Abstract: This paper deals with the localization of the wideband sources. We develop a new approach for estimating the wide band sources parameters. This method is based on the high order statistics of the recorded data in order to eliminate the Gaussian components from the signals received on the various hydrophones.In fact the noise of sea bottom is regarded as being Gaussian. Thanks to the coherent signal subspace algorithm based on the cumulant matrix of the received data instead of the cross-spectral matrix the wideband correlated sources are perfectly located in the very noisy environment. We demonstrate the performance of the proposed algorithm on the real data recorded during an underwater acoustics experiments.
Abstract: Nigeria is considered as one of the many countries in
sub-Saharan Africa with a weak economy and gross deficiencies in technology and engineering. Available data from international monitoring and regulatory organizations show that technology is pivotal to determining the economic strengths of nations all over the
world. Education is critical to technology acquisition, development,
dissemination and adaptation. Thus, this paper seeks to critically
assess and discuss issues and challenges facing technological
advancement in Nigeria, particularly in the education sector, and also
proffers solutions to resuscitate the Nigerian education system
towards achieving national technological and economic sustainability
such that Nigeria can compete favourably with other technologicallydriven
economies of the world in the not-too-distant future.
Abstract: In this paper, a methodology of a model based on
predicting the tool forces oblique machining are introduced by
adopting the orthogonal technique. The applied analytical calculation
is mostly based on Devries model and some parts of the methodology
are employed from Amareggo-Brown model. Model validation is
performed by comparing experimental data with the prediction results
on machining titanium alloy (Ti-6Al-4V) based on micro-cutting tool
perspective. Good agreements with the experiments are observed. A
detailed friction form that affected the tool forces also been examined
with reasonable results obtained.
Abstract: The first generation of Mobile Agents based Intrusion
Detection System just had two components namely data collection
and single centralized analyzer. The disadvantage of this type of
intrusion detection is if connection to the analyzer fails, the entire
system will become useless. In this work, we propose novel hybrid
model for Mobile Agent based Distributed Intrusion Detection
System to overcome the current problem. The proposed model has
new features such as robustness, capability of detecting intrusion
against the IDS itself and capability of updating itself to detect new
pattern of intrusions. In addition, our proposed model is also capable
of tackling some of the weaknesses of centralized Intrusion Detection
System models.
Abstract: A reliability, availability and maintainability (RAM) model has been built for acid gas removal plant for system analysis that will play an important role in any process modifications, if required, for achieving its optimum performance. Due to the complexity of the plant, the model was based on a Reliability Block Diagram (RBD) with a Monte Carlo simulation engine. The model has been validated against actual plant data as well as local expert opinions, resulting in an acceptable simulation model. The results from the model showed that the operation and maintenance can be further improved, resulting in reduction of the annual production loss.
Abstract: In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.
Abstract: The fluid mechanics principle is used extensively in
designing axial flow fans and their associated equipment. This paper presents a computational fluid dynamics (CFD) modeling of air flow
distribution from a radiator axial flow fan used in an acid pump truck Tier4 (APT T4) Repower. This axial flow fan augments the transfer
of heat from the engine mounted on the APT T4.
CFD analysis was performed for an area weighted average static pressure difference at the inlet and outlet of the fan. Pressure contours, velocity vectors, and path lines were plotted for detailing
the flow characteristics for different orientations of the fan blade. The results were then compared and verified against known theoretical observations and actual experimental data. This study
shows that a CFD simulation can be very useful for predicting and understanding the flow distribution from a radiator fan for further
research work.
Abstract: In this paper, cloud resource broker using goalbased
request in medical application is proposed. To handle recent
huge production of digital images and data in medical informatics
application, the cloud resource broker could be used by medical
practitioner for proper process in discovering and selecting correct
information and application. This paper summarizes several
reviewed articles to relate medical informatics application with
current broker technology and presents a research work in applying
goal-based request in cloud resource broker to optimize the use of
resources in cloud environment. The objective of proposing a new
kind of resource broker is to enhance the current resource
scheduling, discovery, and selection procedures. We believed that
it could help to maximize resources allocation in medical
informatics application.
