Abstract: This paper addresses an efficient technique to embed and detect digital fingerprint code. Orthogonal modulation method is a straightforward and widely used approach for digital fingerprinting but shows several limitations in computational cost and signal efficiency. Coded modulation method can solve these limitations in theory. However it is difficult to perform well in practice if host signals are not available during tracing colluders, other kinds of attacks are applied, and the size of fingerprint code becomes large. In this paper, we propose a hybrid modulation method, in which the merits of or-thogonal modulation and coded modulation method are combined so that we can achieve low computational cost and high signal efficiency. To analyze the performance, we design a new fingerprint code based on GD-PBIBD theory and modulate this code into images by our method using spread-spectrum watermarking on frequency domain. The results show that the proposed method can efficiently handle large fingerprint code and trace colluders against averaging attacks.
Abstract: Increasing growth of information volume in the
internet causes an increasing need to develop new (semi)automatic
methods for retrieval of documents and ranking them according to
their relevance to the user query. In this paper, after a brief review
on ranking models, a new ontology based approach for ranking
HTML documents is proposed and evaluated in various
circumstances. Our approach is a combination of conceptual,
statistical and linguistic methods. This combination reserves the
precision of ranking without loosing the speed. Our approach
exploits natural language processing techniques for extracting
phrases and stemming words. Then an ontology based conceptual
method will be used to annotate documents and expand the query.
To expand a query the spread activation algorithm is improved so
that the expansion can be done in various aspects. The annotated
documents and the expanded query will be processed to compute
the relevance degree exploiting statistical methods. The outstanding
features of our approach are (1) combining conceptual, statistical
and linguistic features of documents, (2) expanding the query with
its related concepts before comparing to documents, (3) extracting
and using both words and phrases to compute relevance degree, (4)
improving the spread activation algorithm to do the expansion based
on weighted combination of different conceptual relationships and
(5) allowing variable document vector dimensions. A ranking
system called ORank is developed to implement and test the
proposed model. The test results will be included at the end of the
paper.
Abstract: This study examined the effects of two dynamic
visualizations on 60 Malaysian primary school student-s performance
(time on task), retention and transference. The independent variables
in this study were the two dynamic visualizations, the video and the
animated instructions. The dependent variables were the gain score of
performance, retention and transference. The results showed that the
students in the animation group significantly outperformed the
students in the video group in retention. There were no significant
differences in terms of gain scores in the performance and
transference among the animation and the video groups, although the
scores were slightly higher in the animation group compared to the
video group. The conclusion of this study is that the animation
visualization is superior compared to the video in the retention for a
procedural task.
Abstract: Conventional approaches in the implementation of logic programming applications on embedded systems are solely of software nature. As a consequence, a compiler is needed that transforms the initial declarative logic program to its equivalent procedural one, to be programmed to the microprocessor. This approach increases the complexity of the final implementation and reduces the overall system's performance. On the contrary, presenting hardware implementations which are only capable of supporting logic programs prevents their use in applications where logic programs need to be intertwined with traditional procedural ones, for a specific application. We exploit HW/SW codesign methods to present a microprocessor, capable of supporting hybrid applications using both programming approaches. We take advantage of the close relationship between attribute grammar (AG) evaluation and knowledge engineering methods to present a programmable hardware parser that performs logic derivations and combine it with an extension of a conventional RISC microprocessor that performs the unification process to report the success or failure of those derivations. The extended RISC microprocessor is still capable of executing conventional procedural programs, thus hybrid applications can be implemented. The presented implementation is programmable, supports the execution of hybrid applications, increases the performance of logic derivations (experimental analysis yields an approximate 1000% increase in performance) and reduces the complexity of the final implemented code. The proposed hardware design is supported by a proposed extended C-language called C-AG.
Abstract: Global climate change has become the preeminent
threat to human security in the 21st century. From mitigation perspective, this study aims to evaluate the performance of biogas
renewable project under clean development mechanism activities
(namely Korat-Waste-to-Energy) in Thailand and to assess local perceptions towards the significance of climate change mitigation and
sustainability of such project in their community. Questionnaire was
developed based on the national sustainable development criteria and
was distributed among systematically selected households within
project boundaries (n=260). Majority of the respondents strongly agreed with the reduction of odor problems (81%) and air pollution
(76%). However, they were unsure about greenhouse gas reduction from such project and ignorant about the key issues of climate change. A lesson learned suggested that there is a need to further
investigate the possible socio-psychological barriers may significantly shape public perception and understandings of climate
change in the local context.
Abstract: In this work, we successfully extended one-dimensional differential transform method (DTM), by presenting and proving some theorems, to solving nonlinear high-order multi-pantograph equations. This technique provides a sequence of functions which converges to the exact solution of the problem. Some examples are given to demonstrate the validity and applicability of the present method and a comparison is made with existing results.
