Abstract: This paper presents comparative study on recent
integer DCTs and a new method to construct a low sensitive structure
of integer DCT for colored input signals. The method refers to
sensitivity of multiplier coefficients to finite word length as an
indicator of how word length truncation effects on quality of output
signal. The sensitivity is also theoretically evaluated as a function of
auto-correlation and covariance matrix of input signal. The structure of
integer DCT algorithm is optimized by combination of lower sensitive
lifting structure types of IRT. It is evaluated by the sensitivity of
multiplier coefficients to finite word length expression in a function of
covariance matrix of input signal. Effectiveness of the optimum
combination of IRT in integer DCT algorithm is confirmed by quality
improvement comparing with existing case. As a result, the optimum
combination of IRT in each integer DCT algorithm evidently improves
output signal quality and it is still compatible with the existing one.
Abstract: The increasing importance of data stream arising in a
wide range of advanced applications has led to the extensive study of
mining frequent patterns. Mining data streams poses many new
challenges amongst which are the one-scan nature, the unbounded
memory requirement and the high arrival rate of data streams. In this
paper, we propose a new approach for mining itemsets on data
stream. Our approach SFIDS has been developed based on FIDS
algorithm. The main attempts were to keep some advantages of the
previous approach and resolve some of its drawbacks, and
consequently to improve run time and memory consumption. Our
approach has the following advantages: using a data structure similar
to lattice for keeping frequent itemsets, separating regions from each
other with deleting common nodes that results in a decrease in search
space, memory consumption and run time; and Finally, considering
CPU constraint, with increasing arrival rate of data that result in
overloading system, SFIDS automatically detect this situation and
discard some of unprocessing data. We guarantee that error of results
is bounded to user pre-specified threshold, based on a probability
technique. Final results show that SFIDS algorithm could attain
about 50% run time improvement than FIDS approach.
Abstract: With the advent of emerging personal computing paradigms such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide range of mobile devices. Since these devices do not have the same rendering capabilities, Web contents need to be adapted for transparent access from a variety of client agents. Such content adaptation is exploited for either an individual element or a set of consecutive elements in a Web document and results in better rendering and faster delivery to the client device. Nevertheless, Web content adaptation sets new challenges for semantic markup. This paper presents an advanced components platform, called SMC, enabling the development of mobility applications and services according to a channel model based on the principles of Services Oriented Architecture (SOA). It then goes on to describe the potential for integration with the Semantic Web through a novel framework of external semantic annotation that prescribes a scheme for representing semantic markup files and a way of associating Web documents with these external annotations. The role of semantic annotation in this framework is to describe the contents of individual documents themselves, assuring the preservation of the semantics during the process of adapting content rendering. Semantic Web content adaptation is a way of adding value to Web contents and facilitates repurposing of Web contents (enhanced browsing, Web Services location and access, etc).
Abstract: The ability of UML to handle the modeling process of complex industrial software applications has increased its popularity to the extent of becoming the de-facto language in serving the design purpose. Although, its rich graphical notation naturally oriented towards the object-oriented concept, facilitates the understandability, it hardly successes to report all domainspecific aspects in a satisfactory way. OCL, as the standard language for expressing additional constraints on UML models, has great potential to help improve expressiveness. Unfortunately, it suffers from a weak formalism due to its poor semantic resulting in many obstacles towards the build of tools support and thus its application in the industry field. For this reason, many researches were established to formalize OCL expressions using a more rigorous approach. Our contribution join this work in a complementary way since it focuses specifically on OCL predefined properties which constitute an important part in the construction of OCL expressions. Using formal methods, we mainly succeed in expressing rigorously OCL predefined functions.
Abstract: This paper is motivated by the aspect of uncertainty in
financial decision making, and how artificial intelligence and soft
computing, with its uncertainty reducing aspects can be used for
algorithmic trading applications that trade in high frequency.
This paper presents an optimized high frequency trading system that
has been combined with various moving averages to produce a hybrid
system that outperforms trading systems that rely solely on moving
averages. The paper optimizes an adaptive neuro-fuzzy inference
system that takes both the price and its moving average as input,
learns to predict price movements from training data consisting of
intraday data, dynamically switches between the best performing
moving averages, and performs decision making of when to buy or
sell a certain currency in high frequency.
Abstract: The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.
