Abstract: Noise level has critical effects on the diagnostic
performance of signal-averaged electrocardiogram (SAECG), because
the true starting and end points of QRS complex would be masked by
the residual noise and sensitive to the noise level. Several studies and
commercial machines have used a fixed number of heart beats
(typically between 200 to 600 beats) or set a predefined noise level
(typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform
SAECG analysis. However different criteria or methods used to
perform SAECG would cause the discrepancies of the noise levels
among study subjects. According to the recommendations of 1991
ESC, AHA and ACC Task Force Consensus Document for the use of
SAECG, the determinations of onset and offset are related closely to
the mean and standard deviation of noise sample. Hence this study
would try to perform SAECG using consistent root-mean-square
(RMS) noise levels among study subjects and analyze the noise level
effects on SAECG. This study would also evaluate the differences
between normal subjects and chronic renal failure (CRF) patients in
the time-domain SAECG parameters.
The study subjects were composed of 50 normal Taiwanese and 20
CRF patients. During the signal-averaged processing, different RMS
noise levels were adjusted to evaluate their effects on three time
domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS
voltage of the last QRS 40 ms (RMS40), and (3) duration of the low
amplitude signals below 40 μV (LAS40). The study results
demonstrated that the reduction of RMS noise level can increase
fQRSD and LAS40 and decrease the RMS40, and can further increase
the differences of fQRSD and RMS40 between normal subjects and
CRF patients. The SAECG may also become abnormal due to the
reduction of RMS noise level. In conclusion, it is essential to establish
diagnostic criteria of SAECG using consistent RMS noise levels for
the reduction of the noise level effects.
Abstract: An autonomous environmental monitoring system
(Smart Landfill) has been constructed for the quantitative
measurement of the components of landfill gas found at borehole
wells at the perimeter of landfill sites. The main components of
landfill gas are the greenhouse gases, methane and carbon dioxide
and have been monitored in the range 0-5 % volume. This monitoring
system has not only been tested in the laboratory but has been
deployed in multiple field trials and the data collected successfully
compared with on-site monitors. This success shows the potential of
this system for application in environments where reliable gas
monitoring is crucial.
Abstract: This article proposes a new methodology to be used by SMEs (Small and Medium enterprises) to characterize their performance in quality, highlighting weaknesses and area for improvement. The methodology aims to identify the principal causes of quality problems and help to prioritize improvement initiatives. This is a self-assessment methodology that intends to be easy to implement by companies with low maturity level in quality. The methodology is organized in six different steps which includes gathering information about predetermined processes and subprocesses of quality management, defined based on the well-known Juran-s trilogy for quality management (Quality planning, quality control and quality improvement) and, predetermined results categories, defined based on quality concept. A set of tools for data collecting and analysis, such as interviews, flowcharts, process analysis diagrams and Failure Mode and effects Analysis (FMEA) are used. The article also presents the conclusions obtained in the application of the methodology in two cases studies.
Abstract: In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.
To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.
Abstract: This study, focusing on the importance of encouraging
outdoor activities for children, aims to propose and implement a Web-GIS based outdoor education program for junior high schools,
which will then be evaluated by users. Specifically, for the purpose of improved outdoor activities in the junior high school education, the
outdoor education program, with chiefly using the Web-GIS that provides a good information provision and sharing tool, is proposed
and implemented before being evaluated by users. The conclusion of
this study can be summarized in the following two points.
(1) A five -step outdoor education program based on Web-GIS was
proposed for a “second school" at junior high schools that was then implemented before being evaluated by teachers as users.
(2) Based on the results of evaluation by teachers, it was clear that the general operation of Web-GIS based outdoor education program with them only is difficult due to their lack of knowledge regarding Web-GIS and that support staff who can effectively utilize Web-GIS are essential.
Abstract: In this paper, a direct method based on variable step
size Block Backward Differentiation Formula which is referred as
BBDF2 for solving second order Ordinary Differential Equations
(ODEs) is developed. The advantages of the BBDF2 method over the
corresponding sequential variable step variable order Backward
Differentiation Formula (BDFVS) when used to solve the same
problem as a first order system are pointed out. Numerical results are
given to validate the method.
Abstract: Lignocellulosic materials are new targeted source to
produce second generation biofuels like biobutanol. However, this
process is significantly resisted by the native structure of biomass.
Therefore, pretreatment process is always essential to remove
hemicelluloses and lignin prior to the enzymatic hydrolysis.
The goals of pretreatment are removing hemicelluloses and
lignin, increasing biomass porosity, and increasing the enzyme
accessibility. The main goal of this research is to study the important
variables such as pretreatment temperature and time, which can give
the highest total sugar yield in pretreatment step by using dilute
phosphoric acid. After pretreatment, the highest total sugar yield of
13.61 g/L was obtained under an optimal condition at 140°C for 10
min of pretreatment time by using 1.75% (w/w) H3PO4 and at 15:1
liquid to solid ratio. The total sugar yield of two-stage process
(pretreatment+enzymatic hydrolysis) of 27.38 g/L was obtained.
