Abstract: Let D = 1 be a positive non-square integer and let δ = √D or 1+√D 2 be a real quadratic irrational with trace t =δ + δ and norm n = δδ. Let γ = P+δ Q be a quadratic irrational for positive integers P and Q. Given a quadratic irrational γ, there exist a quadratic ideal Iγ = [Q, δ + P] and an indefinite quadratic form Fγ(x, y) = Q(x−γy)(x−γy) of discriminant Δ = t
2−4n. In the first section, we give some preliminaries form binary quadratic forms, quadratic irrationals and quadratic ideals. In the second section, we obtain some results on γ, Iγ and Fγ for some specific values of Q and P.
Abstract: In this paper, we evaluate the performance of some wavelet based coding algorithms such as 3D QT-L, 3D SPIHT and JPEG2K. In the first step we achieve an objective comparison between three coders, namely 3D SPIHT, 3D QT-L and JPEG2K. For this purpose, eight MRI head scan test sets of 256 x 256x124 voxels have been used. Results show superior performance of 3D SPIHT algorithm, whereas 3D QT-L outperforms JPEG2K. The second step consists of evaluating the robustness of 3D SPIHT and JPEG2K coding algorithm over wireless transmission. Compressed dataset images are then transmitted over AWGN wireless channel or over Rayleigh wireless channel. Results show the superiority of JPEG2K over these two models. In fact, it has been deduced that JPEG2K is more robust regarding coding errors. Thus we may conclude the necessity of using corrector codes in order to protect the transmitted medical information.
Abstract: This paper investigates the control of a bouncing
ball using Model Predictive Control. Bouncing ball is a benchmark
problem for various rhythmic tasks such as juggling, walking,
hopping and running. Humans develop intentions which may be
perceived as our reference trajectory and tries to track it. The
human brain optimizes the control effort needed to track its
reference; this forms the central theme for control of bouncing ball
in our investigations.
Abstract: In many data mining applications, it is a priori known
that the target function should satisfy certain constraints imposed
by, for example, economic theory or a human-decision maker. In this
paper we consider partially monotone prediction problems, where the
target variable depends monotonically on some of the input variables
but not on all. We propose a novel method to construct prediction
models, where monotone dependences with respect to some of
the input variables are preserved by virtue of construction. Our
method belongs to the class of mixture models. The basic idea is to
convolute monotone neural networks with weight (kernel) functions
to make predictions. By using simulation and real case studies,
we demonstrate the application of our method. To obtain sound
assessment for the performance of our approach, we use standard
neural networks with weight decay and partially monotone linear
models as benchmark methods for comparison. The results show that
our approach outperforms partially monotone linear models in terms
of accuracy. Furthermore, the incorporation of partial monotonicity
constraints not only leads to models that are in accordance with the
decision maker's expertise, but also reduces considerably the model
variance in comparison to standard neural networks with weight
decay.
Abstract: Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.
Abstract: Emerging Bio-engineering fields such as Brain
Computer Interfaces, neuroprothesis devices and modeling and
simulation of neural networks have led to increased research activity
in algorithms for the detection, isolation and classification of Action
Potentials (AP) from noisy data trains. Current techniques in the field
of 'unsupervised no-prior knowledge' biosignal processing include
energy operators, wavelet detection and adaptive thresholding. These
tend to bias towards larger AP waveforms, AP may be missed due to
deviations in spike shape and frequency and correlated noise
spectrums can cause false detection. Also, such algorithms tend to
suffer from large computational expense.
A new signal detection technique based upon the ideas of phasespace
diagrams and trajectories is proposed based upon the use of a
delayed copy of the AP to highlight discontinuities relative to
background noise. This idea has been used to create algorithms that
are computationally inexpensive and address the above problems.
Distinct AP have been picked out and manually classified from
real physiological data recorded from a cockroach. To facilitate
testing of the new technique, an Auto Regressive Moving Average
(ARMA) noise model has been constructed bases upon background
noise of the recordings. Along with the AP classification means this
model enables generation of realistic neuronal data sets at arbitrary
signal to noise ratio (SNR).
