Abstract: In this paper, we study the cooperative communications where multiple cognitive radio (CR) transmit-receive pairs competitive maximize their own throughputs. In CR networks, the influences of primary users and the spectrum availability are usually different among CR users. Due to the existence of multiple relay nodes and the different spectrum availability, each CR transmit-receive pair should not only select the relay node but also choose the appropriate channel. For this distributed problem, we propose a game theoretic framework to formulate this problem and we apply a regret-matching learning algorithm which is leading to correlated equilibrium. We further formulate a modified regret-matching learning algorithm which is fully distributed and only use the local information of each CR transmit-receive pair. This modified algorithm is more practical and suitable for the cooperative communications in CR network. Simulation results show the algorithm convergence and the modified learning algorithm can achieve comparable performance to the original regretmatching learning algorithm.
Abstract: The conventional production of biodiesel from crude
palm oil which contains large amounts of free fatty acids in the
presence of a homogeneous base catalyst confronts the problems of
soap formation and very low yield of biodiesel. To overcome these
problems, free fatty acids must be esterified to their esters in the
presence of an acid catalyst prior to alkaline-catalyzed
transesterification. Sulfated metal oxides are a promising group of
catalysts due to their very high acidity. In this research, aluminadoped
sulfated tin oxide (SO4
2-/Al2O3-SnO2) catalysts were prepared
and used for esterification of free fatty acids in crude palm oil in a
batch reactor. The SO4
2-/Al2O3-SnO2 catalysts were prepared from
different Al precursors. The results showed that different Al
precursors gave different activities of the SO4
2-/Al2O3-SnO2 catalysts.
The esterification of free fatty acids in crude palm oil with methanol
in the presence of SO4
2-/Al2O3-SnO2 catalysts followed first-order
kinetics.
Abstract: For complete support of Quality of Service, it is better that environment itself predicts resource requirements of a job by using special methods in the Grid computing. The exact and correct prediction causes exact matching of required resources with available resources. After the execution of each job, the used resources will be saved in the active database named "History". At first some of the attributes will be exploit from the main job and according to a defined similarity algorithm the most similar executed job will be exploited from "History" using statistic terms such as linear regression or average, resource requirements will be predicted. The new idea in this research is based on active database and centralized history maintenance. Implementation and testing of the proposed architecture results in accuracy percentage of 96.68% to predict CPU usage of jobs and 91.29% of memory usage and 89.80% of the band width usage.
Abstract: Environmental awareness and the recent
environmental policies have forced many electric utilities to
restructure their operational practices to account for their emission
impacts. One way to accomplish this is by reformulating the
traditional economic dispatch problem such that emission effects are
included in the mathematical model. This paper presents a Particle
Swarm Optimization (PSO) algorithm to solve the Economic-
Emission Dispatch problem (EED) which gained recent attention due
to the deregulation of the power industry and strict environmental
regulations. The problem is formulated as a multi-objective one with
two competing functions, namely economic cost and emission
functions, subject to different constraints. The inequality constraints
considered are the generating unit capacity limits while the equality
constraint is generation-demand balance. A novel equality constraint
handling mechanism is proposed in this paper. PSO algorithm is
tested on a 30-bus standard test system. Results obtained show that
PSO algorithm has a great potential in handling multi-objective
optimization problems and is capable of capturing Pareto optimal
solution set under different loading conditions.
Abstract: This article presents a resistorless current-mode firstorder allpass filter based on second generation current controlled current conveyors (CCCIIs). The features of the circuit are that: the pole frequency can be electronically controlled via the input bias current: the circuit description is very simple, consisting of 2 CCCIIs and single grounded capacitor, without any external resistors and component matching requirements. Consequently, the proposed circuit is very appropriate to further develop into an integrated circuit. Low input and high output impedances of the proposed configuration enable the circuit to be cascaded in current-mode without additional current buffers. The PSpice simulation results are depicted. The given results agree well with the theoretical anticipation. The application example as a current-mode quadrature oscillator is included.
