Abstract: Coagulation of water involves the use of coagulating
agents to bring the suspended matter in the raw water together for
settling and the filtration stage. Present study is aimed to examine the
effects of aluminum sulfate as coagulant in conjunction with Moringa
Oleifera Coagulant Protein as coagulant aid on turbidity, hardness,
and bacteria in turbid water. A conventional jar test apparatus was
employed for the tests. The best removal was observed at a pH of 7
to 7.5 for all turbidities. Turbidity removal efficiency was resulted
between % 80 to % 99 by Moringa Oleifera Coagulant Protein as
coagulant aid. Dosage of coagulant and coagulant aid decreased with
increasing turbidity. In addition, Moringa Oleifera Coagulant Protein
significantly has reduced the required dosage of primary coagulant.
Residual Al+3 in treated water were less than 0.2 mg/l and meets the
environmental protection agency guidelines. The results showed that
turbidity reduction of % 85.9- % 98 paralleled by a primary
Escherichia coli reduction of 1-3 log units (99.2 – 99.97%) was
obtained within the first 1 to 2 h of treatment. In conclusions,
Moringa Oleifera Coagulant Protein as coagulant aid can be used for
drinking water treatment without the risk of organic or nutrient
release. We demonstrated that optimal design method is an efficient
approach for optimization of coagulation-flocculation process and
appropriate for raw water treatment.
Abstract: In this work we develop an object extraction method
and propose efficient algorithms for object motion characterization.
The set of proposed tools serves as a basis for development of objectbased
functionalities for manipulation of video content. The
estimators by different algorithms are compared in terms of quality
and performance and tested on real video sequences. The proposed
method will be useful for the latest standards of encoding and
description of multimedia content – MPEG4 and MPEG7.
Abstract: The importance of nurturing, accumulating, and efficiently deploying knowledge resources through formal structures and organisational mechanisms is well understood. Recent trends in knowledge management (KM) highlight that the effective creation and transfer of knowledge can also rely upon extra-organisational channels, such as, informal networks. The perception exists that the role of informal networks in knowledge creation and performance has been underestimated in the organisational context. Literature indicates that many managers fail to comprehend and successfully exploit the potential role of informal networks to create value for their organisations. This paper investigates: 1) whether managers share work-specific knowledge with informal contacts within and outside organisational boundaries; and 2) what do they think is the importance of this knowledge collaboration in their learning and work outcomes.
Abstract: Semantic Web services will enable the semiautomatic
and automatic annotation, advertisement, discovery,
selection, composition, and execution of inter-organization business
logic, making the Internet become a common global platform where
organizations and individuals communicate with each other to carry
out various commercial activities and to provide value-added
services. There is a growing consensus that Web services alone will
not be sufficient to develop valuable solutions due the degree of
heterogeneity, autonomy, and distribution of the Web. This paper
deals with two of the hottest R&D and technology areas currently
associated with the Web – Web services and the Semantic Web. It
presents the synergies that can be created between Web Services and
Semantic Web technologies to provide a new generation of eservices.
Abstract: The mechanical properties including flexural and
tensile of neat vinyl ester and polymer based on layered silicate
nanocomposite materials are discussed. The addition of layered
silicate into the polymer matrix increased the tensile and flexural
modulus up to 1 wt.% clay loading. The incorporation of more clay
resulted in decreasing the mechanical properties which was traced to
the existence of aggregation layers. Likewise, up to 1 wt.% clay
loading, the thermal behaviour showed significant improvements and
at higher clay loading the thermal pattern was reduced. The
aggregation layers imparted a negative impact on the overall
mechanical and thermal properties. Wide Angle X-ray Diffraction,
Scanning Electron Microscopy and Transmission Electron
Microscopy were utilised in order to characterise the interlamellar
structure of nanocomposites.
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: One of the main environmental problems which affect extensive areas in the world is soil salinity. Traditional data collection methods are neither enough for considering this important environmental problem nor accurate for soil studies. Remote sensing data could overcome most of these problems. Although satellite images are commonly used for these studies, however there are still needs to find the best calibration between the data and real situations in each specified area. Neyshaboor area, North East of Iran was selected as a field study of this research. Landsat satellite images for this area were used in order to prepare suitable learning samples for processing and classifying the images. 300 locations were selected randomly in the area to collect soil samples and finally 273 locations were reselected for further laboratory works and image processing analysis. Electrical conductivity of all samples was measured. Six reflective bands of ETM+ satellite images taken from the study area in 2002 were used for soil salinity classification. The classification was carried out using common algorithms based on the best composition bands. The results showed that the reflective bands 7, 3, 4 and 1 are the best band composition for preparing the color composite images. We also found out, that hybrid classification is a suitable method for identifying and delineation of different salinity classes in the area.
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: Residues are produced in all stages of human activities
in terms of composition and volume which vary according to
consumption practices and to production methods. Forms of
significant harm to the environment are associated to volume of
generated material as well as to improper disposal of solid wastes,
whose negative effects are noticed more frequently in the long term.
The solution to this problem constitutes a challenge to the
government, industry and society, because they involve economic,
social, environmental and, especially, awareness of the population in
general. The main concerns are focused on the impact it can have on
human health and on the environment (soil, water, air and sights).
The hazardous waste produced mainly by industry, are particularly
worrisome because, when improperly managed, they become a
serious threat to the environment. In view of this issue, this study
aimed to evaluate the management system of solid waste of a coprocessing
industrial waste company, to propose improvements to the
rejects generation management in a specific step of the Blending
production process.
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: In this paper, a generalized synchronization scheme, which is called function synchronization, for chaotic systems is studied. Based on Lyapunov method and active control method, we design the synchronization controller for the system such that the error dynamics between master and slave chaotic systems is asymptotically stable. For verification of our theory, computer and circuit simulations for a specific chaotic system is conducted.
Abstract: In this paper, the noise maps for the area encircled by
the Second Ring Road in Riyadh city are developed based on real
measured data. Sound level meters, GPS receivers to determine
measurement position, a database program to manage the measured
data, and a program to develop the maps are used. A baseline noise
level has been established at each short-term site so subsequent
monitoring may be conducted to describe changes in Riyadh-s noise
environment. Short-term sites are used to show typical daytime and
nighttime noise levels at specific locations by short duration grab
sampling.
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: Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.
Abstract: Nowadays, people are going more and more mobile, both in terms of devices and associated applications. Moreover, services that these devices are offering are getting wider and much more complex. Even though actual handheld devices have considerable computing power, their contexts of utilization are different. These contexts are affected by the availability of connection, high latency of wireless networks, battery life, size of the screen, on-screen or hard keyboard, etc. Consequently, development of mobile applications and their associated mobile Web services, if any, should follow a concise methodology so they will provide a high Quality of Service. The aim of this paper is to highlight and discuss main issues to consider when developing mobile applications and mobile Web services and then propose a framework that leads developers through different steps and modules toward development of efficient and secure mobile applications. First, different challenges in developing such applications are elicited and deeply discussed. Second, a development framework is presented with different modules addressing each of these challenges. Third, the paper presents an example of a mobile application, Eivom Cinema Guide, which benefits from following our development framework.
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: 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.