Abstract: In this paper we consider the issue of distributed adaptive estimation over sensor networks. To deal with more realistic scenario, different variance for observation noise is assumed for sensors in the network. To solve the problem of different variance of observation noise, the proposed method is divided into two phases: I) Estimating each sensor-s observation noise variance and II) using the estimated variances to obtain the desired parameter. Our proposed algorithm is based on a diffusion least mean square (LMS) implementation with linear combiner model. In the proposed algorithm, the step-size parameter the coefficients of linear combiner are adjusted according to estimated observation noise variances. As the simulation results show, the proposed algorithm considerably improves the diffusion LMS algorithm given in literature.
Abstract: This study was conducted to investigate the incidence
of pathogenic bacteria: Salmonella, Shigella, Escherichia coli O157
and Staphylococcus aureus in cakes and tarts collected from thirtyfive
confectionery producing and selling premises located within
Tripoli city, Libya. The results revealed an incidence of S. aureus
with 94.4 and 48.0 %, E. coli O157 with 14.7 and 4.0 % and Salmonella
sp. with 5.9 and 8.0 % in cakes and tarts samples respectively;
while Shigella was not detected in all samples. In order to determine
the source of these pathogenic bacteria, cotton swabs were taken
from the hands of workers on the production line, the surfaces of
preparation tables and cream whipping instruments. The results
showed that the cotton swabs obtained from the hands of workers
contained S. aureus and Salmonella sp. with an incidence of 42.9 and
2.9 %, the cotton swabs obtained from the surfaces of preparation
tables 22.9 and 2.9 % and the cotton swabs obtained from the cream
whipping instruments 14.3 and 0.0 % respectively; while E. coli
O157 and Shigella sp. were not detected in all swabs. Additionally,
other bacteria were isolated from the hands of workers and the Surfaces
of producing equipments included: Aeromonas sp., Pseudomonas
sp., E. coli, Klebsiella sp., Enterobacter sp., Citrobacter sp.,
Proteus sp., Serratia sp. and Acinetobacter sp. These results indicate
that some of the cakes and tarts might pose threat to consumer's
health. Meanwhile, occurrences of pathogenic bacteria on the hands
of those who are working in production line and the surfaces of
equipments reflect poor hygienic practices at most confectionery
premises examined in this study. Thus, firm and continuous surveillance
of these premises is needed to insure the consumer's health and
safety.
Abstract: In working mode some unexpected changes could
be arise in inner structure of electromagnetic device. They
influence modification in electromagnetic field propagation map.
The field values at an observed boundary are also changed. The
development of the process has to be watched because the arising
structural changes would provoke the device to be gone out later.
The probabilistic assessment of the state is possible to be made.
The numerical assessment points if the resulting changes have
only accidental character or they are due to the essential inner
structural disturbances.
The presented application example is referring to the 200MW
turbine-generator. A part of the stator core end teeth zone is
simulated broken. Quasi three-dimensional electromagnetic and
temperature field are solved applying FEM. The stator core state
diagnosis is proposed to be solved as an identification problem on
the basis of a statistical criterion.
Abstract: An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).
Abstract: Mobile IPv6 (MIPv6) describes how mobile node can change its point of attachment from one access router to another. As a demand for wireless mobile devices increases, many enhancements for macro-mobility (inter-domain) protocols have been proposed, designed and implemented in Mobile IPv6. Hierarchical Mobile IPv6 (HMIPv6) is one of them that is designed to reduce the amount of signaling required and to improve handover speed for mobile connections. This is achieved by introducing a new network entity called Mobility Anchor Point (MAP). This report presents a comparative study of the Hierarchical Mobility IPv6 and Mobile IPv6 protocols and we have narrowed down the scope to micro-mobility (intra-domain). The architecture and operation of each protocol is studied and they are evaluated based on the Quality of Service (QoS) parameter; handover latency. The simulation was carried out by using the Network Simulator-2. The outcome from this simulation has been discussed. From the results, it shows that, HMIPv6 performs best under intra-domain mobility compared to MIPv6. The MIPv6 suffers large handover latency. As enhancement we proposed to HMIPv6 to locate the MAP to be in the middle of the domain with respect to all Access Routers. That gives approximately same distance between MAP and Mobile Node (MN) regardless of the new location of MN, and possible shorter distance. This will reduce the delay since the distance is shorter. As a future work performance analysis is to be carried for the proposed HMIPv6 and compared to HMIPv6.
