Abstract: Artificial neural networks have gained a lot of interest
as empirical models for their powerful representational capacity,
multi input and output mapping characteristics. In fact, most feedforward
networks with nonlinear nodal functions have been proved to
be universal approximates. In this paper, we propose a new
supervised method for color image classification based on selforganizing
feature maps (SOFM). This algorithm is based on
competitive learning. The method partitions the input space using
self-organizing feature maps to introduce the concept of local
neighborhoods. Our image classification system entered into RGB
image. Experiments with simulated data showed that separability of
classes increased when increasing training time. In additional, the
result shows proposed algorithms are effective for color image
classification.
Abstract: The growth of organic farming practices in the last
few decades is continuing to stimulate the international debate about
this alternative food market. As a part of a PhD project research
about embeddedness in Alternative Food Networks (AFNs), this
paper focuses on the promotional aspects of organic farms websites
from the Madrid region. As a theoretical tool, some knowledge
categories drawn on the geographic studies literature are used to
classify the many ideas expressed in the web pages. By analysing
texts and pictures of 30 websites, the study aims to question how and
to what extent actors from organic world communicate to the
potential customers their personal beliefs about farming practices,
products qualities, and ecological and social benefits. Moreover, the
paper raises the question of whether organic farming laws and
regulations lack of completeness about the social and cultural aspects
of food.
Abstract: Several parameters are established in order to measure
biodiesel quality. One of them is the iodine value, which is an
important parameter that measures the total unsaturation within a
mixture of fatty acids. Limitation of unsaturated fatty acids is
necessary since warming of higher quantity of these ones ends in
either formation of deposits inside the motor or damage of lubricant.
Determination of iodine value by official procedure tends to be very
laborious, with high costs and toxicity of the reagents, this study uses
artificial neural network (ANN) in order to predict the iodine value
property as an alternative to these problems. The methodology of
development of networks used 13 esters of fatty acids in the input
with convergence algorithms of back propagation of back
propagation type were optimized in order to get an architecture of
prediction of iodine value. This study allowed us to demonstrate the
neural networks’ ability to learn the correlation between biodiesel
quality properties, in this caseiodine value, and the molecular
structures that make it up. The model developed in the study reached
a correlation coefficient (R) of 0.99 for both network validation and
network simulation, with Levenberg-Maquardt algorithm.
Abstract: Cloud computing has emerged as a promising
direction for cost efficient and reliable service delivery across data
communication networks. The dynamic location of service facilities
and the virtualization of hardware and software elements are stressing
the communication networks and protocols, especially when data
centres are interconnected through the internet. Although the
computing aspects of cloud technologies have been largely
investigated, lower attention has been devoted to the networking
services without involving IT operating overhead. Cloud computing
has enabled elastic and transparent access to infrastructure services
without involving IT operating overhead. Virtualization has been a
key enabler for cloud computing. While resource virtualization and
service abstraction have been widely investigated, networking in
cloud remains a difficult puzzle. Even though network has significant
role in facilitating hybrid cloud scenarios, it hasn't received much
attention in research community until recently. We propose Network
as a Service (NaaS), which forms the basis of unifying public and
private clouds. In this paper, we identify various challenges in
adoption of hybrid cloud. We discuss the design and implementation
of a cloud platform.
Abstract: This study found that most corporate personnel are
using social media to communicate with colleagues to make the
process of working more efficient. Complete satisfaction occurred on
the use of security within the University’s computer network. The
social network usage for communication, collaboration,
entertainment and demonstrating concerns accounted for fifty percent
of variance to predict interpersonal relationships of corporate
personnel. This evaluation on the effectiveness of social networking
involved 213 corporate personnel’s. The data was collected by
questionnaires. This data was analyzed by using percentage, mean,
and standard deviation.
The results from the analysis and the effectiveness of using online
social networks were derived from the attitude of private users and
safety data within the security system. The results showed that the
effectiveness on the use of an online social network for corporate
personnel of Suan Sunandha Rajabhat University was specifically at
a good level, and the overall effects of each aspect was (Ẋ=3.11).
Abstract: The new era of digital communication has brought up
many challenges that network operators need to overcome. The high
demand of mobile data rates require improved networks, which is a
challenge for the operators in terms of maintaining the quality of
experience (QoE) for their consumers. In live video transmission,
there is a sheer need for live surveillance of the videos in order to
maintain the quality of the network. For this purpose objective
algorithms are employed to monitor the quality of the videos that are
transmitted over a network. In order to test these objective algorithms,
subjective quality assessment of the streamed videos is required, as the
human eye is the best source of perceptual assessment. In this paper we
have conducted subjective evaluation of videos with varying spatial
and temporal impairments. These videos were impaired with frame
freezing distortions so that the impact of frame freezing on the quality
of experience could be studied. We present subjective Mean Opinion
Score (MOS) for these videos that can be used for fine tuning the
objective algorithms for video quality assessment.
Abstract: This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.
Abstract: Designing cost-efficient, secure network protocols for
Wireless Sensor Networks (WSNs) is a challenging problem because
sensors are resource-limited wireless devices. Security services such
as authentication and improved pairwise key establishment are
critical to high efficient networks with sensor nodes. For sensor
nodes to correspond securely with each other efficiently, usage of
cryptographic techniques is necessary. In this paper, two key
predistribution schemes that enable a mobile sink to establish a
secure data-communication link, on the fly, with any sensor nodes.
The intermediate nodes along the path to the sink are able to verify
the authenticity and integrity of the incoming packets using a
predicted value of the key generated by the sender’s essential power.
