Abstract: This research examines possible effects of climatic
change focusing on global warming and its impacts on world
agricultural product markets, by using a world food model developed
to consider climate changes. GDP and population for each scenario
were constructed by IPCC and climate data for each scenario was
reported by the Hadley Center and are used in this research to consider
results in different contexts. Production and consumption of primary
agriculture crops of the world for each socio-economic scenario are
obtained and investigated by using the modified world food model.
Simulation results show that crop production in some countries or
regions will have different trends depending on the context. These
alternative contexts depend on the rate of GDP growth, population,
temperature, and rainfall. Results suggest that the development of
environment friendly technologies lead to more consumption of food
in many developing countries. Relationships among environmental
policy, clean energy development, and poverty elimination warrant
further investigation.
Abstract: This paper proposes a new approach to perform the
problem of real-time face detection. The proposed method combines
primitive Haar-Like feature and variance value to construct a new
feature, so-called Variance based Haar-Like feature. Face in image
can be represented with a small quantity of features using this
new feature. We used SVM instead of AdaBoost for training and
classification. We made a database containing 5,000 face samples
and 10,000 non-face samples extracted from real images for learning
purposed. The 5,000 face samples contain many images which have
many differences of light conditions. And experiments showed that
face detection system using Variance based Haar-Like feature and
SVM can be much more efficient than face detection system using
primitive Haar-Like feature and AdaBoost. We tested our method on
two Face databases and one Non-Face database. We have obtained
96.17% of correct detection rate on YaleB face database, which is
higher 4.21% than that of using primitive Haar-Like feature and
AdaBoost.
Abstract: The author present PID controller design for
following control of hard disk drive by characteristic ratio assignment
method. The study in this paper concerns design of a PID controller
which sufficiently robust to the disturbances and plant perturbations
on following control of hard disk drive. Characteristic Ratio
Assignment (CRA) is shown to be an efficient control technique to
serve this requirement. The controller design by CRA is based on the
choice of the coefficients of the characteristic polynomial of the
closed loop system according to the convenient performance criteria
such as equivalent time constant and ration of characteristic
coefficient. Hence, in this study, CRA method is applied in PID
controller design for following control of hard disk drive. Matlab
simulation results shown that CRA design is fairly stable and robust
whilst giving the convenience in controller-s parameters adjustment.
Abstract: Multimedia courseware has been accepted as a tool
that can support teaching and learning process. 'Li2D' courseware
was developed to assist student-s visualization on the topic of Loci in
Two Dimension. This paper describes an evaluation on the
effectiveness and usability of a 'Li2D' courseware. The quasi
experiment was used for the effectiveness evaluation. Usability
evaluation was accomplished based on four constructs of usability,
namely: efficiency, learnability, screen design and satisfaction. An
evaluation on the multimedia elements was also conducted. A total of
63 students of Form Two are involved in the study. The students are
divided into two groups: control and experimental. The experimental
group had to interact with 'Li2D' courseware as part of the learning
activities while the control group used the conventional learning
methods. The results indicate that the experimental group performed
better than the control group in understanding the Loci in Two
Dimensions topic. In terms of usability, the results showed that the
students agreed on the usability in multimedia elements in the 'Li2D'
courseware.
Abstract: In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.
Abstract: Effective cooling of electronic equipment has emerged
as a challenging and constraining problem of the new century. In the
present work the feasibility and effectiveness of jet impingement
cooling on electronics were investigated numerically and
experimentally. Studies have been conducted to see the effect of the
geometrical parameters such as jet diameter (D), jet to target
spacing (Z) and ratio of jet spacing to jet diameter (Z/D) on the heat
transfer characteristics. The values of Reynolds numbers considered
are in the range 7000 to 42000. The results obtained from the
numerical studies are validated by conducting experiments. From the
studies it is found that the optimum value of Z/D ratio is 5. For a
given Reynolds number, the Nusselt number increases by about 28%
if the diameter of the nozzle is increased from 1mm to 2mm.
Correlations are proposed for Nusselt number in terms of Reynolds
number and these are valid for air as the cooling medium.
Abstract: We estimate snow velocity and snow drift density on hilly terrain under the assumption that the drifting snow mass can be represented using a micro-continuum approach (i.e. using a nonclassical mechanics approach assuming a class of fluids for which basic equations of mass, momentum and energy have been derived). In our model, the theory of coupled stress fluids proposed by Stokes [1] has been employed for the computation of flow parameters. Analyses of bulk drift velocity, drift density, drift transport and mass transport of snow particles have been carried out and computations made, considering various parametric effects. Results are compared with those of classical mechanics (logarithmic wind profile). The results indicate that particle size affects the flow characteristics significantly.