Abstract: The Multi-Layered Perceptron (MLP) Neural
networks have been very successful in a number of signal processing
applications. In this work we have studied the possibilities and the
met difficulties in the application of the MLP neural networks for the
prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in
term of the statistical indicators, with a linear model most used in
literature, is also performed, and the obtained results show that the
neural networks are more efficient and gave the best results.
Abstract: Applicability of tuning the controller gains for Stewart manipulator using genetic algorithm as an efficient search technique is investigated. Kinematics and dynamics models were introduced in detail for simulation purpose. A PD task space control scheme was used. For demonstrating technique feasibility, a Stewart manipulator numerical-model was built. A genetic algorithm was then employed to search for optimal controller gains. The controller was tested onsite a generic circular mission. The simulation results show that the technique is highly convergent with superior performance operating for different payloads.
Abstract: Nowadays, the challenge in hydraulic turbine design is
the multi-objective design of turbine runner to reach higher
efficiency. The hydraulic performance of a turbine is strictly depends
on runner blades shape. The present paper focuses on the application
of the multi-objective optimization algorithm to the design of a small
Francis turbine runner. The optimization exercise focuses on the
efficiency improvement at the best efficiency operating point (BEP)
of the GAMM Francis turbine. A global optimization method based
on artificial neural networks (ANN) and genetic algorithms (GA)
coupled by 3D Navier-Stokes flow solver has been used to improve
the performance of an initial geometry of a Francis runner. The
results show the good ability of optimization algorithm and the final
geometry has better efficiency with initial geometry. The goal was to
optimize the geometry of the blades of GAMM turbine runner which
leads to maximum total efficiency by changing the design parameters
of camber line in at least 5 sections of a blade. The efficiency of the
optimized geometry is improved from 90.7% to 92.5%. Finally,
design parameters and the way of selection have been considered and
discussed.
Abstract: The speech signal conveys information about the
identity of the speaker. The area of speaker identification is
concerned with extracting the identity of the person speaking the
utterance. As speech interaction with computers becomes more
pervasive in activities such as the telephone, financial transactions
and information retrieval from speech databases, the utility of
automatically identifying a speaker is based solely on vocal
characteristic. This paper emphasizes on text dependent speaker
identification, which deals with detecting a particular speaker from a
known population. The system prompts the user to provide speech
utterance. System identifies the user by comparing the codebook of
speech utterance with those of the stored in the database and lists,
which contain the most likely speakers, could have given that speech
utterance. The speech signal is recorded for N speakers further the
features are extracted. Feature extraction is done by means of LPC
coefficients, calculating AMDF, and DFT. The neural network is
trained by applying these features as input parameters. The features
are stored in templates for further comparison. The features for the
speaker who has to be identified are extracted and compared with the
stored templates using Back Propogation Algorithm. Here, the
trained network corresponds to the output; the input is the extracted
features of the speaker to be identified. The network does the weight
adjustment and the best match is found to identify the speaker. The
number of epochs required to get the target decides the network
performance.
Abstract: Traffic Management and Information Systems, which rely on a system of sensors, aim to describe in real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art focuses on data analysis, little is done in the sense of prediction. In this paper, we describe a machine learning system for traffic flow management and control for a prediction of traffic flow problem. This new algorithm is obtained by combining Random Forests algorithm into Adaboost algorithm as a weak learner. We show that our algorithm performs relatively well on real data, and enables, according to the Traffic Flow Evaluation model, to estimate and predict whether there is congestion or not at a given time on road intersections.
Abstract: In this paper, we propose an energy efficient cluster
based communication protocol for wireless sensor network. Our
protocol considers both the residual energy of sensor nodes and the
distance of each node from the BS when selecting cluster-head. This
protocol can successfully prolong the network-s lifetime by 1)
reducing the total energy dissipation on the network and 2) evenly
distributing energy consumption over all sensor nodes. In this
protocol, the nodes with more energy and less distance from the BS
are probable to be selected as cluster-head. Simulation results with
MATLAB show that proposed protocol could increase the lifetime of
network more than 94% for first node die (FND), and more than 6%
for the half of the nodes alive (HNA) factor as compared with
conventional protocols.
