Abstract: The paper proposes a methodology to process the signals coming from the Transcranial Magnetic Stimulation (TMS) in order to identify the pathology and evaluate the therapy to treat the patients affected by demency diseases. In particular, a fuzzy model is developed to identify the demency of the patients affected by Subcortical Ischemic Vascular Dementia (SIVD) and to measure the effect of a repetitive TMS on their motor performances. A tool is also presented to support the mentioned analysis.
Abstract: In this paper, we analyze and test a scheme for the
estimation of electrical fundamental frequency signals from the
harmonic load current and voltage signals.
The scheme was based on using two different Multi Layer
Artificial Neural Networks (ML-ANN) one for the current and the
other for the voltage.
This study also analyzes and tests the effect of choosing the
optimum artificial neural networks- sizes which determine the quality
and accuracy of the estimation of electrical fundamental frequency
signals.
The simulink tool box of the Matlab program for the simulation of
the test system and the test of the neural networks has been used.
Abstract: One of the approaches enabling people with amputated
limbs to establish some sort of interface with the real world includes
the utilization of the myoelectric signal (MES) from the remaining
muscles of those limbs. The MES can be used as a control input to a
multifunction prosthetic device. In this control scheme, known as the
myoelectric control, a pattern recognition approach is usually utilized
to discriminate between the MES signals that belong to different
classes of the forearm movements. Since the MES is recorded using
multiple channels, the feature vector size can become very large. In
order to reduce the computational cost and enhance the generalization
capability of the classifier, a dimensionality reduction method is
needed to identify an informative yet moderate size feature set. This
paper proposes a new fuzzy version of the well known Fisher-s
Linear Discriminant Analysis (LDA) feature projection technique.
Furthermore, based on the fact that certain muscles might contribute
more to the discrimination process, a novel feature weighting scheme
is also presented by employing Particle Swarm Optimization (PSO)
for estimating the weight of each feature. The new method, called
PSOFLDA, is tested on real MES datasets and compared with other
techniques to prove its superiority.
Abstract: In this paper, the position control of an electronic
throttle actuator is outlined. The dynamic behavior of the actuator is
described with the help of an uncertain plant model. This motivates
the controller design based on the ideas of higher-order slidingmodes.
As a consequence anti-chattering techniques can be omitted.
It is shown that the same concept is applicable to estimate unmeasureable
signals. The control law and the observer are implemented on
an electronic control unit. Results achieved by numerical simulations
and real world experiments are presented and discussed.
Abstract: Many systems in the natural world exhibit chaos or non-linear behavior, the complexity of which is so great that they appear to be random. Identification of chaos in experimental data is essential for characterizing the system and for analyzing the predictability of the data under analysis. The Lyapunov exponents provide a quantitative measure of the sensitivity to initial conditions and are the most useful dynamical diagnostic for chaotic systems. However, it is difficult to accurately estimate the Lyapunov exponents of chaotic signals which are corrupted by a random noise. In this work, a method for estimation of Lyapunov exponents from noisy time series using unscented transformation is proposed. The proposed methodology was validated using time series obtained from known chaotic maps. In this paper, the objective of the work, the proposed methodology and validation results are discussed in detail.
Abstract: In practice, wireless networks has the property that
the signal strength attenuates with respect to the distance from the
base station, it could be better if the nodes at two hop away are
considered for better quality of service. In this paper, we propose a
procedure to identify delay preserving substructures for a given
wireless ad-hoc network using a new graph operation G 2 – E (G) =
G* (Edge difference of square graph of a given graph and the
original graph). This operation helps to analyze some induced
substructures, which preserve delay in communication among them.
This operation G* on a given graph will induce a graph, in which 1-
hop neighbors of any node are at 2-hop distance in the original
network. In this paper, we also identify some delay preserving
substructures in G*, which are (i) set of all nodes, which are mutually
at 2-hop distance in G that will form a clique in G*, (ii) set of nodes
which forms an odd cycle C2k+1 in G, will form an odd cycle in G*
and the set of nodes which form a even cycle C2k in G that will form
two disjoint companion cycles ( of same parity odd/even) of length k
in G*, (iii) every path of length 2k+1 or 2k in G will induce two
disjoint paths of length k in G*, and (iv) set of nodes in G*, which
induces a maximal connected sub graph with radius 1 (which
identifies a substructure with radius equal 2 and diameter at most 4 in
G). The above delay preserving sub structures will behave as good
clusters in the original network.
Abstract: Carrier mobility has become the most important
characteristic of high speed low dimensional devices. Due to
development of very fast switching semiconductor devices, speed of
computer and communication equipment has been increasing day by
day and will continue to do so in future. As the response of any
device depends on the carrier motion within the devices, extensive
studies of carrier mobility in the devices has been established
essential for the growth in the field of low dimensional devices.
