Abstract: This paper describes a new supervised fusion (hybrid)
electrocardiogram (ECG) classification solution consisting of a new
QRS complex geometrical feature extraction as well as a new version
of the learning vector quantization (LVQ) classification algorithm
aimed for overcoming the stability-plasticity dilemma. Toward this
objective, after detection and delineation of the major events of ECG
signal via an appropriate algorithm, each QRS region and also its
corresponding discrete wavelet transform (DWT) are supposed as
virtual images and each of them is divided into eight polar sectors.
Then, the curve length of each excerpted segment is calculated
and is used as the element of the feature space. To increase the
robustness of the proposed classification algorithm versus noise,
artifacts and arrhythmic outliers, a fusion structure consisting of
five different classifiers namely as Support Vector Machine (SVM),
Modified Learning Vector Quantization (MLVQ) and three Multi
Layer Perceptron-Back Propagation (MLP–BP) neural networks with
different topologies were designed and implemented. The new proposed
algorithm was applied to all 48 MIT–BIH Arrhythmia Database
records (within–record analysis) and the discrimination power of the
classifier in isolation of different beat types of each record was
assessed and as the result, the average accuracy value Acc=98.51%
was obtained. Also, the proposed method was applied to 6 number
of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging
to 20 different records of the aforementioned database (between–
record analysis) and the average value of Acc=95.6% was achieved.
To evaluate performance quality of the new proposed hybrid learning
machine, the obtained results were compared with similar peer–
reviewed studies in this area.
Abstract: Information is power. Geographical information is an
emerging science that is advancing the development of knowledge to
further help in the understanding of the relationship of “place" with
other disciplines such as crime. The researchers used crime data for
the years 2004 to 2007 from the Baguio City Police Office to
determine the incidence and actual locations of crime hotspots.
Combined qualitative and quantitative research methodology was
employed through extensive fieldwork and observation, geographic
visualization with Geographic Information Systems (GIS) and Global
Positioning Systems (GPS), and data mining. The paper discusses
emerging geographic visualization and data mining tools and
methodologies that can be used to generate baseline data for
environmental initiatives such as urban renewal and rejuvenation.
The study was able to demonstrate that crime hotspots can be
computed and were seen to be occurring to some select places in the
Central Business District (CBD) of Baguio City. It was observed that
some characteristics of the hotspot places- physical design and milieu
may play an important role in creating opportunities for crime. A list
of these environmental attributes was generated. This derived
information may be used to guide the design or redesign of the urban
environment of the City to be able to reduce crime and at the same
time improve it physically.
Abstract: Although services play a crucial role in economy,
service did not gain as much importance as productivity management
in manufacturing. This paper presents key findings from literature
and practice. Based on an initial definition of complex services, seven
productivity concepts are briefly presented and assessed by relevant,
complex service specific criteria. Following the findings a complex
service productivity model is proposed. The novel model comprises
of all specific dimensions of service provision from both, the
provider-s as well as costumer-s perspective. A clear assignment of
identified value drivers and relationships between them is presented.
In order to verify the conceptual service productivity model a case
study from a project engineering department of a chemical plant
development and construction company is presented.
Abstract: Emergence of smartphones brings to live the concept
of converged devices with the availability of web amenities. Such
trend also challenges the mobile devices manufactures and service
providers in many aspects, such as security on mobile phones,
complex and long time design flow, as well as higher development
cost. Among these aspects, security on mobile phones is getting more
and more attention. Microkernel based virtualization technology will
play a critical role in addressing these challenges and meeting mobile
market needs and preferences, since virtualization provides essential
isolation for security reasons and it allows multiple operating systems
to run on one processor accelerating development and cutting development
cost. However, virtualization benefits do not come for free.
As an additional software layer, it adds some inevitable virtualization
overhead to the system, which may decrease the system performance.
In this paper we evaluate and analyze the virtualization performance
cost of L4 microkernel based virtualization on a competitive mobile
phone by comparing the L4Linux, a para-virtualized Linux on top of
L4 microkernel, with the native Linux performance using lmbench
and a set of typical mobile phone applications.
Abstract: The last Assessment Report of the Intergovernmental
Panel on Climate Change, stating that the greatest risk in climate
change affects sustainability is now widely known and accepted.
However, it has not provoked substantial reaction and attention in
Hungary, while international and national efforts have also not
achieved expected results so far. Still, there are numerous examples
on different levels (national, regional, local, household) making
considerable progress in limiting their own emissions and making
steps toward mitigation of and adaptation to climate change. The
local level is exceptionally important in sustainability adaptation, as
local communities are often able to adapt more flexibly to changes in
the natural environment.The aim of this paper is to attempt a review
of the national climate policy and the local climate change strategies
in Hungary considering sustainable development.
