Abstract: In this paper, we have proposed two novel plasmonic demultiplexing structures based on metal-insulator-metal surfaces which, beside their compact size, have a very good transmission spectrum. The impact of the key internal parameters on the transmission spectrum is numerically analyzed by using the twodimensional (2D) finite difference time domain (FDTD) method. The proposed structures could be used to develop ultra-compact photonic wavelength demultiplexing devices for large-scale photonic integration.
Abstract: Mobile IP has been developed to provide the
continuous information network access to mobile users. In IP-based
mobile networks, location management is an important component of
mobility management. This management enables the system to track
the location of mobile node between consecutive communications. It
includes two important tasks- location update and call delivery.
Location update is associated with signaling load. Frequent updates
lead to degradation in the overall performance of the network and the
underutilization of the resources. It is, therefore, required to devise
the mechanism to minimize the update rate. Mobile IPv6 (MIPv6)
and Hierarchical MIPv6 (HMIPv6) have been the potential
candidates for deployments in mobile IP networks for mobility
management. HMIPv6 through studies has been shown with better
performance as compared to MIPv6. It reduces the signaling
overhead traffic by making registration process local. In this paper,
we present performance analysis of MIPv6 and HMIPv6 using an
analytical model. Location update cost function is formulated based
on fluid flow mobility model. The impact of cell residence time, cell
residence probability and user-s mobility is investigated. Numerical
results are obtained and presented in graphical form. It is shown that
HMIPv6 outperforms MIPv6 for high mobility users only and for low
mobility users; performance of both the schemes is almost equivalent
to each other.
Abstract: In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.
Abstract: Small cracks or chips of a product appear very
frequently in the course of continuous production of an automatic
press process system. These phenomena become the cause of not only
defective product but also damage of a press mold. In order to solve
this problem AE system was introduced. AE system was expected to
be very effective to real time detection of the defective product and to
prevention of the damage of the press molds.
In this study, for pick and analysis of AE signals generated from the
press process, AE sensors/pre-amplifier/analysis and processing board
were used as frequently found in the other similar cases. For analysis
and processing the AE signals picked in real time from the good or bad
products, specialized software called cdm8 was used. As a result of
this work it was conformed that intensity and shape of the various AE
signals differ depending on the weight and thickness of metal sheet
and process type.
Abstract: In this paper, the details of an experimental method to measure the clamping force value at bolted connections due to application of wrenching torque to tighten the nut have been presented. A simplified bolted joint including a holed plate with a single bolt was considered to carry out the experiments. This method was designed based on Hooke-s law by measuring compressive axial strain of a steel bush placed between the nut and the plate. In the experimental procedure, the values of clamping force were calculated for seven different levels of applied torque, and this process was repeated three times for each level of the torque. Moreover, the effect of lubrication of threads on the clamping value was studied using the same method. In both conditions (dry and lubricated threads), relation between the torque and the clamping force have been displayed in graphs.
Abstract: A novel PDE solver using the multidimensional wave
digital filtering (MDWDF) technique to achieve the solution of a 2D
seismic wave system is presented. In essence, the continuous physical
system served by a linear Kirchhoff circuit is transformed to an
equivalent discrete dynamic system implemented by a MD wave
digital filtering (MDWDF) circuit. This amounts to numerically
approximating the differential equations used to describe elements of a
MD passive electronic circuit by a grid-based difference equations
implemented by the so-called state quantities within the passive
MDWDF circuit. So the digital model can track the wave field on a
dense 3D grid of points. Details about how to transform the continuous
system into a desired discrete passive system are addressed. In
addition, initial and boundary conditions are properly embedded into
the MDWDF circuit in terms of state quantities. Graphic results have
clearly demonstrated some physical effects of seismic wave (P-wave
and S–wave) propagation including radiation, reflection, and
refraction from and across the hard boundaries. Comparison between
the MDWDF technique and the finite difference time domain (FDTD)
approach is also made in terms of the computational efficiency.
Abstract: Long term rainfall analysis and prediction is a
challenging task especially in the modern world where the impact of
global warming is creating complications in environmental issues.
These factors which are data intensive require high performance
computational modeling for accurate prediction. This research paper
describes a prototype which is designed and developed on grid
environment using a number of coupled software infrastructural
building blocks. This grid enabled system provides the demanding
computational power, efficiency, resources, user-friendly interface,
secured job submission and high throughput. The results obtained
using sequential execution and grid enabled execution shows that
computational performance has enhanced among 36% to 75%, for
decade of climate parameters. Large variation in performance can be
attributed to varying degree of computational resources available for
job execution.
Grid Computing enables the dynamic runtime selection, sharing
and aggregation of distributed and autonomous resources which plays
an important role not only in business, but also in scientific
implications and social surroundings. This research paper attempts to
explore the grid enabled computing capabilities on weather indices
from HOAPS data for climate impact modeling and change
detection.
