Abstract: Recently, much research has been conducted for
security for wireless sensor networks and ubiquitous computing.
Security issues such as authentication and data integrity are major
requirements to construct sensor network systems. Advanced
Encryption Standard (AES) is considered as one of candidate
algorithms for data encryption in wireless sensor networks. In this
paper, we will present the hardware architecture to implement low
power AES crypto module. Our low power AES crypto module has
optimized architecture of data encryption unit and key schedule unit
which could be applicable to wireless sensor networks. We also details
low power design methods used to design our low power AES crypto
module.
Abstract: The aim of this paper is to explain what a multienterprise tie is, what evidence its analysis provides and how does the cooperation mechanism influence the establishment of a multienterprise tie. The study focuses on businesses of smaller dimension, geographically dispersed and whose businessmen are learning to cooperate in an international environment. The empirical evidence obtained at this moment permits to conclude the following: The tie is not long-lasting, it has an end; opportunism is an opportunity to learn; the multi-enterprise tie is a space to learn about the cooperation mechanism; the local tie permits a businessman to alternate between competition and cooperation strategies; the disappearance of a tie is an experience of learning for a businessman, diminishing the possibility of failure in the next tie; the cooperation mechanism tends to eliminate hierarchical relations; the multienterprise tie diminishes the asymmetries and permits SME-s to have a better position when they negotiate with large companies; the multi-enterprise tie impacts positively on the local system. The collection of empirical evidence was done trough the following instruments: direct observation in a business encounter to which the businesses attended in 2003 (202 Mexican agro industry SME-s), a survey applied in 2004 (129), a questionnaire applied in 2005 (86 businesses), field visits to the businesses during the period 2006-2008 and; a survey applied by telephone in 2008 (55 Mexican agro industry SME-s).
Abstract: We propose a method for discrimination and
classification of ovarian with benign, malignant and normal tissue
using independent component analysis and neural networks. The
method was tested for a proteomic patters set from A database, and
radial basis functions neural networks. The best performance was
obtained with probabilistic neural networks, resulting I 99% success
rate, with 98% of specificity e 100% of sensitivity.
Abstract: Because nodes are usually battery-powered, the energy
presents a very scarce resource in wireless sensor networks. For this
reason, the design of medium access control had to take energy
efficiency as one of its hottest concerns. Accordingly, in order to
improve the energy performance of MAC schemes in wireless sensor
networks, several ways can be followed. In fact, some researchers try
to limit idle listening while others focus on mitigating overhearing
(i.e. a node can hear a packet which is destined to another node)
or reducing the number of the used control packets. We, in this
paper, propose a new hybrid MAC protocol termed ELE-MAC
(i.e. Energy Latency Efficient MAC). The ELE-MAC major design
goals are energy and latency efficiencies. It adopts less control
packets than SMAC in order to preserve energy. We carried out ns-
2 simulations to evaluate the performance of the proposed protocol.
Thus, our simulation-s results prove the ELE-MAC energy efficiency.
Additionally, our solution performs statistically the same or better
latency characteristic compared to adaptive SMAC.
Abstract: We present a hybrid architecture of recurrent neural
networks (RNNs) inspired by hidden Markov models (HMMs). We
train the hybrid architecture using genetic algorithms to learn and
represent dynamical systems. We train the hybrid architecture on a
set of deterministic finite-state automata strings and observe the
generalization performance of the hybrid architecture when presented
with a new set of strings which were not present in the training data
set. In this way, we show that the hybrid system of HMM and RNN
can learn and represent deterministic finite-state automata. We ran
experiments with different sets of population sizes in the genetic
algorithm; we also ran experiments to find out which weight
initializations were best for training the hybrid architecture. The
results show that the hybrid architecture of recurrent neural networks
inspired by hidden Markov models can train and represent dynamical
systems. The best training and generalization performance is
achieved when the hybrid architecture is initialized with random real
weight values of range -15 to 15.
