Abstract: Recently, in some places, optical-fibre access
networks have been used with GPON technology belonging to
organizations (in most cases public bodies) that act as neutral
operators. These operators simultaneously provide network services
to various telecommunications operators that offer integrated voice,
data and television services. This situation creates new problems
related to quality of service, since the interests of the users are
intermingled with the interests of the operators. In this paper, we
analyse this problem and consider solutions that make it possible to
provide guaranteed quality of service for voice over IP, data services
and interactive digital television.
Abstract: This paper covered a series of key points in terms of 2D to 3D stereoscopic conversion. A successfully applied stereoscopic conversion approach in current visual effects industry was presented. The purpose of this paper is to cover a detailed workflow and concept, which has been successfully used in 3D stereoscopic conversion for feature films in visual effects industry, and therefore to clarify the process in stereoscopic conversion production and provide a clear idea for those entry-level artists to improve an overall understanding of 3D stereoscopic in digital compositing field as well as to the higher education factor of visual effects and hopefully inspire further collaboration and participants particularly between academia and industry.
Abstract: The ElectroEncephaloGram (EEG) is useful for
clinical diagnosis and biomedical research. EEG signals often
contain strong ElectroOculoGram (EOG) artifacts produced
by eye movements and eye blinks especially in EEG recorded
from frontal channels. These artifacts obscure the underlying
brain activity, making its visual or automated inspection
difficult. The goal of ocular artifact removal is to remove
ocular artifacts from the recorded EEG, leaving the underlying
background signals due to brain activity. In recent times,
Independent Component Analysis (ICA) algorithms have
demonstrated superior potential in obtaining the least
dependent source components. In this paper, the independent
components are obtained by using the JADE algorithm (best
separating algorithm) and are classified into either artifact
component or neural component. Neural Network is used for
the classification of the obtained independent components.
Neural Network requires input features that exactly represent
the true character of the input signals so that the neural
network could classify the signals based on those key
characters that differentiate between various signals. In this
work, Auto Regressive (AR) coefficients are used as the input
features for classification. Two neural network approaches
are used to learn classification rules from EEG data. First, a
Polynomial Neural Network (PNN) trained by GMDH (Group
Method of Data Handling) algorithm is used and secondly,
feed-forward neural network classifier trained by a standard
back-propagation algorithm is used for classification and the
results show that JADE-FNN performs better than JADEPNN.
Abstract: Certifications such as the Passive House Standard aim to reduce the final space heating energy demand of residential buildings. Space conditioning, notably heating, is responsible for nearly 70% of final residential energy consumption in Europe. There is therefore significant scope for the reduction of energy consumption through improvements to the energy efficiency of residential buildings. However, these certifications totally overlook the energy embodied in the building materials used to achieve this greater operational energy efficiency. The large amount of insulation and the triple-glazed high efficiency windows require a significant amount of energy to manufacture. While some previous studies have assessed the life cycle energy demand of passive houses, including their embodied energy, these rely on incomplete assessment techniques which greatly underestimate embodied energy and can lead to misleading conclusions. This paper analyses the embodied and operational energy demands of a case study passive house using a comprehensive hybrid analysis technique to quantify embodied energy. Results show that the embodied energy is much more significant than previously thought. Also, compared to a standard house with the same geometry, structure, finishes and number of people, a passive house can use more energy over 80 years, mainly due to the additional materials required. Current building energy efficiency certifications should widen their system boundaries to include embodied energy in order to reduce the life cycle energy demand of residential buildings.
Abstract: In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).
Abstract: This paper proposes the application of a hierarchical fuzzy system (HFS) based on multi-input power system stabilizer (MPSS) and also Static Var Compensator (SVC) in multi-machine environment.The number of rules grows exponentially with the number of variables in a conventional fuzzy logic system. The proposed HFS method is developed to solve this problem. To reduce the number of rules the HFS consists of a number of low-dimensional fuzzy systems in a hierarchical structure. In fact, by using HFS the total number of involved rules increases only linearly with the number of input variables. In the MPSS, to have better efficiency an auxiliary signal of reactive power deviation (ΔQ) is added with ΔP+ Δω input type Power system stabilizer (PSS). Phasor model of SVC is described and used in this paper. The performances of MPSS, Conventional power system stabilizer (CPSS), hierarchical Fuzzy Multi-input Power System Stabilizer (HFMPSS) and the proposed method in damping inter-area mode of oscillation are examined in response to disturbances. By using digital simulations the comparative study is illustrated. It can be seen that the proposed PSS is performing satisfactorily within the whole range of disturbances.