Abstract: In text categorization problem the most used method
for documents representation is based on words frequency vectors
called VSM (Vector Space Model). This representation is based only
on words from documents and in this case loses any “word context"
information found in the document. In this article we make a
comparison between the classical method of document representation
and a method called Suffix Tree Document Model (STDM) that is
based on representing documents in the Suffix Tree format. For the
STDM model we proposed a new approach for documents
representation and a new formula for computing the similarity
between two documents. Thus we propose to build the suffix tree
only for any two documents at a time. This approach is faster, it has
lower memory consumption and use entire document representation
without using methods for disposing nodes. Also for this method is
proposed a formula for computing the similarity between documents,
which improves substantially the clustering quality. This
representation method was validated using HAC - Hierarchical
Agglomerative Clustering. In this context we experiment also the
stemming influence in the document preprocessing step and highlight
the difference between similarity or dissimilarity measures to find
“closer" documents.
Abstract: In mobile environments, unspecified numbers of transactions
arrive in continuous streams. To prove correctness of their
concurrent execution a method of modelling an infinite number of
transactions is needed. Standard database techniques model fixed
finite schedules of transactions. Lately, techniques based on temporal
logic have been proposed as suitable for modelling infinite schedules.
The drawback of these techniques is that proving the basic
serializability correctness condition is impractical, as encoding (the
absence of) conflict cyclicity within large sets of transactions results
in prohibitively large temporal logic formulae. In this paper, we show
that, under certain common assumptions on the graph structure of
data items accessed by the transactions, conflict cyclicity need only
be checked within all possible pairs of transactions. This results in
formulae of considerably reduced size in any temporal-logic-based
approach to proving serializability, and scales to arbitrary numbers
of transactions.
Abstract: This paper describes an automatic algorithm to restore
the shape of three-dimensional (3D) left ventricle (LV) models created
from magnetic resonance imaging (MRI) data using a geometry-driven
optimization approach. Our basic premise is to restore the LV shape
such that the LV epicardial surface is smooth after the restoration. A
geometrical measure known as the Minimum Principle Curvature (κ2)
is used to assess the smoothness of the LV. This measure is used to
construct the objective function of a two-step optimization process.
The objective of the optimization is to achieve a smooth epicardial
shape by iterative in-plane translation of the MRI slices.
Quantitatively, this yields a minimum sum in terms of the magnitude
of κ
2, when κ2 is negative. A limited memory quasi-Newton algorithm,
L-BFGS-B, is used to solve the optimization problem. We tested our
algorithm on an in vitro theoretical LV model and 10 in vivo
patient-specific models which contain significant motion artifacts. The
results show that our method is able to automatically restore the shape
of LV models back to smoothness without altering the general shape of
the model. The magnitudes of in-plane translations are also consistent
with existing registration techniques and experimental findings.
Abstract: In order to develop forest management strategies in
tropical forest in Malaysia, surveying the forest resources and
monitoring the forest area affected by logging activities is essential.
There are tremendous effort has been done in classification of land
cover related to forest resource management in this country as it is a
priority in all aspects of forest mapping using remote sensing and
related technology such as GIS. In fact classification process is a
compulsory step in any remote sensing research. Therefore, the main
objective of this paper is to assess classification accuracy of
classified forest map on Landsat TM data from difference number of
reference data (200 and 388 reference data). This comparison was
made through observation (200 reference data), and interpretation
and observation approaches (388 reference data). Five land cover
classes namely primary forest, logged over forest, water bodies, bare
land and agricultural crop/mixed horticultural can be identified by
the differences in spectral wavelength. Result showed that an overall
accuracy from 200 reference data was 83.5 % (kappa value
0.7502459; kappa variance 0.002871), which was considered
acceptable or good for optical data. However, when 200 reference
data was increased to 388 in the confusion matrix, the accuracy
slightly improved from 83.5% to 89.17%, with Kappa statistic
increased from 0.7502459 to 0.8026135, respectively. The accuracy
in this classification suggested that this strategy for the selection of
training area, interpretation approaches and number of reference data
used were importance to perform better classification result.
Abstract: Flow movement in unsaturated soil can be expressed
by a partial differential equation, named Richards equation. The
objective of this study is the finding of an appropriate implicit
numerical solution for head based Richards equation. Some of the
well known finite difference schemes (fully implicit, Crank Nicolson
and Runge-Kutta) have been utilized in this study. In addition, the
effects of different approximations of moisture capacity function,
convergence criteria and time stepping methods were evaluated. Two
different infiltration problems were solved to investigate the
performance of different schemes. These problems include of vertical
water flow in a wet and very dry soils. The numerical solutions of
two problems were compared using four evaluation criteria and the
results of comparisons showed that fully implicit scheme is better
than the other schemes. In addition, utilizing of standard chord slope
method for approximation of moisture capacity function, automatic
time stepping method and difference between two successive
iterations as convergence criterion in the fully implicit scheme can
lead to better and more reliable results for simulation of fluid
movement in different unsaturated soils.
