Abstract: The threshold voltage and capacitance voltage characteristics of ultra-thin Silicon-on-Insulator MOSFET are greatly influenced by the thickness and doping concentration of the silicon film. In this work, the capacitance voltage characteristics and threshold voltage of the device have been analyzed with quantum mechanical effects using the Self-Consistent model. Reduction of channel thickness and adding doping impurities cause an increase in the threshold voltage. Moreover, the temperature effects cause a significant amount of threshold voltage shift. The temperature dependence of threshold voltage has also been observed with Self- Consistent approach which are well supported from experimental performance of practical devices.
Abstract: A numerical analysis of wave and hydrodynamic models
is used to investigate the influence of WAve and Storm Surge
(WASS) in the regional and coastal zones. The numerical analyzed
system consists of the WAve Model Cycle 4 (WAMC4) and the
Princeton Ocean Model (POM) which used to solve the energy
balance and primitive equations respectively. The results of both
models presented the incorporated surface wave in the regional
zone affected the coastal storm surge zone. Specifically, the results
indicated that the WASS generally under the approximation is not
only the peak surge but also the coastal water level drop which
can also cause substantial impact on the coastal environment. The
wave–induced surface stress affected the storm surge can significantly
improve storm surge prediction. Finally, the calibration of wave
module according to the minimum error of the significant wave height
(Hs) is not necessarily result in the optimum wave module in the
WASS analyzed system for the WASS prediction.
Abstract: Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
Abstract: In the context of sensor networks, where every few
dB saving counts, the novel node cooperation schemes are reviewed
where MIMO techniques play a leading role. These methods could be
treated as joint approach for designing physical layer of their
communication scenarios. Then we analyzed the BER performance
of transmission diversity schemes under a general fading channel
model and proposed a power allocation strategy to the transmitting
sensor nodes. This approach is then compared to an equal-power
assignment method and its performance enhancement is verified by
the simulation. Another key point of the contribution lies in the
combination of optimal power allocation and sensor nodes-
cooperation in a transmission diversity regime (MISO). Numerical
results are given through figures to demonstrate the optimality and
efficiency of proposed combined approach.
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: The development and extension of large cities induced
a need for shallow tunnel in soft ground of building areas. Estimation
of ground settlement caused by the tunnel excavation is important
engineering point. In this paper, prediction of surface subsidence
caused by tunneling in one section of seventh line of Tehran subway
is considered. On the basis of studied geotechnical conditions of the
region, tunnel with the length of 26.9km has been excavated applying
a mechanized method using an EPB-TBM with a diameter of 9.14m.
In this regard, settlement is estimated utilizing both analytical and
numerical finite element method. The numerical method shows that
the value of settlement in this section is 5cm. Besides, the analytical
consequences (Bobet and Loganathan-Polous) are 5.29 and 12.36cm,
respectively. According to results of this study, due tosaturation of
this section, there are good agreement between Bobet and numerical
methods. Therefore, tunneling processes in this section needs a
special consolidation measurement and support system before the
passage of tunnel boring machine.
Abstract: Coated tool inserts can be considered as the backbone
of machining processes due to their wear and heat resistance.
However, defects of coating can degrade the integrity of these inserts
and the number of these defects should be minimized or eliminated if
possible. Recently, the advancement of coating processes and
analytical tools open a new era for optimizing the coating tools.
First, an overview is given regarding coating technology for cutting
tool inserts. Testing techniques for coating layers properties, as well
as the various coating defects and their assessment are also surveyed.
Second, it is introduced an experimental approach to examine the
possible coating defects and flaws of worn multicoated carbide
inserts using two important techniques namely scanning electron
microscopy and atomic force microscopy. Finally, it is
recommended a simple procedure for investigating manufacturing
defects and flaws of worn inserts.
Abstract: This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.
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: This paper presents a design and prototype
implementation of new home automation system that uses WiFi
technology as a network infrastructure connecting its parts. The
proposed system consists of two main components; the first part is
the server (web server), which presents system core that manages,
controls, and monitors users- home. Users and system administrator
can locally (LAN) or remotely (internet) manage and control system
code. Second part is hardware interface module, which provides
appropriate interface to sensors and actuator of home automation
system. Unlike most of available home automation system in the
market the proposed system is scalable that one server can manage
many hardware interface modules as long as it exists on WiFi
network coverage. System supports a wide range of home
automation devices like power management components, and
security components. The proposed system is better from the
scalability and flexibility point of view than the commercially
available home automation systems.
Abstract: This paper introduces and studies new indexing techniques for content-based queries in images databases. Indexing is the key to providing sophisticated, accurate and fast searches for queries in image data. This research describes a new indexing approach, which depends on linear modeling of signals, using bases for modeling. A basis is a set of chosen images, and modeling an image is a least-squares approximation of the image as a linear combination of the basis images. The coefficients of the basis images are taken together to serve as index for that image. The paper describes the implementation of the indexing scheme, and presents the findings of our extensive evaluation that was conducted to optimize (1) the choice of the basis matrix (B), and (2) the size of the index A (N). Furthermore, we compare the performance of our indexing scheme with other schemes. Our results show that our scheme has significantly higher performance.
