Abstract: Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.
Abstract: Wireless Sensor Networks (WSNs), which sense
environmental data with battery-powered nodes, require multi-hop
communication. This power-demanding task adds an extra workload
that is unfairly distributed across the network. As a result, nodes run
out of battery at different times: this requires an impractical
individual node maintenance scheme. Therefore we investigate a new
Cooperative Sensing approach that extends the WSN operational life
and allows a more practical network maintenance scheme (where all
nodes deplete their batteries almost at the same time). We propose a
novel cooperative algorithm that derives a piecewise representation
of the sensed signal while controlling approximation accuracy.
Simulations show that our algorithm increases WSN operational life
and spreads communication workload evenly. Results convey a
counterintuitive conclusion: distributing workload fairly amongst
nodes may not decrease the network power consumption and yet
extend the WSN operational life. This is achieved as our cooperative
approach decreases the workload of the most burdened cluster in the
network.
Abstract: Recent investigations have demonstrated the global
sea level rise due to climate change impacts. In this study, climate
changes study the effects of increasing water level in the strait of
Hormuz. The probable changes of sea level rise should be
investigated to employ the adaption strategies. The climatic output
data of a GCM (General Circulation Model) named CGCM3 under
climate change scenario of A1b and A2 were used. Among different
variables simulated by this model, those of maximum correlation
with sea level changes in the study region and least redundancy
among themselves were selected for sea level rise prediction by using
stepwise regression. One of models (Discrete Wavelet artificial
Neural Network) was developed to explore the relationship between
climatic variables and sea level changes. In these models, wavelet
was used to disaggregate the time series of input and output data into
different components and then ANN was used to relate the
disaggregated components of predictors and input parameters to each
other. The results showed in the Shahid Rajae Station for scenario
A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea
level rise is among 90 t0 105 cm. Furthermore, the result showed a
significant increase of sea level at the study region under climate
change impacts, which should be incorporated in coastal areas
management.
Abstract: The paper is focused on the operational model for
transport the single wagon consignments on railway network by
using two different models of train formation. The paper gives an
overview of possibilities of improving the quality of transport
services. Paper deals with two models used in problematic of train
formatting - time continuously and time discrete. By applying these
models in practice, the transport company can guarantee a higher
quality of service and expect increasing of transport performance.
The models are also applicable into others transport networks. The
models supplement a theoretical problem of train formation by new
ways of looking to affecting the organization of wagon flows.
Abstract: In this paper, a novel fuzzy approach is developed
while solving the Dynamic Routing and Wavelength Assignment
(DRWA) problem in optical networks with Wavelength Division
Multiplexing (WDM). In this work, the effect of nonlinear and linear
impairments such as Four Wave Mixing (FWM) and amplifier
spontaneous emission (ASE) noise are incorporated respectively. The
novel algorithm incorporates fuzzy logic controller (FLC) to reduce
the effect of FWM noise and ASE noise on a requested lightpath
referred in this work as FWM aware fuzzy dynamic routing and
wavelength assignment algorithm. The FWM crosstalk products and
the static FWM noise power per link are pre computed in order to
reduce the set up time of a requested lightpath, and stored in an
offline database. These are retrieved during the setting up of a
lightpath and evaluated online taking the dynamic parameters like
cost of the links into consideration.
Abstract: Underwater acoustic networks have attracted great
attention in the last few years because of its numerous applications.
High data rate can be achieved by efficiently modeling the physical
layer in the network protocol stack. In Acoustic medium,
propagation speed of the acoustic waves is dependent on many
parameters such as temperature, salinity, density, and depth.
Acoustic propagation speed cannot be modeled using standard
empirical formulas such as Urick and Thorp descriptions. In this
paper, we have modeled the acoustic channel using real time data of
temperature, salinity, and speed of Bay of Bengal (Indian Coastal
Region). We have modeled the acoustic channel by using Mackenzie
speed equation and real time data obtained from National Institute of
Oceanography and Technology. It is found that acoustic propagation
speed varies between 1503 m/s to 1544 m/s as temperature and
depth differs. The simulation results show that temperature, salinity,
depth plays major role in acoustic propagation and data rate
increases with appropriate data sets substituted in the simulated
model.
