Abstract: Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency
Abstract: This conference paper discusses a risk allocation problem for subprime investing banks involving investment in subprime structured mortgage products (SMPs) and Treasuries. In order to solve this problem, we develop a L'evy process-based model of jump diffusion-type for investment choice in subprime SMPs and Treasuries. This model incorporates subprime SMP losses for which credit default insurance in the form of credit default swaps (CDSs) can be purchased. In essence, we solve a mean swap-at-risk (SaR) optimization problem for investment which determines optimal allocation between SMPs and Treasuries subject to credit risk protection via CDSs. In this regard, SaR is indicative of how much protection investors must purchase from swap protection sellers in order to cover possible losses from SMP default. Here, SaR is defined in terms of value-at-risk (VaR). Finally, we provide an analysis of the aforementioned optimization problem and its connections with the subprime mortgage crisis (SMC).
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
Abstract: This research sought to discover the forms of
promotion and dissemination of traditional local wisdom that are
used to create occupations among the elderly at Noanmueng
Community, Muang Sub-District, Baan Doong District, Udornthani
Province. The criteria used to select the research sample group were:
having a role involved in the promotion and dissemination of
traditional local wisdom to create occupations among the elderly;
being an experienced person who the residents of Noanmueng
Community find trustworthy; and having lived in Noanmueng
Community for a long time so as to be able to see the development
and change that occurs. A total of 16 persons were thus selected. Data
was gathered through a qualitative study, using semi-structured indepth
interviews. The collected data was then summarized and
discussed according to the research objectives. Finally, the data was
presented in narrative format. Results found that the identifying
traditional local wisdom of the community (which grew from the
residents’ experience and beneficial usage in daily life, passed down
from generation to generation) was the weaving of cloth and
basketry. As for the manner of promotion and dissemination of
traditional local wisdom, these skills were passed down through
teaching by example to family members, relatives and others in the
community. This was largely the initiative of the elders or elderly
members of the community. In order for the promotion and
dissemination of traditional local wisdom to create occupations
among the elderly, the traditional local wisdom should be supported
in every way through participation of the community members. For
example, establish a museum of traditional local wisdom for the
collection of traditional local wisdom in various fields, both from the
past and present innovations. This would be a source of pride for the
community, simultaneously helping traditional local wisdom to
become widely known and to create income for the community’s
elderly. Additional ways include organizing exhibitions of products
made by traditional local wisdom, finding both domestic and
international markets, as well as building both domestic and
international networks aiming to find opportunities to market
products made by traditional local wisdom.
Abstract: All practical real-time scheduling algorithms in multiprocessor systems present a trade-off between their computational complexity and performance. In real-time systems, tasks have to be performed correctly and timely. Finding minimal schedule in multiprocessor systems with real-time constraints is shown to be NP-hard. Although some optimal algorithms have been employed in uni-processor systems, they fail when they are applied in multiprocessor systems. The practical scheduling algorithms in real-time systems have not deterministic response time. Deterministic timing behavior is an important parameter for system robustness analysis. The intrinsic uncertainty in dynamic real-time systems increases the difficulties of scheduling problem. To alleviate these difficulties, we have proposed a fuzzy scheduling approach to arrange real-time periodic and non-periodic tasks in multiprocessor systems. Static and dynamic optimal scheduling algorithms fail with non-critical overload. In contrast, our approach balances task loads of the processors successfully while consider starvation prevention and fairness which cause higher priority tasks have higher running probability. A simulation is conducted to evaluate the performance of the proposed approach. Experimental results have shown that the proposed fuzzy scheduler creates feasible schedules for homogeneous and heterogeneous tasks. It also and considers tasks priorities which cause higher system utilization and lowers deadline miss time. According to the results, it performs very close to optimal schedule of uni-processor systems.
Abstract: This paper evaluates performances of an adaptive noise
cancelling (ANC) based target detection algorithm on a set of real test
data supported by the Defense Evaluation Research Agency (DERA
UK) for multi-target wideband active sonar echolocation system. The
hybrid algorithm proposed is a combination of an adaptive ANC
neuro-fuzzy scheme in the first instance and followed by an iterative
optimum target motion estimation (TME) scheme. The neuro-fuzzy
scheme is based on the adaptive noise cancelling concept with the
core processor of ANFIS (adaptive neuro-fuzzy inference system) to
provide an effective fine tuned signal. The resultant output is then
sent as an input to the optimum TME scheme composed of twogauge
trimmed-mean (TM) levelization, discrete wavelet denoising
(WDeN), and optimal continuous wavelet transform (CWT) for
further denosing and targets identification. Its aim is to recover the
contact signals in an effective and efficient manner and then determine
the Doppler motion (radial range, velocity and acceleration) at very
low signal-to-noise ratio (SNR). Quantitative results have shown that
the hybrid algorithm have excellent performance in predicting targets-
Doppler motion within various target strength with the maximum
false detection of 1.5%.
