Abstract: This paper presents two different sequential switching hybrid-modulation strategies and implemented for cascaded multilevel inverters. Hybrid modulation strategies represent the combinations of Fundamental-frequency pulse width modulation (FFPWM) and Multilevel sinusoidal-modulation (MSPWM) strategies, and are designed for performance of the well-known Alternative Phase opposition disposition (APOD), Phase shifted carrier (PSC). The main characteristics of these modulations are the reduction of switching losses with good harmonic performance, balanced power loss dissipation among the devices with in a cell, and among the series-connected cells. The feasibility of these modulations is verified through spectral analysis, power loss analysis and simulation.
Abstract: In this work we introduce an efficient method to limit
the impact of the hiding process on the quality of the cover speech.
Vector quantization of the speech spectral information reduces drastically
the number of the secret speech parameters to be embedded
in the cover signal. Compared to scalar hiding, vector quantization
hiding technique provides a stego signal that is indistinguishable from
the cover speech. The objective and subjective performance measures
reveal that the current hiding technique attracts no suspicion about the
presence of the secret message in the stego speech, while being able
to recover an intelligible copy of the secret message at the receiver
side.
Abstract: Non-stationary trend in R-R interval series is
considered as a main factor that could highly influence the evaluation
of spectral analysis. It is suggested to remove trends in order to obtain
reliable results. In this study, three detrending methods, the
smoothness prior approach, the wavelet and the empirical mode
decomposition, were compared on artificial R-R interval series with
four types of simulated trends. The Lomb-Scargle periodogram was
used for spectral analysis of R-R interval series. Results indicated that
the wavelet method showed a better overall performance than the other
two methods, and more time-saving, too. Therefore it was selected for
spectral analysis of real R-R interval series of thirty-seven healthy
subjects. Significant decreases (19.94±5.87% in the low frequency
band and 18.97±5.78% in the ratio (p
Abstract: Gliding during night without electric power is an efficient method to enhance endurance performance of solar aircrafts. The properties of maximum gliding endurance path are studied in this paper. The problem is formulated as an optimization problem about maximum endurance can be sustained by certain potential energy storage with dynamic equations and aerodynamic parameter constrains. The optimal gliding path is generated based on gauss pseudo-spectral method. In order to analyse relationship between altitude, velocity of solar UAVs and its endurance performance, the lift coefficient in interval of [0.4, 1.2] and flight envelopes between 0~30km are investigated. Results show that broad range of lift coefficient can improve solar aircrafts- long endurance performance, and it is possible for a solar aircraft to achieve the aim of long endurance during whole night just by potential energy storage.
Abstract: Ferroresonance is an electrical phenomenon in
nonlinear character, which frequently occurs in power system due to
transmission line faults and single or more-phase switching on the
lines as well as usage of the saturable transformers. In this study, the
ferroresonance phenomena are investigated under the modeling of the
West Anatolian Electric Power Network of 380 kV in Turkey. The
ferroresonance event is observed as a result of removing the loads at
the end of the lines. In this sense, two different cases are considered.
At first, the switching is applied at 2nd second and the ferroresonance
affects are observed between 2nd and 4th seconds in the voltage
variations of the phase-R. Hence the ferroresonance and nonferroresonance
parts of the overall data are compared with each
others using the Fourier transform techniques to show the
ferroresonance affects.
Abstract: Recently, Uhlig [Numer. Algorithms, 52(3):335-353, 2009] proposed open questions about the ratios between the spectral norm, the numerical radius and the spectral radius of a square matrix. In this note, we provide some observations to answer these questions.
Abstract: A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.
Abstract: Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time
Abstract: This paper proposes to use ETM+ multispectral data
and panchromatic band as well as texture features derived from the
panchromatic band for land cover classification. Four texture features
including one 'internal texture' and three GLCM based textures
namely correlation, entropy, and inverse different moment were used
in combination with ETM+ multispectral data. Two data sets
involving combination of multispectral, panchromatic band and its
texture were used and results were compared with those obtained by
using multispectral data alone. A decision tree classifier with and
without boosting were used to classify different datasets. Results
from this study suggest that the dataset consisting of panchromatic
band, four of its texture features and multispectral data was able to
increase the classification accuracy by about 2%. In comparison, a
boosted decision tree was able to increase the classification accuracy
by about 3% with the same dataset.