Abstract: In this paper, a novel feature-based image
watermarking scheme is proposed. Zernike moments which have
invariance properties are adopted in the scheme. In the proposed
scheme, feature points are first extracted from host image and several
circular patches centered on these points are generated. The patches
are used as carriers of watermark information because they can be
regenerated to locate watermark embedding positions even when
watermarked images are severely distorted. Zernike transform is then
applied to the patches to calculate local Zernike moments. Dither
modulation is adopted to quantize the magnitudes of the Zernike
moments followed by false alarm analysis. Experimental results show
that quality degradation of watermarked image is visually
transparent. The proposed scheme is very robust against image
processing operations and geometric attacks.
Abstract: This paper focuses on a novel method for semantic
searching and retrieval of information about learning materials.
Metametadata encapsulate metadata instances by using the properties
and attributes provided by ontologies rather than describing learning
objects. A novel metametadata taxonomy has been developed which
provides the basis for a semantic search engine to extract, match and
map queries to retrieve relevant results. The use of ontological views
is a foundation for viewing the pedagogical content of metadata
extracted from learning objects by using the pedagogical attributes
from the metametadata taxonomy. Using the ontological approach
and metametadata (based on the metametadata taxonomy) we present
a novel semantic searching mechanism.These three strands – the
taxonomy, the ontological views, and the search algorithm – are
incorporated into a novel architecture (OMESCOD) which has been
implemented.
Abstract: In MPEG and H.26x standards, to eliminate the
temporal redundancy we use motion estimation. Given that the
motion estimation stage is very complex in terms of computational
effort, a hardware implementation on a re-configurable circuit is
crucial for the requirements of different real time multimedia
applications. In this paper, we present hardware architecture for
motion estimation based on "Full Search Block Matching" (FSBM)
algorithm. This architecture presents minimum latency, maximum
throughput, full utilization of hardware resources such as embedded
memory blocks, and combining both pipelining and parallel
processing techniques. Our design is described in VHDL language,
verified by simulation and implemented in a Stratix II
EP2S130F1020C4 FPGA circuit. The experiment result show that the
optimum operating clock frequency of the proposed design is 89MHz
which achieves 160M pixels/sec.
Abstract: techniques are examined to overcome the
performance degradation caused by the channel dispersion using
slow frequency hopping (SFH) with dynamic frequency hopping
(DFH) pattern adaptation. In DFH systems, the frequency slots are
selected by continuous quality monitoring of all frequencies available
in a system and modification of hopping patterns for each individual
link based on replacing slots which its signal to interference ratio
(SIR) measurement is below a required threshold. Simulation results
will show the improvements in BER obtained by DFH in comparison
with matched frequency hopping (MFH), random frequency hopping
(RFH) and multi-carrier code division multiple access (MC-CDMA)
in multipath slowly fading dispersive channels using a generalized
bandpass two-path transfer function model, and will show the
improvement obtained according to the threshold selection.
