Abstract: To tackle the air pollution issues, Plug-in Hybrid
Electric Vehicles (PHEVs) are proposed as an appropriate solution.
Charging a large amount of PHEV batteries, if not controlled, would
have negative impacts on the distribution system. The control process
of charging of these vehicles can be centralized in parking lots that
may provide a chance for better coordination than the individual
charging in houses. In this paper, an optimization-based approach is
proposed to determine the optimum PHEV parking capacities in
candidate nodes of the distribution system. In so doing, a profile for
charging and discharging of PHEVs is developed in order to flatten
the network load profile. Then, this profile is used in solving an
optimization problem to minimize the distribution system losses. The
outputs of the proposed method are the proper place for PHEV
parking lots and optimum capacity for each parking. The application
of the proposed method on the IEEE-34 node test feeder verifies the
effectiveness of the method.
Abstract: ANDASA is a knowledge management platform for
the capitalization of knowledge and cultural assets for the artistic and
cultural sectors. It was built based on the priorities expressed by the
participating artists. Through mapping artistic activities and
specificities, it enables to highlight various aspects of the artistic
research and production. Such instrument will contribute to create
networks and partnerships, as it enables to evidentiate who does
what, in what field, using which methodology. The platform is
accessible to network participants and to the general public.
Abstract: Residential block construction of big cities in China
began in the 1950s, and four models had far-reaching influence on
modern residential block in its development process, including unit
compound and residential district in 1950s to 1980s, and gated
community and open community in 1990s to now. Based on analysis
of the four models’ fabric, the article takes residential blocks in
Hangzhou west area as an example and carries on the studies from
urban structure level and block spacial level, mainly including urban
road network, land use, community function, road organization, public
space and building fabric. At last, the article puts forward “Semi-open
Sub-community” strategy to improve the current fabric.
Abstract: Most of the existing video streaming protocols
provide video services without considering security aspects in
decentralized mobile ad-hoc networks. The security policies adapted
to the currently existing non-streaming protocols, do not comply with
the live video streaming protocols resulting in considerable
vulnerability, high bandwidth consumption and unreliability which
cause severe security threats, low bandwidth and error prone
transmission respectively in video streaming applications. Therefore
a synergized methodology is required to reduce vulnerability and
bandwidth consumption, and enhance reliability in the video
streaming applications in MANET. To ensure the security measures
with reduced bandwidth consumption and improve reliability of the
video streaming applications, a Secure Low-bandwidth Video
Streaming through Reliable Multipath Propagation (SLVRMP)
protocol architecture has been proposed by incorporating the two
algorithms namely Secure Low-bandwidth Video Streaming
Algorithm and Reliable Secure Multipath Propagation Algorithm
using Layered Video Coding in non-overlapping zone routing
network topology. The performances of the proposed system are
compared to those of the other existing secure multipath protocols
Sec-MR, SPREAD using NS 2.34 and the simulation results show
that the performances of the proposed system get considerably
improved.
Abstract: Live video streaming is one of the most widely used
service among end users, yet it is a big challenge for the network
operators in terms of quality. The only way to provide excellent
Quality of Experience (QoE) to the end users is continuous
monitoring of live video streaming. For this purpose, there are several
objective algorithms available that monitor the quality of the video in
a live stream. Subjective tests play a very important role in fine
tuning the results of objective algorithms. As human perception is
considered to be the most reliable source for assessing the quality of a
video stream subjective tests are conducted in order to develop more
reliable objective algorithms. Temporal impairments in a live video
stream can have a negative impact on the end users. In this paper we
have conducted subjective evaluation tests on a set of video
sequences containing temporal impairment known as frame freezing.
Frame Freezing is considered as a transmission error as well as a
hardware error which can result in loss of video frames on the
reception side of a transmission system. In our subjective tests, we
have performed tests on videos that contain a single freezing event
and also for videos that contain multiple freezing events. We have
recorded our subjective test results for all the videos in order to give a
comparison on the available No Reference (NR) objective
algorithms. Finally, we have shown the performance of no reference
algorithms used for objective evaluation of videos and suggested the
algorithm that works better. The outcome of this study shows the
importance of QoE and its effect on human perception. The results
for the subjective evaluation can serve the purpose for validating
objective algorithms.
Abstract: In this paper, we consider a cognitive relay network
(CRN) in which the primary receiver (PR) is protected by peak
transmit power ¯PST and/or peak interference power Q constraints.
In addition, the interference effect from the primary transmitter (PT)
is considered to show its impact on the performance of the CRN. We
investigate the outage probability (OP) and outage capacity (OC) of
the CRN by deriving closed-form expressions over Rayleigh fading
channel. Results show that both the OP and OC improve by increasing
the cooperative relay nodes as well as when the PT is far away from
the SR.
Abstract: Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.
