International Science Index
On the Efficiency and Robustness of Commingle Wiener and Lévy Driven Processes for Vasciek Model
The driven processes of Wiener and Lévy are known
self-standing Gaussian-Markov processes for fitting non-linear
dynamical Vasciek model. In this paper, a coincidental Gaussian
density stationarity condition and autocorrelation function of the
two driven processes were established. This led to the conflation
of Wiener and Lévy processes so as to investigate the efficiency
of estimates incorporated into the one-dimensional Vasciek model
that was estimated via the Maximum Likelihood (ML) technique.
The conditional laws of drift, diffusion and stationarity process
was ascertained for the individual Wiener and Lévy processes as
well as the commingle of the two processes for a fixed effect
and Autoregressive like Vasciek model when subjected to financial
series; exchange rate of Naira-CFA Franc. In addition, the model
performance error of the sub-merged driven process was miniature
compared to the self-standing driven process of Wiener and Lévy.
Heteromolecular Structure Formation in Aqueous Solutions of Ethanol, Tetrahydrofuran and Dimethylformamide
The refractometric method has been used to determine optical properties of concentration features of aqueous solutions of ethanol, tetrahydrofuran and dimethylformamide at the room temperature. Changes in dielectric permittivity of aqueous solutions of ethanol, tetrahydrofuran and dimethylformamide in a wide range of concentrations (0÷1.0 molar fraction) have been studied using molecular dynamics method. The curves depending on the concentration of experimental data on excess refractive indices and excess dielectric permittivity were compared. It has been shown that stable heteromolecular complexes in binary solutions are formed in the concentration range of 0.3÷0.4 mole fractions. The real and complex part of dielectric permittivity was obtained from dipole-dipole autocorrelation functions of molecules. At the concentrations of C = 0.3 / 0.4 m.f. the heteromolecular structures with hydrogen bonds are formed. This is confirmed by the extremum values of excessive dielectric permittivity and excessive refractive index of aqueous solutions.
Energy Detection Based Sensing and Primary User Traffic Classification for Cognitive Radio
As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.
Formulating the Stochastic Finite Elements for Free Vibration Analysis of Plates with Variable Elastic Modulus
In this study, the effect of uncertainty in elastic modulus of a plate on free vibration response is investigated. For this purpose, the elastic modulus of the plate is modeled as stochastic variable with normal distribution. Moreover, the distance autocorrelation function is used for stochastic field. Then, by applying the finite element method and Monte Carlo simulation, stochastic finite element relations are extracted. Finally, with a numerical test, the effect of uncertainty in the elastic modulus on free vibration response of a plate is studied. The results show that the effect of uncertainty in elastic modulus of the plate cannot play an important role on the free vibration response.
Long Term Variability of Temperature in Armenia in the Context of Climate Change
The purpose of this study is to analyze the temporal
and spatial variability of thermal conditions in the Republic of
Armenia. The paper describes annual fluctuations in air temperature.
Research has been focused on case study region of Armenia and
surrounding areas, where long–term measurements and observations
of weather conditions have been performed within the National
Meteorological Service of Armenia and its surrounding areas. The
study contains yearly air temperature data recorded between 1961-
2012. Mann-Kendal test and the autocorrelation function were
applied to detect the change trend of annual mean temperature, as
well as other parametric and non-parametric tests searching to find
the presence of some breaks in the long term evolution of
temperature. The analysis of all records reveals a tendency mostly
towards warmer years, with increased temperatures especially in
valleys and inner basins. The maximum temperature increase is up to
1,5°C. Negative results have not been observed in Armenia. The
patterns of temperature change have been observed since the 1990’s
over much of the Armenian territory. The climate in Armenia was
influenced by global change in the last 2 decades, as results from the
methods employed within the study.