Abstract: RF performance of SOI CMOS device has attracted
significant amount of interest recently. In order to improve RF
parameters, Strained Si/Relaxed Si0.8Ge0.2 investigated as a
replacement for Si technology .Enhancement of carrier mobility
associated with strain engineering makes Strained Si a promising
candidate for improving RF performance of CMOS technology.
From the simulation, the cut-off frequency is estimated to be 224
GHZ, whereas in SOI at similar bias is about 188 GHZ. Therefore,
Strained Si exhibits 19% improvement in cut-off frequency over
similar Si counterpart. In this paper, Ion/Ioff ratio is studied as one of
the key parameters in logic and digital application. Strained Si/SiGe
demonstrates better Ion/Ioff characteristic than SOI, in similar channel
length of 100 nm.Another important key analog figures of merit such
as Early Voltage (VEA) ,transconductance vs drain current (gm /Ids)
are studied. They introduce the efficiency of the devices to convert
dc power into ac frequency.
Abstract: Single-pole switching scheme is widely used in the
Extra High Voltage system. However, the substantial negativesequence
current injected to the turbine-generators imposes the
electromagnetic (E/M) torque of double system- frequency
components during the dead time (between single-pole clearing and
line reclosing). This would induce supersynchronous resonance
(SPSR) torque amplifications on low pressure turbine generator
blades and even lead to fatigue damage. This paper proposes the
design of a mechanical filter (MF) with natural frequency close to
double-system frequency. From the simulation results, it is found that
such a filter not only successfully damps the resonant effect, but also
has the characteristics of feasibility and compact.
Abstract: The modern telecommunication industry demands
higher capacity networks with high data rate. Orthogonal frequency
division multiplexing (OFDM) is a promising technique for high data
rate wireless communications at reasonable complexity in wireless
channels. OFDM has been adopted for many types of wireless
systems like wireless local area networks such as IEEE 802.11a, and
digital audio/video broadcasting (DAB/DVB). The proposed research
focuses on a concatenated coding scheme that improve the
performance of OFDM based wireless communications. It uses a
Redundant Residue Number System (RRNS) code as the outer code
and a convolutional code as the inner code. Here, a direct conversion
of analog signal to residue domain is done to reduce the conversion
complexity using sigma-delta based parallel analog-to-residue
converter. The bit error rate (BER) performances of the proposed
system under different channel conditions are investigated. These
include the effect of additive white Gaussian noise (AWGN),
multipath delay spread, peak power clipping and frame start
synchronization error. The simulation results show that the proposed
RRNS-Convolutional concatenated coding (RCCC) scheme provides
significant improvement in the system performance by exploiting the
inherent properties of RRNS.
Abstract: Selection of maize (Zea mays) hybrids with wide adaptability across diverse farming environments is important, prior to recommending them to achieve a high rate of hybrid adoption. Grain yield of 14 maize hybrids, tested in a randomized completeblock design with four replicates across 22 environments in Iran, was analyzed using site regression (SREG) stability model. The biplot technique facilitates a visual evaluation of superior genotypes, which is useful for cultivar recommendation and mega-environment identification. The objectives of this study were (i) identification of suitable hybrids with both high mean performance and high stability (ii) to determine mega-environments for maize production in Iran. Biplot analysis identifies two mega-environments in this study. The first mega-environments included KRM, KSH, MGN, DZF A, KRJ, DRB, DZF B, SHZ B, and KHM, where G10 hybrid was the best performing hybrid. The second mega-environment included ESF B, ESF A, and SHZ A, where G4 hybrid was the best hybrid. According to the ideal-hybrid biplot, G10 hybrid was better than all other hybrids, followed by the G1 and G3 hybrids. These hybrids were identified as best hybrids that have high grain yield and high yield stability. GGE biplot analysis provided a framework for identifying the target testing locations that discriminates genotypes that are high yielding and stable.
Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: One of the main advantages of the LO paradigm is to
allow the availability of good quality, shareable learning material
through the Web. The effectiveness of the retrieval process requires a
formal description of the resources (metadata) that closely fits the
user-s search criteria; in spite of the huge international efforts in this
field, educational metadata schemata often fail to fulfil this
requirement. This work aims to improve the situation, by the
definition of a metadata model capturing specific didactic features of
shareable learning resources. It classifies LOs into “teacher-oriented"
and “student-oriented" categories, in order to describe the role a LO
is to play when it is integrated into the educational process. This
article describes the model and a first experimental validation process
that has been carried out in a controlled environment.