Abstract: The kinematics of manipulators is a central problem in the automatic control of robot manipulators. Theoretical background for the analysis of the 5 Dof Lynx-6 educational Robot Arm kinematics is presented in this paper. The kinematics problem is defined as the transformation from the Cartesian space to the joint space and vice versa. The Denavit-Harbenterg (D-H) model of representation is used to model robot links and joints in this study. Both forward and inverse kinematics solutions for this educational manipulator are presented, An effective method is suggested to decrease multiple solutions in inverse kinematics. A visual software package, named MSG, is also developed for testing Motional Characteristics of the Lynx-6 Robot arm. The kinematics solutions of the software package were found to be identical with the robot arm-s physical motional behaviors.
Abstract: Full adders are important components in applications
such as digital signal processors (DSP) architectures and
microprocessors. In addition to its main task, which is adding two
numbers, it participates in many other useful operations such as
subtraction, multiplication, division,, address calculation,..etc. In
most of these systems the adder lies in the critical path that
determines the overall speed of the system. So enhancing the
performance of the 1-bit full adder cell (the building block of the
adder) is a significant goal.Demands for the low power VLSI have
been pushing the development of aggressive design methodologies to
reduce the power consumption drastically. To meet the growing
demand, we propose a new low power adder cell by sacrificing the
MOS Transistor count that reduces the serious threshold loss
problem, considerably increases the speed and decreases the power
when compared to the static energy recovery full (SERF) adder. So a
new improved 14T CMOS l-bit full adder cell is presented in this
paper. Results show 50% improvement in threshold loss problem,
45% improvement in speed and considerable power consumption
over the SERF adder and other different types of adders with
comparable performance.
Abstract: In this paper, a fibre laser at 546 nm has been studied
for a signal power of -30 dB. Er3+-doped ZBLAN fibre has been used
by upconversion pumping of a 980 nm laser diode. Gain saturation
effect has been investigated in detail. Laser performance has also been
discussed. An efficiency of 35% has been calculated with a length of 5
mm fibre laser. Results show that Er3+-doped ZBLAN is a promising
candidate for optical amplification at 546 nm.
Abstract: In this work, a radial basis function (RBF) neural network is developed for the identification of hyperbolic distributed parameter systems (DPSs). This empirical model is based only on process input-output data and used for the estimation of the controlled variables at specific locations, without the need of online solution of partial differential equations (PDEs). The nonlinear model that is obtained is suitably transformed to a nonlinear state space formulation that also takes into account the model mismatch. A stable robust control law is implemented for the attenuation of external disturbances. The proposed identification and control methodology is applied on a long duct, a common component of thermal systems, for a flow based control of temperature distribution. The closed loop performance is significantly improved in comparison to existing control methodologies.
Abstract: The purpose of this research is to study motivation
factors and also to study factors relation to job performance to
compare motivation factors under the personal factor classification
such as gender, age, income, educational level, marital status, and
working duration; and to study the relationship between Motivation
Factors and Job Performance with job satisfactions. The sample
groups utilized in this research were 400 Suan Sunandha Rajabhat
University employees. This research is a quantitative research using
questionnaires as research instrument. The statistics applied for data
analysis including percentage, mean, and standard deviation. In
addition, the difference analysis was conducted by t value computing,
one-way analysis of variance and Pearson’s correlation coefficient
computing. The findings of the study results were as follows the
findings showed that the aspects of job promotion and salary were at
the moderate levels. Additionally, the findings also showed that the
motivations that affected the revenue branch chiefs’ job performance
were job security, job accomplishment, policy and management, job
promotion, and interpersonal relation.
Abstract: Repeated observation of a given area over time yields
potential for many forms of change detection analysis. These
repeated observations are confounded in terms of radiometric
consistency due to changes in sensor calibration over time,
differences in illumination, observation angles and variation in
atmospheric effects.
This paper demonstrates applicability of an empirical relative
radiometric normalization method to a set of multitemporal cloudy
images acquired by Resourcesat1 LISS III sensor. Objective of this
study is to detect and remove cloud cover and normalize an image
radiometrically. Cloud detection is achieved by using Average
Brightness Threshold (ABT) algorithm. The detected cloud is
removed and replaced with data from another images of the same
area. After cloud removal, the proposed normalization method is
applied to reduce the radiometric influence caused by non surface
factors. This process identifies landscape elements whose reflectance
values are nearly constant over time, i.e. the subset of non-changing
pixels are identified using frequency based correlation technique. The
quality of radiometric normalization is statistically assessed by R2
value and mean square error (MSE) between each pair of analogous
band.