Abstract: American public schools should be the place that reflects America-s diverse society. The recent Supreme Court decision to discontinue the use of race as a factor in school admission policies has caused major setbacks in America-s effort to repair its racial divide, to improve public schools, and to provide opportunities for all people, regardless of race or creed. However, educators should not allow such legal decision to hinder their ability to teach children tolerance of others in schools and classrooms in America.
Abstract: A model of vortex wake is suggested to determine the
induced power during animal hovering flight. The wake is modeled
by a series of equi-spaced rigid rectangular vortex plates, positioned
horizontally and moving vertically downwards with identical speeds;
each plate is generated during powering of the functionally wing
stroke. The vortex representation of the wake considered in the
current theory allows a considerable loss of momentum to occur. The
current approach accords well with the nature of the wingbeat since it
considers the unsteadiness in the wake as an important fluid
dynamical characteristic. Induced power in hovering is calculated as
the aerodynamic power required to generate the vortex wake system.
Specific mean induced power to mean wing tip velocity ratio is
determined by solely the normal spacing parameter (f) for a given
wing stroke amplitude. The current theory gives much higher specific
induced power estimate than anticipated by classical methods.
Abstract: The aim of our work is to study phase composition,
particle size and magnetic response of Fe2O3/TiO2 nanocomposites
with respect to the final annealing temperature. Those nanomaterials
are considered as smart catalysts, separable from a liquid/gaseous
phase by applied magnetic field. The starting product was obtained
by an ecologically acceptable route, based on heterogeneous
precipitation of the TiO2 on modified g-Fe2O3 nanocrystals dispersed
in water. The precursor was subsequently annealed on air at
temperatures ranging from 200 oC to 900 oC. The samples were
investigated by synchrotron X-ray powder diffraction (S-PXRD),
magnetic measurements and Mössbauer spectroscopy. As evidenced
by S-PXRD and Mössbauer spectroscopy, increasing the annealing
temperature causes evolution of the phase composition from
anatase/maghemite to rutile/hematite, finally above 700 oC the
pseudobrookite (Fe2TiO5) also forms. The apparent particle size of
the various Fe2O3/TiO2 phases has been determined from the highquality
S-PXRD data by using two different approaches: the Rietveld
refinement and the Debye method. Magnetic response of the samples
is discussed in considering the phase composition and the particle
size.
Abstract: Brand name plays a vital role for in-shop buying
behavior of consumers and mutated brand name may affect the
selling of leading branded products. In Indian market, there are many
products with mutated brand names which are either orthographically
or phonologically similar. Due to presence of such products, Indian
consumers very often fall under confusion when buying some
regularly used stuff. Authors of the present paper have attempted to
demonstrate relationship between less attention and false recognition
of mutated brand names during a product selection process. To
achieve this goal, visual attention study was conducted on 15 male
college students using eye-tracker against a mutated brand name and
errors in recognition were noted using questionnaire. Statistical
analysis of the acquired data revealed that there was more false
recognition of mutated brand name when less attention was paid
during selection of favorite product. Moreover, it was perceived that
eye tracking is an effective tool for analyzing false recognition of
brand name mutation.
Abstract: Target tracking and localization are important applications
in wireless sensor networks. In these applications, sensor nodes
collectively monitor and track the movement of a target. They have
limited energy supplied by batteries, so energy efficiency is essential
for sensor networks. Most existing target tracking protocols need to
wake up sensors periodically to perform tracking. Some unnecessary
energy waste is thus introduced. In this paper, an energy efficient
protocol for target localization is proposed. In order to preserve
energy, the protocol fixes the number of sensors for target tracking,
but it retains the quality of target localization in an acceptable
level. By selecting a set of sensors for target localization, the other
sensors can sleep rather than periodically wake up to track the target.
Simulation results show that the proposed protocol saves a significant
amount of energy and also prolongs the network lifetime.
Abstract: Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.
Abstract: The purpose of this paper is to investigate the adjust-
ment of solar shading devices in office buildings in two different
seasons by occupants, and its influence on the lighting control and
indoor illuminance levels. The results show that occupants take
inappropriate measures both in reducing solar radiation in summer
and in admitting solar gains in winter, resulting in an increase in
lighting energy and a reduction in indoor illuminance. Therefore,
movable shading devices, controlled automatically, are suitable for
building applications to reduce energy consumption.
Abstract: Documents clustering become an essential technology
with the popularity of the Internet. That also means that fast and
high-quality document clustering technique play core topics. Text
clustering or shortly clustering is about discovering semantically
related groups in an unstructured collection of documents. Clustering
has been very popular for a long time because it provides unique
ways of digesting and generalizing large amounts of information.
