Abstract: In this paper a fast motion estimation method for
H.264/AVC named Triplet Search Motion Estimation (TS-ME) is
proposed. Similar to some of the traditional fast motion estimation
methods and their improved proposals which restrict the search points
only to some selected candidates to decrease the computation
complexity, proposed algorithm separate the motion search process to
several steps but with some new features. First, proposed algorithm try
to search the real motion area using proposed triplet patterns instead of
some selected search points to avoid dropping into the local minimum.
Then, in the localized motion area a novel 3-step motion search
algorithm is performed. Proposed search patterns are categorized into
three rings on the basis of the distance from the search center. These
three rings are adaptively selected by referencing the surrounding
motion vectors to early terminate the motion search process. On the
other hand, computation reduction for sub pixel motion search is also
discussed considering the appearance probability of the sub pixel
motion vector. From the simulation results, motion estimation speed
improved by a factor of up to 38 when using proposed algorithm than
that of the reference software of H.264/AVC with ignorable picture
quality loss.
Abstract: A new numerical method for solving the twodimensional,
steady, incompressible, viscous flow equations on a
Curvilinear staggered grid is presented in this paper. The proposed
methodology is finite difference based, but essentially takes
advantage of the best features of two well-established numerical
formulations, the finite difference and finite volume methods. Some
weaknesses of the finite difference approach are removed by
exploiting the strengths of the finite volume method. In particular,
the issue of velocity-pressure coupling is dealt with in the proposed
finite difference formulation by developing a pressure correction
equation in a manner similar to the SIMPLE approach commonly
used in finite volume formulations. However, since this is purely a
finite difference formulation, numerical approximation of fluxes is
not required. Results obtained from the present method are based on
the first-order upwind scheme for the convective terms, but the
methodology can easily be modified to accommodate higher order
differencing schemes.
Abstract: This paper deals with the application for contentbased
image retrieval to extract color feature from natural images
stored in the image database by segmenting the image through
clustering. We employ a class of nonparametric techniques in which
the data points are regarded as samples from an unknown probability
density. Explicit computation of the density is avoided by using the
mean shift procedure, a robust clustering technique, which does not
require prior knowledge of the number of clusters, and does not
constrain the shape of the clusters. A non-parametric technique for
the recovery of significant image features is presented and
segmentation module is developed using the mean shift algorithm to
segment each image. In these algorithms, the only user set parameter
is the resolution of the analysis and either gray level or color images
are accepted as inputs. Extensive experimental results illustrate
excellent performance.
Abstract: This research aimed to study the market feasibility for
new brand coffee house, the case study of Thailand.. This study is a
mixed methods research combining quantitative research and the
qualitative research. Primary data 350 sets of questionnaires were
distributed, and the high quality completed questionnaires of 320 sets
returned. Research samples are identified as customers’ of Hi-end
department stores in Thailand. The sources of secondary data were
critical selected from highly reliable sources, both from public and
private sectors. The results were used to classify the customer group
into two main groups, the younger than 25 and the older than 25years
old. Results of the younger group, are give priority to the dimension
of coffee house and its services dimension more than others, then
branding dimension and the product dimension respectively. On the
other hand, the older group give the difference result as they rate the
important of the branding, coffee house and its services, then the
product respectively. Coffee consuming is not just the trend but it
has become part of people lifestyle. And the new cultures also created
by the wise businessman. Coffee was long produced and consumed in
Thailand. But it is surprisingly the hi-end brand coffee houses in Thai
market are mostly imported brands. The café business possibility for
Thai brand coffee house in Thai market were discussed in the paper.
Abstract: This article proposes a novel Pareto-based multiobjective
meta-heuristic algorithm named non-dominated ranking
genetic algorithm (NRGA) to solve multi-facility location-allocation
problem. In NRGA, a fitness value representing rank is assigned to
each individual of the population. Moreover, two features ranked
based roulette wheel selection including select the fronts and choose
solutions from the fronts, are utilized. The proposed solving
methodology is validated using several examples taken from the
specialized literature. The performance of our approach shows that
NRGA algorithm is able to generate true and well distributed Pareto
optimal solutions.
Abstract: The main objective of our study is to collect data
about the profile of the asthmatic patients in Assam and thereby have
a comprehensive knowledge of the factors influencing the asthmatic
patients of the state and their medication pattern. We developed a
search strategy to find any publication about the community based
survey asthma related and used. These to search the MEDLINE
(1996 to current literature) CINAHL DOAJ pubmed databases using
the key phrases, Asthma, Respiratory disorders, Drug therapy of
Asthma, database decision support system and asthma. The
appropriate literature was printed out from the online source and
library (Journal) source. The study was conducted through a set of
structured and non-structured questionnaires targeted on the
asthmatic patients belonging to the rural and urban areas of Assam,
during the month of Dec 2006 to July 2007, 138 cases were studied
in Gauwathi Medical College & Hospital located in Bhangagarh,
Assam in India. The demographic characteristics a factor in 138
patients with asthma with allergic rhinitis (cases) gives the detail
profile of asthmatic patient-s distribution of Assam as classified on
the basis of age and sex. It is evident from the study that male
populations (66%) are more prone to asthma as compared to the
females (34%).Another striking features that emerged from this
survey is the maximum prevalence of asthma in the age group of 20-
30 years followed by infants belonging to the age group of 7 (0.05%)
0-10years among both male and female populations of Assam. The
high incidence of asthma in the age group of 20-30 years may
probably be due to the allergy arising out of sudden exposure to dust
and pollen which the children face while playing and going to the
school. The rural females in the age group of 30-40 years are more
prone to asthma than urban females in the same age group may be
due to sex differentiation among the tribal population of the state.