Abstract: Object Relational Databases (ORDB) are complex in
nature than traditional relational databases because they combine the
characteristics of both object oriented concepts and relational
features of conventional databases. Design of an ORDB demands
efficient and quality schema considering the structural, functional
and componential traits. This internal quality of the schema is
assured by metrics that measure the relevant attributes. This is
extended to substantiate the understandability, usability and
reliability of the schema, thus assuring external quality of the
schema. This work institutes a formalization of ORDB metrics;
metric definition, evaluation methodology and the calibration of the
metric. Three ORDB schemas were used to conduct the evaluation
and the formalization of the metrics. The metrics are calibrated using
content and criteria related validity based on the measurability,
consistency and reliability of the metrics. Nominal and summative
scales are derived based on the evaluated metric values and are
standardized. Future works pertaining to ORDB metrics forms the
concluding note.
Abstract: Genetic Zone Routing Protocol (GZRP) is a new
hybrid routing protocol for MANETs which is an extension of ZRP
by using Genetic Algorithm (GA). GZRP uses GA on IERP and BRP
parts of ZRP to provide a limited set of alternative routes to the
destination in order to load balance the network and robustness
during node/link failure during the route discovery process. GZRP is
studied for its performance compared to ZRP in many folds like
scalability for packet delivery and proved with improved results. This
paper presents the results of the effect of load balancing on GZRP.
The results show that GZRP outperforms ZRP while balancing the
load.
Abstract: Magnesium wastes and scraps, one of the metal wastes, are produced by many industrial activities, all over the world. Their growing size is becoming a future problem for the world. In this study, the use of magnesium wastes as a raw material in the production of the magnesium borate hydrates are aimed. The method used in the experiments is hydrothermal synthesis. The conditions are set to, waste magnesium to B2O3, 1:3 as a molar ratio. Four different reaction times are studied which are 30, 60, 120 and 240 minutes. For the identification analyses X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR) and Raman spectroscopy techniques are used. As a result at all the reaction times magnesium borate hydrates are synthesized and the most crystalline forms are obtained at a reaction time of 120 minutes. The overall yields of the production are found between the values of 65-80 %.
Abstract: Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.
Abstract: Landscape connectivity combines a description of the
physical structure of the landscape with special species- response to
that structure, which forms the theoretical background of applying
landscape connectivity principles in the practices of landscape
planning and design. In this study, a residential development project in
the southern United States was used to explore the meaning of
landscape connectivity and its application in town planning. The vast
rural landscape in the southern United States is conspicuously
characterized by the hedgerow trees or groves. The patchwork
landscape of fields surrounded by high hedgerows is a traditional and
familiar feature of the American countryside. Hedgerows are in effect
linear strips of trees, groves, or woodlands, which are often critical
habitats for wildlife and important for the visual quality of the
landscape. Based on geographic information system (GIS) and
statistical analysis (FRAGSTAT), this study attempts to quantify the
landscape connectivity characterized by hedgerows in south Alabama
where substantial areas of authentic hedgerow landscape are being
urbanized due to the ever expanding real estate industry and high
demand for new residential development. The results of this study
shed lights on how to balance the needs of new urban development and
biodiversity conservation by maintaining a higher level of landscape
connectivity, thus will inform the design intervention.
Abstract: Cheating on standardized tests has been a major
concern as it potentially minimizes measurement precision. One
major way to reduce cheating by collusion is to administer multiple
forms of a test. Even with this approach, potential collusion is still
quite large. A Latin-square treatment structure for distributing
multiple forms is proposed to further reduce the colluding potential.
An index to measure the extent of colluding potential is also
proposed. Finally, with a simple algorithm, the various Latin-squares
were explored to find the best structure to keep the colluding
potential to a minimum.
Abstract: This paper presents the benchmarking results and
performance evaluation of differentclustersbuilt atthe National Center
for High-Performance Computingin Taiwan. Performance of
processor, memory subsystem andinterconnect is a critical factor in the
overall performance of high performance computing platforms. The
evaluation compares different system architecture and software
platforms. Most supercomputer used HPL to benchmark their system
performance, in accordance with the requirement of the TOP500 List.
In this paper we consider system memory access factors that affect
benchmark performance, such as processor and memory
performance.We hope these works will provide useful information for
future development and construct cluster system.
Abstract: Optimization of extraction of phenolic compounds
from Avicennia marina using response surface methodology was
carried out during the present study. Five levels, three factors
rotatable design (CCRD) was utilized to examine the optimum
combination of extraction variables based on the TPC of Avicennia
marina leaves. The best combination of response function was 78.41
°C, drying temperature; 26.18°C; extraction temperature and 36.53
minutes of extraction time. However, the procedure can be promptly
extended to the study of several others pharmaceutical processes like
purification of bioactive substances, drying of extracts and
development of the pharmaceutical dosage forms for the benefit of
consumers.