Abstract: This paper focuses on sovereign credit risk meaning a
hot topic related to the current Eurozone crisis. In the light of the
recent financial crisis, market perception of the creditworthiness of
individual sovereigns has changed significantly. Before the outbreak
of the financial crisis, market participants did not differentiate
between credit risk born by individual states despite different levels
of public indebtedness. In the proceeding of the financial crisis, the
market participants became aware of the worsening fiscal situation in
the European countries and started to discriminate among
government issuers. Concerns about the increasing sovereign risk
were reflected in surging sovereign risk premium. The main of this
paper is to shed light on the characteristics of the sovereign risk with
the special attention paid to the mutual relation between credit spread
and the CDS premium as the main measures of the sovereign risk
premium.
Abstract: A challenging problem in radar signal processing is to
achieve reliable target detection in the presence of interferences. In
this paper, we propose a novel algorithm for automatic censoring of
radar interfering targets in log-normal clutter. The proposed
algorithm, termed the forward automatic censored cell averaging
detector (F-ACCAD), consists of two steps: removing the corrupted
reference cells (censoring) and the actual detection. Both steps are
performed dynamically by using a suitable set of ranked cells to
estimate the unknown background level and set the adaptive
thresholds accordingly. The F-ACCAD algorithm does not require
any prior information about the clutter parameters nor does it require
the number of interfering targets. The effectiveness of the F-ACCAD
algorithm is assessed by computing, using Monte Carlo simulations,
the probability of censoring and the probability of detection in
different background environments.
Abstract: Object-oriented programming is a wonderful way to
make programming of huge real life tasks much easier than by using
procedural languages. In order to teach those ideas to students, it
is important to find a good task that shows the advantages of OOprogramming
very naturally. This paper gives an example, the game
Battleship, which seems to work excellent for teaching the OO ideas
(using Java, [1], [2], [3], [4]).
A three-step task is presented for how to teach OO-programming
using just one example suitable to convey many of the OO ideas.
Observations are given at the end and conclusions about how the
whole teaching course worked out.
Abstract: Smoothing or filtering of data is first preprocessing step
for noise suppression in many applications involving data analysis.
Moving average is the most popular method of smoothing the data,
generalization of this led to the development of Savitzky-Golay filter.
Many window smoothing methods were developed by convolving
the data with different window functions for different applications;
most widely used window functions are Gaussian or Kaiser. Function
approximation of the data by polynomial regression or Fourier
expansion or wavelet expansion also gives a smoothed data. Wavelets
also smooth the data to great extent by thresholding the wavelet
coefficients. Almost all smoothing methods destroys the peaks and
flatten them when the support of the window is increased. In certain
applications it is desirable to retain peaks while smoothing the data
as much as possible. In this paper we present a methodology called
as peak-wise smoothing that will smooth the data to any desired level
without losing the major peak features.
Abstract: The population structure of the Tor tambroides was
investigated with morphometric data (i.e. morphormetric
measurement and truss measurement). A morphometric analysis was
conducted to compare specimens from three waterfalls: Sunanta, Nan
Chong Fa and Wang Muang waterfalls at Khao Nan National Park,
Nakhon Si Thammarat, Southern Thailand. The results of stepwise
discriminant analysis on seven morphometric variables and 21 truss
variables per individual were the same as from a neural network. Fish
from three waterfalls were separated into three groups based on their
morphometric measurements. The morphometric data shows that the
nerual network model performed better than the stepwise
discriminant analysis.
Abstract: Rarefied gas flows are often occurred in micro electro
mechanical systems and classical CFD could not precisely anticipate
the flow and thermal behavior due to the high Knudsen number.
Therefore, the heat transfer and the fluid dynamics characteristics of
rarefied gas flows in both a two-dimensional simple microchannel
and geometry similar to single Knudsen compressor have been
investigated with a goal of increasing performance of a actual
Knudsen compressor by using a particle simulation method. Thermal
transpiration and thermal creep, which are rarefied gas dynamic
phenomena, that cause movement of the flow from less to higher
temperature is generated by using two different longitude temperature
gradients (Linear, Step) along the walls of the flow microchannel. In
this study the influence of amount of temperature gradient and
governing pressure in various Knudsen numbers and length-to-height
ratios have been examined.
Abstract: Human activity is a major concern in a wide variety of
applications, such as video surveillance, human computer interface
and face image database management. Detecting and recognizing
faces is a crucial step in these applications. Furthermore, major
advancements and initiatives in security applications in the past years
have propelled face recognition technology into the spotlight. The
performance of existing face recognition systems declines significantly
if the resolution of the face image falls below a certain level.
This is especially critical in surveillance imagery where often, due to
many reasons, only low-resolution video of faces is available. If these
low-resolution images are passed to a face recognition system, the
performance is usually unacceptable. Hence, resolution plays a key
role in face recognition systems. In this paper we introduce a new
low resolution face recognition system based on mixture of expert
neural networks. In order to produce the low resolution input images
we down-sampled the 48 × 48 ORL images to 12 × 12 ones using
the nearest neighbor interpolation method and after that applying
the bicubic interpolation method yields enhanced images which is
given to the Principal Component Analysis feature extractor system.
Comparison with some of the most related methods indicates that
the proposed novel model yields excellent recognition rate in low
resolution face recognition that is the recognition rate of 100% for
the training set and 96.5% for the test set.
Abstract: In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.