Pharmacists should educate the asthmatics how to use inhalers
considering growing menace of asthma in the state. Safer drugs
should be produced in the form of aerosol so that easy administration
by the asthmatic patients and physicians of the state is possible for
curing asthma. The health centers should be more equipped with the
medicines to cure asthma in the state like Assam.
Abstract: Due to important issues, such as deadlock, starvation,
communication, non-deterministic behavior and synchronization,
concurrent systems are very complex, sensitive, and error-prone.
Thus ensuring reliability and accuracy of these systems is very
essential. Therefore, there has been a big interest in the formal
specification of concurrent programs in recent years. Nevertheless,
some features of concurrent systems, such as dynamic process
creation, scheduling and starvation have not been specified formally
yet. Also, some other features have been specified partially and/or
have been described using a combination of several different
formalisms and methods whose integration needs too much effort. In
other words, a comprehensive and integrated specification that could
cover all aspects of concurrent systems has not been provided yet.
Thus, this paper makes two major contributions: firstly, it provides a
comprehensive formal framework to specify all well-known features
of concurrent systems. Secondly, it provides an integrated
specification of these features by using just a single formal notation,
i.e., the Z language.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
three feature selection methods are evaluated: Random Selection,
Information Gain (IG) and Support Vector Machine feature selection
(called SVM_FS). We show that the best results were obtained with
SVM_FS method for a relatively small dimension of the feature
vector. Also we present a novel method to better correlate SVM
kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: When it comes to last, it is regarded as the critical
foundation of shoe design and development. A computer aided
methodology for various last form designs is proposed in this study.
The reverse engineering is mainly applied to the process of scanning
for the last form. Then with the minimum energy for revision of
surface continuity, the surface reconstruction of last is rebuilt by the
feature curves of the scanned last. When the surface reconstruction of
last is completed, the weighted arithmetic mean method is applied to
the computation on the shape morphing for the control mesh of last,
thus 3D last form of different sizes is generated from its original form
feature with functions remained. In the end, the result of this study is
applied to an application for 3D last reconstruction system. The
practicability of the proposed methodology is verified through later
case studies.
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: Although lots of research work has been done for
human pose recognition, the view-point of cameras is still critical
problem of overall recognition system. In this paper, view-point
insensitive human pose recognition is proposed. The aims of the
proposed system are view-point insensitivity and real-time processing.
Recognition system consists of feature extraction module, neural
network and real-time feed forward calculation. First, histogram-based
method is used to extract feature from silhouette image and it is
suitable for represent the shape of human pose. To reduce the
dimension of feature vector, Principle Component Analysis(PCA) is
used. Second, real-time processing is implemented by using Compute
Unified Device Architecture(CUDA) and this architecture improves
the speed of feed-forward calculation of neural network. We
demonstrate the effectiveness of our approach with experiments on
real environment.
Abstract: In this paper, we proposed the distribution of mesh
normal vector direction as a feature descriptor of a 3D model. A
normal vector shows the entire shape of a model well. The
distribution of normal vectors was sampled in proportion to each
polygon's area so that the information on the surface with less surface
area may be less reflected on composing a feature descriptor in order
to enhance retrieval performance. At the analysis result of ANMRR,
the enhancement of approx. 12.4%~34.7% compared to the existing
method has also been indicated.
Abstract: The purpose of our study was to compare spontaneous
re-epithelisation characteristics versus assisted re-epithelisation. In
order to assess re-epithelisation of the injured skin, we have imagined
and designed a burn wound model on Wistar rat skin. Our aim was to
create standardised, easy reproducible and quantifiable skin lesions
involving entire epidermis and superficial dermis. We then have
applied the above mentioned therapeutic strategies to compare
regeneration of epidermis and dermis, local and systemic parameter
changes in different conditions. We have enhanced the reepithelisation
process under a moist atmosphere of a polyurethane
wound dress modified with helium non-thermal plasma, and with the
aid of direct cold-plasma treatment respectively. We have followed
systemic parameters change: hematologic and biochemical
parameters, and local features: oxidative stress markers and histology
of skin in the above mentioned conditions. Re-epithelisation is just a
part of the skin regeneration process, which recruits cellular
components, with the aid of epidermal and dermal interaction via
signal molecules.
