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: 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: Dynamic of phytoplankton blooms in the Baltic Sea
has been analyzed applying the numerical ecosystem model 3D
CEMBS. The model consists of the hydrodynamic model (POP,
version 2.1) and the ice model (CICE, version 4.0), which are
imposed by the atmospheric data model (DATM7). The 3D
model has an ecosystem module, activated in 2012 in the operational
mode. The ecosystem model consists of 11 main variables: biomass
of small-size phytoplankton and large-size phytoplankton
and cyanobacteria, zooplankton biomass, dissolved and molecular
detritus, dissolved oxygen concentration, as well as concentrations of
nutrients, including: nitrates, ammonia, phosphates and silicates. The
3D-CEMBS model is an effective tool for solving problems related to
phytoplankton blooms dynamic in the Baltic Sea
Abstract: The aim of this paper is to present the kinematic
analysis and mechanism design of an assistive robotic leg for
hemiplegic and hemiparetic patients. In this work, the priority is to
design and develop the lightweight, effective and single driver
mechanism on the basis of experimental hip and knee angles- data for
walking speed of 1 km/h. A mechanism of cam-follower with three
links is suggested for this purpose. The kinematic analysis is carried
out and analysed using commercialized MATLAB software based on
the prototype-s links sizes and kinematic relationships. In order to
verify the kinematic analysis of the prototype, kinematic analysis data
are compared with the experimental data. A good agreement between
them proves that the anthropomorphic design of the lower extremity
exoskeleton follows the human walking gait.
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: Since 1992, year where Hugo de Garis has published
the first paper on Evolvable Hardware (EHW), a period of intense
creativity has followed. It has been actively researched, developed
and applied to various problems. Different approaches have been
proposed that created three main classifications: extrinsic, mixtrinsic
and intrinsic EHW. Each of these solutions has a real interest.
Nevertheless, although the extrinsic evolution generates some
excellent results, the intrinsic systems are not so advanced. This
paper suggests 3 possible solutions to implement the run-time
configuration intrinsic EHW system: FPGA-based Run-Time
Configuration system, JBits-based Run-Time Configuration system
and Multi-board functional-level Run-Time Configuration system.
The main characteristic of the proposed architectures is that they are
implemented on Field Programmable Gate Array. A comparison of
proposed solutions demonstrates that multi-board functional-level
run-time configuration is superior in terms of scalability, flexibility
and the implementation easiness.
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: Effects of bio-nitrogen fertilizer (bio-N), as a partial
alternative to mineral-nitrogen fertilizer (mineral-N), on growth,
yield and yield quality of broccoli plants were investigated. Bio-N
was applied at 1, 2 or 3 doses in combination with 65% of the
recommended dose of mineral-N (bio-N1, bio-N2 or bio-N3 +
⅔mineral-N). However, 100% of the recommended dose of mineral-
N was applied as a control. Significant positive influences of the bio-
N3 + ⅔mineral-N treatment were observed on growth traits, leaf
contents of nitrogen, phosphorus, potassium, nitrate and nitrite, and
yield quality when compared to the other two combined treatments.
In contrast, there were no significant differences in these parameters
between the bio-N3 + ⅔mineral-N and the control treatments, except
for leaf contents of nitrate and nitrite. They showed lower contents in
the bio-N3 + ⅔mineral-N treatment than the control. Therefore, we
recommend using bio-N as a partial alternative to mineral-N for
healthy nutrition.
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: There are reports of gas and oil wells fire due to different accidents. Many different methods are used for fire fighting in gas and oil industry. Traditional fire extinguishing techniques are mostly faced with many problems and are usually time consuming and needs lots of equipments. Besides, they cause damages to facilities, and create health and environmental problems. This article proposes innovative approach in fire extinguishing techniques in oil and gas industry, especially applicable for burning oil wells located offshore. Fire extinguishment employing a turbojet is a novel approach which can help to extinguishment the fire in short period of time. Divergent and convergent turbojets modeled in laboratory scale along with a high pressure flame were used. Different experiments were conducted to determine the relationship between output discharges of trumpet and oil wells. The results were corrected and the relationship between dimensionless parameters of flame and fire extinguishment distances and also the output discharge of turbojet and oil wells in specified distances are demonstrated by specific curves.
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: This paper proposes a method that discovers sequential patterns corresponding to user-s interests from sequential data. This method expresses the interests as constraint patterns. The constraint patterns can define relationships among attributes of the items composing the data. The method recursively decomposes the constraint patterns into constraint subpatterns. The method evaluates the constraint subpatterns in order to efficiently discover sequential patterns satisfying the constraint patterns. Also, this paper applies the method to the sequential data composed of stock price indexes and verifies its effectiveness through comparing it with a method without using the constraint patterns.
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: The purpose of this paper is to analyze the case of the
U.S. Pivot and to suggest an appropriate model including entry
strategies and success factors for QPS of Cable TV. The
telecommunication companies have been operating QPS including
IPTV service, which enables them to cross over broadcasting areas.
Due to this circumstance, the Cable TV operators are now concerned
and are planning to add QPS with the mobile service. Based on the
Porter's five forces model, an analytical framework has been proposed
to MVNO in Cable TV industry in the United States. As a result of this
study, MVNO in Cable TV industry has to have a clear killer
application with their sufficient contents. Subsequently, the direction
of the future Cable TV industry is proposed.
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.
Abstract: This paper presents an intelligent speed control
system based on fuzzy logic for a voltage source PWM inverter-fed
indirect vector controlled induction motor drive. Traditional indirect
vector control system of induction motor introduces conventional PI
regulator in outer speed loop; it is proved that the low precision of the
speed regulator debases the performance of the whole system. To
overcome this problem, replacement of PI controller by an intelligent
controller based on fuzzy set theory is proposed. The performance of
the intelligent controller has been investigated through digital
simulation using MATLAB-SIMULINK package for different
operating conditions such as sudden change in reference speed and
load torque. The simulation results demonstrate that the performance
of the proposed controller is better than that of the conventional PI
controller.