Abstract: Modern applications realized onto FPGAs exhibit high connectivity demands. Throughout this paper we study the routing constraints of Virtex devices and we propose a systematic methodology for designing a novel general-purpose interconnection network targeting to reconfigurable architectures. This network consists of multiple segment wires and SB patterns, appropriately selected and assigned across the device. The goal of our proposed methodology is to maximize the hardware utilization of fabricated routing resources. The derived interconnection scheme is integrated on a Virtex style FPGA. This device is characterized both for its high-performance, as well as for its low-energy requirements. Due to this, the design criterion that guides our architecture selections was the minimal Energy×Delay Product (EDP). The methodology is fully-supported by three new software tools, which belong to MEANDER Design Framework. Using a typical set of MCNC benchmarks, extensive comparison study in terms of several critical parameters proves the effectiveness of the derived interconnection network. More specifically, we achieve average Energy×Delay Product reduction by 63%, performance increase by 26%, reduction in leakage power by 21%, reduction in total energy consumption by 11%, at the expense of increase of channel width by 20%.
Abstract: The recognition of handwritten numeral is an
important area of research for its applications in post office, banks
and other organizations. This paper presents automatic recognition of
handwritten Kannada numerals based on structural features. Five
different types of features, namely, profile based 10-segment string,
water reservoir; vertical and horizontal strokes, end points and
average boundary length from the minimal bounding box are used in
the recognition of numeral. The effect of each feature and their
combination in the numeral classification is analyzed using nearest
neighbor classifiers. It is common to combine multiple categories of
features into a single feature vector for the classification. Instead,
separate classifiers can be used to classify based on each visual
feature individually and the final classification can be obtained based
on the combination of separate base classification results. One
popular approach is to combine the classifier results into a feature
vector and leaving the decision to next level classifier. This method
is extended to extract a better information, possibility distribution,
from the base classifiers in resolving the conflicts among the
classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy
k-NN) as base classifier for individual feature sets, the results of
which together forms the feature vector for the final k Nearest
Neighbor (k-NN) classifier. Testing is done, using different features,
individually and in combination, on a database containing 1600
samples of different numerals and the results are compared with the
results of different existing methods.
Abstract: Most of fuzzy clustering algorithms have some
discrepancies, e.g. they are not able to detect clusters with convex
shapes, the number of the clusters should be a priori known, they
suffer from numerical problems, like sensitiveness to the
initialization, etc. This paper studies the synergistic combination of
the hierarchical and graph theoretic minimal spanning tree based
clustering algorithm with the partitional Gath-Geva fuzzy clustering
algorithm. The aim of this hybridization is to increase the robustness
and consistency of the clustering results and to decrease the number
of the heuristically defined parameters of these algorithms to
decrease the influence of the user on the clustering results. For the
analysis of the resulted fuzzy clusters a new fuzzy similarity measure
based tool has been presented. The calculated similarities of the
clusters can be used for the hierarchical clustering of the resulted
fuzzy clusters, which information is useful for cluster merging and
for the visualization of the clustering results. As the examples used
for the illustration of the operation of the new algorithm will show,
the proposed algorithm can detect clusters from data with arbitrary
shape and does not suffer from the numerical problems of the
classical Gath-Geva fuzzy clustering algorithm.
Abstract: This study proposes a hybrid minimal repair policy
which combines periodic maintenance policy with age-based maintenance policy for a serial production system. Parameters of such policy are defined as and which indicate as hybrid minimal
repair time and planned preventive maintenance time
respectively . Under this hybrid policy, the system is
repaired minimally if it fails during , . A perfect repair is
conducted on the first failure after at any machines. At the same time, we take opportunity to advance the preventive maintenance of
other machines simultaneously. If the system is still operating
properly up to , then the preventive maintenance is carried out as its
predetermined schedule. For a given , we obtain the optimal value which minimizes the expected cost per time unit. Numerical
example is presented to illustrate the properties of the optimal solution.
