Abstract: Thai language is difficult in all four language skills,
especially reading. The first year students may have different abilities
in reading, so a teacher is required to find out a student’s reading
level so that the teacher can help and support them till they can
develop and resolve each problem themselves. This research is aimed
to study the prosody problem among Thai students and will be
focused on first year Thai students in the second semester. A total of
58 students were involved in this study. Four obstacles were found:
1. Interpretation from what they read and write
2. Incorrectness Pronunciation of Prosody
3. Incorrectness in Rhythm of the Poem
4. Incorrectness of the Thai Poem Pronunciation
Abstract: Electrocardiogram (ECG) data compression algorithm
is needed that will reduce the amount of data to be transmitted, stored
and analyzed, but without losing the clinical information content. A
wavelet ECG data codec based on the Set Partitioning In Hierarchical
Trees (SPIHT) compression algorithm is proposed in this paper. The
SPIHT algorithm has achieved notable success in still image coding.
We modified the algorithm for the one-dimensional (1-D) case and
applied it to compression of ECG data.
By this compression method, small percent root mean square
difference (PRD) and high compression ratio with low
implementation complexity are achieved. Experiments on selected
records from the MIT-BIH arrhythmia database revealed that the
proposed codec is significantly more efficient in compression and in
computation than previously proposed ECG compression schemes.
Compression ratios of up to 48:1 for ECG signals lead to acceptable
results for visual inspection.
Abstract: ECG analysis method was developed using ROC
analysis of PVC detecting algorithm. ECG signal of MIT-BIH
arrhythmia database was analyzed by MATLAB. First of all, the
baseline was removed by median filter to preprocess the ECG signal.
R peaks were detected for ECG analysis method, and normal VCG
was extracted for VCG analysis method. Four PVC detecting
algorithm was analyzed by ROC curve, which parameters are
maximum amplitude of QRS complex, width of QRS complex, r-r
interval and geometric mean of VCG. To set cut-off value of
parameters, ROC curve was estimated by true-positive rate
(sensitivity) and false-positive rate. sensitivity and false negative rate
(specificity) of ROC curve calculated, and ECG was analyzed using
cut-off value which was estimated from ROC curve. As a result, PVC
detecting algorithm of VCG geometric mean have high availability,
and PVC could be detected more accurately with amplitude and width
of QRS complex.
Abstract: It is known that an analog Hopfield neural network
with time delay can generate the outputs which are similar to the
human electroencephalogram. To gain deeper insights into the
mechanisms of rhythm generation by the Hopfield neural networks
and to study the effects of noise on their activities, we investigated
the behaviors of the networks with symmetric and asymmetric
interneuron connections. The neural network under the study consists
of 10 identical neurons. For symmetric (fully connected) networks all
interneuron connections aij = +1; the interneuron connections for
asymmetric networks form an upper triangular matrix with non-zero
entries aij = +1. The behavior of the network is described by 10
differential equations, which are solved numerically. The results of
simulations demonstrate some remarkable properties of a Hopfield
neural network, such as linear growth of outputs, dependence of
synchronization properties on the connection type, huge
amplification of oscillation by the external uniform noise, and the
capability of the neural network to transform one type of noise to
another.
Abstract: This paper investigates the control of a bouncing
ball using Model Predictive Control. Bouncing ball is a benchmark
problem for various rhythmic tasks such as juggling, walking,
hopping and running. Humans develop intentions which may be
perceived as our reference trajectory and tries to track it. The
human brain optimizes the control effort needed to track its
reference; this forms the central theme for control of bouncing ball
in our investigations.
Abstract: The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.
Abstract: The analysis to detect arrhythmias and life-threatening
conditions are highly essential in today world and this analysis
can be accomplished by advanced non-linear processing methods
for accurate analysis of the complex signals of heartbeat dynamics.
In this perspective, recent developments in the field of multiscale
information content have lead to the Microcanonical Multiscale
Formalism (MMF). We show that such framework provides several
signal analysis techniques that are especially adapted to the
study of heartbeat dynamics. In this paper, we just show first hand
results of whether the considered heartbeat dynamics signals have
the multiscale properties by computing local preticability exponents
(LPEs) and the Unpredictable Points Manifold (UPM), and thereby
computing the singularity spectrum.