Abstract: Utilities use operating reserve for frequency regulation.To ensure that the operating frequency and system security are well maintained, the operating grid codes always specify that the reserve quantity and response rate should meet some prescribed levels. This paper proposes a methodology to evaluate system's contingency reserve for an isolated power network. With the presented algorithm to estimate system's frequency response characteristic, an online allocation of contingency reserve would be feasible to meet the grid codes for contingency operation. Test results from the simulated conditions, and from the actual operating data verify the merits of the proposed methodology to system's frequency control, and security.
Abstract: Investment in a constructed facility represents a cost in
the short term that returns benefits only over the long term use of the
facility. Thus, the costs occur earlier than the benefits, and the owners
of facilities must obtain the capital resources to finance the costs of
construction. A project cannot proceed without an adequate
financing, and the cost of providing an adequate financing can be
quite large. For these reasons, the attention to the project finance is an
important aspect of project management. Finance is also a concern to
the other organizations involved in a project such as the general
contractor and material suppliers. Unless an owner immediately and
completely covers the costs incurred by each participant, these
organizations face financing problems of their own. At a more
general level, the project finance is the only one aspect of the general
problem of corporate finance. If numerous projects are considered
and financed together, then the net cash flow requirements constitute
the corporate financing problem for capital investment. Whether
project finance is performed at the project or at the corporate level
does not alter the basic financing problem .In this paper, we will first
consider facility financing from the owner's perspective, with due
consideration for its interaction with other organizations involved in a
project. Later, we discuss the problems of construction financing
which are crucial to the profitability and solvency of construction
contractors. The objective of this paper is to present the steps utilized
to determine the best combination of minimum project financing.
The proposed model considers financing; schedule and maximum net
area .The proposed model is called Project Financing and Schedule
Integration using Genetic Algorithms "PFSIGA". This model
intended to determine more steps (maximum net area) for any project
with a subproject. An illustrative example will demonstrate the
feature of this technique. The model verification and testing are put
into consideration.
Abstract: Today, the Internet based communication has widen
the opportunity of event monitoring system in the medical field.
There is always a need of analyzing and designing secure and reliable
mobile communication between the hospital and biomedical
engineers mobile units. This study has been carried out to find
possible solution using SIP-based event notification for alerting the
technical staff about the Biomedical Device (BMD) status and
Patients treatment session. The Session Initiation Protocol (SIP) can
be used to create a medical event notification system. SIP can work
on a variety of devices. Its adoption as the protocol of choice for third
generation wireless networks allows for a robust and scalable
environment. One of the advantages of SIP is that it supports personal
mobility through the separation of user addressing and device
addressing. The solution for Telemed alert notification system is
based on SIP - Specific Event Notification. The aim of this project is
to extend mobility service to the hospital technicians who are using
Telemedicine system.
Abstract: The technical realization of data transmission using
glass fiber began after the development of diode laser in year 1962.
The erbium doped fiber amplifiers (EDFA's) in high speed networks
allow information to be transmitted over longer distances without
using of signal amplification repeaters. These kinds of fibers are
doped with erbium atoms which have energy levels in its atomic
structure for amplifying light at 1550nm. When a carried signal wave
at 1550nm enters the erbium fiber, the light stimulates the excited
erbium atoms which pumped with laser beam at 980nm as additional
light. The wavelength and intensity of the semiconductor lasers
depend on the temperature of active zone and the injection current.
The present paper shows the effect of the diode lasers temperature
and injection current on the optical amplification. From the results of
in- and output power one may calculate the max. optical gain by
erbium doped fiber amplifier.