Abstract: DNA Barcode provides good sources of needed
information to classify living species. The classification problem has
to be supported with reliable methods and algorithms. To analyze
species regions or entire genomes, it becomes necessary to use the
similarity sequence methods. A large set of sequences can be
simultaneously compared using Multiple Sequence Alignment which
is known to be NP-complete. However, all the used methods are still
computationally very expensive and require significant computational
infrastructure. Our goal is to build predictive models that are highly
accurate and interpretable. In fact, our method permits to avoid the
complex problem of form and structure in different classes of
organisms. The empirical data and their classification performances
are compared with other methods. Evenly, in this study, we present
our system which is consisted of three phases. The first one, is called
transformation, is composed of three sub steps; Electron-Ion
Interaction Pseudopotential (EIIP) for the codification of DNA
Barcodes, Fourier Transform and Power Spectrum Signal Processing.
Moreover, the second phase step is an approximation; it is
empowered by the use of Multi Library Wavelet Neural Networks
(MLWNN). Finally, the third one, is called the classification of DNA
Barcodes, is realized by applying the algorithm of hierarchical
classification.
Abstract: The lifetime of a wireless sensor network can be
effectively increased by using scheduling operations. Once the
sensors are randomly deployed, the task at hand is to find the largest
number of disjoint sets of sensors such that every sensor set provides
complete coverage of the target area. At any instant, only one of these
disjoint sets is switched on, while all other are switched off. This
paper proposes a heuristic search method to find the maximum
number of disjoint sets that completely cover the region. A
population of randomly initialized members is made to explore the
solution space. A set of heuristics has been applied to guide the
members to a possible solution in their neighborhood. The heuristics
escalate the convergence of the algorithm. The best solution explored
by the population is recorded and is continuously updated. The
proposed algorithm has been tested for applications which require
sensing of multiple target points, referred to as point coverage
applications. Results show that the proposed algorithm outclasses the
existing algorithms. It always finds the optimum solution, and that
too by making fewer number of fitness function evaluations than the
existing approaches.
Abstract: Superabsorbent polymers received much attention and
are used in many fields because of their superior characters to
traditional absorbents, e.g., sponge and cotton. So, it is very
important but challenging to prepare highly and fast-swelling
superabsorbents. A reliable, efficient and low-cost technique for
removing heavy metal ions from wastewater is the adsorption using
bio-adsorbents obtained from biological materials, such as
polysaccharides-based hydrogels superabsorbents. In this study, novel multi-functional superabsorbent composites
type semi-interpenetrating polymer networks (Semi-IPNs) were
prepared via graft polymerization of acrylamide onto chitosan
backbone in presence of gelatin, CTS-g-PAAm/Ge, using potassium
persulfate and N,N’-methylene bisacrylamide as initiator and
crosslinker, respectively. These hydrogels were also partially
hydrolyzed to achieve superabsorbents with ampholytic properties
and uppermost swelling capacity. The formation of the grafted
network was evidenced by Fourier Transform Infrared Spectroscopy
(ATR-FTIR) and Thermogravimetric Analysis (TGA). The porous
structures were observed by Scanning Electron Microscope (SEM).
From TGA analysis, it was concluded that the incorporation of the Ge
in the CTS-g-PAAm network has marginally affected its thermal
stability. The effect of gelatin content on the swelling capacities of
these superabsorbent composites was examined in various media
(distilled water, saline and pH-solutions). The water absorbency was
enhanced by adding Ge in the network, where the optimum value was
reached at 2 wt. % of Ge. Their hydrolysis has not only greatly
optimized their absorption capacity but also improved the swelling
kinetic.These materials have also showed reswelling ability. We
believe that these super-absorbing materials would be very effective
for the adsorption of harmful metal ions from wastewater.
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: In this paper, influence of harmonics on medium
voltage distribution system of Bogazici Electricity Distribution Inc.
(BEDAS) which takes place at Istanbul/Turkey is investigated. A ring
network consisting of residential loads is taken into account for this
study. Real system parameters and measurement results are used for
simulations. Also, probable working conditions of the system are
analyzed for 50%, 75%, and 100% loading of transformers with
similar harmonic contents. Results of the study are exhibited the
influence of nonlinear loads on %THDV, P.F. and technical losses of
the medium voltage distribution system.
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: This study suggests the estimation method of stress
distribution for the beam structures based on TLS (Terrestrial Laser
Scanning). The main components of method are the creation of the
lattices of raw data from TLS to satisfy the suitable condition and
application of CSSI (Cubic Smoothing Spline Interpolation) for
estimating stress distribution. Estimation of stress distribution for the
structural member or the whole structure is one of the important
factors for safety evaluation of the structure. Existing sensors which
include ESG (Electric strain gauge) and LVDT (Linear Variable
Differential Transformer) can be categorized as contact type sensor
which should be installed on the structural members and also there are
various limitations such as the need of separate space where the
network cables are installed and the difficulty of access for sensor
installation in real buildings. To overcome these problems inherent in
the contact type sensors, TLS system of LiDAR (light detection and
ranging), which can measure the displacement of a target in a long
range without the influence of surrounding environment and also get
the whole shape of the structure, has been applied to the field of
structural health monitoring. The important characteristic of TLS
measuring is a formation of point clouds which has many points
including the local coordinate. Point clouds are not linear distribution
but dispersed shape. Thus, to analyze point clouds, the interpolation is
needed vitally. Through formation of averaged lattices and CSSI for
the raw data, the method which can estimate the displacement of
simple beam was developed. Also, the developed method can be
extended to calculate the strain and finally applicable to estimate a
stress distribution of a structural member. To verify the validity of the
method, the loading test on a simple beam was conducted and TLS
measured it. Through a comparison of the estimated stress and
reference stress, the validity of the method is confirmed.