Abstract: In this paper, we describe an application for face
recognition. Many studies have used local descriptors to characterize
a face, the performance of these local descriptors remain low by
global descriptors (working on the entire image). The application of
local descriptors (cutting image into blocks) must be able to store
both the advantages of global and local methods in the Discrete
Cosine Transform (DCT) domain. This system uses neural network
techniques. The letter method provides a good compromise between
the two approaches in terms of simplifying of calculation and
classifying performance. Finally, we compare our results with those
obtained from other local and global conventional approaches.
Abstract: The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).
Abstract: The use of wireless technology in industrial networks
has gained vast attraction in recent years. In this paper, we have
thoroughly analyzed the effect of contention window (CW) size on
the performance of IEEE 802.11-based industrial wireless networks
(IWN), from delay and reliability perspective. Results show that the
default values of CWmin, CWmax, and retry limit (RL) are far from
the optimum performance due to the industrial application
characteristics, including short packet and noisy environment. In this
paper, an adaptive CW algorithm (payload-dependent) has been
proposed to minimize the average delay. Finally a simple, but
effective CW and RL setting has been proposed for industrial
applications which outperforms the minimum-average-delay solution
from maximum delay and jitter perspective, at the cost of a little
higher average delay. Simulation results show an improvement of up
to 20%, 25%, and 30% in average delay, maximum delay and jitter
respectively.
Abstract: Hydrogels are three-dimensional, hydrophilic,
polymeric networks composed of homopolymers or copolymers and
are insoluble in water due to the presence of chemical or physical
cross-links. When hydrogels come in contact with aqueous solutions,
they can effectively sorb and retain the dissolved substances,
depending on the nature of the monomeric units comprising the
hydrogel. For this reason, hydrogels have been proposed in several
studies as water purification agents. At the present work anionic
hydrogels bearing negatively charged –COO- groups were prepared
and investigated. These gels are based on sodium acrylate (ANa),
either homopolymerized (poly(sodiumacrylate), PANa) or
copolymerized (P(DMAM-co-ANa)) with N,N Dimethylacrylamide
(DMAM). The hydrogels were used to extract some model organic
dyes from water. It is found that cationic dyes are strongly sorbed and
retained by the hydrogels, while sorption of anionic dyes was
negligible. In all cases it was found that both maximum sorption
capacity and equilibrium binding constant varied from one dye to the
other depending on the chemical structure of the dye, the presence of
functional chemical groups and the hydrophobic-hydrophilic balance.
Finally, the nonionic hydrogel of the homopolymer poly(N,Ndimethylacrylamide),
PDMAM, was also used for reasons of
comparison.
Abstract: The energy need is growing rapidly due to the
population growth and the large new usage of power. Several works
put considerable efforts to make the electricity grid more intelligent
to reduce essentially energy consumption and provide efficiency and
reliability of power systems. The Smart Grid is a complex
architecture that covers critical devices and systems vulnerable to
significant attacks. Hence, security is a crucial factor for the success
and the wide deployment of Smart Grids. In this paper, we present
security issues of the Smart Grid architecture and we highlight open
issues that will make the Smart Grid security a challenging research
area in the future.
Abstract: The health care must be a right for people around the
world, but in order to guarantee the access to all, it is necessary to
overcome geographical barriers. Telemedicine take advantage of
Information Communication Technologies to deploy health care
services around the world. To achieve those goals, it is necessary to
use existing last mile solution to create access for home users, which
is why is necessary to establish the channel characteristics for those
kinds of services. This paper presents an analysis of network
performance of last mile solution for the use of IPTV broadcasting
with the application of streaming for telemedicine apps.
