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: In this paper genetic based test data compression is
targeted for improving the compression ratio and for reducing the
computation time. The genetic algorithm is based on extended pattern
run-length coding. The test set contains a large number of X value
that can be effectively exploited to improve the test data
compression. In this coding method, a reference pattern is set and its
compatibility is checked. For this process, a genetic algorithm is
proposed to reduce the computation time of encoding algorithm. This
coding technique encodes the 2n compatible pattern or the inversely
compatible pattern into a single test data segment or multiple test data
segment. The experimental result shows that the compression ratio
and computation time is reduced.
Abstract: This paper focuses on the orbit avoidance strategy of
the optical remote sensing satellite. The optical remote sensing
satellite, moving along the Sun-synchronous orbit, is equipped with
laser warning equipment to alert CCD camera from laser attacks. This
paper explores the strategy of satellite avoidance to protect the CCD
camera and also the satellite. The satellite could evasive to several
target points in the orbital coordinates of virtual satellite. The so-called
virtual satellite is a passive vehicle which superposes the satellite at the
initial stage of avoidance. The target points share the consistent cycle
time and the same semi-major axis with the virtual satellite, which
ensures the properties of the satellite’s Sun-synchronous orbit remain
unchanged. Moreover, to further strengthen the avoidance capability
of satellite, it can perform multi-target-points avoid maneuvers. On
occasions of fulfilling the satellite orbit tasks, the orbit can be restored
back to virtual satellite through orbit maneuvers. There into, the avoid
maneuvers adopts pulse guidance. In addition, the fuel consumption is
optimized. The avoidance strategy discussed in this article is
applicable to optical remote sensing satellite when it is encountered
with hostile attack of space-based laser anti-satellite.
Abstract: Increasingly complex modern power systems require
stability, especially for transient and small disturbances. Transient
stability plays a major role in stability during fault and large
disturbance. This paper compares a power system stabilizer (PSS)
and static Var compensator (SVC) to improve damping oscillation
and enhance transient stability. The effectiveness of a PSS connected
to the exciter and/or governor in damping electromechanical
oscillations of isolated synchronous generator was tested. The SVC
device is a member of the shunt FACTS (flexible alternating current
transmission system) family, utilized in power transmission systems.
The designed model was tested with a multi-machine system
consisting of four machines six bus, using MATLAB/SIMULINK
software. The results obtained indicate that SVC solutions are better
than PSS.
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: Hydraulic fracturing is one of the most important
stimulation techniques available to the petroleum engineer to extract
hydrocarbons in tight gas sandstones. It allows more oil and gas
production in tight reservoirs as compared to conventional means.
The main aim of the study is to optimize the hydraulic fracturing as
technique and for this purpose three multi-zones layer formation is
considered and fractured contemporaneously. The three zones are
named as Zone1 (upper zone), Zone2 (middle zone) and Zone3
(lower zone) respectively and they all occur in shale rock. Simulation was performed with Mfrac integrated software which
gives a variety of 3D fracture options. This simulation process
yielded an average fracture efficiency of 93.8%for the three
respective zones and an increase of the average permeability of the
rock system. An average fracture length of 909 ft with net height
(propped height) of 210 ft (average) was achieved. Optimum
fracturing results was also achieved with maximum fracture width of
0.379 inches at an injection rate of 13.01 bpm with 17995 Mscf of
gas production.
Abstract: Detecting changes in multiple images of the same
scene has recently seen increased interest due to the many
contemporary applications including smart security systems, smart
homes, remote sensing, surveillance, medical diagnosis, weather
forecasting, speed and distance measurement, post-disaster forensics
and much more. These applications differ in the scale, nature, and
speed of change. This paper presents an application of image
processing techniques to implement a real-time change detection
system. Change is identified by comparing the RGB representation of
two consecutive frames captured in real-time. The detection threshold
can be controlled to account for various luminance levels. The
comparison result is passed through a filter before decision making to
reduce false positives, especially at lower luminance conditions. The
system is implemented with a MATLAB Graphical User interface
with several controls to manage its operation and performance.
Abstract: Presently various computational techniques are used
in modeling and analyzing environmental engineering data. In the
present study, an intra-comparison of polynomial and radial basis
kernel functions based on Support Vector Regression and, in turn, an
inter-comparison with Multi Linear Regression has been attempted in
modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ
multiple plunging jets (varying from 1 to 16 numbers). The data set
used in this study consists of four input parameters with a total of
eighty eight cases, forty four each for vertical and inclined multiple
plunging jets. For testing, tenfold cross validation was used.
Correlation coefficient values of 0.971 and 0.981 along with
corresponding root mean square error values of 0.0025 and 0.0020
were achieved by using polynomial and radial basis kernel functions
based Support Vector Regression respectively. An intra-comparison
suggests improved performance by radial basis function in
comparison to polynomial kernel based Support Vector Regression.
Further, an inter-comparison with Multi Linear Regression
(correlation coefficient = 0.973 and root mean square error = 0.0024)
reveals that radial basis kernel functions based Support Vector
Regression performs better in modeling and estimating mass transfer
by multiple plunging jets.
