Abstract: This paper presents a novel algorithm for secure,
reliable and flexible transmission of big data in two hop wireless
networks using cooperative jamming scheme. Two hop wireless
networks consist of source, relay and destination nodes. Big data has
to transmit from source to relay and from relay to destination by
deploying security in physical layer. Cooperative jamming scheme
determines transmission of big data in more secure manner by
protecting it from eavesdroppers and malicious nodes of unknown
location. The novel algorithm that ensures secure and energy balance
transmission of big data, includes selection of data transmitting
region, segmenting the selected region, determining probability ratio
for each node (capture node, non-capture and eavesdropper node) in
every segment, evaluating the probability using binary based
evaluation. If it is secure transmission resume with the two- hop
transmission of big data, otherwise prevent the attackers by
cooperative jamming scheme and transmit the data in two-hop
transmission.
Abstract: Cooperative spectrum sensing is a crucial challenge in
cognitive radio networks. Cooperative sensing can increase the
reliability of spectrum hole detection, optimize sensing time and
reduce delay in cooperative networks. In this paper, an efficient
central capacity optimization algorithm is proposed to minimize
cooperative sensing time in a homogenous sensor network using OR
decision rule subject to the detection and false alarm probabilities
constraints. The evaluation results reveal significant improvement in
the sensing time and normalized capacity of the cognitive sensors.
Abstract: Heat transfer due to forced convection of copper water
based nanofluid has been predicted by Artificial Neural network
(ANN). The present nanofluid is formed by mixing copper
nanoparticles in water and the volume fractions are considered here
are 0% to 15% and the Reynolds number are kept constant at 100.
The back propagation algorithm is used to train the network. The
present ANN is trained by the input and output data which has been
obtained from the numerical simulation, performed in finite volume
based Computational Fluid Dynamics (CFD) commercial software
Ansys Fluent. The numerical simulation based results are compared
with the back propagation based ANN results. It is found that the
forced convection heat transfer of water based nanofluid can be
predicted correctly by ANN. It is also observed that the back
propagation ANN can predict the heat transfer characteristics of
nanofluid very quickly compared to standard CFD method.
Abstract: Thousands of organisations store important and
confidential information related to them, their customers, and their
business partners in databases all across the world. The stored data
ranges from less sensitive (e.g. first name, last name, date of birth) to
more sensitive data (e.g. password, pin code, and credit card
information). Losing data, disclosing confidential information or
even changing the value of data are the severe damages that
Structured Query Language injection (SQLi) attack can cause on a
given database. It is a code injection technique where malicious SQL
statements are inserted into a given SQL database by simply using a
web browser. In this paper, we propose an effective pattern
recognition neural network model for detection and classification of
SQLi attacks. The proposed model is built from three main elements
of: a Uniform Resource Locator (URL) generator in order to generate
thousands of malicious and benign URLs, a URL classifier in order
to: 1) classify each generated URL to either a benign URL or a
malicious URL and 2) classify the malicious URLs into different
SQLi attack categories, and a NN model in order to: 1) detect either a
given URL is a malicious URL or a benign URL and 2) identify the
type of SQLi attack for each malicious URL. The model is first
trained and then evaluated by employing thousands of benign and
malicious URLs. The results of the experiments are presented in
order to demonstrate the effectiveness of the proposed approach.
Abstract: This article presents two methods for the
compensation of harmonics generated by a nonlinear load. The first is
the classic method P-Q. The second is the controller by modern
method of artificial intelligence specifically fuzzy logic. Both
methods are applied to a shunt Active Power Filter (sAPF) based on a
three-phase voltage converter at five levels NPC topology. In
calculating the harmonic currents of reference, we use the algorithm
P-Q and pulse generation, we use the intersective PWM. For
flexibility and dynamics, we use fuzzy logic. The results give us clear
that the rate of Harmonic Distortion issued by fuzzy logic is better
than P-Q.
Abstract: In this paper, we propose a system for preventing gas
risks through the use of wireless communication modules and
intelligent gas safety appliances. Our system configuration consists of
an automatic extinguishing system, detectors, a wall-pad, and a
microcomputer controlled micom gas meter to monitor gas flow and
pressure as well as the occurrence of earthquakes. The automatic fire
extinguishing system checks for both combustible gaseous leaks and
monitors the environmental temperature, while the detector array
measures smoke and CO gas concentrations. Depending on detected
conditions, the micom gas meter cuts off an inner valve and generates
a warning, the automatic fire-extinguishing system cuts off an external
valve and sprays extinguishing materials, or the sensors generate
signals and take further action when smoke or CO are detected.
