Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: The use of wireless technology in industrial networks
has gained vast attraction in recent years. In this paper, we have
thoroughly analyzed the effect of contention window (CW) size on
the performance of IEEE 802.11-based industrial wireless networks
(IWN), from delay and reliability perspective. Results show that the
default values of CWmin, CWmax, and retry limit (RL) are far from
the optimum performance due to the industrial application
characteristics, including short packet and noisy environment. In this
paper, an adaptive CW algorithm (payload-dependent) has been
proposed to minimize the average delay. Finally a simple, but
effective CW and RL setting has been proposed for industrial
applications which outperforms the minimum-average-delay solution
from maximum delay and jitter perspective, at the cost of a little
higher average delay. Simulation results show an improvement of up
to 20%, 25%, and 30% in average delay, maximum delay and jitter
respectively.
Abstract: This article describes the results of research focused
on quality of railway freight transport services. Improvement of these
services has a crucial importance in customer considering on the
future use of railway transport. Processes filling the customer
demands and output quality assessment were defined as a part of the
research. In this contribution is introduced the map of quality
planning and the algorithm of applied methodology. It characterizes a
model which takes into account characters of transportation with
linking a perception services quality in ordinary and extraordinary
operation. Despite the fact that rail freight transport has its solid
position in the transport market, lots of carriers worldwide have been
experiencing a stagnation for a couple of years. Therefore, specific
results of the research have a significant importance and belong to
numerous initiatives aimed to develop and support railway transport
not only by creating a single railway area or reducing noise but also
by promoting railway services. This contribution is focused also on
the application of dynamic quality models which represent an
innovative method of evaluation quality services. Through this
conception, time factor, expected, and perceived quality in each
moment of the transportation process can be taken into account.
Abstract: In this article, the radial displacement error correction
capability of a high precision spindle grinding caused by unbalance
force was investigated. The spindle shaft is considered as a flexible
rotor mounted on two sets of angular contact ball bearing. Finite
element methods (FEM) have been adopted for obtaining the
equation of motion of the spindle. In this paper, firstly, natural
frequencies, critical frequencies, and amplitude of the unbalance
response caused by residual unbalance are determined in order to
investigate the spindle behaviors. Furthermore, an optimization
design algorithm is employed to minimize radial displacement of the
spindle which considers dimension of the spindle shaft, the dynamic
characteristics of the bearings, critical frequencies and amplitude of
the unbalance response, and computes optimum spindle diameters
and stiffness and damping of the bearings. Numerical simulation
results show that by optimizing the spindle diameters, and stiffness
and damping in the bearings, radial displacement of the spindle can
be reduced. A spindle about 4 μm radial displacement error can be
compensated with 2 μm accuracy. This certainly can improve the
accuracy of the product of machining.
Abstract: Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.
Abstract: People, throughout the history, have made estimates
and inferences about the future by using their past experiences.
Developing information technologies and the improvements in the
database management systems make it possible to extract useful
information from knowledge in hand for the strategic decisions.
Therefore, different methods have been developed. Data mining by
association rules learning is one of such methods. Apriori algorithm,
one of the well-known association rules learning algorithms, is not
commonly used in spatio-temporal data sets. However, it is possible
to embed time and space features into the data sets and make Apriori
algorithm a suitable data mining technique for learning spatiotemporal
association rules. Lake Van, the largest lake of Turkey, is a
closed basin. This feature causes the volume of the lake to increase or
decrease as a result of change in water amount it holds. In this study,
evaporation, humidity, lake altitude, amount of rainfall and
temperature parameters recorded in Lake Van region throughout the
years are used by the Apriori algorithm and a spatio-temporal data
mining application is developed to identify overflows and newlyformed
soil regions (underflows) occurring in the coastal parts of
Lake Van. Identifying possible reasons of overflows and underflows
may be used to alert the experts to take precautions and make the
necessary investments.
Abstract: The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.