Abstract: Spectrum handover is a significant topic in the
cognitive radio networks to assure an efficient data transmission in
the cognitive radio user’s communications. This paper proposes a
comparison between three spectrum handover models: VIKOR, SAW
and MEW. Four evaluation metrics are used. These metrics are,
accumulative average of failed handover, accumulative average of
handover performed, accumulative average of transmission
bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three
spectrum handover models was validated with captured data of
spectrum occupancy in experiments performed at the GSM frequency
band (824 MHz - 849 MHz). These data represent the actual behavior
of the licensed users for this wireless frequency band. The results of the comparison show that VIKOR Algorithm
provides a 15.8% performance improvement compared to SAW
Algorithm and, it is 12.1% better than the MEW Algorithm.
Abstract: This paper develops a multiple channel assignment
model, which allows to take advantage of spectrum opportunities in
cognitive radio networks in the most efficient way. The developed
scheme allows making several assignments of available and
frequency adjacent channel, which require a bigger bandwidth, under
an equality environment. The hybrid assignment model it is made by
two algorithms, one that makes the ranking and selects available
frequency channels and the other one in charge of establishing the
Max-Min Fairness for not restrict the spectrum opportunities for all
the other secondary users, who also claim to make transmissions.
Measurements made were done for average bandwidth, average
delay, as well as fairness computation for several channel
assignments. Reached results were evaluated with experimental
spectrum occupational data from captured GSM frequency band. The
developed model shows evidence of improvement in spectrum
opportunity use and a wider average transmission bandwidth for each
secondary user, maintaining equality criteria in channel assignment.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: The use of information technology in education have
changed not only the learners learning style but also the way they
taught, where nowadays learners are connected with diversity of
information sources with means of knowledge available everywhere.
The advantage of network wireless technologies and mobility
technologies used in the education and learning processes lead to
mobile learning as a new model of learning technology. Currently,
most of mobile learning applications are developed for the formal
education and learning environment. Despite the long history and
large amount of research on mobile learning and instruction design
model still there is a need of well-defined process in designing
mobile learning applications. Based on this situation, this paper
emphasizes on identifying instruction design phase’s considerations
and influencing factors in developing mobile learning application.
This set of instruction design steps includes analysis, design,
development, implementation, evaluation and continuous has been
built from a literature study, with focus on standards for learning,
mobile application software quality and guidelines. The effort is part
of an Omani-funded research project investigating the development,
adoption and dissemination of mobile learning in Oman.
Abstract: Analytical techniques for measuring and planning
railway capacity expansion activities have been considered in this
article. A preliminary mathematical framework involving track
duplication and section sub divisions is proposed for this task. In
railways, these features have a great effect on network performance
and for this reason they have been considered. Additional motivations
have also arisen from the limitations of prior models that have not
included them.
Abstract: In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
Abstract: The first layer of defense against data loss is the backup
data. This paper implements an agent-based network backup system
used the backup, server-storage and server-backup agent these
tripartite construction, and the snapshot and hierarchical index are
used in the NSBS. It realizes the control command and data flow
separation, balances the system load, thereby improving efficiency of
the system backup and recovery. The test results show the agent-based
network backup system can effectively improve the task-based
concurrency, reasonably allocate network bandwidth, the system
backup performance loss costs smaller and improves data recovery
efficiency by 20%.
Abstract: Since the last decade, there has been a rapid growth in
digital multimedia, such as high-resolution media files and threedimentional
movies. Hence, there is a need for large digital storage
such as Hard Disk Drive (HDD). As such, users expect to have a
quieter HDD in their laptop. In this paper, a jury test has been
conducted on a group of 34 people where 17 of them are students
who are the potential consumer, and the remaining are engineers who
know the HDD. A total 13 HDD sound samples have been selected
from over hundred HDD noise recordings. These samples are
selected based on an agreed subjective feeling. The samples are
played to the participants using head acoustic playback system, which
enabled them to experience as similar as possible the same
environment as have been recorded. Analysis has been conducted and
the obtained results have indicated different group has different
perception over the noises. Two neural network-based acoustic
annoyance models are established based on back propagation neural
network. Four psychoacoustic metrics, loudness, sharpness,
roughness and fluctuation strength, are used as the input of the
model, and the subjective evaluation results are taken as the output.
The developed models are reasonably accurate in simulating both
training and test samples.