Information on intelligent measures taken by the gas safety appliances
and sensors are transmitted to the wall-pad, which in turn relays this as
real time data to a server that can be monitored via an external network
(BcN) connection to a web or mobile application for the management
of gas safety. To validate this smart-home gas management system, we
field-tested its suitability for use in Korean apartments under several
scenarios.
Abstract: In the present study, RBF neural networks were used
for predicting the performance and emission parameters of a
biodiesel engine. Engine experiments were carried out in a 4 stroke
diesel engine using blends of diesel and Honge methyl ester as the
fuel. Performance parameters like BTE, BSEC, Tex and emissions
from the engine were measured. These experimental results were
used for ANN modeling.
RBF center initialization was done by random selection and by
using Clustered techniques. Network was trained by using fixed and
varying widths for the RBF units. It was observed that RBF results
were having a good agreement with the experimental results.
Networks trained by using clustering technique gave better results
than using random selection of centers in terms of reduced MRE and
increased prediction accuracy. The average MRE for the performance
parameters was 3.25% with the prediction accuracy of 98% and for
emissions it was 10.4% with a prediction accuracy of 80%.
Abstract: In this research article of modeling Underwater
Wireless Sensor Network Simulators, we provide a comprehensive
overview of the various currently available simulators used in UWSN
modeling. In this work, we compare their working environment,
software platform, simulation language, key features, limitations and
corresponding applications. Based on extensive experimentation and
performance analysis, we provide their efficiency for specific
applications. We have also provided guidelines for developing
protocols in different layers of the protocol stack, and finally these
parameters are also compared and tabulated. This analysis is
significant for researchers and designers to find the right simulator
for their research activities.
Abstract: We propose new multiple-channel piezoelectric (PZT)
actuated tunable optical filter based on racetrack multi-ring
resonators for wavelength de-multiplexing network applications. We
design tunable eight-channel wavelength de-multiplexer consisting of
eight cascaded PZT actuated tunable multi-ring resonator filter with a
channel spacing of 1.6nm. The filter for each channel is basically
structured on a suspended beam, sandwiched with piezoelectric
material and built in integrated ring resonators which are placed on
the middle of the beam to gain uniform stress and linearly varying
longitudinal strain. A reference single mode serially coupled multi
stage racetrack ring resonator with the same radii and coupling length
is designed with a line width of 0.8974nm with a flat top pass band at
1dB of 0.5205nm and free spectral range of about 14.9nm. In each
channel, a small change in the perimeter of the rings is introduced to
establish the shift in resonance wavelength as per the defined channel
spacing. As a result, when a DC voltage is applied, the beams will
elongate, which involves mechanical deformation of the ring
resonators that induces a stress and a strain, which brings a change in
refractive index and perimeter of the rings leading to change in the
output spectrum shift providing the tunability of central wavelength
in each channel. Simultaneous wave length shift as high as
45.54pm/
Abstract: Due to the continuous increment of the load demand,
identification of weaker buses, improvement of voltage profile and
power losses in the context of the voltage stability problems has
become one of the major concerns for the larger, complex,
interconnected power systems. The objective of this paper is to
review the impact of Flexible AC Transmission System (FACTS)
controller in Wind generators connected electrical network for
maintaining voltage stability. Wind energy could be the growing
renewable energy due to several advantages. The influence of wind
generators on power quality is a significant issue; non uniform power
production causes variations in system voltage and frequency.
Therefore, wind farm requires high reactive power compensation; the
advances in high power semiconducting devices have led to the
development of FACTS. The FACTS devices such as for example
SVC inject reactive power into the system which helps in maintaining
a better voltage profile. The performance is evaluated on an IEEE 14
bus system, two wind generators are connected at low voltage buses
to meet the increased load demand and SVC devices are integrated at
the buses with wind generators to keep voltage stability. Power
flows, nodal voltage magnitudes and angles of the power network are
obtained by iterative solutions using MIPOWER.
