Abstract: This paper reviews a number of theoretical aspects
for implementing an explicit spatial perspective in econometrics
for modelling non-continuous data, in general, and count data, in
particular. It provides an overview of the several spatial econometric
approaches that are available to model data that are collected with
reference to location in space, from the classical spatial econometrics
approaches to the recent developments on spatial econometrics to
model count data, in a Bayesian hierarchical setting. Considerable
attention is paid to the inferential framework, necessary for
structural consistent spatial econometric count models, incorporating
spatial lag autocorrelation, to the corresponding estimation and
testing procedures for different assumptions, to the constrains and
implications embedded in the various specifications in the literature. This review combines insights from the classical spatial
econometrics literature as well as from hierarchical modeling and
analysis of spatial data, in order to look for new possible directions
on the processing of count data, in a spatial hierarchical Bayesian
econometric context.
Abstract: Decision support systems are usually based on
multidimensional structures which use the concept of hypercube.
Dimensions are the axes on which facts are analyzed and form a
space where a fact is located by a set of coordinates at the
intersections of members of dimensions. Conventional
multidimensional structures deal with discrete facts linked to discrete
dimensions. However, when dealing with natural continuous
phenomena the discrete representation is not adequate. There is a
need to integrate spatiotemporal continuity within multidimensional
structures to enable analysis and exploration of continuous field data.
Research issues that lead to the integration of spatiotemporal
continuity in multidimensional structures are numerous. In this paper,
we discuss research issues related to the integration of continuity in
multidimensional structures, present briefly a multidimensional
model for continuous field data. We also define new aggregation
operations. The model and the associated operations and measures
are validated by a prototype.
Abstract: Linear convolutive filters are fast in calculation and in application, and thus, often used for real-time processing of continuous data streams. In the case of transient signals, a filter has not only to detect the presence of a specific waveform, but to estimate its arrival time as well. In this study, a measure is presented which indicates the performance of detectors in achieving both of these tasks simultaneously. Furthermore, a new sub-class of linear filters within the class of filters which minimize the quadratic response is proposed. The proposed filters are more flexible than the existing ones, like the adaptive matched filter or the minimum power distortionless response beamformer, and prove to be superior with respect to that measure in certain settings. Simulations of a real-time scenario confirm the advantage of these filters as well as the usefulness of the performance measure.
Abstract: In this paper, an automated algorithm to estimate and remove the continuous baseline from measured spectra containing both continuous and discontinuous bands is proposed. The algorithm uses previous information contained in a Continuous Database Spectra (CDBS) to obtain a linear basis, with minimum number of sampled vectors, capable of representing a continuous baseline. The proposed algorithm was tested by using a CDBS of flame spectra where Principal Components Analysis and Non-negative Matrix Factorization were used to obtain linear bases. Thus, the radical emissions of natural gas, oil and bio-oil flames spectra at different combustion conditions were obtained. In order to validate the performance in the baseline estimation process, the Goodness-of-fit Coefficient and the Root Mean-squared Error quality metrics were evaluated between the estimated and the real spectra in absence of discontinuous emission. The achieved results make the proposed method a key element in the development of automatic monitoring processes strategies involving discontinuous spectral bands.
Abstract: In wireless sensor network (WSN) the use of mobile
sink has been attracting more attention in recent times. Mobile sinks
are more effective means of balancing load, reducing hotspot
problem and elongating network lifetime. The sensor nodes in WSN
have limited power supply, computational capability and storage and
therefore for continuous data delivery reliability becomes high
priority in these networks. In this paper, we propose a Reliable
Energy-efficient Data Dissemination (REDD) scheme for WSNs with
multiple mobile sinks. In this strategy, sink first determines the
location of source and then directly communicates with the source
using geographical forwarding. Every forwarding node (FN) creates a
local zone comprising some sensor nodes that can act as
representative of FN when it fails. Analytical and simulation study
reveals significant improvement in energy conservation and reliable
data delivery in comparison to existing schemes.
Abstract: This article outlines conceptualization and
implementation of an intelligent system capable of extracting
knowledge from databases. Use of hybridized features of both the
Rough and Fuzzy Set theory render the developed system flexibility
in dealing with discreet as well as continuous datasets. A raw data set
provided to the system, is initially transformed in a computer legible
format followed by pruning of the data set. The refined data set is
then processed through various Rough Set operators which enable
discovery of parameter relationships and interdependencies. The
discovered knowledge is automatically transformed into a rule base
expressed in Fuzzy terms. Two exemplary cancer repository datasets
(for Breast and Lung Cancer) have been used to test and implement
the proposed framework.
Abstract: This paper studies questions of continuous data dependence and uniqueness for solutions of initial boundary value problems in linear micropolar thermoelastic mixtures. Logarithmic convexity arguments are used to establish results with no definiteness assumptions upon the internal energy.