Abstract: In this research, we propose to use the discrete cosine
transform to approximate the cumulative distributions of data cube
cells- values. The cosine transform is known to have a good energy
compaction property and thus can approximate data distribution
functions easily with small number of coefficients. The derived
estimator is accurate and easy to update. We perform experiments to
compare its performance with a well-known technique - the (Haar)
wavelet. The experimental results show that the cosine transform
performs much better than the wavelet in estimation accuracy, speed,
space efficiency, and update easiness.
Abstract: Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for preprocessing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based preprocessing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.
Abstract: Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
Abstract: This article proposes an Ant Colony Optimization
(ACO) metaheuristic to minimize total makespan for scheduling a set
of jobs and assign workers for uniformly related parallel machines.
An algorithm based on ACO has been developed and coded on a
computer program Matlab®, to solve this problem. The paper
explains various steps to apply Ant Colony approach to the problem
of minimizing makespan for the worker assignment & jobs
scheduling problem in a parallel machine model and is aimed at
evaluating the strength of ACO as compared to other conventional
approaches. One data set containing 100 problems (12 Jobs, 03
machines and 10 workers) which is available on internet, has been
taken and solved through this ACO algorithm. The results of our
ACO based algorithm has shown drastically improved results,
especially, in terms of negligible computational effort of CPU, to
reach the optimal solution. In our case, the time taken to solve all 100
problems is even lesser than the average time taken to solve one
problem in the data set by other conventional approaches like GA
algorithm and SPT-A/LMC heuristics.
Abstract: Modeling of Panel Zone (PZ) seismic behavior,
because of its role in overall ductility and lateral stiffness of steel
moment frames, has been considered a challenge for years. There are
some studies regarding the effects of different doubler plates
thicknesses and geometric properties of PZ on its seismic behavior.
However, there is not much investigation on the effects of number of
provided continuity plates in case of presence of one triangular
haunch, two triangular haunches and rectangular haunch (T shape
haunches) for exterior columns. In this research first detailed finite
element models of 12tested connection of SAC joint venture were
created and analyzed then obtained cyclic behavior backbone curves
of these models besides other FE models for similar tests were used
for neural network training. Then seismic behavior of these data is
categorized according to continuity plate-s arrangements and
differences in type of haunches. PZ with one-sided haunches have
little plastic rotation. As the number of continuity plates increases
due to presence of two triangular haunches (four continuity plate),
there will be no plastic rotation, in other words PZ behaves in its
elastic range. In the case of rectangular haunch, PZ show more plastic
rotation in comparison with one-sided triangular haunch and
especially double-sided triangular haunches. Moreover, the models
that will be presented in case of triangular one-sided and double-
sided haunches and rectangular haunches as a result of this study
seem to have a proper estimation of PZ seismic behavior.
Abstract: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.
Abstract: It is sometimes difficult to differentiate between
innocent murmurs and pathological murmurs during auscultation. In
these difficult cases, an intelligent stethoscope with decision support
abilities would be of great value. In this study, using a dog model,
phonocardiographic recordings were obtained from 27 boxer dogs
with various degrees of aortic stenosis (AS) severity. As a reference
for severity assessment, continuous wave Doppler was used. The data
were analyzed with recurrence quantification analysis (RQA) with
the aim to find features able to distinguish innocent murmurs from
murmurs caused by AS. Four out of eight investigated RQA features
showed significant differences between innocent murmurs and
pathological murmurs. Using a plain linear discriminant analysis
classifier, the best pair of features (recurrence rate and entropy)
resulted in a sensitivity of 90% and a specificity of 88%. In
conclusion, RQA provide valid features which can be used for
differentiation between innocent murmurs and murmurs caused by
AS.
Abstract: MBMS is a unidirectional point-to-multipoint bearer
service in which data are transmitted from a single source entity to
multiple recipients. For a mobile to support the MBMS, MBMS client
functions as well as MBMS radio protocols should be designed and
implemented. In this paper, we analyze the MBMS client functions
and describe the implementation of them in our mobile test-bed. User
operations and signaling flows between protocol entities to control the
MBMS functions are designed in detail. Service announcement
utilizing the file download MBMS service and four MBMS user
services are demonstrated in the test-bed to verify the MBMS client
functions.