Abstract: One of the challenges that higher education faces is to
find how to approach the sustainability in an inclusive way to the
student within all the different academic areas, how to move the
sustainable development from the abstract field to the operational
field. This research comes from the ecoliteracy and the pedagogical
praxis as tools for rebuilding the teaching processes inside of
universities. The purpose is to determine and describe which are the
factors involved in the process of learning particularly in the
Greenhouse-School Siembra UV. In the Greenhouse-School Siembra UV, of the University of
Veracruz, are cultivated vegetables, medicinal plants and small
cornfields under the usage of eco-technologies such as hydroponics,
Wickingbed and Hugelkultur, which main purpose is the saving of
space, labor and natural resources, as well as function as agricultural
production alternatives in the urban and periurban zones. The sample was formed with students from different academic
areas and who are actively involved in the greenhouse, as well as
institutes from the University of Veracruz and governmental and nongovernmental
departments. This project comes from a pedagogic praxis approach, from filling
the needs that the different professional profiles of the university
students have. All this with the purpose of generate a pragmatic
dialogue with the sustainability. It also comes from the necessity to
understand the factors that intervene in the students’ praxis. In this
manner is how the students are the fundamental unit in the sphere of
sustainability. As a result, it is observed that those University of Veracruz
students who are involved in the Greenhouse-school, Siembra UV,
have enriched in different levels the sense of urban and periurban
agriculture because of the diverse academic approaches they have
and the interaction between them. It is concluded that the ecotechnologies
act as fundamental tools for ecoliteracy in society,
where it is strengthen the nutritional and food security from a
sustainable development approach.
Abstract: To study the dynamic mechanics response of asphalt
pavement under the temperature load and vehicle loading, asphalt
pavement was regarded as multilayered elastic half-space system, and
theory analysis was conducted by regarding dynamic modulus of
asphalt mixture as the parameter. Firstly, based on the dynamic
modulus test of asphalt mixture, function relationship between the
dynamic modulus of representative asphalt mixture and temperature
was obtained. In addition, the analytical solution for thermal stress in
single layer was derived by using Laplace integral transformation and
Hankel integral transformation respectively by using thermal
equations of equilibrium. The analytical solution of calculation model
of thermal stress in asphalt pavement was derived by transfer matrix
of thermal stress in multilayer elastic system. Finally, the variation of
thermal stress in pavement structure was analyzed. The result shows
that there is obvious difference between the thermal stress based on
dynamic modulus and the solution based on static modulus. So the
dynamic change of parameter in asphalt mixture should be taken into
consideration when theoretical analysis is taken out.
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: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: The subject of this paper is to review, comparative
analysis and simulation of selected components of power electronic
systems (PES), consistent with the concept of a more electric aircraft
(MEA). Comparative analysis and simulation in software
environment MATLAB / Simulink were carried out on the base of a
group of representatives of civil aircraft (B-787, A-380) and military
(F-22 Raptor, F-35) in the context of multi-pulse converters used in
them (6- and 12-pulse, and 18- and 24-pulse), which are key
components of high-tech electronics on-board power systems of
autonomous power systems (ASE) of modern aircraft (airplanes of
the future).
Abstract: Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.
Abstract: Software testing has become a mandatory process in
assuring the software product quality. Hence, test management is
needed in order to manage the test activities conducted in the
software test life cycle. This paper discusses on the challenges faced
in the software test life cycle, and how the test processes and test
activities, mainly on test cases creation, test execution, and test
reporting is being managed and automated using several test
automation tools, i.e. Jira, Robot Framework, and Jenkins.
Abstract: The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.
Abstract: This paper evaluates the accrual based scheduling for
cloud in single and multi-resource system. Numerous organizations
benefit from Cloud computing by hosting their applications. The
cloud model provides needed access to computing with potentially
unlimited resources. Scheduling is tasks and resources mapping to a
certain optimal goal principle. Scheduling, schedules tasks to virtual
machines in accordance with adaptable time, in sequence under
transaction logic constraints. A good scheduling algorithm improves
CPU use, turnaround time, and throughput. In this paper, three realtime
cloud services scheduling algorithm for single resources and
multiple resources are investigated. Experimental results show
Resource matching algorithm performance to be superior for both
single and multi-resource scheduling when compared to benefit first
scheduling, Migration, Checkpoint algorithms.
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: This study aims to assess the students' needs for the
tour planning e-guide. The study is developing on the contribution
and importance of the Educational Tour Planning Guide (ETP) is a
multimedia course ware as one of the effective methods in teaching
and learning of environmental science among the students in primary
schools of the Ministry of Education, Malaysia. It is to provide the
student with knowledge and experience about tourism, environmental
science activities and process. E-guide to ETP also hopes to
strengthen the student understanding toward the subject learn in the
tourism environmental science. In order to assess the student's needs
on the e-Guide to Educational Tour Planning in Environmental
Science, the study has produced a similar e-Guide to ETP in the form
as a course ware to be tested during the study. The study has involved
several steps in order to be completed. It is such as the formulation of
the problem, the review of the literature, the formulation of the study
methodology, the production of the e-Guide to ETP, field survey and
finally the analyses and discussion made on the data gathered during
the study. The survey has involved 100 respondents among the
students in standard six primary schools in Kluang Johor. Through
the findings, the study indicates that the current tested product is
acceptable among the students in learning environmental science as a
guide to plan for the tour. The findings also show a slight difference
between the respondents who are using the e-Guide to ETP, and those
who are not on the basis of the e-Guide to ETP results. Due the
important for the study, the researcher hopes to be having a fair
discussion and excellence, recommendation for the development of
the product of the current study. This report is written also important
to provide a written reference for the future related study.
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.