Abstract: In this paper, an approach to reduce the computation steps required by fast neural networksfor the searching process is presented. The principle ofdivide and conquer strategy is applied through imagedecomposition. Each image is divided into small in sizesub-images and then each one is tested separately usinga fast neural network. The operation of fast neuralnetworks based on applying cross correlation in thefrequency domain between the input image and theweights of the hidden neurons. Compared toconventional and fast neural networks, experimentalresults show that a speed up ratio is achieved whenapplying this technique to locate human facesautomatically in cluttered scenes. Furthermore, fasterface detection is obtained by using parallel processingtechniques to test the resulting sub-images at the sametime using the same number of fast neural networks. Incontrast to using only fast neural networks, the speed upratio is increased with the size of the input image whenusing fast neural networks and image decomposition.
Abstract: This paper discusses the development of wireless
structure control of an induction motor scalar drives. This was
realised up on the wireless WiFi networks. This strategy of control is
ensured by the use of Wireless ad hoc networks and a virtual network
interface based on VNC which is used to make possible to take the
remote control of a PC connected on a wireless Ethernet network.
Verification of the proposed strategy of control is provided by
experimental realistic tests on scalar controlled induction motor
drives. The experimental results of the implementations with their
analysis are detailed.
Abstract: Combining classifiers is a useful method for solving
complex problems in machine learning. The ECOC (Error Correcting
Output Codes) method has been widely used for designing combining
classifiers with an emphasis on the diversity of classifiers. In this
paper, in contrast to the standard ECOC approach in which individual
classifiers are chosen homogeneously, classifiers are selected
according to the complexity of the corresponding binary problem. We
use SATIMAGE database (containing 6 classes) for our experiments.
The recognition error rate in our proposed method is %10.37 which
indicates a considerable improvement in comparison with the
conventional ECOC and stack generalization methods.
Abstract: This paper presents a multi-objective model for addressing two main objectives in designing rural roads networks: minimization of user operation costs and maximization of population covered. As limited budgets often exist, a reasonable trade-off must be obtained in order to account for both cost and social benefits in this type of networks. For a real-world rural road network, the model is solved, where all non-dominated solutions were obtained. Afterwards, an analysis is made on the (possibly) most interesting solutions (the ones providing better trade-offs). This analysis, coupled with the knowledge of the real world scenario (typically provided by decision makers) provides a suitable method for the evaluation of road networks in rural areas of developing countries.
Abstract: With the exponentially increasing demand for
wireless communications the capacity of current cellular systems will
soon become incapable of handling the growing traffic. Since radio
frequencies are diminishing natural resources, there seems to be a
fundamental barrier to further capacity increase. The solution can be
found in smart antenna systems.
Smart or adaptive antenna arrays consist of an array of antenna
elements with signal processing capability, that optimize the
radiation and reception of a desired signal, dynamically. Smart
antennas can place nulls in the direction of interferers via adaptive
updating of weights linked to each antenna element. They thus cancel
out most of the co-channel interference resulting in better quality of
reception and lower dropped calls. Smart antennas can also track the
user within a cell via direction of arrival algorithms. This implies that
they are more advantageous than other antenna systems. This paper
focuses on few issues about the smart antennas in mobile radio
networks.
Abstract: In this paper, the problem of finding the optimal
topological configuration of a deregulated distribution network is
considered. The new features of this paper are proposing a multiobjective
function and its application on deregulated distribution
networks for finding the optimal configuration. The multi-objective
function will be defined for minimizing total Energy Supply Costs
(ESC) and energy losses subject to load flow constraints. The
optimal configuration will be obtained by using Binary Genetic
Algorithm (BGA).The proposed method has been tested to analyze a
sample and a practical distribution networks.