Abstract: For the first incumbent operator it is very important
to understand how to react when the second operator comes to the
market. In this paper which is prepared for preliminary study of
GSM market in Iran, we have studied five MENA markets
according to the similarity point of view. This paper aims at
analyzing the impact of second entrants in selected markets on
certain marketing key performance indicators (KPI) such as:
Market shares (by operator), prepaid share, minutes of use (MoU),
Price and average revenue per user (ARPU) (for total market
each).
Abstract: This paper explores the effectiveness of machine
learning techniques in detecting firms that issue fraudulent financial
statements (FFS) and deals with the identification of factors
associated to FFS. To this end, a number of experiments have been
conducted using representative learning algorithms, which were
trained using a data set of 164 fraud and non-fraud Greek firms in the
recent period 2001-2002. The decision of which particular method to
choose is a complicated problem. A good alternative to choosing
only one method is to create a hybrid forecasting system
incorporating a number of possible solution methods as components
(an ensemble of classifiers). For this purpose, we have implemented
a hybrid decision support system that combines the representative
algorithms using a stacking variant methodology and achieves better
performance than any examined simple and ensemble method. To
sum up, this study indicates that the investigation of financial
information can be used in the identification of FFS and underline the
importance of financial ratios.
Abstract: Chronic conditions carry with them strong emotions
and often lead to charged relationships between patients and their
health providers and, by extension, patients and health researchers.
Persons are both autonomous and relational and a purely cognitive
model of autonomy neglects the social and relational basis of chronic
illness. Ensuring genuine informed consent in research requires a
thorough understanding of how participants perceive a study and
their reasons for participation. Surveys may not capture the
complexities of reasoning that underlies study participation.
Contradictory reasons for participation, for instance an initial claim
of altruism as rationale and a subsequent claim of personal benefit
(therapeutic misconception), affect the quality of informed consent.
Individuals apply principles through the filter of personal values and
lived experience. Authentic autonomy, and hence authentic consent
to research, occurs within the context of patients- unique life
narratives and illness experiences.
Abstract: In this paper we propose a family of algorithms based
on 3rd and 4th order cumulants for blind single-input single-output
(SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR)
channel estimation driven by non-Gaussian signal. The input signal
represents the signal used in 10GBASE-T (or IEEE 802.3an-2006)
as a Tomlinson-Harashima Precoded (THP) version of random
Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The
proposed algorithms are tested using three non-minimum phase
channel for different Signal-to-Noise Ratios (SNR) and for different
data input length. Numerical simulation results are presented to
illustrate the performance of the proposed algorithms.
Abstract: Grid composite structures have many applications in aerospace industry in which deal with transverse loadings abundantly. In present paper a stiffened composite cylindrical shell with clamped-free boundary condition under transverse end load experimentally and numerically was studied. Some electrical strain gauges were employed to measure the strains. Also a finite element analysis was done for validation of experimental result. The FEM software used was ANSYS11. In addition, the results between stiffened composite shell and unstiffened composite shell were compared. It was observed that intersection of two stiffeners has an important effect in decrease of stress in the shell. Fairly good agreements were observed between the numerical and the measured results. According to recent studies about grid composite structures, it should be noted that any investigation like this research has not been reported.
Abstract: In Korea, the technology of a load fo nuclear power plant has been being developed.
automatic controller which is able to control temperature and axial power distribution was developed. identification algorithm and a model predictive contact former transforms the nuclear reactor status into
numerically. And the latter uses them and ge
manipulated values such as two kinds of control ro
this automatic controller, the performance of a coperation was evaluated. As a result, the automatic generated model parameters of a nuclear react to nuclear reactor average temperature and axial power the desired targets during a daily load follow.
Abstract: In this competitive age, one of the key tools of most successful organizations is knowledge management. Today some organizations measure their current knowledge and use it as an indicator for rating the organization on their reports. Noting that the universities and colleges of medical science have a great role in public health of societies, their access to newest scientific research and the establishment of organizational knowledge management systems is very important. In order to explore the Application of Knowledge Management Factors, a national study was undertaken. The main purpose of this study was to find the rate of the application of knowledge management factors and some ways to establish more application of knowledge management system in Esfahan University-s Medical College (EUMC). Esfahan is the second largest city after Tehran, the capital city of Iran, and the EUMC is the biggest medical college in Esfahan. To rate the application of knowledge management, this study uses a quantitative research methodology based on Probst, Raub and Romhardt model of knowledge management. A group of 267 faculty members and staff of the EUMC were asked via questionnaire. Finding showed that the rate of the application of knowledge management factors in EUMC have been lower than average. As a result, an interview with ten faculty members conducted to find the guidelines to establish more applications of knowledge management system in EUMC.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.