Abstract: In this paper, we propose a face recognition algorithm
using AAM and Gabor features. Gabor feature vectors which are well
known to be robust with respect to small variations of shape, scaling,
rotation, distortion, illumination and poses in images are popularly
employed for feature vectors for many object detection and
recognition algorithms. EBGM, which is prominent among face
recognition algorithms employing Gabor feature vectors, requires
localization of facial feature points where Gabor feature vectors are
extracted. However, localization method employed in EBGM is based
on Gabor jet similarity and is sensitive to initial values. Wrong
localization of facial feature points affects face recognition rate. AAM
is known to be successfully applied to localization of facial feature
points. In this paper, we devise a facial feature point localization
method which first roughly estimate facial feature points using AAM
and refine facial feature points using Gabor jet similarity-based facial
feature localization method with initial points set by the rough facial
feature points obtained from AAM, and propose a face recognition
algorithm using the devised localization method for facial feature
localization and Gabor feature vectors. It is observed through
experiments that such a cascaded localization method based on both
AAM and Gabor jet similarity is more robust than the localization
method based on only Gabor jet similarity. Also, it is shown that the
proposed face recognition algorithm using this devised localization
method and Gabor feature vectors performs better than the
conventional face recognition algorithm using Gabor jet
similarity-based localization method and Gabor feature vectors like
EBGM.
Abstract: A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank, it captures vocal tract characteristics more effectively in the lower frequency regions. This paper proposes a new set of features using a complementary filter bank structure which improves distinguishability of speaker specific cues present in the higher frequency zone. Unlike high level features that are difficult to extract, the proposed feature set involves little computational burden during the extraction process. When combined with MFCC via a parallel implementation of speaker models, the proposed feature set outperforms baseline MFCC significantly. This proposition is validated by experiments conducted on two different kinds of public databases namely YOHO (microphone speech) and POLYCOST (telephone speech) with Gaussian Mixture Models (GMM) as a Classifier for various model orders.
Abstract: Concerns about low levels of children-s physical activity and motor skill development, prompted the Ministry of Education to trial a physical activity pilot project (PAPP) in 16 New Zealand primary schools. The project comprised professional development and training in physical education for lead teachers and introduced four physical activity coordinators to liaise with and increase physical activity opportunities in the pilot schools. A survey of generalist teachers (128 baseline, 155 post-intervention) from these schools looked at timetabled physical activity sessions and issues related to teaching physical education. The authors calculated means and standard deviations of data relating to timetabled PE sessions and used a one-way analysis of variance to determine significant differences. Results indicated time devoted to physical activity related subjects significantly increased over the course of the intervention. Teacher-s reported improved confidence and competence, which resulted in an improvement in quality physical education delivered more often.
Abstract: The Block Sorting problem is to sort a given
permutation moving blocks. A block is defined as a substring
of the given permutation, which is also a substring of the
identity permutation. Block Sorting has been proved to be
NP-Hard. Until now two different 2-Approximation algorithms
have been presented for block sorting. These are the best known
algorithms for Block Sorting till date. In this work we present
a different characterization of Block Sorting in terms of a
transposition cycle graph. Then we suggest a heuristic,
which we show to exhibit a 2-approximation performance
guarantee for most permutations.
Abstract: In this paper a new definition of adjacency matrix in
the simple graphs is presented that is called fuzzy adjacency matrix,
so that elements of it are in the form of 0 and
n N
n
1 , ∈
that are
in the interval [0, 1], and then some charactristics of this matrix are
presented with the related examples . This form matrix has complete
of information of a graph.
Abstract: Octree compression techniques have been used
for several years for compressing large three dimensional data
sets into homogeneous regions. This compression technique
is ideally suited to datasets which have similar values in
clusters. Oil engineers represent reservoirs as a three dimensional
grid where hydrocarbons occur naturally in clusters. This
research looks at the efficiency of storing these grids using
octree compression techniques where grid cells are broken
into active and inactive regions. Initial experiments yielded
high compression ratios as only active leaf nodes and their
ancestor, header nodes are stored as a bitstream to file on
disk. Savings in computational time and memory were possible
at decompression, as only active leaf nodes are sent to the
graphics card eliminating the need of reconstructing the original
matrix. This results in a more compact vertex table, which can
be loaded into the graphics card quicker and generating shorter
refresh delay times.
Abstract: this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.
Abstract: Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Competitive Neural Network used for classification. Ferroresonance data and other transients was obtained by simulation using EMTP program. Using Daubechies wavelet transform signals has been decomposed till six levels. The energy of six detailed signals that obtained by wavelet transform are used for training and trailing Competitive Neural Network. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.
