Abstract: In this work, we study the problem of determining
the minimum scheduling length that can satisfy end-to-end (ETE)
traffic demand in scheduling-based multihop WSNs with cooperative
multiple-input multiple-output (MIMO) transmission scheme. Specifically,
we present a cross-layer formulation for the joint routing,
scheduling and stream control problem by incorporating various
power and rate adaptation schemes, and taking into account an
antenna beam pattern model and the signal-to-interference-and-noise
(SINR) constraint at the receiver. In the context, we also propose
column generation (CG) solutions to get rid of the complexity
requiring the enumeration of all possible sets of scheduling links.
Abstract: An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers- social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).
Abstract: Automatic reusability appraisal is helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the
software from scratch. But the issue of how to identify reusable
components from existing systems has remained relatively
unexplored. In this research work, structural attributes of software
components are explored using software metrics and quality of the
software is inferred by different Neural Network based approaches,
taking the metric values as input. The calculated reusability value
enables to identify a good quality code automatically. It is found that
the reusability value determined is close to the manual analysis used
to be performed by the programmers or repository managers. So, the
developed system can be used to enhance the productivity and
quality of software development.
Abstract: This paper reported an experimental research of
steady-state heat transfer behaviour of a gas flowing through a fixed
bed under the different operating conditions. Studies had been carried
out in a fixed-bed packed methanol synthesis catalyst percolated by air
at appropriate flow rate. Both radial and axial direction temperature
distribution had been investigated under the different operating
conditions. The effects of operating conditions including the reactor
inlet air temperature, the heating pipe temperature and the air flow rate
on temperature distribution was investigated and the experimental
results showed that a higher inlet air temperature was conducive to
uniform temperature distribution in the fixed bed. A large temperature
drop existed at the radial direction, and the temperature drop increased
with the heating pipe temperature increasing under the experimental
conditions; the temperature profile of the vicinity of the heating pipe
was strongly affected by the heating pipe temperature. A higher air
flow rate can improve the heat transfer in the fixed bed. Based on the
thermal distribution, heat transfer models of the fixed bed could be
established, and the characteristics of the temperature distribution in
the fixed bed could be finely described, that had an important practical
significance.
Abstract: Wireless mesh networks based on IEEE 802.11
technology are a scalable and efficient solution for next generation
wireless networking to provide wide-area wideband internet access to
a significant number of users. The deployment of these wireless mesh
networks may be within different authorities and without any
planning, they are potentially overlapped partially or completely in
the same service area. The aim of the proposed model is design a new
model to Enhancement Throughput of Unplanned Wireless Mesh
Networks Deployment Using Partitioning Hierarchical Cluster
(PHC), the unplanned deployment of WMNs are determinates there
performance. We use throughput optimization approach to model the
unplanned WMNs deployment problem based on partitioning
hierarchical cluster (PHC) based architecture, in this paper the
researcher used bridge node by allowing interworking traffic between
these WMNs as solution for performance degradation.
Abstract: This paper presents an analysis result of relationship
between business and information technology (IT) in business process
reengineering (BPR). 258 Japanese firm-level data collected have been
analyzed using structural equation modeling. This analysis was aimed
to illuminating success factors of achieve effective BPR. Analysis was
focused on management factors (including organizational factors) and
implementing management method (e.g. balanced score card, internal
control, etc.).These results would contribute for achieving effective
BPR by showing effective tasks and environment to be focused.
Abstract: Image compression can improve the performance of
the digital systems by reducing time and cost in image storage
and transmission without significant reduction of the image quality.
Furthermore, the discrete cosine transform has emerged as the new
state-of-the art standard for image compression. In this paper, a
hybrid image compression technique based on reversible blockade
transform coding is proposed. The technique, implemented over
regions of interest (ROIs), is based on selection of the coefficients
that belong to different transforms, depending on the coefficients is
proposed. This method allows: (1) codification of multiple kernals
at various degrees of interest, (2) arbitrary shaped spectrum,and (3)
flexible adjustment of the compression quality of the image and the
background. No standard modification for JPEG2000 decoder was
required. The method was applied over different types of images.