Abstract: This paper presents a novel template-based method to
detect objects of interest from real images by shape matching. To
locate a target object that has a similar shape to a given template
boundary, the proposed method integrates three components: contour
grouping, partial shape matching, and boundary verification. In the
first component, low-level image features, including edges and
corners, are grouped into a set of perceptually salient closed contours
using an extended ratio-contour algorithm. In the second component,
we develop a partial shape matching algorithm to identify the
fractions of detected contours that partly match given template
boundaries. Specifically, we represent template boundaries and
detected contours using landmarks, and apply a greedy algorithm to
search the matched landmark subsequences. For each matched
fraction between a template and a detected contour, we estimate an
affine transform that transforms the whole template into a hypothetic
boundary. In the third component, we provide an efficient algorithm
based on oriented edge lists to determine the target boundary from
the hypothetic boundaries by checking each of them against image
edges. We evaluate the proposed method on recognizing and
localizing 12 template leaves in a data set of real images with clutter
back-grounds, illumination variations, occlusions, and image noises.
The experiments demonstrate the high performance of our proposed
method1.
Abstract: This manuscript presents, palmprint recognition by
combining different texture extraction approaches with high accuracy.
The Region of Interest (ROI) is decomposed into different frequencytime
sub-bands by wavelet transform up-to two levels and only the
approximate image of two levels is selected, which is known as
Approximate Image ROI (AIROI). This AIROI has information of
principal lines of the palm. The Competitive Index is used as the
features of the palmprint, in which six Gabor filters of different
orientations convolve with the palmprint image to extract the orientation
information from the image. The winner-take-all strategy
is used to select dominant orientation for each pixel, which is
known as Competitive Index. Further, PCA is applied to select highly
uncorrelated Competitive Index features, to reduce the dimensions of
the feature vector, and to project the features on Eigen space. The
similarity of two palmprints is measured by the Euclidean distance
metrics. The algorithm is tested on Hong Kong PolyU palmprint
database. Different AIROI of different wavelet filter families are also
tested with the Competitive Index and PCA. AIROI of db7 wavelet
filter achievs Equal Error Rate (EER) of 0.0152% and Genuine
Acceptance Rate (GAR) of 99.67% on the palm database of Hong
Kong PolyU.
Abstract: Advertising is one of the important marketing
strategies and the choice of media is an important aspect of
effectiveness of advertising media. The two most popular advertising
media, TV and web media are highly effective in creating successful
advertisements as they influence the purchase decision of the
viewers. Although TV and web are electronic media, they are unique
in their features and traits of advertising. Hence, the present study
attempts to analyze the influence of these two media towards buying
behavior of the viewers. The two media are analyzed separately to
determine its level of influence towards buying behavior and finally a
comparative analysis of these media is attempted to find the
difference in their level of influence.
Abstract: Electrophysiological signals were recorded from primary cultures of dissociated rat cortical neurons coupled to Micro-Electrode Arrays (MEAs). The neuronal discharge patterns may change under varying physiological and pathological conditions. For this reason, we developed a new burst detection method able to identify bursts with peculiar features in different experimental conditions (i.e. spontaneous activity and under the effect of specific drugs). The main feature of our algorithm (i.e. Burst On Hurst), based on the auto-similarity or fractal property of the recorded signal, is the independence from the chosen spike detection method since it works directly on the raw data.
Abstract: Based on the Lagrangian for the Gross –Pitaevskii
equation as derived by H. Sakaguchi and B.A Malomed [5] we have
derived a double well model for the nonlinear optical lattice. This
model explains the various features of nonlinear optical lattices.
Further, from this model we obtain and simulate the probability for
tunneling from one well to another which agrees with experimental
results [4].
Abstract: This article is based on the technique which is called
Discrete Parameter Tracking (DPT). First introduced by A. A. Azab
[8] which is applicable for less order reference model. The order of
the reference model is (n-l) and n is the number of the adjustable
parameters in the physical plant.
The technique utilizes a modified gradient method [9] where the
knowledge of the exact order of the nonadaptive system is not
required, so, as to eliminate the identification problem. The
applicability of the mentioned technique (DPT) was examined
through the solution of several problems.
This article introduces the solution of a third order system with
three adjustable parameters, controlled according to second order
reference model. The adjustable parameters have great initial error
which represent condition.
Computer simulations for the solution and analysis are provided
to demonstrate the simplicity and feasibility of the technique.
Abstract: In this paper a combined feature selection method is
proposed which takes advantages of sample domain filtering,
resampling and feature subset evaluation methods to reduce
dimensions of huge datasets and select reliable features. This method
utilizes both feature space and sample domain to improve the process
of feature selection and uses a combination of Chi squared with
Consistency attribute evaluation methods to seek reliable features.
This method consists of two phases. The first phase filters and
resamples the sample domain and the second phase adopts a hybrid
procedure to find the optimal feature space by applying Chi squared,
Consistency subset evaluation methods and genetic search.
Experiments on various sized datasets from UCI Repository of
Machine Learning databases show that the performance of five
classifiers (Naïve Bayes, Logistic, Multilayer Perceptron, Best First
Decision Tree and JRIP) improves simultaneously and the
classification error for these classifiers decreases considerably. The
experiments also show that this method outperforms other feature
selection methods.