Abstract: Microwave energy is a superior alternative to several other thermal treatments. Extraction techniques are widely employed for the isolation of bioactive compounds and vegetable oils from oil seeds. Among the different and new available techniques, microwave pretreatment of seeds is a simple and desirable method for production of high quality vegetable oils. Microwave pretreatment for oil extraction has many advantages as follow: improving oil extraction yield and quality, direct extraction capability, lower energy consumption, faster processing time and reduced solvent levels compared with conventional methods. It allows also for better retention and availability of desirable nutraceuticals, such as phytosterols and tocopherols, canolol and phenolic compounds in the extracted oil such as rapeseed oil. This can be a new step to produce nutritional vegetable oils with improved shelf life because of high antioxidant content.
Abstract: This paper investigates the issue of building decision
trees from data with imprecise class values where imprecision is
encoded in the form of possibility distributions. The Information
Affinity similarity measure is introduced into the well-known gain
ratio criterion in order to assess the homogeneity of a set of
possibility distributions representing instances-s classes belonging to
a given training partition. For the experimental study, we proposed an
information affinity based performance criterion which we have used
in order to show the performance of the approach on well-known
benchmarks.
Abstract: The Multi-Layered Perceptron (MLP) Neural
networks have been very successful in a number of signal processing
applications. In this work we have studied the possibilities and the
met difficulties in the application of the MLP neural networks for the
prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in
term of the statistical indicators, with a linear model most used in
literature, is also performed, and the obtained results show that the
neural networks are more efficient and gave the best results.
Abstract: Due to the ever growing amount of publications about
protein-protein interactions, information extraction from text is
increasingly recognized as one of crucial technologies in
bioinformatics. This paper presents a Protein Interaction Extraction
System using a Link Grammar Parser from biomedical abstracts
(PIELG). PIELG uses linkage given by the Link Grammar Parser to
start a case based analysis of contents of various syntactic roles as
well as their linguistically significant and meaningful combinations.
The system uses phrasal-prepositional verbs patterns to overcome
preposition combinations problems. The recall and precision are
74.4% and 62.65%, respectively. Experimental evaluations with two
other state-of-the-art extraction systems indicate that PIELG system
achieves better performance. For further evaluation, the system is
augmented with a graphical package (Cytoscape) for extracting
protein interaction information from sequence databases. The result
shows that the performance is remarkably promising.
Abstract: This paper addresses the problem of building a unified
structure to describe a peer-to-peer system. Our approach uses the
well-known notations in the P2P area, and provides a global
architecture that puts a separation between the platform specific
characteristics and the logical ones. In order to enable the navigation
of the peer across platforms, a roaming layer is added. The latter
provides a capability to define a unique identification of peer and
assures the mapping between this identification and those used in
each platform. The mapping task is assured by special wrapper. In
addition, ontology is proposed to give a clear presentation of the
structure of the P2P system without interesting in the content and the
resource managed by the peer. The ontology is created according to
the web semantic paradigm and using OWL language; so, the
structure of the system is considered as a web resource.
Abstract: The automatic discrimination of seismic signals is an important practical goal for the earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present new techniques for seismic signals classification: local, regional and global discrimination. These techniques were tested on seismic signals from the data base of the National Geophysical Institute of the Centre National pour la Recherche Scientifique et Technique (Morocco) by using the Moroccan software for seismic signals analysis.
Abstract: The problem addressed herein is the efficient management of the Grid/Cluster intense computation involved, when the preconditioned Bi-CGSTAB Krylov method is employed for the iterative solution of the large and sparse linear system arising from the discretization of the Modified Helmholtz-Dirichlet problem by the Hermite Collocation method. Taking advantage of the Collocation ma-trix's red-black ordered structure we organize efficiently the whole computation and map it on a pipeline architecture with master-slave communication. Implementation, through MPI programming tools, is realized on a SUN V240 cluster, inter-connected through a 100Mbps and 1Gbps ethernet network,and its performance is presented by speedup measurements included.
Abstract: In this paper, we propose a modified version of the
Constant Modulus Algorithm (CMA) tailored for blind Decision
Feedback Equalizer (DFE) of first order Markovian time varying
channels. The proposed NonStationary CMA (NSCMA) is designed
so that it explicitly takes into account the Markovian structure of
the channel nonstationarity. Hence, unlike the classical CMA, the
NSCMA is not blind with respect to the channel time variations.
