38,504 research outputs found
The velocity distribution of nearby stars from Hipparcos data I. The significance of the moving groups
We present a three-dimensional reconstruction of the velocity distribution of
nearby stars (<~ 100 pc) using a maximum likelihood density estimation
technique applied to the two-dimensional tangential velocities of stars. The
underlying distribution is modeled as a mixture of Gaussian components. The
algorithm reconstructs the error-deconvolved distribution function, even when
the individual stars have unique error and missing-data properties. We apply
this technique to the tangential velocity measurements from a kinematically
unbiased sample of 11,865 main sequence stars observed by the Hipparcos
satellite. We explore various methods for validating the complexity of the
resulting velocity distribution function, including criteria based on Bayesian
model selection and how accurately our reconstruction predicts the radial
velocities of a sample of stars from the Geneva-Copenhagen survey (GCS). Using
this very conservative external validation test based on the GCS, we find that
there is little evidence for structure in the distribution function beyond the
moving groups established prior to the Hipparcos mission. This is in sharp
contrast with internal tests performed here and in previous analyses, which
point consistently to maximal structure in the velocity distribution. We
quantify the information content of the radial velocity measurements and find
that the mean amount of new information gained from a radial velocity
measurement of a single star is significant. This argues for complementary
radial velocity surveys to upcoming astrometric surveys
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Data clustering has received a lot of attention and numerous methods,
algorithms and software packages are available. Among these techniques,
parametric finite-mixture models play a central role due to their interesting
mathematical properties and to the existence of maximum-likelihood estimators
based on expectation-maximization (EM). In this paper we propose a new mixture
model that associates a weight with each observed point. We introduce the
weighted-data Gaussian mixture and we derive two EM algorithms. The first one
considers a fixed weight for each observation. The second one treats each
weight as a random variable following a gamma distribution. We propose a model
selection method based on a minimum message length criterion, provide a weight
initialization strategy, and validate the proposed algorithms by comparing them
with several state of the art parametric and non-parametric clustering
techniques. We also demonstrate the effectiveness and robustness of the
proposed clustering technique in the presence of heterogeneous data, namely
audio-visual scene analysis.Comment: 14 pages, 4 figures, 4 table
Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases
In many applications, such as physiology and finance, large time series data
bases are to be analyzed requiring the computation of linear, nonlinear and
other measures. Such measures have been developed and implemented in commercial
and freeware softwares rather selectively and independently. The Measures of
Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle
an arbitrary large set of scalar time series and compute a large variety of
measures on them, allowing for the specification of varying measure parameters
as well. The variety of options with added facilities for visualization of the
results support different settings of time series analysis, such as the
detection of dynamics changes in long data records, resampling (surrogate or
bootstrap) tests for independence and linearity with various test statistics,
and discrimination power of different measures and for different combinations
of their parameters. The basic features of {\tt MATS} are presented and the
implemented measures are briefly described. The usefulness of {\tt MATS} is
illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at
http://eeganalysis.web.auth.gr/indexen.ht
mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks
Knowledge of the battery lifetime of the wireless sensor network is important for many situations,
such as in evaluation of the location of nodes or the estimation of the connectivity,
along time, between devices. However, experimental evaluation is a very time-consuming
task. It depends on many factors, such as the use of the radio transceiver or the distance
between nodes. Simulations reduce considerably this time. They allow the evaluation of
the network behavior before its deployment. This article presents a simulation tool which
helps developers to obtain information about battery state. This simulator extends the
well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications
using an accurate model of the battery consumption and its relation to the radio power
transmission. Although an specific indoor scenario is used in testing of simulation, the simulator
is not limited to this environment. It is possible to work in outdoor scenarios too.
Experimental results validate the proposed model.Junta de AndalucĂa P07-TIC-02476Junta de AndalucĂa TIC-570
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