Abstract: The White Kwao (Pueraria mirifica), a potent phytoestrogenic medicinal plant, has long been use in Thailand as a traditional folkmedicine. However, no scientific information of the direct effect of White Kwao on the development of mammalian embryo was available. Therefore, the purpose of this study was to investigate the effect of White Kwao extract on the in vitro development and implantation rate of mouse embryos. This study was designed into two experiments. In the first experiment, the two-cell stage mouse embryos were collected from the oviduct of superovulated mature female mice, and randomly cultured in three different media, the M16, M16 supplemented with 0.52μg esthinylestradiol-17β, and M16 supplemented with 10 mg/ml White Kwao extract. The culture was incubated in CO2 incubator at 37 oC . After the embryos were cultivated, the developments of embryos were observed every 24 hours for 5 days. The development rate of embryos from the two-cell stage to blastocyst stage in the media was with White Kwao was significantly higher (p
Abstract: commercially produced in Malaysia granular
palm shell activated carbon (PSAC) was biomodified with
bacterial biomass (Bacillus subtilis) to produce a hybrid
biosorbent of higher efficiency. The obtained biosorbent was
evaluated in terms of adsorption capacity to remove copper
and zinc metal ions from aqueous solutions. The adsorption
capacity was evaluated in batch adsorption experiments where
concentrations of metal ions varied from 20 to 350 mg/L. A
range of pH from 3 to 6 of aqueous solutions containing metal
ions was tested. Langmuir adsorption model was used to
interpret the experimental data. Comparison of the adsorption
data of the biomodified and original palm shell activated
carbon showed higher uptake of metal ions by the hybrid
biosorbent. A trend in metal ions uptake increase with the
increase in the solution-s pH was observed. The surface
characterization data indicated a decrease in the total surface
area for the hybrid biosorbent; however the uptake of copper
and zinc by it was at least equal to the original PSAC at pH 4
and 5. The highest capacity of the hybrid biosorbent was
observed at pH 5 and comprised 22 mg/g and 19 mg/g for
copper and zinc, respectively. The adsorption capacity at the
lowest pH of 3 was significantly low. The experimental results
facilitated identification of potential factors influencing the
adsorption of copper and zinc onto biomodified and original
palm shell activated carbon.
Abstract: This paper proposes an Interactive Chinese Character
Learning System (ICCLS) based on pictorial evolution as an
edutainment concept in computer-based learning of language. The
advantage of the language origination itself is taken as a learning
platform due to the complexity in Chinese language as compared to
other types of languages. Users especially children enjoy more by
utilize this learning system because they are able to memories the
Chinese Character easily and understand more of the origin of the
Chinese character under pleasurable learning environment, compares
to traditional approach which children need to rote learning Chinese
Character under un-pleasurable environment. Skeletonization is used
as the representation of Chinese character and object with an animated
pictograph evolution to facilitate the learning of the language. Shortest
skeleton path matching technique is employed for fast and accurate
matching in our implementation. User is required to either write a
word or draw a simple 2D object in the input panel and the matched
word and object will be displayed as well as the pictograph evolution
to instill learning. The target of computer-based learning system is for
pre-school children between 4 to 6 years old to learn Chinese
characters in a flexible and entertaining manner besides utilizing
visual and mind mapping strategy as learning methodology.
Abstract: The finite-difference time-domain (FDTD) method is
one of the most widely used computational methods in
electromagnetic. This paper describes the design of two-dimensional
(2D) FDTD simulation software for transverse magnetic (TM)
polarization using Berenger's split-field perfectly matched layer
(PML) formulation. The software is developed using Matlab
programming language. Numerical examples validate the software.
Abstract: This paper looks into areas not covered by prominent
Agent-Oriented Software Engineering (AOSE) methodologies.
Extensive paper review led to the identification of two issues, first
most of these methodologies almost neglect semantic web and
ontology. Second, as expected, each one has its strength and
weakness and may focus on some phases of the development
lifecycle but not all of the phases. The work presented here builds
extensions to a highly regarded AOSE methodology (MaSE) in order
to cover the areas that this methodology does not concentrate on. The
extensions include introducing an ontology stage for semantic
representation and integrating early requirement specification from a
methodology which mainly focuses on that. The integration involved
developing transformation rules (with the necessary handling of nonmatching
notions) between the two sets of representations and
building the software which automates the transformation. The
application of this integration on a case study is also presented in the
paper. The main flow of MaSE stages was changed to smoothly
accommodate the new additions.
Abstract: Facial features are frequently used to represent local
properties of a human face image in computer vision applications. In
this paper, we present a fast algorithm that can extract the facial
features online such that they can give a satisfying representation of a
face image. It includes one step for a coarse detection of each facial
feature by AdaBoost and another one to increase the accuracy of the
found points by Active Shape Models (ASM) in the regions of interest.