Abstract: Saturated hydraulic conductivity of Soil is an
important property in processes involving water and solute flow in
soils. Saturated hydraulic conductivity of soil is difficult to measure
and can be highly variable, requiring a large number of replicate
samples. In this study, 60 sets of soil samples were collected at
Saqhez region of Kurdistan province-IRAN. The statistics such as
Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean
Bias Error (MBE) and Mean Absolute Error (MAE) were used to
evaluation the multiple linear regression models varied with number
of dataset. In this study the multiple linear regression models were
evaluated when only percentage of sand, silt, and clay content (SSC)
were used as inputs, and when SSC and bulk density, Bd, (SSC+Bd)
were used as inputs. The R, RMSE, MBE and MAE values of the 50
dataset for method (SSC), were calculated 0.925, 15.29, -1.03 and
12.51 and for method (SSC+Bd), were calculated 0.927, 15.28,-1.11
and 12.92, respectively, for relationship obtained from multiple
linear regressions on data. Also the R, RMSE, MBE and MAE values
of the 10 dataset for method (SSC), were calculated 0.725, 19.62, -
9.87 and 18.91 and for method (SSC+Bd), were calculated 0.618,
24.69, -17.37 and 22.16, respectively, which shows when number of
dataset increase, precision of estimated saturated hydraulic
conductivity, increases.
Abstract: As wireless sensor networks are energy constraint networks
so energy efficiency of sensor nodes is the main design issue.
Clustering of nodes is an energy efficient approach. It prolongs the
lifetime of wireless sensor networks by avoiding long distance communication.
Clustering algorithms operate in rounds. Performance of
clustering algorithm depends upon the round time. A large round
time consumes more energy of cluster heads while a small round
time causes frequent re-clustering. So existing clustering algorithms
apply a trade off to round time and calculate it from the initial
parameters of networks. But it is not appropriate to use initial
parameters based round time value throughout the network lifetime
because wireless sensor networks are dynamic in nature (nodes can be
added to the network or some nodes go out of energy). In this paper
a variable round time approach is proposed that calculates round
time depending upon the number of active nodes remaining in the
field. The proposed approach makes the clustering algorithm adaptive
to network dynamics. For simulation the approach is implemented
with LEACH in NS-2 and the results show that there is 6% increase
in network lifetime, 7% increase in 50% node death time and 5%
improvement over the data units gathered at the base station.
Abstract: Some methodologies were compared in providing
erosion maps of surface, rill and gully and erosion features, in
research which took place in the Varamin sub-basin, north-east
Tehran, Iran. A photomorphic unit map was produced from
processed satellite images, and four other maps were prepared by the
integration of different data layers, including slope, plant cover,
geology, land use, rocks erodibility and land units. Comparison of
ground truth maps of erosion types and working unit maps indicated
that the integration of land use, land units and rocks erodibility layers
with satellite image photomorphic units maps provide the best
methods in producing erosion types maps.
Abstract: The objective of this study is to evaluate the
occurrence of fungi in aerobic and anoxic activated sludge from
membrane bioreactors (MBRs). Thirty-six samples of both aerobic
and anoxic activated sludge were taken from 2 MBR treating
domestic wastewater. Over a period of eight months 2 samples from
each plant were taken per month. The samples were prepared for
count and definition of fungi. The obtained data show that, sixty
species belonging to 27 genera were collected from activated sludge
samples under aerobic and anoxic conditions. Regarding to the fungi
definition, under aerobic condition the Geotrichum was found at
(8.8%) followed by Penicillium (75.0%), Yeasts (65.7%) and
Trichoderma (55.5%), while Yeasts (77.1%) Geotrichum
candidumand Penicillium (61.1%) species were the most prevalent in
anoxic activated sludge. The results indicate that activated sludge is
habitat for growth and sporulation of different groups of fungi, both
saprophytic and pathogenic.