Results show a better performance for the selected regions, when
image coding methods were employed for the whole set of images.
We believe that this method is an excellent tool for future image
compression research, mainly on images where image coding can
be of interest, such as the medical imaging modalities and several
multimedia applications. Finally VLSI implementation of proposed
method is shown. It is also shown that the kernal of Hartley and
Cosine transform gives the better performance than any other model.
Abstract: This paper features the modeling and design of a
Robust Decentralized Fast Output Sampling (RDFOS) Feedback
control technique for the active vibration control of a smart flexible
multimodel Euler-Bernoulli cantilever beams for a multivariable
(MIMO) case by retaining the first 6 vibratory modes. The beam
structure is modeled in state space form using the concept of
piezoelectric theory, the Euler-Bernoulli beam theory and the Finite
Element Method (FEM) technique by dividing the beam into 4 finite
elements and placing the piezoelectric sensor / actuator at two finite
element locations (positions 2 and 4) as collocated pairs, i.e., as
surface mounted sensor / actuator, thus giving rise to a multivariable
model of the smart structure plant with two inputs and two outputs.
Five such multivariable models are obtained by varying the
dimensions (aspect ratios) of the aluminium beam. Using model
order reduction technique, the reduced order model of the higher
order system is obtained based on dominant Eigen value retention
and the Davison technique. RDFOS feedback controllers are
designed for the above 5 multivariable-multimodel plant. The closed
loop responses with the RDFOS feedback gain and the magnitudes of
the control input are obtained and the performance of the proposed
multimodel smart structure system is evaluated for vibration control.
Abstract: Patients with diabetes are susceptible to chronic foot
wounds which may be difficult to manage and slow to heal.
Diagnosis and treatment currently rely on the subjective judgement of
experienced professionals. An objective method of tissue assessment
is required. In this paper, a data fusion approach was taken to wound
tissue classification. The supervised Maximum Likelihood and
unsupervised Multi-Modal Expectation Maximisation algorithms
were used to classify tissues within simulated wound models by
weighting the contributions of both colour and 3D depth information.
It was found that, at low weightings, depth information could show
significant improvements in classification accuracy when compared
to classification by colour alone, particularly when using the
maximum likelihood method. However, larger weightings were
found to have an entirely negative effect on accuracy.
Abstract: Worm propagation profiles have significantly changed
since 2003-2004: sudden world outbreaks like Blaster or Slammer
have progressively disappeared and slower but stealthier worms
appeared since, most of them for botnets dissemination. Decreased
worm virulence results in more difficult detection.
In this paper, we describe a stealth worm propagation model
which has been extensively simulated and analysed on a huge virtual
network. The main features of this model is its ability to infect any
Internet-like network in a few seconds, whatever may be its size while
greatly limiting the reinfection attempt overhead of already infected
hosts. The main simulation results shows that the combinatorial
topology of routing may have a huge impact on the worm propagation
and thus some servers play a more essential and significant role than
others. The real-time capability to identify them may be essential to
greatly hinder worm propagation.
Abstract: For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach in order to make a solver quickly. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. It is noted that our formal modeling language is not intend for providing an efficient notation for data mining application, but for facilitating a designer who develops solvers in machine learning.
Abstract: In the paper, a fast high-resolution range profile synthetic algorithm called orthogonal matching pursuit with sensing dictionary (OMP-SD) is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori that the synthetic range profile (SRP) of targets are sparse, SRP can be accomplished even in presence of data lost. Besides, the computation complexity decreases from O(MNDK) flops for OMP to O(M(N + D)K) flops for OMP-SD by introducing sensing dictionary (SD). Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.