Abstract: In distributed resource allocation a set of agents must assign their resources to a set of tasks. This problem arises in many real-world domains such as distributed sensor networks, disaster rescue, hospital scheduling and others. Despite the variety of approaches proposed for distributed resource allocation, a systematic formalization of the problem, explaining the different sources of difficulties, and a formal explanation of the strengths and limitations of key approaches is missing. We take a step towards this goal by using a formalization of distributed resource allocation that represents both dynamic and distributed aspects of the problem. In this paper we present a new idea for target tracking in sensor networks and compare it with previous approaches. The central contribution of the paper is a generalized mapping from distributed resource allocation to DDCSP. This mapping is proven to correctly perform resource allocation problems of specific difficulty. This theoretical result is verified in practice by a simulation on a realworld distributed sensor network.
Abstract: Field mapping activity for an active volcano mainly in
the Torrid Zone is usually hampered by several problems such as steep
terrain and bad atmosphere conditions. In this paper we present a
simple solution for such problem by a combination Synthetic Aperture
Radar (SAR) and geostatistical methods. By this combination, we
could reduce the speckle effect from the SAR data and then estimate
roughness distribution of the pyroclastic flow deposits. The main
purpose of this study is to detect spatial distribution of new pyroclastic
flow deposits termed as P-zone accurately using the β°data from two
RADARSAT-1 SAR level-0 data. Single scene of Hyperion data and
field observation were used for cross-validation of the SAR results.
Mt. Merapi in central Java, Indonesia, was chosen as a study site and
the eruptions in May-June 2006 were examined. The P-zones were
found in the western and southern flanks. The area size and the longest
flow distance were calculated as 2.3 km2 and 6.8 km, respectively. The
grain size variation of the P-zone was mapped in detail from fine to
coarse deposits regarding the C-band wavelength of 5.6 cm.
Abstract: High Speed PM Generators driven by micro-turbines
are widely used in Smart Grid System. So, this paper proposes
comparative study among six classical, optimized and genetic
analytical design cases for 400 kW output power at tip speed 200
m/s. These six design trials of High Speed Permanent Magnet
Synchronous Generators (HSPMSGs) are: Classical Sizing;
Unconstrained optimization for total losses and its minimization;
Constrained optimized total mass with bounded constraints are
introduced in the problem formulation. Then a genetic algorithm is
formulated for obtaining maximum efficiency and minimizing
machine size. In the second genetic problem formulation, we attempt
to obtain minimum mass, the machine sizing that is constrained by
the non-linear constraint function of machine losses. Finally, an
optimum torque per ampere genetic sizing is predicted. All results are
simulated with MATLAB, Optimization Toolbox and its Genetic
Algorithm. Finally, six analytical design examples comparisons are
introduced with study of machines waveforms, THD and rotor losses.
Abstract: SoftBoost is a recently presented boosting algorithm,
which trades off the size of achieved classification margin and
generalization performance. This paper presents a performance
evaluation of SoftBoost algorithm on the generic object recognition
problem. An appearance-based generic object recognition
model is used. The evaluation experiments are performed using
a difficult object recognition benchmark. An assessment with respect
to different degrees of label noise as well as a comparison to
the well known AdaBoost algorithm is performed. The obtained
results reveal that SoftBoost is encouraged to be used in cases
when the training data is known to have a high degree of noise.
Otherwise, using Adaboost can achieve better performance.
Abstract: This paper presents a formant-tracking linear prediction
(FTLP) model for speech processing in noise. The main focus of this
work is the detection of formant trajectory based on Hidden Markov
Models (HMM), for improved formant estimation in noise. The
approach proposed in this paper provides a systematic framework for
modelling and utilization of a time- sequence of peaks which satisfies
continuity constraints on parameter; the within peaks are modelled
by the LP parameters. The formant tracking LP model estimation
is composed of three stages: (1) a pre-cleaning multi-band spectral
subtraction stage to reduce the effect of residue noise on formants
(2) estimation stage where an initial estimate of the LP model of
speech for each frame is obtained (3) a formant classification using
probability models of formants and Viterbi-decoders. The evaluation
results for the estimation of the formant tracking LP model tested
in Gaussian white noise background, demonstrate that the proposed
combination of the initial noise reduction stage with formant tracking
and LPC variable order analysis, results in a significant reduction in
errors and distortions. The performance was evaluated with noisy
natual vowels extracted from international french and English vocabulary
speech signals at SNR value of 10dB. In each case, the
estimated formants are compared to reference formants.
Abstract: The need to evaluate and understand the natural
drainage pattern in a flood prone, and fast developing environment is
of paramount importance. This information will go a long way to
help the town planners to determine the drainage pattern, road
networks and areas where prominent structures are to be located. This
research work was carried out with the aim of studying the Bayelsa
landscape topography using digitized topographic information, and to
model the natural drainage flow pattern that will aid the
understanding and constructions of workable drainages. To achieve
this, digitize information of elevation and coordinate points were
extracted from a global imagery map. The extracted information was
modeled into 3D surfaces. The result revealed that the average
elevation for Bayelsa State is 12 m above sea level. The highest
elevation is 28 m, and the lowest elevation 0 m, along the coastline.