Small-signal ac transport of degenerate two-dimensional hot
electrons in GaAs quantum wells is studied here incorporating
deformation potential acoustic, polar optic and ionized impurity
scattering in the framework of heated drifted Fermi-Dirac carrier
distribution. Delta doping is considered in the calculations to
investigate the effects of double delta doping on millimeter and submillimeter
wave response of two dimensional hot electrons in GaAs
nanostructures. The inclusion of delta doping is found to enhance
considerably the two dimensional electron density which in turn
improves the carrier mobility (both ac and dc) values in the GaAs
quantum wells thereby providing scope of getting higher speed
devices in future.
Abstract: The frequency contents of the non-stationary
signals vary with time. For proper characterization of such
signals, a smart time-frequency representation is necessary.
Classically, the STFT (short-time Fourier transform) is
employed for this purpose. Its limitation is the fixed timefrequency
resolution. To overcome this drawback an enhanced
STFT version is devised. It is based on the signal driven
sampling scheme, which is named as the cross-level sampling.
It can adapt the sampling frequency and the window function
(length plus shape) by following the input signal local
variations. This adaptation results into the proposed technique
appealing features, which are the adaptive time-frequency
resolution and the computational efficiency.
Abstract: This paper aims to select the optimal location and
setting parameters of TCSC (Thyristor Controlled Series
Compensator) controller using Particle Swarm Optimization (PSO)
and Genetic Algorithm (GA) to mitigate small signal oscillations in a
multimachine power system. Though Power System Stabilizers
(PSSs) are prime choice in this issue, installation of FACTS device
has been suggested here in order to achieve appreciable damping of
system oscillations. However, performance of any FACTS devices
highly depends upon its parameters and suitable location in the
power network. In this paper PSO as well as GA based techniques are
used separately and compared their performances to investigate this
problem. The results of small signal stability analysis have been
represented employing eigenvalue as well as time domain response in
face of two common power system disturbances e.g., varying load
and transmission line outage. It has been revealed that the PSO based
TCSC controller is more effective than GA based controller even
during critical loading condition.
Abstract: In this paper, we implement a modern serial backplane
platform for telecommunication inter-rack systems. For combination
high reliability and low cost protocol property, we applied high level
data link control (HDLC) protocol with low voltage differential
signaling (LVDS) bus for card to card communicated over backplane.
HDLC protocol is a high performance with several operation modes
and is famous in telecommunication systems. LVDS bus is a high
reliability with high immunity against electromagnetic interference
(EMI) and noise.
Abstract: Sleep stage scoring is the process of classifying the
stage of the sleep in which the subject is in. Sleep is classified into
two states based on the constellation of physiological parameters.
The two states are the non-rapid eye movement (NREM) and the
rapid eye movement (REM). The NREM sleep is also classified into
four stages (1-4). These states and the state wakefulness are
distinguished from each other based on the brain activity. In this
work, a classification method for automated sleep stage scoring
based on a single EEG recording using wavelet packet decomposition
was implemented. Thirty two ploysomnographic recording from the
MIT-BIH database were used for training and validation of the
proposed method. A single EEG recording was extracted and
smoothed using Savitzky-Golay filter. Wavelet packets
decomposition up to the fourth level based on 20th order Daubechies
filter was used to extract features from the EEG signal. A features
vector of 54 features was formed. It was reduced to a size of 25 using
the gain ratio method and fed into a classifier of regression trees. The
regression trees were trained using 67% of the records available. The
records for training were selected based on cross validation of the
records. The remaining of the records was used for testing the
classifier. The overall correct rate of the proposed method was found
to be around 75%, which is acceptable compared to the techniques in
the literature.
Abstract: The purpose of this research is to develop and apply the
RSCMAC to enhance the dynamic accuracy of Global Positioning
System (GPS). GPS devices provide services of accurate positioning,
speed detection and highly precise time standard for over 98% area on
the earth. The overall operation of Global Positioning System includes
24 GPS satellites in space; signal transmission that includes 2
frequency carrier waves (Link 1 and Link 2) and 2 sets random
telegraphic codes (C/A code and P code), on-earth monitoring stations
or client GPS receivers. Only 4 satellites utilization, the client position
and its elevation can be detected rapidly. The more receivable
satellites, the more accurate position can be decoded. Currently, the
standard positioning accuracy of the simplified GPS receiver is greatly
increased, but due to affected by the error of satellite clock, the
troposphere delay and the ionosphere delay, current measurement
accuracy is in the level of 5~15m. In increasing the dynamic GPS
positioning accuracy, most researchers mainly use inertial navigation
system (INS) and installation of other sensors or maps for the
assistance. This research utilizes the RSCMAC advantages of fast
learning, learning convergence assurance, solving capability of
time-related dynamic system problems with the static positioning
calibration structure to improve and increase the GPS dynamic
accuracy. The increasing of GPS dynamic positioning accuracy can be
achieved by using RSCMAC system with GPS receivers collecting
dynamic error data for the error prediction and follows by using the
predicted error to correct the GPS dynamic positioning data. The
ultimate purpose of this research is to improve the dynamic positioning
error of cheap GPS receivers and the economic benefits will be
enhanced while the accuracy is increased.