One of the issues of clustering is to extract proper feature (concept)
of a problem domain. The existing clustering technology mainly
focuses on term weight calculation. To achieve more accurate
document clustering, more informative features including concept
weight are important. Feature Selection is important for clustering
process because some of the irrelevant or redundant feature may
misguide the clustering results. To counteract this issue, the proposed
system presents the concept weight for text clustering system
developed based on a k-means algorithm in accordance with the
principles of ontology so that the important of words of a cluster can
be identified by the weight values. To a certain extent, it has resolved
the semantic problem in specific areas.
Abstract: This paper proposes a method, combining color and layout features, for identifying documents captured from low-resolution handheld devices. On one hand, the document image color density surface is estimated and represented with an equivalent ellipse and on the other hand, the document shallow layout structure is computed and hierarchically represented. Our identification method first uses the color information in the documents in order to focus the search space on documents having a similar color distribution, and finally selects the document having the most similar layout structure in the remaining of the search space.
Abstract: A cross sectional survey design was used to collect
data from 370 diabetic patients. Two instruments were used in
obtaining data; in-depth interview guide and researchers- developed
questionnaire. Fisher's exact test was used to investigate association
between the identified factors and nonadherence. Factors identified
were: socio-demographic factors such as: gender, age, marital status,
educational level and occupation; psychosocial obstacles such as:
non-affordability of prescribed diet, frustration due to the restriction,
limited spousal support, feelings of deprivation, feeling that
temptation is inevitable, difficulty in adhering in social gatherings
and difficulty in revealing to host that one is diabetic; health care
providers obstacles were: poor attitude of health workers, irregular
diabetes education in clinics , limited number of nutrition education
sessions/ inability of the patients to estimate the desired quantity of
food, no reminder post cards or phone calls about upcoming patient
appointments and delayed start of appointment / time wasting in
clinics.
Abstract: Economically transformers constitute one of the largest investments in a Power system. For this reason, transformer condition assessment and management is a high priority task. If a transformer fails, it would have a significant negative impact on revenue and service reliability. Monitoring the state of health of power transformers has traditionally been carried out using laboratory Dissolved Gas Analysis (DGA) tests performed at periodic intervals on the oil sample, collected from the transformers. DGA of transformer oil is the single best indicator of a transformer-s overall condition and is a universal practice today, which started somewhere in the 1960s. Failure can occur in a transformer due to different reasons. Some failures can be limited or prevented by maintenance. Oil filtration is one of the methods to remove the dissolve gases and prevent the deterioration of the oil. In this paper we analysis the DGA data by regression method and predict the gas concentration in the oil in the future. We bring about a comparative study of different traditional methods of regression and the errors generated out of their predictions. With the help of these data we can deduce the health of the transformer by finding the type of fault if it has occurred or will occur in future. Additional in this paper effect of filtration on the transformer health is highlight by calculating the probability of failure of a transformer with and without oil filtrating.
Abstract: The purpose of the study was to determine if, among
32 brain injured adults in community rehabilitation programs, there is
a statistically significant relationship between the degree of severity
of brain injury and these adults- level of self-esteem and stress. The
researcher hypothesized there would be a statistically significant
difference and a statistically significant relationship in self-esteem
and stress levels among and TBI adults. A Pearson product moment
correlational analysis was implemented and results found a
statistically significant relationship between self-esteem and stress
levels. Future recommendations were suggested upon completion of
research.
Abstract: Supersonic hydrogen-air cylindrical mixing layer is
numerically analyzed to investigate the effect of inlet swirl on
ignition time delay in scramjets. Combustion is treated using detail
chemical kinetics. One-equation turbulence model of Spalart and
Allmaras is chosen to study the problem and advection upstream
splitting method is used as computational scheme. The results show
that swirling both fuel and oxidizer streams may drastically decrease
the ignition distance in supersonic combustion, unlike using the swirl
just in fuel stream which has no helpful effect.
Abstract: In this paper we present semantic assistant agent
(SAA), an open source digital library agent which takes user query
for finding information in the digital library and takes resources-
metadata and stores it semantically. SAA uses Semantic Web to
improve browsing and searching for resources in digital library. All
metadata stored in the library are available in RDF format for
querying and processing by SemanSreach which is a part of SAA
architecture. The architecture includes a generic RDF-based model
that represents relationships among objects and their components.
Queries against these relationships are supported by an RDF triple
store.