Abstract: Drying characteristics of rough rice (variety of lenjan) with an initial moisture content of 25% dry basis (db) was studied in a hot air dryer assisted by infrared heating. Three arrival air temperatures (30, 40 and 500C) and four infrared radiation intensities (0, 0.2 , 0.4 and 0.6 W/cm2) and three arrival air speeds (0.1, 0.15 and 0.2 m.s-1) were studied. Bending strength of brown rice kernel, percentage of cracked kernels and time of drying were measured and evaluated. The results showed that increasing the drying arrival air temperature and radiation intensity of infrared resulted decrease in drying time. High bending strength and low percentage of cracked kernel was obtained when paddy was dried by hot air assisted infrared dryer. Between this factors and their interactive effect were a significant difference (p
Abstract: The evolution of technology and construction techniques has enabled the upgrading of transport networks. In particular, the high-speed rail networks allow convoys to peak at above 300 km/h. These structures, however, often significantly impact the surrounding environment. Among the effects of greater importance are the ones provoked by the soundwave connected to train transit. The wave propagation affects the quality of life in areas surrounding the tracks, often for several hundred metres. There are substantial damages to properties (buildings and land), in terms of market depreciation. The present study, integrating expertise in acoustics, computering and evaluation fields, outlines a useful model to select project paths so as to minimize the noise impact and reduce the causes of possible litigation. It also facilitates the rational selection of initiatives to contain the environmental damage to the already existing railway tracks. The research is developed with reference to the Italian regulatory framework (usually more stringent than European and international standards) and refers to a case study concerning the high speed network in Italy.
Abstract: When consistently innovative business-models can
give companies a competitive advantage, longitudinal empirical
research, which can reflect dynamic business-model changes, has yet
to prove a definitive connection. This study consequently employs a
dynamic perspective in conjunction with innovation theory to examine
the relationship between the types of business-model innovation and
firm value. This study tries to examine various types of
business-model innovation in high-end and low-end technology
industries such as HTC and the 7-Eleven chain stores with research
periods of 14 years and 32 years, respectively. The empirical results
suggest that adopting radical business-model innovation in addition to
expanding new target markets can successfully lead to a competitive
advantage. Sustained advanced technological competences and
service/product innovation are the key successful factors in high-end
and low-end technology industry business-models respectively. In
sum up, the business-model innovation can yield a higher market value
and financial value in high-end technology industries than low-end
ones.
Abstract: A Web-based learning tool, the Learn IN Context
(LINC) system, designed and being used in some institution-s
courses in mixed-mode learning, is presented in this paper. This
mode combines face-to-face and distance approaches to education.
LINC can achieve both collaborative and competitive learning. In
order to provide both learners and tutors with a more natural way to
interact with e-learning applications, a conversational interface has
been included in LINC. Hence, the components and essential features
of LINC+, the voice enhanced version of LINC, are described. We
report evaluation experiments of LINC/LINC+ in a real use context
of a computer programming course taught at the Université de
Moncton (Canada). The findings show that when the learning
material is delivered in the form of a collaborative and voice-enabled
presentation, the majority of learners seem to be satisfied with this
new media, and confirm that it does not negatively affect their
cognitive load.
Abstract: This paper considers the autonomous navigation
problem of multiple n-link nonholonomic mobile manipulators within
an obstacle-ridden environment. We present a set of nonlinear
acceleration controllers, derived from the Lyapunov-based control
scheme, which generates collision-free trajectories of the mobile
manipulators from initial configurations to final configurations in a
constrained environment cluttered with stationary solid objects of
different shapes and sizes. We demonstrate the efficiency of the
control scheme and the resulting acceleration controllers of the
mobile manipulators with results through computer simulations of an
interesting scenario.
Abstract: Detection, feature extraction and pose estimation of
people in images and video is made challenging by the variability of
human appearance, the complexity of natural scenes and the high
dimensionality of articulated body models and also the important
field in Image, Signal and Vision Computing in recent years. In this
paper, four types of people in 2D dimension image will be tested and
proposed. The system will extract the size and the advantage of them
(such as: tall fat, short fat, tall thin and short thin) from image. Fat
and thin, according to their result from the human body that has been
extract from image, will be obtained. Also the system extract every
size of human body such as length, width and shown them in output.
Abstract: A numerical study is presented on buckling and post
buckling behaviour of laminated carbon fiber reinforced plastic
(CFRP) thin-walled cylindrical shells under axial compression using
asymmetric meshing technique (AMT). Asymmetric meshing
technique is a perturbation technique to introduce disturbance without
changing geometry, boundary conditions or loading conditions.
Asymmetric meshing affects predicted buckling load, buckling mode
shape and post-buckling behaviour. Linear (eigenvalue) and nonlinear
(Riks) analyses have been performed to study the effect of
asymmetric meshing in the form of a patch on buckling behaviour.
The reduction in the buckling load using Asymmetric meshing
technique was observed to be about 15%. An isolated dimple formed
near the bifurcation point and the size of which increased to reach a
stable state in the post-buckling region. The load-displacement curve
behaviour applying asymmetric meshing is quite similar to the curve
obtained using initial geometric imperfection in the shell model.