This greatly helps the equalizer in the case of realistic channels, and
avoids frequent transmissions of training sequences.
This paper develops a theoretical analysis of the steady state
performance of the CMA and the NSCMA for DFEs within a time
varying context. Therefore, approximate expressions of the mean
square errors are derived. We prove that in the steady state, the
NSCMA exhibits better performance than the classical CMA. These
new results are confirmed by simulation.
Through an experimental study, we demonstrate that the Bit Error
Rate (BER) is reduced by the NSCMA-DFE, and the improvement
of the BER achieved by the NSCMA-DFE is as significant as the
channel time variations are severe.
Abstract: Flight management system (FMS) is a specialized
computer system that automates a wide variety of in-flight tasks,
reducing the workload on the flight crew to the point that modern
aircraft no longer carry flight engineers or navigators. The primary
function of FMS is to perform the in-flight management of the flight
plan using various sensors (such as GPS and INS often backed up by
radio navigation) to determine the aircraft's position. From the
cockpit FMS is normally controlled through a Control Display Unit
(CDU) which incorporates a small screen and keyboard or touch
screen. This paper investigates the performance of GPS/ INS
integration techniques in which the data fusion process is done using
Kalman filtering. This will include the importance of sensors
calibration as well as the alignment of the strap down inertial
navigation system. The limitations of the inertial navigation systems
are investigated in order to understand why INS sometimes is
integrated with other navigation aids and not just operating in standalone
mode. Finally, both the loosely coupled and tightly coupled
configurations are analyzed for several types of situations and
operational conditions.
Abstract: The motivation of this work was to find a suitable 3D
scanner for human body parts digitalization in the field of prosthetics
and orthotics. The main project objective is to compare the three
hand-held portable scanners (two optical and one laser) and two
optical tripod scanners. The comparison was made with respect of
scanning detail, simplicity of operation and ability to scan directly on
the human body. Testing was carried out on a plaster cast of the
upper limb and directly on a few volunteers. The objective monitored
parameters were time of digitizing and post-processing of 3D data
and resulting visual data quality. Subjectively, it was considered level
of usage and handling of the scanner. The new tripod was developed
to improve the face scanning conditions. The results provide an
overview of the suitability of different types of scanners.
Abstract: Following harvest, fresh produce needs to be cooled
immediately in a room where the air temperature and the relative air
humidity are controlled to maintain the produce quality. In this paper,
an experimental study for forced air cooling of fresh produce
(cauliflower) is performed using a pilot developed within our
laboratory. Furthermore, a numerical simulation of spherical
produces, taking into account the aerodynamic aspect and also the
heat transfer in the produce and in the air, was carried out using a
finite element method. At the end of this communication,
experimental results are presented and compared with the simulation.
Abstract: This paper proposes a declarative language for
knowledge representation (Ibn Rochd), and its environment of
exploitation (DeGSE). This DeGSE system was designed and
developed to facilitate Ibn Rochd writing applications. The system
was tested on several knowledge bases by ascending complexity,
culminating in a system for recognition of a plant or a tree, and
advisors to purchase a car, for pedagogical and academic guidance,
or for bank savings and credit. Finally, the limits of the language and
research perspectives are stated.
Abstract: This paper presents methodologies for developing an
intelligent CAD system assisting in analysis and design of
reconfigurable special machines. It describes a procedure for
determining feasibility of utilizing these machines for a given part
and presents a model for developing an intelligent CAD system. The
system analyzes geometrical and topological information of the given
part to determine possibility of the part being produced by
reconfigurable special machines from a technical point of view. Also
feasibility of the process from a economical point of view is
analyzed. Then the system determines proper positioning of the part
considering details of machining features and operations needed.
This involves determination of operation types, cutting tools and the
number of working stations needed. Upon completion of this stage
the overall layout of the machine and machining equipment required
are determined.
Abstract: This paper describes various stages of design and prototyping of a modular robot for use in various industrial applications. The major goal of current research has been to design and make different robotic joints at low cost capable of being assembled together in any given order for achieving various robot configurations. Five different types of joins were designed and manufactured where extensive research has been carried out on the design of each joint in order to achieve optimal strength, size, modularity, and price. This paper presents various stages of research and development undertaken to engineer these joints that include material selection, manufacturing, and strength analysis. The outcome of this research addresses the birth of a new generation of modular industrial robots with a wider range of applications and greater efficiency.