Abstract: Enzymatic saccharification of biomass for reducing
sugar production is one of the crucial processes in biofuel production
through biochemical conversion. In this study, enzymatic
saccharification of dilute potassium hydroxide (KOH) pre-treated
Tetraselmis suecica biomass was carried out by using cellulase
enzyme obtained from Trichoderma longibrachiatum. Initially, the
pre-treatment conditions were optimised by changing alkali reagent
concentration, retention time for reaction, and temperature. The T.
suecica biomass after pre-treatment was also characterized using
Fourier Transform Infrared Spectra and Scanning Electron
Microscope. These analyses revealed that the functional group such
as acetyl and hydroxyl groups, structure and surface of T. suecica
biomass were changed through pre-treatment, which is favourable for
enzymatic saccharification process. Comparison of enzymatic
saccharification of untreated and pre-treated microalgal biomass
indicated that higher level of reducing sugar can be obtained from
pre-treated T. suecica. Enzymatic saccharification of pre-treated T.
suecica biomass was optimised by changing temperature, pH, and
enzyme concentration to solid ratio ([E]/[S]). Highest conversion of
carbohydrate into reducing sugar of 95% amounted to reducing sugar
yield of 20 (wt%) from pre-treated T. suecica was obtained from
saccharification, at temperature: 40°C, pH: 4.5 and [E]/[S] of 0.1
after 72 h of incubation. Hydrolysate obtained from enzymatic
saccharification of pretreated T. suecica biomass was further
fermented into biobutanol using Clostridium saccharoperbutyliticum
as biocatalyst. The results from this study demonstrate a positive
prospect of application of dilute alkaline pre-treatment to enhance
enzymatic saccharification and biobutanol production from
microalgal biomass.
Abstract: Extraction of lactic acid from aqueous solution using n-butanol as an extractant was studied. Effect of mixing time, pH of the aqueous solution, initial lactic acid concentration, and volume ratio between the organic and the aqueous phase were investigated. Distribution coefficient and degree of lactic acid extraction was found to increase when the pH of aqueous solution was decreased. The pH Effect was substantially pronounced at pH of the aqueous solution less than 1. Initial lactic acid concentration and organic-toaqueous volume ratio appeared to have positive effect on the distribution coefficient and the degree of extraction. Due to the nature of n-butanol that is partially miscible in water, incorporation of aqueous solution into organic phase was observed in the extraction with large organic-to-aqueous volume ratio.
Abstract: In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
Abstract: With the advent of inexpensive 32 bit floating point digital signal processor-s availability in market, many computationally intensive algorithms such as Kalman filter becomes feasible to implement in real time. Dynamic simulation of a self excited DC motor using second order state variable model and implementation of Kalman Filter in a floating point DSP TMS320C6713 is presented in this paper with an objective to introduce and implement such an algorithm, for beginners. A fractional hp DC motor is simulated in both Matlab® and DSP and the results are included. A step by step approach for simulation of DC motor in Matlab® and “C" routines in CC Studio® is also given. CC studio® project file details and environmental setting requirements are addressed. This tutorial can be used with 6713 DSK, which is based on floating point DSP and CC Studio either in hardware mode or in simulation mode.
Abstract: This purpose of this paper is to develop and validate a
model to accurately predict the cell temperature of a PV module that
adapts to various mounting configurations, mounting locations, and
climates while only requiring readily available data from the module
manufacturer. Results from this model are also compared to results
from published cell temperature models. The models were used to
predict real-time performance from a PV water pumping systems in
the desert of Medenine, south of Tunisia using 60-min intervals of
measured performance data during one complete year. Statistical
analysis of the predicted results and measured data highlight possible
sources of errors and the limitations and/or adequacy of existing
models, to describe the temperature and efficiency of PV-cells and
consequently, the accuracy of performance of PV water pumping
systems prediction models.
Abstract: Web-based technologies have created numerous
opportunities for electronic word-of-mouth (eWOM) communication.
There are many factors that affect customer adoption and decisionmaking
process. However, only a few researches focus on some
factors such as the membership time of forum and propensity to trust.
Using a discrete-time event simulation to simulate a diffusion model
along with a consumer decision model, the study shows the effect of
each factor on adoption of opinions on on-line discussion forum. The
purpose of this study is to examine the effect of factor affecting
information adoption and decision making process. The model is
constructed to test quantitative aspects of each factor. The simulation
study shows the membership time and the propensity to trust has an
effect on information adoption and purchasing decision. The result of
simulation shows that the longer the membership time in the
communities and the higher propensity to trust could lead to the
higher demand rates because consumers find it easier and faster to
trust the person in the community and then adopt the eWOM. Other
implications for both researchers and practitioners are provided.
Abstract: These days people love to travel around the world.
Regardless of their location and time, they especially Muslims still
need to perform their prayers. Normally for travelers, they need to
bring maps, compass and for Muslim, they even have to bring Qibla
pointer when they travel. It is slightly difficult to determine the Qibla
direction and to know the time for each prayer. As the technology
grows, many PDA equip with maps and GPS to locate their location.
