Abstract: We present in this work our model of road traffic
emissions (line sources) and dispersion of these emissions, named
DISPOLSPEM (Dispersion of Poly Sources and Pollutants Emission
Model). In its emission part, this model was designed to keep the
consistent bottom-up and top-down approaches. It also allows to
generate emission inventories from reduced input parameters being
adapted to existing conditions in Morocco and in the other developing
countries. While several simplifications are made, all the performance
of the model results are kept. A further important advantage of
the model is that it allows the uncertainty calculation and emission
rate uncertainty according to each of the input parameters. In the
dispersion part of the model, an improved line source model has
been developed, implemented and tested against a reference solution.
It provides improvement in accuracy over previous formulas of line
source Gaussian plume model, without being too demanding in terms
of computational resources. In the case study presented here, the
biggest errors were associated with the ends of line source sections;
these errors will be canceled by adjacent sections of line sources
during the simulation of a road network. In cases where the wind
is parallel to the source line, the use of the combination discretized
source and analytical line source formulas minimizes remarkably the
error. Because this combination is applied only for a small number
of wind directions, it should not excessively increase the calculation
time.
Abstract: We consider fast and accurate solutions of scattering
problems by large perfectly conducting objects (PEC) formulated
by an optimization of the Method of Auxiliary Sources (MAS). We
present various techniques used to reduce the total computational cost
of the scattering problem. The first technique is based on replacing
the object by an array of finite number of small (PEC) object with the
same shape. The second solution reduces the problem on considering
only the half of the object.These t
Abstract: In diversity rich environments, such as in Ultra-
Wideband (UWB) applications, the a priori determination of the
number of strong diversity branches is difficult, because of the considerably large number of diversity paths, which are characterized
by a variety of power delay profiles (PDPs). Several
Rake implementations have been proposed in the past, in order to reduce the number of the estimated and combined paths. To this
aim, we introduce two adaptive Rake receivers, which combine
a subset of the resolvable paths considering simultaneously the
quality of both the total combining output signal-to-noise ratio (SNR) and the individual SNR of each path. These schemes achieve
better adaptation to channel conditions compared to other known receivers, without further increasing the complexity. Their performance
is evaluated in different practical UWB channels, whose models are based on extensive propagation measurements. The
proposed receivers compromise between the power consumption,
complexity and performance gain for the additional paths, resulting in important savings in power and computational resources.
Abstract: In this paper, a new learning approach for network
intrusion detection using naïve Bayesian classifier and ID3 algorithm
is presented, which identifies effective attributes from the training
dataset, calculates the conditional probabilities for the best attribute
values, and then correctly classifies all the examples of training and
testing dataset. Most of the current intrusion detection datasets are
dynamic, complex and contain large number of attributes. Some of
the attributes may be redundant or contribute little for detection
making. It has been successfully tested that significant attribute
selection is important to design a real world intrusion detection
systems (IDS). The purpose of this study is to identify effective
attributes from the training dataset to build a classifier for network
intrusion detection using data mining algorithms. The experimental
results on KDD99 benchmark intrusion detection dataset demonstrate
that this new approach achieves high classification rates and reduce
false positives using limited computational resources.