Abstract: In power systems, protective relays must filter their
inputs to remove undesirable quantities and retain signal quantities of
interest. This job must be performed accurate and fast. A new
method for filtering the undesirable components such as DC and
harmonic components associated with the fundamental system
signals. The method is s based on a dynamic filtering algorithm. The
filtering algorithm has many advantages over some other classical
methods. It can be used as dynamic on-line filter without the need of
parameters readjusting as in the case of classic filters. The proposed
filter is tested using different signals. Effects of number of samples
and sampling window size are discussed. Results obtained are
presented and discussed to show the algorithm capabilities.
Abstract: Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
Abstract: This paper reports a new approach on identifying the
individuality of persons by using parametric classification of multiple
mental thoughts. In the approach, electroencephalogram (EEG)
signals were recorded when the subjects were thinking of one or
more (up to five) mental thoughts. Autoregressive features were
computed from these EEG signals and classified by Linear
Discriminant classifier. The results here indicate that near perfect
identification of 400 test EEG patterns from four subjects was
possible, thereby opening up a new avenue in biometrics.
Abstract: Radio wave propagation on the road surface is a major
problem on wireless sensor network for traffic monitoring. In this
paper, we compare receiving signal strength on two scenarios 1) an
empty road and 2) a road with a vehicle. We investigate the effect of
antenna polarization and antenna height to the receiving signal
strength. The transmitting antenna is installed on the road surface.
The receiving signal is measured 360 degrees around the transmitting
antenna with the radius of 2.5 meters. Measurement results show the
receiving signal fluctuation around the transmitting antenna in both
scenarios. Receiving signal with vertical polarization antenna results
in higher signal strength than horizontal polarization antenna. The
optimum antenna elevation is 1 meter for both horizon and vertical
polarizations with the vehicle on the road. In the empty road, the
receiving signal level is unvarying with the elevation when the
elevation is greater than 1.5 meters.
Abstract: This paper considers the control of the longitudinal
flight dynamics of an F-16 aircraft. The primary design objective
is model-following of the pitch rate q, which is the preferred
system for aircraft approach and landing. Regulation of the aircraft
velocity V (or the Mach-hold autopilot) is also considered, but
as a secondary objective. The problem is challenging because the
system is nonlinear, and also non-affine in the input. A sliding
mode controller is designed for the pitch rate, that exploits the
modal decomposition of the linearized dynamics into its short-period
and phugoid approximations. The inherent robustness of the SMC
design provides a convenient way to design controllers without gain
scheduling, with a steady-state response that is comparable to that
of a conventional polynomial based gain-scheduled approach with
integral control, but with improved transient performance. Integral
action is introduced in the sliding mode design using the recently
developed technique of “conditional integrators", and it is shown that
robust regulation is achieved with asymptotically constant exogenous
signals, without degrading the transient response. Through extensive
simulation on the nonlinear multiple-input multiple-output (MIMO)
longitudinal model of the F-16 aircraft, it is shown that the conditional
integrator design outperforms the one based on the conventional linear
control, without requiring any scheduling.
Abstract: This paper describes the development of a 16-ports optical code division multiple access (OCDMA) encoder prototype based on Arrayed Waveguide Grating (AWG) and optical switches. It is potentially to provide a high security for data transmission due to all data will be transmitted in binary code form. The output signals from AWG are coded with a binary code that given to an optical switch before it signal modulate with the carrier and transmitted to the receiver. The 16-ports encoder used 16 double pole double throw (DPDT) toggle switches to control the polarization of voltage source from +5 V to -5 V for 16 optical switches. When +5 V is given, the optical switch will give code '1' and vice versa. The experimental results showed the insertion loss, crosstalk, uniformity, and optical signal-noise-ratio (OSNR) for the developed prototype are
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.
Abstract: A gene network gives the knowledge of the regulatory
relationships among the genes. Each gene has its activators and
inhibitors that regulate its expression positively and negatively
respectively. Genes themselves are believed to act as activators and
inhibitors of other genes. They can even activate one set of genes and
inhibit another set. Identifying gene networks is one of the most
crucial and challenging problems in Bioinformatics. Most work done
so far either assumes that there is no time delay in gene regulation or
there is a constant time delay. We here propose a Dynamic Time-
Lagged Correlation Based Method (DTCBM) to learn the gene
networks, which uses time-lagged correlation to find the potential
gene interactions, and then uses a post-processing stage to remove
false gene interactions to common parents, and finally uses dynamic
correlation thresholds for each gene to construct the gene network.
DTCBM finds correlation between gene expression signals shifted in
time, and therefore takes into consideration the multi time delay
relationships among the genes. The implementation of our method is
done in MATLAB and experimental results on Saccharomyces
cerevisiae gene expression data and comparison with other methods
indicate that it has a better performance.