Abstract: Visualizing sound and noise often help us to determine
an appropriate control over the source localization. Near-field acoustic
holography (NAH) is a powerful tool for the ill-posed problem.
However, in practice, due to the small finite aperture size, the discrete
Fourier transform, FFT based NAH couldn-t predict the activeregion-
of-interest (AROI) over the edges of the plane. Theoretically
few approaches were proposed for solving finite aperture problem.
However most of these methods are not quite compatible for the
practical implementation, especially near the edge of the source. In
this paper, a zip-stuffing extrapolation approach has suggested with
2D Kaiser window. It is operated on wavenumber complex space
to localize the predicted sources. We numerically form a practice
environment with touch impact databases to test the localization of
sound source. It is observed that zip-stuffing aperture extrapolation
and 2D window with evanescent components provide more accuracy
especially in the small aperture and its derivatives.
Abstract: To extract the important physiological factors related to
diabetes from an oral glucose tolerance test (OGTT) by mathematical
modeling, highly informative but convenient protocols are required.
Current models require a large number of samples and extended
period of testing, which is not practical for daily use. The purpose
of this study is to make model assessments possible even from a
reduced number of samples taken over a relatively short period.
For this purpose, test values were extrapolated using a support
vector machine. A good correlation was found between reference and
extrapolated values in evaluated 741 OGTTs. This result indicates
that a reduction in the number of clinical test is possible through a
computational approach.
Abstract: The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.
Abstract: The mechanical quadrature methods for solving the boundary integral equations of the anisotropic Darcy-s equations with Dirichlet conditions in smooth domains are presented. By applying the collectively compact theory, we prove the convergence and stability of approximate solutions. The asymptotic expansions for the error show that the methods converge with the order O (h3), where h is the mesh size. Based on these analysis, extrapolation methods can be introduced to achieve a higher convergence rate O (h5). An a posterior asymptotic error representation is derived in order to construct self-adaptive algorithms. Finally, the numerical experiments show the efficiency of our methods.
Abstract: The wind resource in the Italian site of Lendinara
(RO) is analyzed through a systematic anemometric campaign
performed on the top of the bell tower, at an altitude of over 100 m
above the ground. Both the average wind speed and the Weibull
distribution are computed. The resulting average wind velocity is in
accordance with the numerical predictions of the Italian Wind Atlas,
confirming the accuracy of the extrapolation of wind data adopted for
the evaluation of wind potential at higher altitudes with respect to the
commonly placed measurement stations.