Abstract: Since “Hello Kitty” was manufactured in the market in
1974, the manufacturer, Sanrio Co., Ltd. gains high profits not only
Kitty’s products but also Kitty license, which gives us a picture of
Sanrio’s sales strategy in the global market. Kitty’s history, its
products, and Sanrio’s sales strategy are researched in this paper.
Comparing it to American Girl, and focusing on KITTYLAB, a type of
attraction where you can enjoy games with Kitty, and choose its parts
to build your own Kitty, the image of the cultural icon can be altered.
Abstract: This study investigates the cleaning performance of
high intensity 360 kHz frequency on removal of nano-dimensional
and sub-micron particles from various surfaces, uniformity of the
cleaning tank and run to run variation of cleaning process. The
uniformity of the cleaning tank was measured by two different
methods i.e. 1. ppbTM meter and 2. Liquid Particle Counting (LPC)
technique. The result indicates that the energy was distributed more
uniformly throughout the entire cleaning vessel even at the corners
and edges of the tank when megasonic sweeping technology is
applied. The result also shows that rinsing the parts with 360 kHz
frequency at final rinse gives lower particle counts, hence higher
cleaning efficiency as compared to other frequencies. When
megasonic sweeping technology is applied each piezoelectric
transducers will operate at their optimum resonant frequency and
generates stronger acoustic cavitational force and higher acoustic
streaming velocity. These combined forces are helping to enhance the
particle removal and at the same time improve the overall cleaning
performance. The multiple extractions study was also carried out for
various frequencies to measure the cleaning potential and asymptote
value.
Abstract: In this paper, we propose an automatic verification
technology of software patches for user virtual environments on IaaS
Cloud to decrease verification costs of patches. In these days, IaaS
services have been spread and many users can customize virtual
machines on IaaS Cloud like their own private servers. Regarding to
software patches of OS or middleware installed on virtual machines,
users need to adopt and verify these patches by themselves. This task
increases operation costs of users. Our proposed method replicates
user virtual environments, extracts verification test cases for user
virtual environments from test case DB, distributes patches to virtual
machines on replicated environments and conducts those test cases
automatically on replicated environments. We have implemented the
proposed method on OpenStack using Jenkins and confirmed the
feasibility. Using the implementation, we confirmed the effectiveness
of test case creation efforts by our proposed idea of 2-tier abstraction
of software functions and test cases. We also evaluated the automatic
verification performance of environment replications, test cases
extractions and test cases conductions.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: The inherent skin patterns created at the joints in the
finger exterior are referred as finger knuckle-print. It is exploited to
identify a person in a unique manner because the finger knuckle print
is greatly affluent in textures. In biometric system, the region of
interest is utilized for the feature extraction algorithm. In this paper,
local and global features are extracted separately. Fast Discrete
Orthonormal Stockwell Transform is exploited to extract the local
features. Global feature is attained by escalating the size of Fast
Discrete Orthonormal Stockwell Transform to infinity. Two features
are fused to increase the recognition accuracy. A matching distance is
calculated for both the features individually. Then two distances are
merged mutually to acquire the final matching distance. The
proposed scheme gives the better performance in terms of equal error
rate and correct recognition rate.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Speech Segmentation is the measure of the change
point detection for partitioning an input speech signal into regions
each of which accords to only one speaker. In this paper, we apply
two features based on multi-scale product (MP) of the clean speech,
namely the spectral centroid of MP, and the zero crossings rate of
MP. We focus on multi-scale product analysis as an important tool
for segmentation extraction. The MP is based on making the product
of the speech wavelet transform coefficients (WTC). We have
estimated our method on the Keele database. The results show the
effectiveness of our method. It indicates that the two features can find
word boundaries, and extracted the segments of the clean speech.
Abstract: Modelling of the earth's surface and evaluation of
urban environment, with 3D models, is an important research topic.
New stereo capabilities of high resolution optical satellites images,
such as the tri-stereo mode of Pleiades, combined with new image
matching algorithms, are now available and can be applied in urban
area analysis. In addition, photogrammetry software packages gained
new, more efficient matching algorithms, such as SGM, as well as
improved filters to deal with shadow areas, can achieve more dense
and more precise results.
This paper describes a comparison between 3D data extracted
from tri-stereo and dual stereo satellite images, combined with pixel
based matching and Wallis filter. The aim was to improve the
accuracy of 3D models especially in urban areas, in order to assess if
satellite images are appropriate for a rapid evaluation of urban
environments.
The results showed that 3D models achieved by Pleiades tri-stereo
outperformed, both in terms of accuracy and detail, the result
obtained from a Geo-eye pair. The assessment was made with
reference digital surface models derived from high resolution aerial
photography. This could mean that tri-stereo images can be
successfully used for the proposed urban change analyses.