Abstract: The paper describes the evaluation of quality of
control for cases of controlled non-minimal phase plants. Control
circuits containing non-minimal phase plants have different
properties, they manifest reversed reaction at the beginning of unit
step response. For these types of plants are developed special
criterion of quality of control, which considers the difference and can
be helpful for synthesis of optimal controller tuning. All results are
clearly presented using Matlab/Simulink models.
Abstract: The curves, of which the square of the distance
between the two points equal to zero, are called minimal or isotropic
curves [4]. In this work, first, necessary and sufficient conditions to
be a Pseudo Helix, which is a special case of such curves, are
presented. Thereafter, it is proven that an isotropic curve-s position
vector and pseudo curvature satisfy a vector differential equation of
fourth order. Additionally, In view of solution of mentioned
equation, position vector of pseudo helices is obtained.
Abstract: This paper is concerned with an improved algorithm
based on the piecewise-smooth Mumford and Shah (MS) functional
for an efficient and reliable segmentation. In order to speed up
convergence, an additional force, at each time step, is introduced
further to drive the evolution of the curves instead of only driven by
the extensions of the complementary functions u + and u - . In our
scheme, furthermore, the piecewise-constant MS functional is
integrated to generate the extra force based on a temporary image that
is dynamically created by computing the union of u + and u - during
segmenting. Therefore, some drawbacks of the original algorithm,
such as smaller objects generated by noise and local minimal problem
also are eliminated or improved. The resulting algorithm has been
implemented in Matlab and Visual Cµ, and demonstrated efficiently
by several cases.
Abstract: The main goal of this work is to propose a way for
combined use of two nontraditional algorithms by solving topological
problems on telecommunications concentrator networks. The
algorithms suggested are the Simulated Annealing algorithm and the
Genetic Algorithm. The Algorithm of Simulated Annealing unifies
the well known local search algorithms. In addition - Simulated
Annealing allows acceptation of moves in the search space witch lead
to decisions with higher cost in order to attempt to overcome any
local minima obtained. The Genetic Algorithm is a heuristic approach
witch is being used in wide areas of optimization works. In the last
years this approach is also widely implemented in
Telecommunications Networks Planning. In order to solve less or
more complex planning problem it is important to find the most
appropriate parameters for initializing the function of the algorithm.
Abstract: The paper addresses a problem of optimal staffing in
open shop environment. The problem is to determine the optimal
number of operators serving a given number of machines to fulfill the
number of independent operations while minimizing staff idle. Using
a Gantt chart presentation of the problem it is modeled as twodimensional
cutting stock problem. A mixed-integer programming
model is used to get minimal job processing time (makespan) for
fixed number of machines' operators. An algorithm for optimal openshop
staffing is developed based on iterative solving of the
formulated optimization task. The execution of the developed
algorithm provides optimal number of machines' operators in the
sense of minimum staff idle and optimal makespan for that number of
operators. The proposed algorithm is tested numerically for a real life
staffing problem. The testing results show the practical applicability
for similar open shop staffing problems.
Abstract: Unstructured peer-to-peer networks are popular due to
its robustness and scalability. Query schemes that are being used in
unstructured peer-to-peer such as the flooding and interest-based
shortcuts suffer various problems such as using large communication
overhead long delay response. The use of routing indices has been a
popular approach for peer-to-peer query routing. It helps the query
routing processes to learn the routing based on the feedbacks
collected. In an unstructured network where there is no global
information available, efficient and low cost routing approach is
needed for routing efficiency.
In this paper, we propose a novel mechanism for query-feedback
oriented routing indices to achieve routing efficiency in unstructured
network at a minimal cost. The approach also applied information
retrieval technique to make sure the content of the query is
understandable and will make the routing process not just based to
the query hits but also related to the query content. Experiments have
shown that the proposed mechanism performs more efficient than
flood-based routing.
Abstract: Accurately predicting non-peak traffic is crucial to
daily traffic for all forecasting models. In the paper, least squares
support vector machines (LS-SVMs) are investigated to solve such a
practical problem. It is the first time to apply the approach and analyze
the forecast performance in the domain. For comparison purpose, two
parametric and two non-parametric techniques are selected because of
their effectiveness proved in past research. Having good
generalization ability and guaranteeing global minima, LS-SVMs
perform better than the others. Providing sufficient improvement in
stability and robustness reveals that the approach is practically
promising.