A Comparative Study between Discrete Wavelet Transform and Maximal Overlap Discrete Wavelet Transform for Testing Stationarity
In this paper the core objective is to apply discrete wavelet transform and maximal overlap discrete wavelet transform functions namely Haar, Daubechies2, Symmlet4, Coiflet2 and discrete approximation of the Meyer wavelets in non stationary financial time series data from Dow Jones index (DJIA30) of US stock market. The data consists of 2048 daily data of closing index from December 17, 2004 to October 23, 2012. Unit root test affirms that the data is non stationary in the level. A comparison between the results to transform non stationary data to stationary data using aforesaid transforms is given which clearly shows that the decomposition stock market index by discrete wavelet transform is better than maximal overlap discrete wavelet transform for original data.
Unambiguous Signal Acquisition Based On Recombination of Sub-Correlations of BOC Signals
Due to side-peaks of autocorrelation function, the binary offset carrier (BOC) signal acquisition suffers from an ambiguity when one of the side-peaks is acquired. In this paper, we first analyze that the BOC autocorrelation is made up of the sum of subcorrelations, and then, remove the side-peaks causing the ambiguity by recombining the sub-correlations. The proposed scheme is shown to remove the side-peaks completely. From numerical results, it is confirmed that the proposed scheme outperforms the conventional schemes in terms of the receiver operating characteristic and mean acquisition time.
Simulation of Sample Paths of Non Gaussian Stationary Random Fields
Mathematical justifications are given for a simulation technique of multivariate nonGaussian random processes and fields based on Rosenblatt-s transformation of Gaussian processes. Different types of convergences are given for the approaching sequence. Moreover an original numerical method is proposed in order to solve the functional equation yielding the underlying Gaussian process autocorrelation function.
A Preliminary Study on the Suitability of Data Driven Approach for Continuous Water Level Modeling
Reliable water level forecasts are particularly
important for warning against dangerous flood and inundation. The
current study aims at investigating the suitability of the adaptive
network based fuzzy inference system for continuous water level
modeling. A hybrid learning algorithm, which combines the least
square method and the back propagation algorithm, is used to
identify the parameters of the network. For this study, water levels
data are available for a hydrological year of 2002 with a sampling
interval of 1-hour. The number of antecedent water level that should
be included in the input variables is determined by two statistical
methods, i.e. autocorrelation function and partial autocorrelation
function between the variables. Forecasting was done for 1-hour until
12-hour ahead in order to compare the models generalization at
higher horizons. The results demonstrate that the adaptive networkbased
fuzzy inference system model can be applied successfully and
provide high accuracy and reliability for river water level estimation.
In general, the adaptive network-based fuzzy inference system
provides accurate and reliable water level prediction for 1-hour ahead
where the MAPE=1.15% and correlation=0.98 was achieved. Up to
12-hour ahead prediction, the model still shows relatively good
performance where the error of prediction resulted was less than
9.65%. The information gathered from the preliminary results
provide a useful guidance or reference for flood early warning
system design in which the magnitude and the timing of a potential
extreme flood are indicated.
Noise Analysis of Single-Ended Input Differential Amplifier using Stochastic Differential Equation
In this paper, we analyze the effect of noise in a single- ended input differential amplifier working at high frequencies. Both extrinsic and intrinsic noise are analyzed using time domain method employing techniques from stochastic calculus. Stochastic differential equations are used to obtain autocorrelation functions of the output noise voltage and other solution statistics like mean and variance. The analysis leads to important design implications and suggests changes in the device parameters for improved noise characteristics of the differential amplifier.
Modeling of Statistically Multiplexed Non Uniform Activity VBR Video
This paper reports the feasibility of the ARMA model
to describe a bursty video source transmitting over a AAL5 ATM link
(VBR traffic). The traffic represents the activity of the action movie
"Lethal Weapon 3" transmitted over the ATM network using the Fore
System AVA-200 ATM video codec with a peak rate of 100 Mbps
and a frame rate of 25. The model parameters were estimated for a
single video source and independently multiplexed video sources. It
was found that the model ARMA (2, 4) is well-suited for the real data
in terms of average rate traffic profile, probability density function,
autocorrelation function, burstiness measure, and the pole-zero
distribution of the filter model.
Motor Imaginary Signal Classification Using Adaptive Recursive Bandpass Filter and Adaptive Autoregressive Models for Brain Machine Interface Designs
The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.