Abstract: In this paper, a novel method for recognition of musical
instruments in a polyphonic music is presented by using an
embedded hidden Markov model (EHMM). EHMM is a doubly
embedded HMM structure where each state of the external HMM
is an independent HMM. The classification is accomplished for
two different internal HMM structures where GMMs are used as
likelihood estimators for the internal HMMs. The results are compared
to those achieved by an artificial neural network with two
hidden layers. Appropriate classification accuracies were achieved
both for solo instrument performance and instrument combinations
which demonstrates that the new approach outperforms the similar
classification methods by means of the dynamic of the signal.
Abstract: The IEEE 802.11e which is an enhanced version of the 802.11 WLAN standards incorporates the Quality of Service (QoS) which makes it a better choice for multimedia and real time applications. In this paper we study various aspects concerned with 802.11e standard. Further, the analysis results for this standard are compared with the legacy 802.11 standard. Simulation results show that IEEE 802.11e out performs legacy IEEE 802.11 in terms of quality of service due to its flow differentiated channel allocation and better queue management architecture. We also propose a method to improve the unfair allocation of bandwidth for downlink and uplink channels by varying the medium access priority level.
Abstract: Business process management (BPM) has become
widely accepted within business community as a means for
improving business performance. However, it is of the highest
importance to incorporate BPM as part of the curriculum at the
university level education in order to achieve the appropriate
acceptance of the method. Goal of the paper is to determine the
current state of education in business process management (BPM) at
the Croatian universities and abroad. It investigates the applied forms
of instruction and teaching methods and gives several proposals for
BPM courses improvement. Since majority of undergraduate and
postgraduate students have limited understanding of business
processes and lack of any practical experience, there is a need for
introducing new teaching approaches. Therefore, we offer some
suggestions for further improvement, among which the introduction
of simulation games environment in BPM education is strongly
recommended.
Abstract: The purpose of planned islanding is to construct a
power island during system disturbances which are commonly
formed for maintenance purpose. However, in most of the cases
island mode operation is not allowed. Therefore distributed
generators (DGs) must sense the unplanned disconnection from the
main grid. Passive technique is the most commonly used method for
this purpose. However, it needs improvement in order to identify the
islanding condition. In this paper an effective method for
identification of islanding condition based on phase space and neural
network techniques has been developed. The captured voltage
waveforms at the coupling points of DGs are processed to extract the
required features. For this purposed a method known as the phase
space techniques is used. Based on extracted features, two neural
network configuration namely radial basis function and probabilistic
neural networks are trained to recognize the waveform class.
According to the test result, the investigated technique can provide
satisfactory identification of the islanding condition in the
distribution system.
Abstract: In the management of industrial waste, conversion from the use of paper invoices to electronic forms is currently under way in developed countries. Difficulties in such computerization include the lack of synchronization between the actual goods and the corresponding data managed by the server. Consequently, a system which utilizes the incorporation of a QR code in connection with the waste material has been developed. The code is read at each stage, from discharge until disposal, and progress at each stage can be easily reported. This system can be linked with Japanese public digital authentication service of waste, taking advantage of its good points, and can be used to submit reports to the regulatory authorities. Its usefulness was confirmed by a verification test, and put into actual practice.
Abstract: Transmission control protocol (TCP) Vegas detects
network congestion in the early stage and successfully prevents
periodic packet loss that usually occurs in TCP Reno. It has been
demonstrated that TCP Vegas outperforms TCP Reno in many
aspects. However, TCP Vegas suffers several problems that affect its
congestion avoidance mechanism. One of the most important
weaknesses in TCP Vegas is that alpha and beta depend on a good
expected throughput estimate, which as we have seen, depends on a
good minimum RTT estimate. In order to make the system more
robust alpha and beta must be made responsive to network conditions
(they are currently chosen statically). This paper proposes a modified
Vegas algorithm, which can be adjusted to present good performance
compared to other transmission control protocols (TCPs). In order to
do this, we use PSO algorithm to tune alpha and beta. The simulation
results validate the advantages of the proposed algorithm in term of
performance.