Abstract: The aim of this article is to assess the existing
business models used by the banks operating in the CEE countries in
the time period from 2006 till 2011.
In order to obtain research results, the authors performed
qualitative analysis of the scientific literature on bank business
models, which have been grouped into clusters that consist of such
components as: 1) capital and reserves; 2) assets; 3) deposits, and 4)
loans.
In their turn, bank business models have been developed based on
the types of core activities of the banks, and have been divided into
four groups: Wholesale, Investment, Retail and Universal Banks.
Descriptive statistics have been used to analyse the models,
determining mean, minimal and maximal values of constituent
cluster components, as well as standard deviation. The analysis of
the data is based on such bank variable indices as Return on Assets
(ROA) and Return on Equity (ROE).
Abstract: This paper presents the design of a ring-shaped tri-axial fore sensor that can be incorporated into the tip of a guidewire for use in minimally invasive surgery (MIS). The designed sensor comprises a ring-shaped structure located at the center of four cantilever beams. The ringdesign allows surgical tools to be easily passed through which largely simplified the integration process. Silicon nanowires (SiNWs) are used aspiezoresistive sensing elementsembeddedon the four cantilevers of the sensor to detect the resistance change caused by the applied load.An integration scheme with new designed guidewire tip structure having two coils at the distal end is presented. Finite element modeling has been employed in the sensor design to find the maximum stress location in order to put the SiNWs at the high stress regions to obtain maximum output. A maximum applicable force of 5 mN is found from modeling. The interaction mechanism between the designed sensor and a steel wire has been modeled by FEM. A linear relationship between the applied load on the steel wire and the induced stress on the SiNWs were observed.
Abstract: Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.
Abstract: Determination of nano particle size is substantial since
the nano particle size exerts a significant effect on various properties
of nano materials. Accordingly, proposing non-destructive, accurate
and rapid techniques for this aim is of high interest. There are some
conventional techniques to investigate the morphology and grain size
of nano particles such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and X-ray diffractometry (XRD).
Vibrational spectroscopy is utilized to characterize different
compounds and applied for evaluation of the average particle size
based on relationship between particle size and near infrared spectra
[1,4] , but it has never been applied in quantitative morphological
analysis of nano materials. So far, the potential application of nearinfrared
(NIR) spectroscopy with its ability in rapid analysis of
powdered materials with minimal sample preparation, has been
suggested for particle size determination of powdered
pharmaceuticals. The relationship between particle size and diffuse
reflectance (DR) spectra in near infrared region has been applied to
introduce a method for estimation of particle size. Back propagation
artificial neural network (BP-ANN) as a nonlinear model was applied
to estimate average particle size based on near infrared diffuse
reflectance spectra. Thirty five different nano TiO2 samples with
different particle size were analyzed by DR-FTNIR spectrometry and
the obtained data were processed by BP- ANN.
Abstract: Minimally invasive surgery (MIS) is now being widely used as a preferred choice for various types of operations. The need to detect various tactile properties, justifies the key role of tactile sensing that is currently missing in MIS. In this regard, Laparoscopy is one of the methods of minimally invasive surgery that can be used in kidney stone removal surgeries. At this moment, determination of the exact location of stone during laparoscopy is one of the limitations of this method that no scientific solution has been found for so far. Artificial tactile sensing is a new method for obtaining the characteristics of a hard object embedded in a soft tissue. Artificial palpation is an important application of artificial tactile sensing that can be used in different types of surgeries. In this study, a new method for determining the exact location of stone during laparoscopy is presented. In the present study, the effects of stone existence on the surface of kidney were investigated using conceptual 3D model of kidney containing a simulated stone. Having imitated palpation and modeled it conceptually, indications of stone existence that appear on the surface of kidney were determined. A number of different cases were created and solved by the software and using stress distribution contours and stress graphs, it is illustrated that the created stress patterns on the surface of kidney show not only the existence of stone inside, but also its exact location. So three-dimensional analysis leads to a novel method of predicting the exact location of stone and can be directly applied to the incorporation of tactile sensing in artificial palpation, helping surgeons in non-invasive procedures.