Abstract: Today-s Information and Knowledge Society has
placed new demands on education and a new paradigm of education
is required. Learning, facilitated by educational systems and the
pedagogic process, is globally undergoing dramatic changes. The aim
of this paper is the development of a simple Instructional Design tool
for E-Learning, named IDEL (Instructional Design for Electronic
Learning), that provides the educators with facilities to create their
own courses with the essential educational material and manage
communication with students. It offers flexibility in the way of
learning and provides ease in employment and reusability of
resources. IDEL is a web-based Instructional System and is designed
to facilitate course design process in accordance with the ADDIE
model and the instructional design principles with emphasis placed
on the use of technology enhanced learning. An example case of
using the ADDIE model to systematically develop a course and its
implementation with the aid of IDEL is given and some results from
student evaluation of the tool and the course are reported.
Abstract: Flash floods are considered natural disasters that can
cause casualties and demolishing of infra structures. The problem is
that flash floods, particularly in arid and semi arid zones, take place
in very short time. So, it is important to forecast flash floods earlier to
its events with a lead time up to 48 hours to give early warning alert
to avoid or minimize disasters. The flash flood took place over Wadi
Watier - Sinai Peninsula, in October 24th, 2008, has been simulated,
investigated and analyzed using the state of the art regional weather
model. The Weather Research and Forecast (WRF) model, which is a
reliable short term forecasting tool for precipitation events, has been
utilized over the study area. The model results have been calibrated
with the real data, for the same date and time, of the rainfall
measurements recorded at Sorah gauging station. The WRF model
forecasted total rainfall of 11.6 mm while the real measured one was
10.8 mm. The calibration shows significant consistency between
WRF model and real measurements results.
Abstract: Business process automation is an important task in an
enterprise business environment software development. The
requirements of processing acceleration and automation level of
enterprises are inherently different from one organization to another.
We present a methodology and system for automation of business
process management system architecture by multi-agent collaboration
based on SOA. Design layer processes are modeled in semantic
markup language for web services application. At the core of our
system is considering certain types of human tasks to their further
automation across over multiple platform environments. An
improved abnormality processing with model for automation of
BPMS architecture by multi-agent collaboration based on SOA is
introduced. Validating system for efficiency of process automation,
an application for educational knowledge base instance would also be
described.
Abstract: This paper addresses the problem of determining the current 3D location of a moving object and robustly tracking it from a sequence of camera images. The approach presented here uses a particle filter and does not perform any explicit triangulation. Only the color of the object to be tracked is required, but not any precisemotion model. The observation model we have developed avoids the color filtering of the entire image. That and the Monte Carlotechniques inside the particle filter provide real time performance.Experiments with two real cameras are presented and lessons learned are commented. The approach scales easily to more than two cameras and new sensor cues.
Abstract: Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.
Abstract: This paper investigates the application of Particle Swarm Optimization (PSO) technique for coordinated design of a Power System Stabilizer (PSS) and a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the power system stability. The design problem of PSS and TCSC-based controllers is formulated as a time domain based optimization problem. PSO algorithm is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. To compare the capability of PSS and TCSC-based controller, both are designed independently first and then in a coordinated manner for individual and coordinated application. The proposed controllers are tested on a weakly connected power system. The eigenvalue analysis and non-linear simulation results are presented to show the effectiveness of the coordinated design approach over individual design. The simulation results show that the proposed controllers are effective in damping low frequency oscillations resulting from various small disturbances like change in mechanical power input and reference voltage setting.
Abstract: This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.
Abstract: A dissimilarity measure between the empiric
characteristic functions of the subsamples associated to the different
classes in a multivariate data set is proposed. This measure can be
efficiently computed, and it depends on all the cases of each class. It
may be used to find groups of similar classes, which could be joined
for further analysis, or it could be employed to perform an
agglomerative hierarchical cluster analysis of the set of classes. The
final tree can serve to build a family of binary classification models,
offering an alternative approach to the multi-class SVM problem. We
have tested this dendrogram based SVM approach with the oneagainst-
one SVM approach over four publicly available data sets,
three of them being microarray data. Both performances have been
found equivalent, but the first solution requires a smaller number of
binary SVM models.