Abstract: In this paper spatial variability of some chemical and
physical soil properties were investigated in mountain rangelands of
Nesho, Mazandaran province, Iran. 110 soil samples from 0-30 cm
depth were taken with systematic method on grid 30×30 m2 in
regions with different vegetation cover and transported to laboratory.
Then soil chemical and physical parameters including Acidity (pH),
Electrical conductivity, Caco3, Bulk density, Particle density, total
phosphorus, total Nitrogen, available potassium, Organic matter,
Saturation moisture, Soil texture (percentage of sand, silt and clay),
Sodium, Calcium, magnesium were measured in laboratory. Data
normalization was performed then was done statistical analysis for
description of soil properties and geostatistical analysis for indication
spatial correlation between these properties and were perpetrated
maps of spatial distribution of soil properties using Kriging method.
Results indicated that in the study area Saturation moisture and
percentage of Sand had highest and lowest spatial correlation
respectively.
Abstract: Most high-performance ac drives utilize a current
controller. The controller switches a voltage source inverter (VSI)
such that the motor current follows a set of reference current
waveforms. Fixed-band hysteresis (FBH) current control has been
widely used for the PWM inverter. We want to apply the same
controller for the PWM AC chopper. The aims of the controller is to
optimize the harmonic content at both input and output sides, while
maintaining acceptable losses in the ac chopper and to control in
wide range the fundamental output voltage. Fixed band controller has
been simulated and analyzed for a single-phase AC chopper and are
easily extended to three-phase systems. Simulation confirmed the
advantages and the excellent performance of the modulation method
applied for the AC chopper.
Abstract: A Web-based learning tool, the Learn IN Context
(LINC) system, designed and being used in some institution-s
courses in mixed-mode learning, is presented in this paper. This
mode combines face-to-face and distance approaches to education.
LINC can achieve both collaborative and competitive learning. In
order to provide both learners and tutors with a more natural way to
interact with e-learning applications, a conversational interface has
been included in LINC. Hence, the components and essential features
of LINC+, the voice enhanced version of LINC, are described. We
report evaluation experiments of LINC/LINC+ in a real use context
of a computer programming course taught at the Université de
Moncton (Canada). The findings show that when the learning
material is delivered in the form of a collaborative and voice-enabled
presentation, the majority of learners seem to be satisfied with this
new media, and confirm that it does not negatively affect their
cognitive load.
Abstract: The effects of equilibrium time, solution pH, and
sorption temperature of cationic methylene blue (MB) adsorption on nanoporous metallosilicoaluminophosphate ZnAPSO-34 was studied
using a batch equilibration method. UV–VIS spectroscopy was used
to obtain the adsorption isotherms at 20° C. The optimum period for
adsorption was 300 min. However, MB removal increased from
81,82 % to 94,81 %. The equilibrium adsorption data was analyzed
by using Langmuir, Freundlich and Temkin isotherm models.
Langmuir isotherm was found to be the better-fitting model and the process followed pseudo second–order kinetics. The results showed
that ZnAPSO-34 could be employed as an effective material and could be an attractive alternative for the removal of dyes and colors
from aqueous solutions.
Abstract: The belief decision tree (BDT) approach is a decision
tree in an uncertain environment where the uncertainty is represented
through the Transferable Belief Model (TBM), one interpretation
of the belief function theory. The uncertainty can appear either in
the actual class of training objects or attribute values of objects to
classify. In this paper, we develop a post-pruning method of belief
decision trees in order to reduce size and improve classification
accuracy on unseen cases. The pruning of decision tree has a
considerable intention in the areas of machine learning.
Abstract: Recent developments in storage technology and
networking architectures have made it possible for broad areas of applications to rely on data streams for quick response and accurate
decision making. Data streams are generated from events of real world so existence of associations, which are among the occurrence of these events in real world, among concepts of data streams is
logical. Extraction of these hidden associations can be useful for prediction of subsequent concepts in concept shifting data streams. In this paper we present a new method for learning association among
concepts of data stream and prediction of what the next concept will be. Knowing the next concept, an informed update of data model will be possible. The results of conducted experiments show that the proposed method is proper for classification of concept shifting data
streams.