Abstract: Margin-Based Principle has been proposed for a long
time, it has been proved that this principle could reduce the
structural risk and improve the performance in both theoretical
and practical aspects. Meanwhile, feed-forward neural network is
a traditional classifier, which is very hot at present with a deeper
architecture. However, the training algorithm of feed-forward neural
network is developed and generated from Widrow-Hoff Principle that
means to minimize the squared error. In this paper, we propose
a new training algorithm for feed-forward neural networks based
on Margin-Based Principle, which could effectively promote the
accuracy and generalization ability of neural network classifiers
with less labelled samples and flexible network. We have conducted
experiments on four UCI open datasets and achieved good results
as expected. In conclusion, our model could handle more sparse
labelled and more high-dimension dataset in a high accuracy while
modification from old ANN method to our method is easy and almost
free of work.
Abstract: The changes of the optical and structural properties of
Bismuth-Boro-Tellurite glasses pre and post gamma irradiation were
studied. Six glass samples, with different composition [(TeO2)0.7
(B2O3)0.3]1-x (Bi2O3)x prepared by melt quenching method were
irradiated with 25kGy gamma radiation at room temperature. The
Fourier Transform Infrared Spectroscopy (FTIR) was used to explore
the structural bonding in the prepared glass samples due to exposure,
while UV-VIS Spectrophotometer was used to evaluate the changes
in the optical properties before and after irradiation. Gamma
irradiation causes profound changes in the peak intensity as shown by
FTIR spectra which is due to the breaking of the network bonding.
Before gamma irradiation, the optical band gap, Eg value decreased
from 2.44 eV to 2.15 eV with the addition of Bismuth content. The
value kept decreasing (from 2.18 eV to 2.00 eV) following exposure
to gamma radiation due to the increase of non-bridging oxygen
(NBO) and the increase of defect in the glass. In conclusion, the glass
with high content of Bi2O3 (0.30Bi) give smallest Eg and show less
changes in FTIR spectra after gamma irradiation which indicate that
this glass is more resistant to gamma radiation compared to other
glasses.
Abstract: Networking is important among students to achieve
better understanding. Social networking plays an important role in the
education. Realizing its huge potential, various organizations,
including institutions of higher learning have moved to the area of
social networks to interact with their students especially through
Facebook. Therefore, measuring the effectiveness of Facebook as a
learning tool has become an area of interest to academicians and
researchers. Therefore, this study tried to integrate and propose new
theoretical and empirical evidences by linking the western idea of
adopting Facebook as an alternative learning platform from a
Malaysian perspective. This study, thus, aimed to fill a gap by being
among the pioneering research that tries to study the effectiveness of
adopting Facebook as a learning platform across other cultural
settings, namely Malaysia. Structural equation modeling was
employed for data analysis and hypothesis testing. This study finding
has provided some insights that would likely affect students’
awareness towards using Facebook as an alternative learning
platform in the Malaysian higher learning institutions. At the end,
future direction is proposed.
Abstract: An efficient remanufacturing network lead to an
efficient design of sustainable manufacturing enterprise. In
remanufacturing network, products are collected from the customer
zone, disassembled and remanufactured at a suitable remanufacturing
facility. In this respect, another issue to consider is how the returned
product to be remanufactured, in other words, what is the best layout
for such facility. In order to achieve a sustainable manufacturing
system, Cellular Manufacturing System (CMS) designs are highly
recommended, CMSs combine high throughput rates of line layouts
with the flexibility offered by functional layouts (job shop).
Introducing the CMS while designing a remanufacturing network will
benefit the utilization of such a network. This paper presents and
analyzes a comprehensive mathematical model for the design of
Dynamic Cellular Remanufacturing Systems (DCRSs). In this paper,
the proposed model is the first one to date that considers CMS and
remanufacturing system simultaneously. The proposed DCRS model
considers several manufacturing attributes such as multi period
production planning, dynamic system reconfiguration, duplicate
machines, machine capacity, available time for workers, worker
assignments, and machine procurement, where the demand is totally
satisfied from a returned product. A numerical example is presented
to illustrate the proposed model.
Abstract: In this paper we propose a novel methodology for
extracting a road network and its nodes from satellite images of
Algeria country.
This developed technique is a progress of our previous research
works. It is founded on the information theory and the mathematical
morphology; the information theory and the mathematical
morphology are combined together to extract and link the road
segments to form a road network and its nodes.
We therefore have to define objects as sets of pixels and to study
the shape of these objects and the relations that exist between them.
In this approach, geometric and radiometric features of roads are
integrated by a cost function and a set of selected points of a crossing
road. Its performances were tested on satellite images of Algeria
country.
Abstract: With the growing of computer and network, digital
data can be spread to anywhere in the world quickly. In addition,
digital data can also be copied or tampered easily so that the security
issue becomes an important topic in the protection of digital data.