In Yenagoa the capital city of Bayelsa were a detail survey was
carried out showed that average elevation is 15 m, the highest
elevation is 25 m and lowest is 3 m above the mean sea level. The
regional elevation in Bayelsa, showed a gradation decrease from the
North Eastern zone to the South Western Zone. Yenagoa showed an
observed elevation lineament, were low depression is flanked by high
elevation that runs from the North East to the South west. Hence,
future drainages in Yenagoa should be directed from the high
elevation, from South East toward the North West and from the
North West toward South East, to the point of convergence which is
at the center that flows from South East toward the North West.
Bayelsa when considered on a regional Scale, the flow pattern is from
the North East to the South West, and also North South. It is
recommended that in the event of any large drainage construction at
municipal scale, it should be directed from North East to the South
West or from North to South. Secondly, detail survey should be
carried out to ascertain the local topography and the drainage pattern
before the design and construction of any drainage system in any part
of Bayelsa.
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: Training neural networks to capture an intrinsic
property of a large volume of high dimensional data is a difficult
task, as the training process is computationally expensive. Input
attributes should be carefully selected to keep the dimensionality of
input vectors relatively small.
Technical indexes commonly used for stock market prediction
using neural networks are investigated to determine its effectiveness
as inputs. The feed forward neural network of Levenberg-Marquardt
algorithm is applied to perform one step ahead forecasting of
NASDAQ and Dow stock prices.
Abstract: The increasing complexity of software development based on peer to peer networks makes necessary the creation of new frameworks in order to simplify the developer-s task. Additionally, some applications, e.g. fire detection or security alarms may require real-time constraints and the high level definition of these features eases the application development. In this paper, a service model based on a component model with real-time features is proposed. The high-level model will abstract developers from implementation tasks, such as discovery, communication, security or real-time requirements. The model is oriented to deploy services on small mobile devices, such as sensors, mobile phones and PDAs, where the computation is light-weight. Services can be composed among them by means of the port concept to form complex ad-hoc systems and their implementation is carried out using a component language called UM-RTCOM. In order to apply our proposals a fire detection application is described.
Abstract: The habitat where the present study has been carried
out is productive in relation to nutrient quality and they may perform
several useful functions, but are also threatened for their existence.
Hence, the proposed work, will add much new information about
biodiversity of macrophytes in drains and their embankment. All the
species were identified with their different stages of growth which
encountered on the three selected sites (I, II and III). The number of
species occurring at each site is grouped seasonally, i.e. summer,
rainy and winter season and the species were further recorded for the
study of phytosociology. Phytosociological characters such as
frequency, density and abundance were influenced by the climatic,
anthropogenic and biotic stresses prevailing at the three study sites.
All the species present at the study sites have shown maximum
values of frequency, density and abundance in rainy season in
comparison to that of summer and winter seasons.
Abstract: This study analyzed environmental health risks and
people-s perceptions of risks related to waste management in poor
settlements of Abidjan, to develop integrated solutions for health and
well-being improvement. The trans-disciplinary approach used relied
on remote sensing, a geographic information system (GIS),
qualitative and quantitative methods such as interviews and a
household survey (n=1800). Mitigating strategies were then
developed using an integrated participatory stakeholder workshop.
Waste management deficiencies resulting in lack of drainage and
uncontrolled solid and liquid waste disposal in the poor settlements
lead to severe environmental health risks. Health problems were
caused by direct handling of waste, as well as through broader
exposure of the population. People in poor settlements had little
awareness of health risks related to waste management in their
community and a general lack of knowledge pertaining to sanitation
systems. This unfortunate combination was the key determinant
affecting the health and vulnerability. For example, an increased
prevalence of malaria (47.1%) and diarrhoea (19.2%) was observed
in the rainy season when compared to the dry season (32.3% and
14.3%). Concerted and adapted solutions that suited all the
stakeholders concerned were developed in a participatory workshop
to allow for improvement of health and well-being.
Abstract: The aim of this study is to compare the effect of the ultrasonic pre treatment on the removal of heavy metals (Iron, Zinc and Copper) from Acid Mine Drainage (AMD) by Denver Cell flotation. Synthetic AMD and individual metal solutions are used in the initial experiments to optimise the process conditions for real AMD. Three different process methods, ultrasound treatment followed by Denver flotation cell, Denver flotation cell alone and ultrasonic treatments run simultaneously with the Denver flotation cell were tested for every sample. Precipitation of the metal solutions by using sodium hydroxide (NaOH) and application of the optimum frother dosage followed by flotation significantly reduced the metal content of the AMD.
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.