The resulted facial features are evaluated by matching with artificial
face models in the applications of physiognomy. The distance measure
between the features and those in the fate models from the database is
carried out by means of the Hausdorff distance. In the experiment, the
proposed method shows the efficient performance in facial feature
extractions and online system of physiognomy.
Abstract: The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.
Abstract: In this paper, we propose a robust scheme to work face alignment and recognition under various influences. For face representation, illumination influence and variable expressions are the important factors, especially the accuracy of facial localization and face recognition. In order to solve those of factors, we propose a robust approach to overcome these problems. This approach consists of two phases. One phase is preprocessed for face images by means of the proposed illumination normalization method. The location of facial features can fit more efficient and fast based on the proposed image blending. On the other hand, based on template matching, we further improve the active shape models (called as IASM) to locate the face shape more precise which can gain the recognized rate in the next phase. The other phase is to process feature extraction by using principal component analysis and face recognition by using support vector machine classifiers. The results show that this proposed method can obtain good facial localization and face recognition with varied illumination and local distortion.
Abstract: Distant-talking voice-based HCI system suffers from
performance degradation due to mismatch between the acoustic
speech (runtime) and the acoustic model (training). Mismatch is
caused by the change in the power of the speech signal as observed at
the microphones. This change is greatly influenced by the change in
distance, affecting speech dynamics inside the room before reaching
the microphones. Moreover, as the speech signal is reflected, its
acoustical characteristic is also altered by the room properties. In
general, power mismatch due to distance is a complex problem. This
paper presents a novel approach in dealing with distance-induced
mismatch by intelligently sensing instantaneous voice power variation
and compensating model parameters. First, the distant-talking speech
signal is processed through microphone array processing, and the
corresponding distance information is extracted. Distance-sensitive
Gaussian Mixture Models (GMMs), pre-trained to capture both
speech power and room property are used to predict the optimal
distance of the speech source. Consequently, pre-computed statistic
priors corresponding to the optimal distance is selected to correct
the statistics of the generic model which was frozen during training.
Thus, model combinatorics are post-conditioned to match the power
of instantaneous speech acoustics at runtime. This results to an
improved likelihood in predicting the correct speech command at
farther distances. We experiment using real data recorded inside two
rooms. Experimental evaluation shows voice recognition performance
using our method is more robust to the change in distance compared
to the conventional approach. In our experiment, under the most
acoustically challenging environment (i.e., Room 2: 2.5 meters), our
method achieved 24.2% improvement in recognition performance
against the best-performing conventional method.
Abstract: Nowadays, with the emerging of the new applications
like robot control in image processing, artificial vision for visual
servoing is a rapidly growing discipline and Human-machine
interaction plays a significant role for controlling the robot. This
paper presents a new algorithm based on spatio-temporal volumes for
visual servoing aims to control robots. In this algorithm, after
applying necessary pre-processing on video frames, a spatio-temporal
volume is constructed for each gesture and feature vector is extracted.
These volumes are then analyzed for matching in two consecutive
stages. For hand gesture recognition and classification we tested
different classifiers including k-Nearest neighbor, learning vector
quantization and back propagation neural networks. We tested the
proposed algorithm with the collected data set and results showed the
correct gesture recognition rate of 99.58 percent. We also tested the
algorithm with noisy images and algorithm showed the correct
recognition rate of 97.92 percent in noisy images.
Abstract: A high energy dual-wavelength extracavity KTA
optical parametric oscillator (OPO) with excellent stability and beam
quality, which is pumped by a Q-switched single-longitudinal-mode
Nd:YAG laser, has been demonstrated based on a type II noncritical
phase matching (NCPM) KTA crystal. The maximum pulse energy of
10.2 mJ with the output stability of better than 4.1% rms at 3.467 μm is
obtained at the repetition rate of 10 Hz and pulse width of 2 ns, and the
11.9 mJ of 1.535 μm radiation is obtained simultaneously. This
extracavity NCPM KTA OPO is very useful when high energy, high
beam quality and smooth time domain are needed.