Digital watermark is a method to protect the ownership of digital data.
Embedding the watermark will influence the quality certainly. In this
paper, Vector Quantization (VQ) is used to embed the watermark into
the image to fulfill the goal of data hiding. This kind of watermarking
is invisible which means that the users will not conscious the existing
of embedded watermark even though the embedded image has tiny
difference compared to the original image. Meanwhile, VQ needs a lot
of computation burden so that we adopt a fast VQ encoding scheme by
partial distortion searching (PDS) and mean approximation scheme to
speed up the data hiding process.
The watermarks we hide to the image could be gray, bi-level and
color images. Texts are also can be regarded as watermark to embed.
In order to test the robustness of the system, we adopt Photoshop to
fulfill sharpen, cropping and altering to check if the extracted
watermark is still recognizable. Experimental results demonstrate that
the proposed system can resist the above three kinds of tampering in
general cases.
Abstract: One of the key aspects of power quality improvement
in power system is the mitigation of voltage sags/swells and flicker.
Custom power devices have been known as the best tools for voltage
disturbances mitigation as well as reactive power compensation.
Dynamic Voltage Restorer (DVR) which is the most efficient and
effective modern custom power device can provide the most
commercial solution to solve several problems of power quality in
distribution networks. This paper deals with analysis and simulation
technique of DVR based on instantaneous power theory which is a
quick control to detect signals. The main purpose of this work is to
remove three important disturbances including voltage sags/swells
and flicker. Simulation of the proposed method was carried out on
two sample systems by using Matlab software environment and the
results of simulation show that the proposed method is able to
provide desirable power quality in the presence of wide range of
disturbances.
Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) is
such a time-vary nonlinear dynamic system. The traditional linear
modeling approach is hard to estimate structure correctly of PEMFC
system. From this reason, this paper presents a nonlinear modeling of
the PEMFC using Neural Network Auto-regressive model with
eXogenous inputs (NNARX) approach. The multilayer perception
(MLP) network is applied to evaluate the structure of the NNARX
model of PEMFC. The validity and accuracy of NNARX model are
tested by one step ahead relating output voltage to input current from
measured experimental of PEMFC. The results show that the obtained
nonlinear NNARX model can efficiently approximate the dynamic
mode of the PEMFC and model output and system measured output
consistently.
Abstract: Design concepts of real-time embedded system can be
realized initially by introducing novel design approaches. In this
literature, model based design approach and in-the-loop testing were
employed early in the conceptual and preliminary phase to formulate
design requirements and perform quick real-time verification. The
design and analysis methodology includes simulation analysis, model
based testing, and in-the-loop testing. The design of conceptual driveby-
wire, or DBW, algorithm for electronic control unit, or ECU, was
presented to demonstrate the conceptual design process, analysis, and
functionality evaluation. The concepts of DBW ECU function can be
implemented in the vehicle system to improve electric vehicle, or EV,
conversion drivability. However, within a new development process,
conceptual ECU functions and parameters are needed to be evaluated.
As a result, the testing system was employed to support conceptual
DBW ECU functions evaluation. For the current setup, the system
components were consisted of actual DBW ECU hardware, electric
vehicle models, and control area network or CAN protocol. The
vehicle models and CAN bus interface were both implemented as
real-time applications where ECU and CAN protocol functionality
were verified according to the design requirements. The proposed
system could potentially benefit in performing rapid real-time
analysis of design parameters for conceptual system or software
algorithm development.
Abstract: Cloud computing is the innovative and leading
information technology model for enabling convenient, on-demand
network access to a shared pool of configurable computing resources
that can be rapidly provisioned and released with minimal
management effort. In this paper, we aim at the development of
workflow management system for cloud computing platforms based
on our previous research on the dynamic allocation of the cloud
computing resources and its workflow process. We took advantage of
the HTML5 technology and developed web-based workflow interface.
In order to enable the combination of many tasks running on the cloud
platform in sequence, we designed a mechanism and developed an
execution engine for workflow management on clouds. We also
established a prediction model which was integrated with job queuing
system to estimate the waiting time and cost of the individual tasks on
different computing nodes, therefore helping users achieve maximum
performance at lowest payment. This proposed effort has the potential
to positively provide an efficient, resilience and elastic environment
for cloud computing platform. This development also helps boost user
productivity by promoting a flexible workflow interface that lets users
design and control their tasks' flow from anywhere.