Abstract: The wind resource in the Italian site of Lendinara
(RO) is analyzed through a systematic anemometric campaign
performed on the top of the bell tower, at an altitude of over 100 m
above the ground. Both the average wind speed and the Weibull
distribution are computed. The resulting average wind velocity is in
accordance with the numerical predictions of the Italian Wind Atlas,
confirming the accuracy of the extrapolation of wind data adopted for
the evaluation of wind potential at higher altitudes with respect to the
commonly placed measurement stations.
Abstract: Concerning the inpatient care the present situation is
characterized by intense charges of medical technology into the
clinical daily routine and an ever stronger integration of special
techniques into the clinical workflow. Medical technology is by now
an integral part of health care according to consisting general
accepted standards. Purchase and operation thereby represent an
important economic position and both are subject of everyday
optimisation attempts. For this purpose by now exists a huge number
of tools which conduce more likely to a complexness of the problem
by a comprehensive implementation. In this paper the advantages of
an integrative information-workflow on the life-cycle-management in
the region of medical technology are shown.
Abstract: The purpose of this study was to evaluate and
compare new indices based on the discrete wavelet transform
with another spectral parameters proposed in the literature as
mean average voltage, median frequency and ratios between
spectral moments applied to estimate acute exercise-induced
changes in power output, i.e., to assess peripheral muscle
fatigue during a dynamic fatiguing protocol. 15 trained
subjects performed 5 sets consisting of 10 leg press, with 2
minutes rest between sets. Surface electromyography was
recorded from vastus medialis (VM) muscle. Several surface
electromyographic parameters were compared to detect
peripheral muscle fatigue. These were: mean average voltage
(MAV), median spectral frequency (Fmed), Dimitrov spectral
index of muscle fatigue (FInsm5), as well as other five
parameters obtained from the discrete wavelet transform
(DWT) as ratios between different scales. The new wavelet
indices achieved the best results in Pearson correlation
coefficients with power output changes during acute dynamic
contractions. Their regressions were significantly different
from MAV and Fmed. On the other hand, they showed the
highest robustness in presence of additive white gaussian
noise for different signal to noise ratios (SNRs). Therefore,
peripheral impairments assessed by sEMG wavelet indices
may be a relevant factor involved in the loss of power output
after dynamic high-loading fatiguing task.
Abstract: This study investigated the number of Aedes larvae,
the key breeding sites of Aedes sp., and the relationship between
climatic factors and the incidence of DHF in Samui Islands. We
conducted our questionnaire and larval surveys from randomly
selected 105 households in Samui Islands in July-September 2006.
Pearson-s correlation coefficient was used to explore the primary
association between the DHF incidence and all climatic factors.
Multiple stepwise regression technique was then used to fit the
statistical model. The results showed that the positive indoor
containers were small jars, cement tanks, and plastic tanks. The
positive outdoor containers were small jars, cement tanks, plastic
tanks, used cans, tires, plastic bottles, discarded objects, pot saucers,
plant pots, and areca husks. All Ae. albopictus larval indices (i.e., CI,
HI, and BI) were higher than Ae. aegypti larval indices in this area.
These larval indices were higher than WHO standard. This indicated
a high risk of DHF transmission at Samui Islands. The multiple
stepwise regression model was y = –288.80 + 11.024xmean temp. The
mean temperature was positively associated with the DHF incidence
in this area.
Abstract: This paper examines the effect of corporate diversification on the profitability of the Financial services sector in Nigeria. The study relied on historic accounting data generated from financial (annual) reports and accounts of sampled banks between the period 1998 and 2007 (a ten-year period). A regression equation was formulated, in line with previous studies to shed light on the effect of corporate diversification on the profitability of the Financial services sector in Nigeria. The results of the regression analysis revealed that diversification impacts strongly on banks profitability. Conclusively the paper produces strong evidence to assert that diversification impacts positively and significantly on banks profitability because among other things such diversified banks can pool their internally generated funds and allocate them properly.
Abstract: The flow of a third grade fluid in an orthogonal rheometer is studied. We employ the admissible velocity field proposed in [5]. We solve the problem and obtain the velocity field as well as the components for the Cauchy tensor. We compare the results with those from [9]. Some diagrams concerning the velocity and Cauchy stress components profiles are presented for different values of material constants and compared with the corresponding values for a linear viscous fluid.
Abstract: Using a scoring system, this paper provides a
comparative assessment of the quality of data between XBRL
formatted financial reports and non-XBRL financial reports. It shows a
major improvement in the quality of data of XBRL formatted financial
reports. Although XBRL formatted financial reports do not show
much advantage in the quality at the beginning, XBRL financial
reports lately display a large improvement in the quality of data in
almost all aspects. With the improved XBRL web data managing,
presentation and analysis applications, XBRL formatted financial
reports have a much better accessibility, are more accurate and better
in timeliness.