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: Air emissions from waste treatment plants often
consist of a combination of Volatile Organic Compounds (VOCs)
and odors. Hydrogen sulfide is one of the major odorous gases
present in the waste emissions coming from municipal wastewater
treatment facilities. Hydrogen sulfide (H2S) is odorous, highly toxic
and flammable. Exposure to lower concentrations can result in eye
irritation, a sore throat and cough, shortness of breath, and fluid in
the lungs. Biofiltration has become a widely accepted technology for
treating air streams containing H2S. When compared with other nonbiological
technologies, biofilter is more cost-effective for treating large
volumes of air containing low concentrations of biodegradable compounds.
Optimization of biofilter media is essential for many reasons such as:
providing a higher surface area for biofilm growth, low pressure drop,
physical stability, and good moisture retention. In this work, a novel
biofilter media is developed and tested at a pumping station of a
municipality located in the United Arab Emirates (UAE). The
media is found to be very effective (>99%) in removing H2S
concentrations that are expected in pumping stations under steady
state and shock loading conditions.
Abstract: Society has grown to rely on Internet services, and the
number of Internet users increases every day. As more and more
users become connected to the network, the window of opportunity
for malicious users to do their damage becomes very great and
lucrative. The objective of this paper is to incorporate different
techniques into classier system to detect and classify intrusion from
normal network packet. Among several techniques, Steady State
Genetic-based Machine Leaning Algorithm (SSGBML) will be used
to detect intrusions. Where Steady State Genetic Algorithm (SSGA),
Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and
Zeroth Level Classifier system are investigated in this research.
SSGA is used as a discovery mechanism instead of SGA. SGA
replaces all old rules with new produced rule preventing old good
rules from participating in the next rule generation. Zeroth Level
Classifier System is used to play the role of detector by matching
incoming environment message with classifiers to determine whether
the current message is normal or intrusion and receiving feedback
from environment. Finally, in order to attain the best results,
Modified SSGA will enhance our discovery engine by using Fuzzy
Logic to optimize crossover and mutation probability. The
experiments and evaluations of the proposed method were performed
with the KDD 99 intrusion detection dataset.
Abstract: This study was conducted to investigate the incidence
of pathogenic bacteria: Salmonella, Shigella, Escherichia coli O157
and Staphylococcus aureus in cakes and tarts collected from thirtyfive
confectionery producing and selling premises located within
Tripoli city, Libya. The results revealed an incidence of S. aureus
with 94.4 and 48.0 %, E. coli O157 with 14.7 and 4.0 % and Salmonella
sp. with 5.9 and 8.0 % in cakes and tarts samples respectively;
while Shigella was not detected in all samples. In order to determine
the source of these pathogenic bacteria, cotton swabs were taken
from the hands of workers on the production line, the surfaces of
preparation tables and cream whipping instruments. The results
showed that the cotton swabs obtained from the hands of workers
contained S. aureus and Salmonella sp. with an incidence of 42.9 and
2.9 %, the cotton swabs obtained from the surfaces of preparation
tables 22.9 and 2.9 % and the cotton swabs obtained from the cream
whipping instruments 14.3 and 0.0 % respectively; while E. coli
O157 and Shigella sp. were not detected in all swabs. Additionally,
other bacteria were isolated from the hands of workers and the Surfaces
of producing equipments included: Aeromonas sp., Pseudomonas
sp., E. coli, Klebsiella sp., Enterobacter sp., Citrobacter sp.,
Proteus sp., Serratia sp. and Acinetobacter sp. These results indicate
that some of the cakes and tarts might pose threat to consumer's
health. Meanwhile, occurrences of pathogenic bacteria on the hands
of those who are working in production line and the surfaces of
equipments reflect poor hygienic practices at most confectionery
premises examined in this study. Thus, firm and continuous surveillance
of these premises is needed to insure the consumer's health and
safety.
Abstract: Underpricing is one anomaly in initial public offerings
(IPO) literature that has been widely observed across different stock
markets with different trends emerging over different time periods.
This study seeks to determine how IPOs on the JSE performed on the
first day, first week and first month over the period of 1996-2011.
Underpricing trends are documented for both hot and cold market
periods in terms of four main sectors (cyclical, defensive, growth
stock and interest rate sensitive stocks). Using a sample of 360 listed
companies on the JSE, the empirical findings established that IPOs
on the JSE are significantly underpriced with an average market
adjusted first day return of 62.9%. It is also established that hot
market IPOs on the JSE are more underpriced than the cold market
IPOs. Also observed is the fact that as the offer price per share
increases above the median price for any given period, the level of
underpricing decreases substantially. While significant differences
exist in the level of underpricing of IPOs in the four different sectors
in the hot and cold market periods, interest rates sensitive stocks
showed a different trend from the other sectors and thus require
further investigation to uncover this pattern.
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: 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.