Abstract: Malaria is transmitted to the human by biting of
infected Anopheles mosquitoes. This disease is a serious, acute and
chronic relapsing infection to humans. Fever, nausea, vomiting, back
pain, increased sweating anemia and splenomegaly (enlargement of
the spleen) are the symptoms of the patients who infected with this
disease. It is caused by the multiplication of protozoa parasite of the
genus Plasmodium. Plasmodium falciparum, Plasmodium vivax,
Plasmodium malariae and Plasmodium ovale are the four types of
Plasmodium malaria. A mathematical model for the transmission of
Plasmodium Malaria is developed in which the human and vector
population are divided into two classes, the susceptible and the
infectious classes. In this paper, we formulate the dynamical model
of Plasmodium falciparum and Plasmodium vivax malaria. The
standard dynamical analysis is used for analyzing the behavior for
the transmission of this disease. The Threshold condition is found
and numerical results are shown to confirm the analytical results.
Abstract: The process of wafer fabrication is arguably the most
technologically complex and capital intensive stage in semiconductor
manufacturing. This large-scale discrete-event process is highly reentrant,
and involves hundreds of machines, restrictions, and
processing steps. Therefore, production control of wafer fabrication
facilities (fab), specifically scheduling, is one of the most challenging
problems that this industry faces. Dispatching rules have been
extensively applied to the scheduling problems in semiconductor
manufacturing. Moreover, lot release policies are commonly used in
this manufacturing setting to further improve the performance of such
systems and reduce its inherent variability. In this work, simulation is
used in the scheduling of re-entrant flow shop manufacturing systems
with an application in semiconductor wafer fabrication; where, a
simulation model has been developed for the Intel Five-Machine Six
Step Mini-Fab using the ExtendTM simulation environment. The
Mini-Fab has been selected as it captures the challenges involved in
scheduling the highly re-entrant semiconductor manufacturing lines.
A number of scenarios have been developed and have been used to
evaluate the effect of different dispatching rules and lot release
policies on the selected performance measures. Results of simulation
showed that the performance of the Mini-Fab can be drastically
improved using a combination of dispatching rules and lot release
policy.
Abstract: In this paper we propose a new approach for flexible document categorization according to the document type or genre instead of topic. Our approach implements two homogenous classifiers: contextual classifier and logical classifier. The contextual classifier is based on the document URL, whereas, the logical classifier use the logical structure of the document to perform the categorization. The final categorization is obtained by combining contextual and logical categorizations. In our approach, each document is assigned to all predefined categories with different membership degrees. Our experiments demonstrate that our approach is best than other genre categorization approaches.
Abstract: The problem of controlling a two link robotic manipulator, consisting of a rotating and a prismatic links, is addressed. The actuations of both links are assumed to have unknown dead zone nonlinearities and friction torques modeled by LuGre friction model. Because of the existence of the unknown dead zone and friction torque at the actuations, unknown parameters and unmeasured states would appear to be part of the overall system dynamics that need for estimation. Unmeasured states observer, unknown parameters estimators, and robust adaptive control laws have been derived such that closed loop global stability is achieved. Simulation results have been performed to show the efficacy of the suggested approach.
Abstract: The inherent flexibilities of XML in both structure
and semantics makes mining from XML data a complex task with
more challenges compared to traditional association rule mining in
relational databases. In this paper, we propose a new model for the
effective extraction of generalized association rules form a XML
document collection. We directly use frequent subtree mining
techniques in the discovery process and do not ignore the tree
structure of data in the final rules. The frequent subtrees based on the
user provided support are split to complement subtrees to form the
rules. We explain our model within multi-steps from data preparation
to rule generation.
Abstract: Supplier selection, in real situation, is affected by
several qualitative and quantitative factors and is one of the most
important activities of purchasing department. Since at the time of
evaluating suppliers against the criteria or factors, decision makers
(DMS) do not have precise, exact and complete information, supplier
selection becomes more difficult. In this case, Grey theory helps us
to deal with this problem of uncertainty. Here, we apply Technique
for Order Preference by Similarity to Ideal Solution (TOPSIS)
method to evaluate and select the best supplier by using interval
fuzzy numbers. Through this article, we compare TOPSIS with some
other approaches and afterward demonstrate that the concept of
TOPSIS is very important for ranking and selecting right supplier.
Abstract: Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.
Abstract: The electrolyte stirring method of anodization etching
process for manufacturing porous silicon (PS) is reported in this work.
Two experimental setups of nature air stirring (PS-ASM) and
electrolyte stirring (PS-ESM) are employed to clarify the influence of
stirring mechanisms on electrochemical etching process. Compared to
traditional fabrication without any stirring apparatus (PS-TM), a large
plateau region of PS surface structure is obtained from samples with
both stirring methods by the 3D-profiler measurement. Moreover, the
light emission response is also improved by both proposed electrolyte
stirring methods due to the cycling force in electrolyte could
effectively enhance etch-carrier distribution while the electrochemical
etching process is made. According to the analysis of statistical
calculation of photoluminescence (PL) intensity, lower standard
deviations are obtained from PS-samples with studied stirring methods,
i.e. the uniformity of PL-intensity is effectively improved. The
calculated deviations of PL-intensity are 93.2, 74.5 and 64,
respectively, for PS-TM, PS-ASM and PS-ESM.