Abstract: In this paper, the improvement by deconvolution of
the depth resolution in Secondary Ion Mass Spectrometry (SIMS)
analysis is considered. Indeed, we have developed a new Tikhonov-
Miller deconvolution algorithm where a priori model of the solution
is included. This is a denoisy and pre-deconvoluted signal obtained
from: firstly, by the application of wavelet shrinkage algorithm,
secondly by the introduction of the obtained denoisy signal in an
iterative deconvolution algorithm. In particular, we have focused the
light on the effect of the iterations number on the evolution of the
deconvoluted signals. The SIMS profiles are multilayers of Boron in
Silicon matrix.
Abstract: This paper presents a new method for the
implementation of a direct rotor flux control (DRFOC) of induction
motor (IM) drives. It is based on the rotor flux components
regulation. The d and q axis rotor flux components feed proportional
integral (PI) controllers. The outputs of which are the target stator
voltages (vdsref and vqsref). While, the synchronous speed is depicted at
the output of rotor speed controller. In order to accomplish variable
speed operation, conventional PI like controller is commonly used.
These controllers provide limited good performances over a wide
range of operations even under ideal field oriented conditions. An
alternate approach is to use the so called fuzzy logic controller. The
overall investigated system is implemented using dSpace system
based on digital signal processor (DSP). Simulation and experimental
results have been presented for a one kw IM drives to confirm the
validity of the proposed algorithms.
Abstract: Multiple sequence alignment is a fundamental part in
many bioinformatics applications such as phylogenetic analysis.
Many alignment methods have been proposed. Each method gives a
different result for the same data set, and consequently generates a
different phylogenetic tree. Hence, the chosen alignment method
affects the resulting tree. However in the literature, there is no
evaluation of multiple alignment methods based on the comparison of
their phylogenetic trees. This work evaluates the following eight
aligners: ClustalX, T-Coffee, SAGA, MUSCLE, MAFFT, DIALIGN,
ProbCons and Align-m, based on their phylogenetic trees (test trees)
produced on a given data set. The Neighbor-Joining method is used
to estimate trees. Three criteria, namely, the dNNI, the dRF and the
Id_Tree are established to test the ability of different alignment
methods to produce closer test tree compared to the reference one
(true tree). Results show that the method which produces the most
accurate alignment gives the nearest test tree to the reference tree.
MUSCLE outperforms all aligners with respect to the three criteria
and for all datasets, performing particularly better when sequence
identities are within 10-20%. It is followed by T-Coffee at lower
sequence identity (30%), trees scores of all methods
become similar.