Abstract: One image is worth more than thousand words.
Images if analyzed can reveal useful information. Low level image
processing deals with the extraction of specific feature from a single
image. Now the question arises: What technique should be used to
extract patterns of very large and detailed image database? The
answer of the question is: “Image Mining”. Image Mining deals with
the extraction of image data relationship, implicit knowledge, and
another pattern from the collection of images or image database. It is
nothing but the extension of Data Mining. In the following paper, not
only we are going to scrutinize the current techniques of image
mining but also present a new technique for mining images using
Genetic Algorithm.
Abstract: Fast changing knowledge systems on the Internet can
be accessed more efficiently with the help of automatic document
summarization and updating techniques. The aim of multi-document
update summary generation is to construct a summary unfolding the
mainstream of data from a collection of documents based on the
hypothesis that the user has already read a set of previous documents.
In order to provide a lot of semantic information from the documents,
deeper linguistic or semantic analysis of the source documents were
used instead of relying only on document word frequencies to select
important concepts. In order to produce a responsive summary,
meaning oriented structural analysis is needed. To address this issue,
the proposed system presents a document summarization approach
based on sentence annotation with aspects, prepositions and named
entities. Semantic element extraction strategy is used to select
important concepts from documents which are used to generate
enhanced semantic summary.
Abstract: Text mining techniques are generally applied for
classifying the text, finding fuzzy relations and structures in data
sets. This research provides plenty text mining capabilities. One
common application is text classification and event extraction,
which encompass deducing specific knowledge concerning incidents
referred to in texts. The main contribution of this paper is the
clarification of a concept graph generation mechanism, which is based
on a text classification and optimal fuzzy relationship extraction.
Furthermore, the work presented in this paper explains the application
of fuzzy relationship extraction and branch and bound (BB) method
to simplify the texts.
Abstract: Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Abstract: This paper investigates the activity of the rectus
femoris (RF) and biceps femoris (BF) in healthy subjects during salat
(prostration) and specific exercise (squat exercise) using
electromyography (EMG). A group of undergraduates aged between
19 to 25 years voluntarily participated in this study. The myoelectric
activity of the muscles were recorded and analyzed. The finding
indicated that there were contractions of the muscles during the salat
and exercise with almost same EMG’s level. From the result,
Wilcoxon’s Rank Sum test showed significant difference between
prostration and squat exercise (p
Abstract: A thermosyphon system is a heat transfer loop which
operates on the basis of gravity and buoyancy forces. It guarantees a
good reliability and low maintenance cost as it does not involve any
mechanical pump. Therefore, it can be used in many industrial
applications such as refrigeration and air conditioning, electronic
cooling, nuclear reactors, geothermal heat extraction, etc. But flow
instabilities and loop configuration are the major problems in this
system. Several previous researchers studied that stabilities can be
suppressed by using nanofluids as loop fluid. In the present study a
rectangular thermosyphon loop with end heat exchangers are
considered for the study. This configuration is more appropriate for
many practical applications such as solar water heater, geothermal
heat extraction, etc. In the present work, steady-state analysis is
carried out on thermosyphon loop with parallel flow coaxial heat
exchangers at heat source and heat sink. In this loop nanofluid is
considered as the loop fluid and water is considered as the external
fluid in both hot and cold heat exchangers. For this analysis onedimensional
homogeneous model is developed. In this model,
conservation equations like conservation of mass, momentum, energy
are discretized using finite difference method. A computer code is
written in MATLAB to simulate the flow in thermosyphon loop. A
comparison in terms of heat transfer is made between water and
nanofluid as working fluids in the loop.
Abstract: In this study, we propose a novel technique for acoustic
echo suppression (AES) during speech recognition under barge-in
conditions. Conventional AES methods based on spectral subtraction
apply fixed weights to the estimated echo path transfer function
(EPTF) at the current signal segment and to the EPTF estimated until
the previous time interval. However, the effects of echo path changes
should be considered for eliminating the undesired echoes. We
describe a new approach that adaptively updates weight parameters in
response to abrupt changes in the acoustic environment due to
background noises or double-talk. Furthermore, we devised a voice
activity detector and an initial time-delay estimator for barge-in speech
recognition in communication networks. The initial time delay is
estimated using log-spectral distance measure, as well as
cross-correlation coefficients. The experimental results show that the
developed techniques can be successfully applied in barge-in speech
recognition systems.
Abstract: In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation,
style, illumination, and can suffer from perspective distortion.
Pre-processing is performed to make the characters scale and
rotation invariant. Since text degradations can not be appropriately
defined using well-known geometric transformations such
as translation, rotation, affine transformation and shearing, we
use the whole character black pixels as our feature vector.