Abstract: The aim of this paper is to present a methodology in
three steps to forecast supply chain demand. In first step, various data
mining techniques are applied in order to prepare data for entering
into forecasting models. In second step, the modeling step, an
artificial neural network and support vector machine is presented
after defining Mean Absolute Percentage Error index for measuring
error. The structure of artificial neural network is selected based on
previous researchers' results and in this article the accuracy of
network is increased by using sensitivity analysis. The best forecast
for classical forecasting methods (Moving Average, Exponential
Smoothing, and Exponential Smoothing with Trend) is resulted based
on prepared data and this forecast is compared with result of support
vector machine and proposed artificial neural network. The results
show that artificial neural network can forecast more precisely in
comparison with other methods. Finally, forecasting methods'
stability is analyzed by using raw data and even the effectiveness of
clustering analysis is measured.
Abstract: The turbulent mixing of coolant streams of different
temperature and density can cause severe temperature fluctuations in
piping systems in nuclear reactors. In certain periodic contraction
cycles these conditions lead to thermal fatigue. The resulting aging
effect prompts investigation in how the mixing of flows over a sharp
temperature/density interface evolves. To study the fundamental
turbulent mixing phenomena in the presence of density gradients,
isokinetic (shear-free) mixing experiments are performed in a square
channel with Reynolds numbers ranging from 2-500 to 60-000.
Sucrose is used to create the density difference. A Wire Mesh Sensor
(WMS) is used to determine the concentration map of the flow in the
cross section. The mean interface width as a function of velocity,
density difference and distance from the mixing point are analyzed
based on traditional methods chosen for the purposes of
atmospheric/oceanic stratification analyses. A definition of the
mixing layer thickness more appropriate to thermal fatigue and based
on mixedness is devised. This definition shows that the thermal
fatigue risk assessed using simple mixing layer growth can be
misleading and why an approach that separates the effects of large
scale (turbulent) and small scale (molecular) mixing is necessary.
Abstract: Mobile agent has motivated the creation of a new
methodology for parallel computing. We introduce a methodology
for the creation of parallel applications on the network. The proposed
Mobile-Agent parallel processing framework uses multiple Javamobile
Agents. Each mobile agent can travel to the specified
machine in the network to perform its tasks. We also introduce the
concept of master agent, which is Java object capable of
implementing a particular task of the target application. Master agent
is dynamically assigns the task to mobile agents. We have developed
and tested a prototype application: Mobile Agent Based Parallel
Computing. Boosted by the inherited benefits of using Java and
Mobile Agents, our proposed methodology breaks the barriers
between the environments, and could potentially exploit in a parallel
manner all the available computational resources on the network.
This paper elaborates performance issues of a mobile agent for
parallel computing.
Abstract: Due to the increasing and varying risks that economic units face with, derivative instruments gain substantial importance, and trading volumes of derivatives have reached very significant level. Parallel with these high trading volumes, researchers have developed many different models. Some are parametric, some are nonparametric. In this study, the aim is to analyse the success of artificial neural network in pricing of options with S&P 100 index options data. Generally, the previous studies cover the data of European type call options. This study includes not only European call option but also American call and put options and European put options. Three data sets are used to perform three different ANN models. One only includes data that are directly observed from the economic environment, i.e. strike price, spot price, interest rate, maturity, type of the contract. The others include an extra input that is not an observable data but a parameter, i.e. volatility. With these detail data, the performance of ANN in put/call dimension, American/European dimension, moneyness dimension is analyzed and whether the contribution of the volatility in neural network analysis make improvement in prediction performance or not is examined. The most striking results revealed by the study is that ANN shows better performance when pricing call options compared to put options; and the use of volatility parameter as an input does not improve the performance.
Abstract: Saudi Arabia is an arid country which depends on
costly desalination plants to satisfy the growing residential water
demand. Prediction of water demand is usually a challenging task
because the forecast model should consider variations in economic
progress, climate conditions and population growth. The task is
further complicated knowing that Mecca city is visited regularly by
large numbers during specific months in the year due to religious
occasions. In this paper, a neural networks model is proposed to
handle the prediction of the monthly and yearly water demand for
Mecca city, Saudi Arabia. The proposed model will be developed
based on historic records of water production and estimated visitors-
distribution. The driving variables for the model include annuallyvarying
variables such as household income, household density, and
city population, and monthly-varying variables such as expected
number of visitors each month and maximum monthly temperature.