Abstract: Partial coherence between two signals removing the contribution of a periodic, deterministic signal is proposed for evaluating the interrelationship in multivariate systems. The estimator expression was derived and shown to be independent of such periodic signal. Simulations were used for obtaining its critical value, which were found to be the same as those for Gaussian signals, as well as for evaluating the technique. An Illustration with eletroencephalografic (EEG) signals during photic stimulation is also provided. The application of the proposed technique in both simulation and real EEG data indicate that it seems to be very specific in removing the contribution of periodic sources. The estimate independence of the periodic signal may widen partial coherence application to signal analysis, since it could be used together with simple coherence to test for contamination in signals by a common, periodic noise source.
Abstract: In order to provide accurate heart rate variability
indices of sympathetic and parasympathetic activity, the low
frequency and high frequency components of an RR heart rate signal
must be adequately separated. This is not always possible by just
applying spectral analysis, as power from the high and low frequency
components often leak into their adjacent bands. Furthermore,
without the respiratory spectra it is not obvious that the low
frequency component is not another respiratory component, which
can appear in the lower band. This paper describes an adaptive filter,
which aids the separation of the low frequency sympathetic and high
frequency parasympathetic components from an ECG R-R interval
signal, enabling the attainment of more accurate heart rate variability
measures. The algorithm is applied to simulated signals and heart rate
and respiratory signals acquired from an ambulatory monitor
incorporating single lead ECG and inductive plethysmography
sensors embedded in a garment. The results show an improvement
over standard heart rate variability spectral measurements.
Abstract: This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.
Abstract: Near-infrared (NIR) spectroscopy is a widely used
method for material identification for laboratory and industrial applications.
While standard spectrometers only allow measurements at
one sampling point at a time, NIR Spectral Imaging techniques can
measure, in real-time, both the size and shape of an object as well as
identify the material the object is made of. The online classification
and sorting of recovered paper with NIR Spectral Imaging (SI)
is used with success in the paper recycling industry throughout
Europe. Recently, the globalisation of the recycling material streams
caused that water-based flexographic-printed newspapers mainly from
UK and Italy appear also in central Europe. These flexo-printed
newspapers are not sufficiently de-inkable with the standard de-inking
process originally developed for offset-printed paper. This de-inking
process removes the ink from recovered paper and is the fundamental
processing step to produce high-quality paper from recovered paper.
Thus, the flexo-printed newspapers are a growing problem for the
recycling industry as they reduce the quality of the produced paper
if their amount exceeds a certain limit within the recovered paper
material.
This paper presents the results of a research project for the
development of an automated entry inspection system for recovered
paper that was jointly conducted by CTR AG (Austria) and PTS
Papiertechnische Stiftung (Germany). Within the project an NIR
SI prototype for the identification of flexo-printed newspaper has
been developed. The prototype can identify and sort out flexoprinted
newspapers in real-time and achieves a detection accuracy
for flexo-printed newspaper of over 95%. NIR SI, the technology the
prototype is based on, allows the development of inspection systems
for incoming goods in a paper production facility as well as industrial
sorting systems for recovered paper in the recycling industry in the
near future.
Abstract: The optimization and control problem for 4D trajectories
is a subject rarely addressed in literature. In the 4D navigation
problem we define waypoints, for each mission, where the arrival
time is specified in each of them. One way to design trajectories for
achieving this kind of mission is to use the trajectory optimization
concepts. To solve a trajectory optimization problem we can use
the indirect or direct methods. The indirect methods are based on
maximum principle of Pontryagin, on the other hand, in the direct
methods it is necessary to transform into a nonlinear programming
problem. We propose an approach based on direct methods with a
pseudospectral integration scheme built on Chebyshev polynomials.
