731,203 research outputs found
Remote Antenna Unit Selection Assisted Seamless Handover for High-Speed Railway Communications with Distributed Antennas
To attain seamless handover and reduce the han- dover failure probability for
high-speed railway (HSR) com- munication systems, this paper proposes a remote
antenna unit (RAU) selection assisted handover scheme where two antennas are
installed on high speed train (HST) and distributed antenna system (DAS) cell
architecture on ground is adopted. The RAU selection is used to provide high
quality received signals for trains moving in DAS cells and the two HST
antennas are employed on trains to realize seamless handover. Moreover, to
efficiently evaluate the system performance, a new met- ric termed as handover
occurrence probability is defined for describing the relation between handover
occurrence position and handover failure probability. We then analyze the
received signal strength, the handover trigger probability, the handover
occurrence probability, the handover failure probability and the communication
interruption probability. Numerical results are provided to compare our
proposed scheme with the current existing ones. It is shown that our proposed
scheme achieves better performances in terms of handover failure probability
and communication interruption probability.Comment: 7 figures, accepted by IEEE VTC-Spring, 201
Traffic Network Optimum Principle - Minimum Probability of Congestion Occurrence
We introduce an optimum principle for a vehicular traffic network with road
bottlenecks. This network breakdown minimization (BM) principle states that the
network optimum is reached, when link flow rates are assigned in the network in
such a way that the probability for spontaneous occurrence of traffic breakdown
at one of the network bottlenecks during a given observation time reaches the
minimum possible value. Based on numerical simulations with a stochastic
three-phase traffic flow model, we show that in comparison to the well-known
Wardrop's principles the application of the BM principle permits considerably
greater network inflow rates at which no traffic breakdown occurs and,
therefore, free flow remains in the whole network.Comment: 22 pages, 6 figure
Integrated probability of coronary heart disease subject to the -308 tumor necrosis factor-alpha SNP: a Bayesian meta-analysis
We present a meta-analysis of independent studies on the potential
implication in the occurrence of coronary heart disease (CHD) of the
single-nucleotide polymorphism (SNP) at the -308 position of the tumor necrosis
factor alpha (TNF-alpha) gene. We use Bayesian analysis to integrate
independent data sets and to infer statistically robust measurements of
correlation. Bayesian hypothesis testing indicates that there is no preference
for the hypothesis that the -308 TNF-alpha SNP is related to the occurrence of
CHD, in the Caucasian or in the Asian population, over the null hypothesis. As
a measure of correlation, we use the probability of occurrence of CHD
conditional on the presence of the SNP, derived as the posterior probability of
the Bayesian meta-analysis. The conditional probability indicates that CHD is
not more likely to occur when the SNP is present, which suggests that the -308
TNF-alpha SNP is not implicated in the occurrence of CHD.Comment: 21 pages, 7 figures, Published in PeerJ (2015
Discrimination with error margin between two states - Case of general occurrence probabilities -
We investigate a state discrimination problem which interpolates
minimum-error and unambiguous discrimination by introducing a margin for the
probability of error. We closely analyze discrimination of two pure states with
general occurrence probabilities. The optimal measurements are classified into
three types. One of the three types of measurement is optimal depending on
parameters (occurrence probabilities and error margin). We determine the three
domains in the parameter space and the optimal discrimination success
probability in each domain in a fully analytic form. It is also shown that when
the states to be discriminated are multipartite, the optimal success
probability can be attained by local operations and classical communication.
For discrimination of two mixed states, an upper bound of the optimal success
probability is obtained.Comment: Final version, 9 pages, references added, presentation improve
Localization of magnetic sources underground by a data adaptive tomographic scanner
A tomography method is proposed to image magnetic anomaly sources buried
below a non-flat ground surface, by developing the expression of the total
power associated with a measured magnetic field. By discretising the integral
relating a static magnetic field to its source terms, the total power can be
written as a sum of crosscorrelation products between the magnetic field data
set and the theoretical expression of the magnetic field generated by a source
element of unitary strength. Then, applying Schwarz's inequality, an occurrence
probability function is derived for imaging any distribution of magnetic
anomaly sources in the subsurface. The tomographic procedure consists in
scanning the half-space below the survey area by the unitary source and in
computing the occurrence probability function at the nodes of a regular grid
within the half-space. The grid values are finally contoured in order to single
out the zones with high probability of occurrence of buried magnetic anomaly
sources. Synthetic and field examples are discussed to test the resolution
power of the proposed tomography.Comment: 15 pages, 17 figure
Evaluating probability forecasts
Probability forecasts of events are routinely used in climate predictions, in
forecasting default probabilities on bank loans or in estimating the
probability of a patient's positive response to treatment. Scoring rules have
long been used to assess the efficacy of the forecast probabilities after
observing the occurrence, or nonoccurrence, of the predicted events. We develop
herein a statistical theory for scoring rules and propose an alternative
approach to the evaluation of probability forecasts. This approach uses loss
functions relating the predicted to the actual probabilities of the events and
applies martingale theory to exploit the temporal structure between the
forecast and the subsequent occurrence or nonoccurrence of the event.Comment: Published in at http://dx.doi.org/10.1214/11-AOS902 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
The probability of SWF occurrence in relation to solar activity
Solar terrestrial researches have revealed substantial meaning of nonsteady events on the Sun, mainly solar flares, for the processes taking place in ionosphere. Solar flares result in the numerous consequences, account and prediction of which become necessary in our days. It is well known, that ionospheric disturbances following solar flares cause strong disturbances in the ionosphere, which severely violate radio systems (communication, navigation, etc.). Possibilities of sudden short wave fadeouts (SWF) prediction are considered
Extreme events and event size fluctuations in biased random walks on networks
Random walk on discrete lattice models is important to understand various
types of transport processes. The extreme events, defined as exceedences of the
flux of walkers above a prescribed threshold, have been studied recently in the
context of complex networks. This was motivated by the occurrence of rare
events such as traffic jams, floods, and power black-outs which take place on
networks. In this work, we study extreme events in a generalized random walk
model in which the walk is preferentially biased by the network topology. The
walkers preferentially choose to hop toward the hubs or small degree nodes. In
this setting, we show that extremely large fluctuations in event-sizes are
possible on small degree nodes when the walkers are biased toward the hubs. In
particular, we obtain the distribution of event-sizes on the network. Further,
the probability for the occurrence of extreme events on any node in the network
depends on its 'generalized strength', a measure of the ability of a node to
attract walkers. The 'generalized strength' is a function of the degree of the
node and that of its nearest neighbors. We obtain analytical and simulation
results for the probability of occurrence of extreme events on the nodes of a
network using a generalized random walk model. The result reveals that the
nodes with a larger value of 'generalized strength', on average, display lower
probability for the occurrence of extreme events compared to the nodes with
lower values of 'generalized strength'
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