Abstract: Neural activity in the human brain starts from the
early stages of prenatal development. This activity or signals
generated by the brain are electrical in nature and represent not only
the brain function but also the status of the whole body. At the
present moment, three methods can record functional and
physiological changes within the brain with high temporal resolution
of neuronal interactions at the network level: the
electroencephalogram (EEG), the magnet oencephalogram (MEG),
and functional magnetic resonance imaging (fMRI); each of these has
advantages and shortcomings. EEG recording with a large number of
electrodes is now feasible in clinical practice. Multichannel EEG
recorded from the scalp surface provides very valuable but indirect
information about the source distribution. However, deep electrode
measurements yield more reliable information about the source
locations intracranial recordings and scalp EEG are used with the
source imaging techniques to determine the locations and strengths of
the epileptic activity. As a source localization method, Low
Resolution Electro-Magnetic Tomography (LORETA) is solved for
the realistic geometry based on both forward methods, the Boundary
Element Method (BEM) and the Finite Difference Method (FDM). In
this paper, we review the findings EEG- LORETA about epilepsy.
Abstract: This study investigates the effects of the lead angle
and chip thickness variation on surface roughness during the
machining of compacted graphite iron using ceramic cutting tools
under dry cutting conditions. Analytical models were developed for
predicting the surface roughness values of the specimens after the
face milling process. Experimental data was collected and imported
to the artificial neural network model. A multilayer perceptron model
was used with the back propagation algorithm employing the input
parameters of lead angle, cutting speed and feed rate in connection
with chip thickness. Furthermore, analysis of variance was employed
to determine the effects of the cutting parameters on surface
roughness. Artificial neural network and regression analysis were
used to predict surface roughness. The values thus predicted were
compared with the collected experimental data, and the
corresponding percentage error was computed. Analysis results
revealed that the lead angle is the dominant factor affecting surface
roughness. Experimental results indicated an improvement in the
surface roughness value with decreasing lead angle value from 88° to
45°.
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: The phatic function of communication is a vital
element of any conversation. This research paper looks into this
function with respect to personal blogs maintained by Indian
bloggers. This paper is a study into the phenomenon of phatic
communication maintained by bloggers through their blogs. Based on
a linguistic analysis of the posts of twenty eight Indian bloggers,
writing in English, studied over a period of three years, the study
indicates that though the blogging phenomenon is not conversational
in the same manner as face-to-face communication, it does make
ample provision for feedback that is conversational in nature.
Ordinary day to day offline conversations use conventionalized
phatic utterances; those on the social media are in a perpetual mode
of innovation and experimentation in order to sustain contact with its
readers. These innovative methods and means are the focus of this
study. Though the personal blogger aims to chronicle his/her personal
life through the blog, the socializing function is crucial to these
bloggers. In comparison to the western personal blogs which focus on
the presentation of the ‘bounded individual self’, we find Indian
personal bloggers engage in the presentation of their ‘social selves’.
These bloggers yearn to reach out to the readers on the internet and
the phatic function serves to initiate, sustain and renew social ties on
the blogosphere thereby consolidating the social network of readers
and bloggers.
Abstract: Mobil Producing Nigeria Unlimited (MPNU), a
subsidiary of ExxonMobil and the highest crude oil & condensate
producer in Nigeria has its operational base and an oil terminal, the
Qua Iboe terminal (QIT) located at Ibeno, Nigeria. Other oil
companies like Network Exploration and Production Nigeria Ltd,
Frontier Oil Ltd; Shell Petroleum Development Company Ltd; Elf
Petroleum Nigeria Ltd and Nigerian Agip Energy, a subsidiary of the
Italian ENI E&P operate onshore, on the continental shelf and in deep
offshore of the Atlantic Ocean, respectively with the coastal waters of
Ibeno, Nigeria as the nearest shoreline. This study was designed to
delineate the oil-polluted sites in Ibeno, Nigeria using
microbiological and physico-chemical characterization of soils,
sediments and ground and surface water samples from the study area.
Results obtained revealed that there have been significant recent
hydrocarbon inputs into this environment as observed from the high
counts of hydrocarbonoclastic microorganisms in excess of 1% at all
the stations sampled. Moreover, high concentrations of THC, BTEX
and heavy metals contents in all the samples analyzed corroborate the
high recent crude oil input into the study area. The results also
showed that the pollution of the different environmental media
sampled were of varying degrees, following the trend: ground water
> surface water > sediments > soils.