model
established, either a large scale model is built or the intersection is
represented by its performance measures and simulation for analysis.
In both cases, it is important to seek the queueing model to represent
the road intersection. In this paper, we propose to model the road
intersection as a BCMP queueing network and we compare this
analytical model against a simulation model for validation.
Abstract: The increasing interest on processing data created by
sensor networks has evolved into approaches to implement sensor
networks as databases. The aggregation operator, which calculates a
value from a large group of data such as computing averages or sums,
etc. is an essential function that needs to be provided when
implementing such sensor network databases. This work proposes to
add the DURING clause into TinySQL to calculate values during a
specific long period and suggests a way to implement the aggregation
service in sensor networks by applying materialized view and
incremental view maintenance techniques that is used in data
warehouses. In sensor networks, data values are passed from child
nodes to parent nodes and an aggregation value is computed at the root
node. As such root nodes need to be memory efficient and low
powered, it becomes a problem to recompute aggregate values from all
past and current data. Therefore, applying incremental view
maintenance techniques can reduce the memory consumption and
support fast computation of aggregate values.
Abstract: This paper presents a new method of analog fault diagnosis based on back-propagation neural networks (BPNNs) using wavelet decomposition and fractal dimension as preprocessors. The proposed method has the capability to detect and identify faulty components in an analog electronic circuit with tolerance by analyzing its impulse response. Using wavelet decomposition to preprocess the impulse response drastically de-noises the inputs to the neural network. The second preprocessing by fractal dimension can extract unique features, which are the fed to a neural network as inputs for further classification. A comparison of our work with [1] and [6], which also employs back-propagation (BP) neural networks, reveals that our system requires a much smaller network and performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.
Abstract: In recent years, everything is trending toward digitalization
and with the rapid development of the Internet technologies,
digital media needs to be transmitted conveniently over the network.
Attacks, misuse or unauthorized access of information is of great
concern today which makes the protection of documents through
digital media a priority problem. This urges us to devise new data
hiding techniques to protect and secure the data of vital significance.
In this respect, steganography often comes to the fore as a tool for
hiding information. Steganography is a process that involves hiding
a message in an appropriate carrier like image or audio. It is of
Greek origin and means "covered or hidden writing". The goal of
steganography is covert communication. Here the carrier can be sent
to a receiver without any one except the authenticated receiver only
knows existence of the information. Considerable amount of work
has been carried out by different researchers on steganography. In this
work the authors propose a novel Steganographic method for hiding
information within the spatial domain of the gray scale image. The
proposed approach works by selecting the embedding pixels using
some mathematical function and then finds the 8 neighborhood of
the each selected pixel and map each bit of the secret message in
each of the neighbor pixel coordinate position in a specified manner.
Before embedding a checking has been done to find out whether the
selected pixel or its neighbor lies at the boundary of the image or not.
This solution is independent of the nature of the data to be hidden
and produces a stego image with minimum degradation.
Abstract: In this paper, we propose a single sample path based
algorithm with state aggregation to optimize the average rewards of
singularly perturbed Markov reward processes (SPMRPs) with a
large scale state spaces. It is assumed that such a reward process
depend on a set of parameters. Differing from the other kinds of
Markov chain, SPMRPs have their own hierarchical structure. Based
on this special structure, our algorithm can alleviate the load in the
optimization for performance. Moreover, our method can be applied
on line because of its evolution with the sample path simulated.
Compared with the original algorithm applied on these problems of
general MRPs, a new gradient formula for average reward
performance metric in SPMRPs is brought in, which will be proved
in Appendix, and then based on these gradients, the schedule of the
iteration algorithm is presented, which is based on a single sample
path, and eventually a special case in which parameters only
dominate the disturbance matrices will be analyzed, and a precise
comparison with be displayed between our algorithm with the old
ones which is aim to solve these problems in general Markov reward
processes. When applied in SPMRPs, our method will approach a fast
pace in these cases. Furthermore, to illustrate the practical value of
SPMRPs, a simple example in multiple programming in computer
systems will be listed and simulated. Corresponding to some practical
model, physical meanings of SPMRPs in networks of queues will be
clarified.
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: The rheological properties, structure and potential synergistic interactions of whey proteins (1-6%) and inulin (20%) in mixed gels in the presence of CaCl2 was the aim of this study. Whey proteins have a strong influence on inulin gel formation. At low concentrations (2%) whey proteins did not impair in inulin gel formation. At higher concentration (4%) whey proteins impaired inulin gelation and inulin impaired the formation of a Ca2+-induced whey protein network. The presence of whey proteins at a level allowing for protein gel network formation (6%) significantly increased the rheological parameters values of the gels. SEM micrographs showed that whey protein structure was coated by inulin moieties which could make the mixed gels firmer. The protein surface hydrophobicity measurements did not exclude synergistic interactions between inulin and whey proteins, however. The use of an electrophoretic technique did not show any stable inulin-whey protein complexes.
Abstract: This paper is mainly concerned with the application of a novel technique of data interpretation to the characterization and classification of measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artifical Neural Networks have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compares with earlier methods.
Abstract: In this paper, we propose a chaotic cipher system consisting of Improved Volterra Filters and the mapping that is created from the actual voice by using Radial Basis Function Network. In order to achieve a practical system, the system supposes to use the digital communication line, such as the Internet, to maintain the parameter matching between the transmitter and receiver sides. Therefore, in order to withstand the attack from outside, it is necessary that complicate the internal state and improve the sensitivity coefficient. In this paper, we validate the robustness of proposed method from three perspectives of "Chaotic properties", "Randomness", "Coefficient sensitivity".
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.