Abstract: This paper evaluates the accrual based scheduling for
cloud in single and multi-resource system. Numerous organizations
benefit from Cloud computing by hosting their applications. The
cloud model provides needed access to computing with potentially
unlimited resources. Scheduling is tasks and resources mapping to a
certain optimal goal principle. Scheduling, schedules tasks to virtual
machines in accordance with adaptable time, in sequence under
transaction logic constraints. A good scheduling algorithm improves
CPU use, turnaround time, and throughput. In this paper, three realtime
cloud services scheduling algorithm for single resources and
multiple resources are investigated. Experimental results show
Resource matching algorithm performance to be superior for both
single and multi-resource scheduling when compared to benefit first
scheduling, Migration, Checkpoint algorithms.
Abstract: Imperialist Competitive Algorithm (ICA) is a recent
meta-heuristic method that is inspired by the social evolutions for
solving NP-Hard problems. The ICA is a population-based algorithm
which has achieved a great performance in comparison to other metaheuristics.
This study is about developing enhanced ICA approach to
solve the Cell Formation Problem (CFP) using sequence data. In
addition to the conventional ICA, an enhanced version of ICA,
namely EICA, applies local search techniques to add more
intensification aptitude and embed the features of exploration and
intensification more successfully. Suitable performance measures are
used to compare the proposed algorithms with some other powerful
solution approaches in the literature. In the same way, for checking
the proficiency of algorithms, forty test problems are presented. Five
benchmark problems have sequence data, and other ones are based on
0-1 matrices modified to sequence based problems. Computational
results elucidate the efficiency of the EICA in solving CFP problems.
Abstract: Spectrum handover is a significant topic in the
cognitive radio networks to assure an efficient data transmission in
the cognitive radio user’s communications. This paper proposes a
comparison between three spectrum handover models: VIKOR, SAW
and MEW. Four evaluation metrics are used. These metrics are,
accumulative average of failed handover, accumulative average of
handover performed, accumulative average of transmission
bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three
spectrum handover models was validated with captured data of
spectrum occupancy in experiments performed at the GSM frequency
band (824 MHz - 849 MHz). These data represent the actual behavior
of the licensed users for this wireless frequency band. The results of the comparison show that VIKOR Algorithm
provides a 15.8% performance improvement compared to SAW
Algorithm and, it is 12.1% better than the MEW Algorithm.
Abstract: This paper develops a multiple channel assignment
model, which allows to take advantage of spectrum opportunities in
cognitive radio networks in the most efficient way. The developed
scheme allows making several assignments of available and
frequency adjacent channel, which require a bigger bandwidth, under
an equality environment. The hybrid assignment model it is made by
two algorithms, one that makes the ranking and selects available
frequency channels and the other one in charge of establishing the
Max-Min Fairness for not restrict the spectrum opportunities for all
the other secondary users, who also claim to make transmissions.
Measurements made were done for average bandwidth, average
delay, as well as fairness computation for several channel
assignments. Reached results were evaluated with experimental
spectrum occupational data from captured GSM frequency band. The
developed model shows evidence of improvement in spectrum
opportunity use and a wider average transmission bandwidth for each
secondary user, maintaining equality criteria in channel assignment.
Abstract: This paper presents an extensive review of literature
relevant to the modelling techniques adopted in sediment yield and
hydrological modelling. Several studies relating to sediment yield are
discussed. Many research areas of sedimentation in rivers, runoff and
reservoirs are presented. Different types of hydrological models,
different methods employed in selecting appropriate models for
different case studies are analysed. Applications of evolutionary
algorithms and artificial intelligence techniques are discussed and
compared especially in water resources management and modelling.
This review concentrates on Genetic Programming (GP) and fully
discusses its theories and applications. The successful applications of
GP as a soft computing technique were reviewed in sediment
modelling. Some fundamental issues such as benchmark,
generalization ability, bloat, over-fitting and other open issues
relating to the working principles of GP are highlighted. This paper
concludes with the identification of some research gaps in
hydrological modelling and sediment yield.