Abstract: School physical education, through its objectives and
contents, efficiently valorizes the pupils- abilities, developing them,
especially the coordinative skill component, which is the basis of
movement learning, of the development of the daily motility and also
of the special, refined motility required by the practice of certain
sports. Medium school age offers the nervous and motor substratum
needed for the acquisition of complex motor habits, a substratum that
is essential for the coordinative skill. Individuals differ as to the level
at which this function is performed, the extent to which this function
turns an individual into a person that is adapted and adaptable to
complex and various situations. Spatio-temporal orientation, together
with movement combination and coupling, and with kinesthetic,
balance, motor reaction, movement transformation and rhythm
differentiation form the coordinative skills. From our viewpoint,
these are characteristic features with high levels of manifestation in a
complex psychomotor act - valorizing the quality of one-s talent - as
well as indices pertaining to one-s psychomotor intelligence and
creativity.
Abstract: There is an ongoing controversy in the literature related
to the biological effects of weak, low frequency electromagnetic
fields. The physical arguments and interpretation of the experimental
evidence are inconsistent, where some physical arguments and
experimental demonstrations tend to reject the likelihood of any
effect of the fields at extremely low level. The problem arises of
explaining, how the low-energy influences of weak magnetic fields
can compete with the thermal and electrical noise of cells at normal
temperature using the theoretical studies. The magnetoreception in
animals involve radical pair mechanism. The same mechanism has
been shown to be involved in the circadian rhythm synchronization in
mammals. These reactions can be influenced by the weak magnetic
fields. Hence, it is postulated the biological clock can be affected
by weak magnetic fields and these disruptions to the rhythm can
cause adverse biological effects. In this paper, likelihood of altering
the biological clock via the radical pair mechanism is analyzed to
simplify these studies of controversy.
Abstract: This paper illustrates the use of a combined neural
network model for classification of electrocardiogram (ECG) beats.
We present a trainable neural network ensemble approach to develop
customized electrocardiogram beat classifier in an effort to further
improve the performance of ECG processing and to offer
individualized health care.
We process a three stage technique for detection of premature
ventricular contraction (PVC) from normal beats and other heart
diseases. This method includes a denoising, a feature extraction and a
classification. At first we investigate the application of stationary
wavelet transform (SWT) for noise reduction of the
electrocardiogram (ECG) signals. Then feature extraction module
extracts 10 ECG morphological features and one timing interval
feature. Then a number of multilayer perceptrons (MLPs) neural
networks with different topologies are designed.
The performance of the different combination methods as well as
the efficiency of the whole system is presented. Among them,
Stacked Generalization as a proposed trainable combined neural
network model possesses the highest recognition rate of around 95%.
Therefore, this network proves to be a suitable candidate in ECG
signal diagnosis systems. ECG samples attributing to the different
ECG beat types were extracted from the MIT-BIH arrhythmia
database for the study.
Abstract: In the national and professional music of oral tradition
of many people in the East there is the metric formula called “ussuli",
that is to say rhythmic constructions of different character and a
composition. Ussuli in translation from Arabic means the law. The
cultural contacts of the ancient and medieval inhabitants of the
Central Asia, India, China, East Turkestan, Iraq, Afghanistan,
Turkey, and Iran have played a certain role in formation of both
musical and dancing heritage of each of these people. During
theatrical shows many dances were performed under the
accompaniment of percussion instruments as nagra, dayulpaz, doll.
The abovementioned tools are used as the obligatory accompanying
tool in an orchestra and at support of dancing acts as the solo tool.
Dynamics of development of a dancing composition, at times
execution of technique of movement depends on various
combinations of ussuli and their receptions of execution.
Abstract: Electrocardiogram (ECG) is considered to be the
backbone of cardiology. ECG is composed of P, QRS & T waves and
information related to cardiac diseases can be extracted from the
intervals and amplitudes of these waves. The first step in extracting
ECG features starts from the accurate detection of R peaks in the
QRS complex. We have developed a robust R wave detector using
wavelets. The wavelets used for detection are Daubechies and
Symmetric. The method does not require any preprocessing therefore,
only needs the ECG correct recordings while implementing the
detection. The database has been collected from MIT-BIH arrhythmia
database and the signals from Lead-II have been analyzed. MatLab
7.0 has been used to develop the algorithm. The ECG signal under
test has been decomposed to the required level using the selected
wavelet and the selection of detail coefficient d4 has been done based
on energy, frequency and cross-correlation analysis of decomposition
structure of ECG signal. The robustness of the method is apparent
from the obtained results.