Classification is performed with minimum distance classifier
using the maximum likelihood criterion, which delivers very
promising Character Recognition Rate (CRR) of 89%. We
achieve considerably higher Word Recognition Rate (WRR) of
99% when using lower level linguistic knowledge about product
words during the recognition process.
Abstract: In this study, we are interested in a species of the
family of Asteraceae (Tagetes erecta). This family is considered as a
source of antimicrobial extracts with strong capacity. The extraction
of the flavonoids is carried out by the method of liquid/liquid with the
use of successive solvents. Afterwards, we evaluated the biological
activity of the flavonoids on five pathogenic bacterial stocks such as
Escherichia coli, Bacillus subtilis, Klebsiella pneumoniae,
Pseudomonas aeruginosa and Staphylococcus aureus and two stocks
of yeasts to knowing Candida albicans) and Saccharomyces
cerevisiae, by employing the method of the aromatogramme starting
from a solid disc. The result of the antimicrobial activity shows an
action and a variable degree of sensitivity according to bacterial
stocks tested. It will be noted that the flavonoids have an inhibiting
effect on E. coli, B. subtilis, K. pneumoniae and S. aureus. But a
resistance with respect to the extract by P. aeruginosa, C. albicans
and S. cerevisiae is to be mentioned.
Abstract: The growth in the volume of text data such as books
and articles in libraries for centuries has imposed to establish
effective mechanisms to locate them. Early techniques such as
abstraction, indexing and the use of classification categories have
marked the birth of a new field of research called "Information
Retrieval". Information Retrieval (IR) can be defined as the task of
defining models and systems whose purpose is to facilitate access to
a set of documents in electronic form (corpus) to allow a user to find
the relevant ones for him, that is to say, the contents which matches
with the information needs of the user.
Most of the models of information retrieval use a specific data
structure to index a corpus which is called "inverted file" or "reverse
index".
This inverted file collects information on all terms over the corpus
documents specifying the identifiers of documents that contain the
term in question, the frequency of each term in the documents of the
corpus, the positions of the occurrences of the word...
In this paper we use an oriented object database (db4o) instead of
the inverted file, that is to say, instead to search a term in the inverted
file, we will search it in the db4o database.
The purpose of this work is to make a comparative study to see if
the oriented object databases may be competing for the inverse index
in terms of access speed and resource consumption using a large
volume of data.
Abstract: In this paper, Fuzzy C-Means clustering with
Expectation Maximization-Gaussian Mixture Model based hybrid
modeling algorithm is proposed for Continuous Tamil Speech
Recognition. The speech sentences from various speakers are used
for training and testing phase and objective measures are between the
proposed and existing Continuous Speech Recognition algorithms.
From the simulated results, it is observed that the proposed algorithm
improves the recognition accuracy and F-measure up to 3% as
compared to that of the existing algorithms for the speech signal from
various speakers. In addition, it reduces the Word Error Rate, Error
Rate and Error up to 4% as compared to that of the existing
algorithms. In all aspects, the proposed hybrid modeling for Tamil
speech recognition provides the significant improvements for speechto-
text conversion in various applications.
Abstract: Brown seaweeds are abundant in Portuguese coastline
and represent an almost unexploited marine economic resource. One
of the most common species, easily available for harvesting in the
northwest coast, is Saccorhiza polyschides grows in the lowest shore
and costal rocky reefs. It is almost exclusively used by local farmers
as natural fertilizer, but contains a substantial amount of valuable
compounds, particularly alginates, natural biopolymers of high
interest for many industrial applications.
Alginates are natural polysaccharides present in cell walls of
brown seaweed, highly biocompatible, with particular properties that
make them of high interest for the food, biotechnology, cosmetics
and pharmaceutical industries. Conventional extraction processes are
based on thermal treatment. They are lengthy and consume high
amounts of energy and solvents. In recent years, microwave-assisted
extraction (MAE) has shown enormous potential to overcome major
drawbacks that outcome from conventional plant material extraction
(thermal and/or solvent based) techniques, being also successfully
applied to the extraction of agar, fucoidans and alginates. In the
present study, acid pretreatment of brown seaweed Saccorhiza
polyschides for subsequent microwave-assisted extraction (MAE) of
alginate was optimized. Seaweeds were collected in Northwest
Portuguese coastal waters of the Atlantic Ocean between May and
August, 2014. Experimental design was used to assess the effect of
temperature and acid pretreatment time in alginate extraction.
Response surface methodology allowed the determination of the
optimum MAE conditions: 40 mL of HCl 0.1 M per g of dried
seaweed with constant stirring at 20ºC during 14h. Optimal acid
pretreatment conditions have enhanced significantly MAE of
alginates from Saccorhiza polyschides, thus contributing for the
development of a viable, more environmental friendly alternative to
conventional processes.