Abstract: In this article, we propose an Intelligent Medical
Diagnostic System (IMDS) accessible through common
web-based interface, to on-line perform initial screening for
osteoporosis. The fundamental approaches which construct the
proposed system are mainly based on the fuzzy-neural theory,
which can exhibit superiority over other conventional technologies
in many fields. In diagnosis process, users simply answer
a series of directed questions to the system, and then they
will immediately receive a list of results which represents the
risk degrees of osteoporosis. According to clinical testing results,
it is shown that the proposed system can provide the general
public or even health care providers with a convenient, reliable,
inexpensive approach to osteoporosis risk assessment.
Abstract: KREISIG is a computer simulation program, firstly developed by Munawar (1994) in Germany to optimize signalized roundabout. The traffic movement is based on the car following theory. Turbine method has been implemented for signal setting. The program has then been further developed in Indonesia to meet the traffic characteristics in Indonesia by adjusting the sensitivity of the drivers. Trial and error method has been implemented to adjust the saturation flow. The saturation flow output has also been compared to the calculation method according to 1997 Indonesian Highway Capacity Manual. It has then been implemented to optimize signalized roundabout at Kleringan roundabout in Malioboro area, Yogyakarta, Indonesia. It is found that this method can optimize the signal setting of this roundabout. Therefore, it is recommended to use this program to optimize signalized roundabout.
Abstract: Parsing is important in Linguistics and Natural
Language Processing to understand the syntax and semantics of a
natural language grammar. Parsing natural language text is
challenging because of the problems like ambiguity and inefficiency.
Also the interpretation of natural language text depends on context
based techniques. A probabilistic component is essential to resolve
ambiguity in both syntax and semantics thereby increasing accuracy
and efficiency of the parser. Tamil language has some inherent
features which are more challenging. In order to obtain the solutions,
lexicalized and statistical approach is to be applied in the parsing
with the aid of a language model. Statistical models mainly focus on
semantics of the language which are suitable for large vocabulary
tasks where as structural methods focus on syntax which models
small vocabulary tasks. A statistical language model based on Trigram
for Tamil language with medium vocabulary of 5000 words has
been built. Though statistical parsing gives better performance
through tri-gram probabilities and large vocabulary size, it has some
disadvantages like focus on semantics rather than syntax, lack of
support in free ordering of words and long term relationship. To
overcome the disadvantages a structural component is to be
incorporated in statistical language models which leads to the
implementation of hybrid language models. This paper has attempted
to build phrase structured hybrid language model which resolves
above mentioned disadvantages. In the development of hybrid
language model, new part of speech tag set for Tamil language has
been developed with more than 500 tags which have the wider
coverage. A phrase structured Treebank has been developed with 326
Tamil sentences which covers more than 5000 words. A hybrid
language model has been trained with the phrase structured Treebank
using immediate head parsing technique. Lexicalized and statistical
parser which employs this hybrid language model and immediate
head parsing technique gives better results than pure grammar and
trigram based model.
Abstract: This paper provides an analysis of corporate income
tax (CIT) incentives in the Western Balkan countries: Slovenia,
Croatia, Serbia, Montenegro, Macedonia and Albania. Western
Balkan countries, as other transition and developing countries, use
large number of the corporate income tax incentives (CIT) to attract
foreign investments and to stimulate economic activity. The main
goal of this paper is to investigate how often these countries use CIT
incentives and provide review of existing tax incentives in Western
Balkan countries. Paper will focus on reduced CIT rates, tax
holidays, and other investment incentives which imply incentives
like accelerated depreciation, tax allowances and tax credits.
Abstract: In this study, workplace environmental monitoring
systems were established using USN(Ubiquitous Sensor Networks)
and LabVIEW. Although existing direct sampling methods enable
finding accurate values as of the time points of measurement, those
methods are disadvantageous in that continuous management and
supervision are difficult and costs for are high when those methods are
used. Therefore, the efficiency and reliability of workplace
management by supervisors are relatively low when those methods are
used. In this study, systems were established so that information on
workplace environmental factors such as temperatures, humidity and
noises is measured and transmitted to the PC in real time to enable
supervisors to monitor workplaces through LabVIEW on the PC.
When any accidents have occurred in workplaces, supervisors can
immediately respond through the monitoring system and this system
enables integrated workplace management and the prevention of
safety accidents. By introducing these monitoring systems, safety
accidents due to harmful environmental factors in workplaces can be
prevented and these monitoring systems will be also helpful in finding
out the correlation between safety accidents and occupational diseases
by comparing and linking databases established by this monitoring
system with existing statistical data.
Abstract: In order to research Internet quantificationally and
better model the performance of network, this paper proposes a novel
AS level network performance model (MNPM), it takes autonomous
system (AS) as basic modeling unit, measures E2E performance
between any two outdegrees of an AS and organizes measurement
results into matrix form which called performance matrix (PM).
Inter-AS performance calculation is defined according to performance
information stored in PM. Simulation has been implemented to verify
the correctness of MNPM and a practical application of MNPM
(network congestion detection) is given.