Abstract: Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.
Abstract: As we know, most differential equations concerning
physical phenomenon could not be solved by analytical method. Even if we use Series Method, some times we need an appropriate change of variable, and even when we can, their closed form solution may be
so complicated that using it to obtain an image or to examine the structure of the system is impossible. For example, if we consider Schrodinger equation, i.e.,
We come to a three-term recursion relations, which work with it takes, at least, a little bit time to get a series solution[6]. For this
reason we use a change of variable such as or when we consider the orbital angular momentum[1], it will be
necessary to solve. As we can observe, working with this equation is tedious. In this paper, after introducing Clenshaw method, which is a kind of Spectral method, we try to solve some of such equations.
Abstract: A variational method is used to obtain the growth rate of a transverse long-wavelength perturbation applied to the soliton solution of a nonlinear Schr¨odinger equation with a three-half order potential. We demonstrate numerically that this unstable perturbed soliton will eventually transform into a cylindrical soliton.
Abstract: Electroencephalogram (EEG) recordings are often
contaminated with ocular and muscle artifacts. In this paper, the
canonical correlation analysis (CCA) is used as blind source
separation (BSS) technique (BSS-CCA) to decompose the artifact
contaminated EEG into component signals. We combine the BSSCCA
technique with wavelet filtering approach for minimizing both
ocular and muscle artifacts simultaneously, and refer the proposed
method as wavelet enhanced BSS-CCA. In this approach, after
careful visual inspection, the muscle artifact components are
discarded and ocular artifact components are subjected to wavelet
filtering to retain high frequency cerebral information, and then clean
EEG is reconstructed. The performance of the proposed wavelet
enhanced BSS-CCA method is tested on real EEG recordings
contaminated with ocular and muscle artifacts, for which power
spectral density is used as a quantitative measure. Our results suggest
that the proposed hybrid approach minimizes ocular and muscle
artifacts effectively, minimally affecting underlying cerebral activity
in EEG recordings.
Abstract: Elastic light single-scattering spectroscopy system
with a single optical fiber probe was employed to differentiate cancerous prostate tissue from non-cancerous prostate tissue ex-vivo just after radical prostatectomy. First, ELSSS spectra were acquired
from cancerous prostate tissue to define its spectral features. Then,
spectra were acquired from normal prostate tissue to define difference in spectral features between the cancerous and normal
prostate tissues. Of the total 66 tissue samples were evaluated from
nine patients by ELSSS system. Comparing of histopathology results
and ELSSS measurements revealed that sign of the spectral slopes of
cancerous prostate tissue is negative and non-cancerous tissue is positive in the wavelength range from 450 to 750 nm. Based on the
correlation between histopathology results and sign of the spectral
slopes, ELSSS system differentiates cancerous prostate tissue from
non- cancerous with a sensitivity of 0.95 and a specificity of 0.94.
Abstract: Multicarrier transmission system such as Orthogonal
Frequency Division Multiplexing (OFDM) is a promising technique
for high bit rate transmission in wireless communication system.
OFDM is a spectrally efficient modulation technique that can achieve
high speed data transmission over multipath fading channels without
the need for powerful equalization techniques. However the price
paid for this high spectral efficiency and less intensive equalization
is low power efficiency. OFDM signals are very sensitive to nonlinear
effects due to the high Peak-to-Average Power Ratio (PAPR),
which leads to the power inefficiency in the RF section of the
transmitter. This paper investigates the effect of PAPR reduction on
the performance parameter of multicarrier communication system.
Performance parameters considered are power consumption of Power
Amplifier (PA) and Digital-to-Analog Converter (DAC), power amplifier
efficiency, SNR of DAC and BER performance of the system.
From our analysis it is found that irrespective of PAPR reduction
technique being employed, the power consumption of PA and DAC
reduces and power amplifier efficiency increases due to reduction in
PAPR. Moreover, it has been shown that for a given BER performance
the requirement of Input-Backoff (IBO) reduces with reduction in
PAPR.