In this paper we present a new electronic device called Mobile Qibla
and Prayer Time Finder to locate the Qibla direction and to
determine each prayer time based on the current user-s location using
PDA. This device use PIC microcontroller equipped with digital
compass where it will communicate with PDA using Bluetooth
technology and display the exact Qibla direction and prayer time
automatically at any place in the world. This device is reliable and
accurate in determining the Qibla direction and prayer time.
Abstract: Previous the 3D model texture generation from multi-view images and mapping algorithms has issues in the texture chart generation which are the self-intersection and the concentration of the texture in texture space. Also we may suffer from some problems due to the occluded areas, such as inside parts of thighs. In this paper we propose a texture mapping technique for 3D models using multi-view images on the GPU. We do texture mapping directly on the GPU fragment shader per pixel without generation of the texture map. And we solve for the occluded area using the 3D model depth information. Our method needs more calculation on the GPU than previous works, but it has shown real-time performance and previously mentioned problems do not occur.
Abstract: A ten-year grazing study was conducted at the
Agriculture and Agri-Food Canada Brandon Research Centre in
Manitoba to study the effect of alfalfa inclusion and fertilizer (N, P,
K, and S) addition on economics and efficiency of non-renewable
energy use in meadow brome grass-based pasture systems for beef
production. Fertilizing grass-only or alfalfa-grass pastures to full soil
test recommendations improved pasture productivity, but did not
improve profitability compared to unfertilized pastures. Fertilizing
grass-only pastures resulted in the highest net loss of any pasture
management strategy in this study. Adding alfalfa at the time of
seeding, with no added fertilizer, was economically the best pasture
improvement strategy in this study. Because of moisture limitations,
adding commercial fertilizer to full soil test recommendations is
probably not economically justifiable in most years, especially with
the rising cost of fertilizer. Improving grass-only pastures by adding
fertilizer and/or alfalfa required additional non-renewable energy
inputs; however, the additional energy required for unfertilized
alfalfa-grass pastures was minimal compared to the fertilized
pastures. Of the four pasture management strategies, adding alfalfa
to grass pastures without adding fertilizer had the highest efficiency
of energy use. Based on energy use and economic performance, the
unfertilized alfalfa-grass pasture was the most efficient and
sustainable pasture system.
Abstract: Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, the given image may
contain a number of distinct gray levels with narrow difference with
their neighborhood pixels. If the gray levels of the pixels in an image
and their neighbors are mapped in such a way that the difference in
the gray levels of the neighbors with the pixel is minimum, then
compression ratio as well as the convergence of the network can be
improved. To achieve this, a Cumulative distribution function is
estimated for the image and it is used to map the image pixels. When
the mapped image pixels are used, the Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.
Abstract: Urban disaster risks and vulnerabilities are great problems for Turkey. The annual loss of life and property through disaster in the world-s major metropolitan areas is increasing. Urban concentrations of the poor and less-informed in environmentally fragile locations suffer the impact of disaster disproportionately. Gecekondu (squatter) developments will compound the inherent risks associated with high-density environments, in appropriate technologies, and inadequate infrastructure. On the other hand, there are many geological disadvantages such as sitting on top of active tectonic plate boundaries, and why having avalanche, flood, and landslide and drought prone areas in Turkey. However, this natural formation is inevitable; the only way to survive in such a harsh geography is to be aware of importance of these natural events and to take political and physical measures. The main aim of this research is to bring up the magnitude of natural hazard risks in Izmir built-up zone, not being taken into consideration adequately. Because the dimensions of the peril are not taken seriously enough, the natural hazard risks, which are commonly well known, are not considered important or they are being forgotten after some time passes. Within this research, the magnitude of natural hazard risks for Izmir is being presented in the scope of concrete and local researches over Izmir risky areas.
Abstract: In this paper, the dynamics of a system of two van der Pol oscillators with delayed position and velocity is studied. We provide an approximate solution for this system using parameterexpansion method. Also, we obtain approximate values for frequencies of the system. The parameter-expansion method is more efficient than the perturbation method for this system because the method is independent of perturbation parameter assumption.
Abstract: Despite the recent surge of research in control of
worm propagation, currently, there is no effective defense system
against such cyber attacks. We first design a distributed detection
architecture called Detection via Distributed Blackholes (DDBH).
Our novel detection mechanism could be implemented via virtual
honeypots or honeynets. Simulation results show that a worm can be
detected with virtual honeypots on only 3% of the nodes. Moreover,
the worm is detected when less than 1.5% of the nodes are infected.
We then develop two control strategies: (1) optimal dynamic trafficblocking,
for which we determine the condition that guarantees
minimum number of removed nodes when the worm is contained and
(2) predictive dynamic traffic-blocking–a realistic deployment of
the optimal strategy on scale-free graphs. The predictive dynamic
traffic-blocking, coupled with the DDBH, ensures that more than
40% of the network is unaffected by the propagation at the time
when the worm is contained.