Abstract: The study was conducted to investigate the profile of
hepatitis in Kingdom of Saudi Arabia, and to determine which age
group hepatitis viruses most commonly infect. The epidemiology of
viral hepatitis in Saudi Arabia has undergone major changes,
concurrent with major socioeconomic developments over the last two
to three decades. This disease represents a major public health
problem in Saudi Arabia resulting in the need for considerable
healthcare resources. A retrospective cross sectional analysis of the
reported cases of viral hepatitis was conducted based on the reports
of The Ministry of Health in Saudi Arabia about Hepatitis A, B and C
infections in all regions from the period of January 2006 to December
2010. The study demonstrated that incidence of viral Hepatitis is
decreasing, except for Hepatitis B that showed minimal increase. Of
hepatitis A, B, and C, Hepatitis B virus (HBV) was the most
predominant type, accounting for (53%) of the cases, followed by
Hepatitis C virus (HCV) (30%) and HAV (17%). HAV infection
predominates in children (5–14 years) with 60% of viral hepatitis
cases, HBV in young adults (15–44 years) with 69% of viral hepatitis
cases, and HCV in older adults (>45 years) with 59% of viral
hepatitis cases. Despite significant changes in the prevalence of viral
hepatitis A, B and C, it remains a major public health problem in
Saudi Arabia; however, it showed a significant decline in the last two
decades that could be attributed to the vaccination programs and the
improved health facilities. Further researches are needed to identify
the risk factors making a specific age group or a specific region in
Saudi Arabia targeted for a specific type of hepatitis viruses.
Abstract: The back propagation algorithm calculates the weight
changes of artificial neural networks, and a common approach is to
use a training algorithm consisting of a learning rate and a
momentum factor. The major drawbacks of above learning algorithm
are the problems of local minima and slow convergence speeds. The
addition of an extra term, called a proportional factor reduces the
convergence of the back propagation algorithm. We have applied the
three term back propagation to multiplicative neural network
learning. The algorithm is tested on XOR and parity problem and
compared with the standard back propagation training algorithm.
Abstract: The study of a real function of two real variables can be supported by visualization using a Computer Algebra System (CAS). One type of constraints of the system is due to the algorithms implemented, yielding continuous approximations of the given function by interpolation. This often masks discontinuities of the function and can provide strange plots, not compatible with the mathematics. In recent years, point based geometry has gained increasing attention as an alternative surface representation, both for efficient rendering and for flexible geometry processing of complex surfaces. In this paper we present different artifacts created by mesh surfaces near discontinuities and propose a point based method that controls and reduces these artifacts. A least squares penalty method for an automatic generation of the mesh that controls the behavior of the chosen function is presented. The special feature of this method is the ability to improve the accuracy of the surface visualization near a set of interior points where the function may be discontinuous. The present method is formulated as a minimax problem and the non uniform mesh is generated using an iterative algorithm. Results show that for large poorly conditioned matrices, the new algorithm gives more accurate results than the classical preconditioned conjugate algorithm.
Abstract: This paper presents a new sensor-based online method for generating collision-free near-optimal paths for mobile robots pursuing a moving target amidst dynamic and static obstacles. At each iteration, first the set of all collision-free directions are calculated using velocity vectors of the robot relative to each obstacle and target, forming the Directive Circle (DC), which is a novel concept. Then, a direction close to the shortest path to the target is selected from feasible directions in DC. The DC prevents the robot from being trapped in deadlocks or local minima. It is assumed that the target's velocity is known, while the speeds of dynamic obstacles, as well as the locations of static obstacles, are to be calculated online. Extensive simulations and experimental results demonstrated the efficiency of the proposed method and its success in coping with complex environments and obstacles.