Abstract: In recent years, fast neural networks for object/face detection have been introduced based on cross correlation in the frequency domain between the input matrix and the hidden weights of neural networks. In our previous papers [3,4], fast neural networks for certain code detection was introduced. It was proved in [10] that for fast neural networks to give the same correct results as conventional neural networks, both the weights of neural networks and the input matrix must be symmetric. This condition made those fast neural networks slower than conventional neural networks. Another symmetric form for the input matrix was introduced in [1-9] to speed up the operation of these fast neural networks. Here, corrections for the cross correlation equations (given in [13,15,16]) to compensate for the symmetry condition are presented. After these corrections, it is proved mathematically that the number of computation steps required for fast neural networks is less than that needed by classical neural networks. Furthermore, there is no need for converting the input data into symmetric form. Moreover, such new idea is applied to increase the speed of neural networks in case of processing complex values. Simulation results after these corrections using MATLAB confirm the theoretical computations.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
Abstract: The cellular network is one of the emerging areas of
communication, in which the mobile nodes act as member for one
base station. The cluster based communication is now an emerging
area of wireless cellular multimedia networks. The cluster renders
fast communication and also a convenient way to work with
connectivity. In our scheme we have proposed an optimization
technique for the fuzzy cluster nodes, by categorizing the group
members into three categories like long refreshable member, medium
refreshable member and short refreshable member. By considering
long refreshable nodes as static nodes, we compute the new
membership values for the other nodes in the cluster. We compare
their previous and present membership value with the threshold value
to categorize them into three different members. By which, we
optimize the nodes in the fuzzy clusters. The simulation results show
that there is reduction in the cluster computational time and
iterational time after optimization.
Abstract: The important issue considered in the widespread deployment of Wireless Sensor Networks (WSNs) is an efficiency of the energy consumption. In this paper, we present a study of the optimal relay station planning problems using Binary Integer Linear Programming (BILP) model to minimize the energy consumption in WSNs. Our key contribution is that the proposed model not only ensures the required network lifetime but also guarantees the radio connectivity at high level of communication quality. Specially, we take into account effects of noise, signal quality limitation and bit error rate characteristics. Numerical experiments were conducted in various network scenarios. We analyzed the effects of different sensor node densities and distribution on the energy consumption.
Abstract: The improvement of irrigation systems in the Nile
Delta is one of the most important attempts in Egypt to implement
more effective irrigation technology by improving the existing
irrigation networks. Demand delivery system in the existing irrigation
network is using of mechanical gates structures to automatically
divert water from one portion of an agricultural field to another in the
desired amount and sequence. This paper discusses evaluating main
irrigation networks system under the government managed before
and after improvement systems in the Nile Delta. The overall results
indicate that policy of using the demand delivery concept through
irrigation networks is successful by improving water delivery
performance among them than the rotation delivery concept that used
before. It is provided fair share of water delivery among irrigation
districts and available water in the end of irrigation network,
although this system located in an end of irrigation networks in the
Nile Delta.
Abstract: This paper shows the results obtained in the analysis
of the impact of distributed generation (DG) on distribution losses
and presents a new algorithm to the optimal allocation of distributed
generation resources in distribution networks. The optimization is
based on a Hybrid Genetic Algorithm and Particle Swarm
Optimization (HGAPSO) aiming to optimal DG allocation in
distribution network. Through this algorithm a significant
improvement in the optimization goal is achieved. With a numerical
example the superiority of the proposed algorithm is demonstrated in
comparison with the simple genetic algorithm.
Abstract: The paper shows how the CASMAS modeling language,
and its associated pervasive computing architecture, can be
used to facilitate continuity of care by providing members of patientcentered
communities of care with a support to cooperation and
knowledge sharing through the usage of electronic documents and
digital devices. We consider a scenario of clearly fragmented care to
show how proper mechanisms can be defined to facilitate a better
integration of practices and information across heterogeneous care
networks. The scenario is declined in terms of architectural components
and cooperation-oriented mechanisms that make the support
reactive to the evolution of the context where these communities
operate.
Abstract: The winding hot-spot temperature is one of the most
critical parameters that affect the useful life of the power
transformers. The winding hot-spot temperature can be calculated as
function of the top-oil temperature that can estimated by using the
ambient temperature and transformer loading measured data. This
paper proposes the estimation of the top-oil temperature by using a
method based on Least Squares Support Vector Machines approach.
The estimated top-oil temperature is compared with measured data of
a power transformer in operation. The results are also compared with
methods based on the IEEE Standard C57.91-1995/2000 and
Artificial Neural Networks. It is shown that the Least Squares
Support Vector Machines approach presents better performance than
the methods based in the IEEE Standard C57.91-1995/2000 and
artificial neural networks.