Abstract: This paper integrates Octagon and Square Search
pattern (OCTSS) motion estimation algorithm into H.264/AVC
(Advanced Video Coding) video codec in Adaptive Group of Pictures
(AGOP) mode. AGOP structure is computed based on scene change
in the video sequence. Octagon and square search pattern block-based
motion estimation method is implemented in inter-prediction process
of H.264/AVC. Both these methods reduce bit rate and computational
complexity while maintaining the quality of the video sequence
respectively. Experiments are conducted for different types of video
sequence. The results substantially proved that the bit rate,
computation time and PSNR gain achieved by the proposed method
is better than the existing H.264/AVC with fixed GOP and AGOP.
With a marginal gain in quality of 0.28dB and average gain in bitrate
of 132.87kbps, the proposed method reduces the average computation
time by 27.31 minutes when compared to the existing state-of-art
H.264/AVC video codec.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
Abstract: A mixed method for model order reduction is
presented in this paper. The denominator polynomial is derived by
matching both Markov parameters and time moments, whereas
numerator polynomial derivation and error minimization is done
using Genetic Algorithm. The efficiency of the proposed method can
be investigated in terms of closeness of the response of reduced order
model with respect to that of higher order original model and a
comparison of the integral square error as well.
Abstract: This study presents a kinematic positioning approach
that uses a global positioning system (GPS) buoy for precise ocean
surface monitoring. The GPS buoy data from the two experiments are
processed using an accurate, medium-range differential kinematic
technique. In each case, the data from a nearby coastal site are
collected at a high rate (1 Hz) for more than 24 hours, and
measurements are conducted in neighboring tidal stations to verify
the estimated sea surface heights. The GPS buoy kinematic
coordinates are estimated using epoch-wise pre-elimination and a
backward substitution algorithm. Test results show that centimeterlevel
accuracy can be successfully achieved in determining sea
surface height using the proposed technique. The centimeter-level
agreement between the two methods also suggests the possibility of
using this inexpensive and more flexible GPS buoy equipment to
enhance (or even replace) current tidal gauge stations.
Abstract: Optimizing the parameters in the controller plays a
vital role in the control theory and its applications. Optimizing the
PID parameters is finding out the best value from the feasible
solutions. Finding the optimal value is an optimization problem.
Inverted Pendulum is a very good platform for control engineers to
verify and apply different logics in the field of control theory. It is
necessary to find an optimization technique for the controller to tune
the values automatically in order to minimize the error within the
given bounds. In this paper, the algorithmic concepts of Harmony
search (HS) and Genetic Algorithm (GA) have been analyzed for the
given range of values. The experimental results show that HS
performs well than GA.
Abstract: This study and the field test comparisons were carried
out on the Algerian Derguna – Setif transmission systems. The
transmission line of normal voltage 225 kV is 65 km long,
transported and uses twin bundle conductors protected with two
shield wires of transposed galvanized steel. An iterative finite-element method is used to solve Poisons
equation. Two algorithms are proposed for satisfying the current
continuity condition and updating the space-charge density. A new approach to the problem of corona discharge in
transmission system has been described in this paper. The effect of
varying the configurations and wires number is also investigated. The
analysis of this steady is important in the design of HVDC
transmission lines. The potential and electric field have been
calculating in locations singular points of the system.
Abstract: Current study established for EEG signal analysis in
patients with language disorder. Language disorder can be defined as
meaningful delay in the use or understanding of spoken or written
language. The disorder can include the content or meaning of
language, its form, or its use. Here we applied Z-score, power
spectrum, and coherence methods to discriminate the language
disorder data from healthy ones. Power spectrum of each channel in
alpha, beta, gamma, delta, and theta frequency bands was measured.
In addition, intra hemispheric Z-score obtained by scoring algorithm.
Obtained results showed high Z-score and power spectrum in
posterior regions. Therefore, we can conclude that peoples with
language disorder have high brain activity in frontal region of brain
in comparison with healthy peoples. Results showed that high coherence correlates with irregularities
in the ERP and is often found during complex task, whereas low
coherence is often found in pathological conditions. The results of the
Z-score analysis of the brain dynamics showed higher Z-score peak
frequency in delta, theta and beta sub bands of Language Disorder
patients. In this analysis there were activity signs in both hemispheres
and the left-dominant hemisphere was more active than the right.