The proposed schemes are based on the pairwise key with the mobile
sink, our analytical results clearly show that our schemes perform
better in terms of network resilience to node capture than existing
schemes if used in wireless sensor networks with mobile sinks.
Abstract: Security can be defined as the degree of resistance to, or protection from harm. It applies to any vulnerable and valuable assets, such as persons, dwellings, communities, nations or organizations. Cybercrime is any crime committed or facilitated via the Internet. It is any criminal activity involving computers and networks. It can range from fraud to unsolicited emails (spam). It includes the distant theft of government or corporate secrets through criminal trespass into remote systems around the globe. Nigeria like any other nations of the world is currently having her own share of the menace that has been used even as tools by terrorists. This paper is an attempt at presenting cyber security as an issue that requires a coordinated national response. It also acknowledges and advocates the key roles to be played by stakeholders and the importance of forging strong partnerships to prevent and tackle cybercrime in Nigeria.
Abstract: To explore how the brain may recognise objects in its
general,accurate and energy-efficient manner, this paper proposes the
use of a neuromorphic hardware system formed from a Dynamic
Video Sensor (DVS) silicon retina in concert with the SpiNNaker
real-time Spiking Neural Network (SNN) simulator. As a first step
in the exploration on this platform a recognition system for dynamic
hand postures is developed, enabling the study of the methods used
in the visual pathways of the brain. Inspired by the behaviours of
the primary visual cortex, Convolutional Neural Networks (CNNs)
are modelled using both linear perceptrons and spiking Leaky
Integrate-and-Fire (LIF) neurons.
In this study’s largest configuration using these approaches, a
network of 74,210 neurons and 15,216,512 synapses is created and
operated in real-time using 290 SpiNNaker processor cores in parallel
and with 93.0% accuracy. A smaller network using only 1/10th of the
resources is also created, again operating in real-time, and it is able
to recognise the postures with an accuracy of around 86.4% - only
6.6% lower than the much larger system. The recognition rate of the
smaller network developed on this neuromorphic system is sufficient
for a successful hand posture recognition system, and demonstrates
a much improved cost to performance trade-off in its approach.
Abstract: This article proposes a new method for application in
communication circuit systems that increase efficiency, PAE, output
power and gain in the circuit. The proposed method is based on a
combination of switching class-E and class-J and has been termed
class-EJ. This method was investigated using both theory and
simulation to confirm ∼72% PAE and output power of >39dBm. The
combination and design of the proposed power amplifier accrues gain
of over 15dB in the 2.9 to 3.5GHz frequency bandwidth. This circuit
was designed using MOSFET and high power transistors. The loadand
source-pull method achieved the best input and output networks
using lumped elements. The proposed technique was investigated for
fundamental and second harmonics having desirable amplitudes for
the output signal.
Abstract: The Trustworthy link failure recovery algorithm is
introduced in this paper, to provide the forwarding continuity even
with compound link failures. The ephemeral failures are common in
IP networks and it also has some proposals based on local rerouting.
To ensure forwarding continuity, we are introducing the compound
link failure recovery algorithm, even with compound link failures.
For forwarding the information, each packet carries a blacklist, which
is a min set of failed links encountered along its path, and the next
hop is chosen by excluding the blacklisted links. Our proposed
method describes how it can be applied to ensure forwarding to all
reachable destinations in case of any two or more link or node
failures in the network. After simulating with NS2 contains lot of
samples proved that the proposed protocol achieves exceptional
concert even under elevated node mobility using Trustworthy link
Failure Recovery Algorithm.
Abstract: The 5th generation of mobile networks is term used in
various research papers and projects to identify the next major phase
of mobile telecommunications standards. 5G wireless networks will
support higher peak data rate, lower latency and provide best
connections with QoS guarantees.
In this article, we discuss various promising technologies for 5G
wireless communication systems, such as IPv6 support, World Wide
Wireless Web (WWWW), Dynamic Adhoc Wireless Networks
(DAWN), BEAM DIVISION MULTIPLE ACCESS (BDMA), Cloud
Computing, cognitive radio technology and FBMC/OQAM.
This paper is organized as follows: First, we will give introduction
to 5G systems, present some goals and requirements of 5G. In the
next, basic differences between 4G and 5G are given, after we talk
about key technology innovations of 5G systems and finally we will
conclude in last Section.
Abstract: This paper presents an optimization method for
reducing the number of input channels and the complexity of the
feed-forward NARX neural network (NN) without compromising the
accuracy of the NN model. By utilizing the correlation analysis
method, the most significant regressors are selected to form the input
layer of the NN structure. An application of vehicle dynamic model
identification is also presented in this paper to demonstrate the
optimization technique and the optimal input layer structure and the
optimal number of neurons for the neural network is investigated.
Abstract: Some plants of genus Schinus have been used in the
folk medicine as topical antiseptic, digestive, purgative, diuretic,
analgesic or antidepressant, and also for respiratory and urinary
infections. Chemical composition of essential oils of S. molle and S.
terebinthifolius had been evaluated and presented high variability
according with the part of the plant studied and with the geographic
and climatic regions. The pharmacological properties, namely
antimicrobial, anti-tumoural and anti-inflammatory activities are
conditioned by chemical composition of essential oils. Taking into
account the difficulty to infer the pharmacological properties of
Schinus essential oils without hard experimental approach, this work
will focus on the development of a decision support system, in terms
of its knowledge representation and reasoning procedures, under a
formal framework based on Logic Programming, complemented with
an approach to computing centered on Artificial Neural Networks
and the respective Degree-of-Confidence that one has on such an
occurrence.
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: 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: 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.