1,344 research outputs found
Conditioning super-Brownian motion on its boundary statistics, and fragmentation
We condition super-Brownian motion on "boundary statistics" of the exit
measure from a bounded domain . These are random variables defined on
an auxiliary probability space generated by sampling from the exit measure
. Two particular examples are: conditioning on a Poisson random measure
with intensity and conditioning on itself. We find the
conditional laws as -transforms of the original SBM law using Dynkin's
formulation of -harmonic functions. We give explicit expression for the
(extended) -harmonic functions considered. We also obtain explicit
constructions of these conditional laws in terms of branching particle systems.
For example, we give a fragmentation system description of the law of SBM
conditioned on , in terms of a particle system, called the backbone.
Each particle in the backbone is labeled by a measure ,
representing its descendants' total contribution to the exit measure. The
particle's spatial motion is an -transform of Brownian motion, where
depends on . At the particle's death two new particles are born,
and is passed to the newborns by fragmentation.Comment: Published in at http://dx.doi.org/10.1214/12-AOP778 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Ön Gerilmeli Piezoelektrik Örtü Tabakası Ve Ön Gerilmeli Elastik Yarı Düzlemden Oluşan Sistemde Genelleştirilmiş Rayleigh Dalgalarının Dispersiyonu
Konferans Bildirisi -- Teorik ve Uygulamalı Mekanik Türk Milli Komitesi, 2013Conference Paper -- Theoretical and Applied Mechanical Turkish National Committee, 2013Bu çalışmada doğrusallaştırılmış dalga teorisi kullanılarak ön gerilmeli piezoelektrik tabaka ile örtülü ön gerilmeli yarı düzlem ortamında genelleştirilmiş Rayleigh dalgalarının dispersiyonu incelenmiştir.In this paper, generalized Rayleigh wave dispersion in a pre-stressed half-plane covered with a pre-stressed piezoelectric layer is studied using linearized wave theory
A Novel Oil-Water Emulsion Burner Concept for Offshore Oil Spill Clean Up
PresentationIn-situ burning has been considered as a primary spill response option for oil spills since offshore drilling began in the Beaufort Sea (1970s). Since then, many studies and tests have been performed but researchers are still looking for a more efficient, simple and low cost way to burn the oil faster and as completely as possible. In this study, a new burner concept capable of enhanced combustion of oil-water emulsions and requiring no atomizing nozzles, moving parts and compressed gas for operation is discussed. The operating principle is based on use of immersed noncombustible objects of suitable geometry to transfer the heat generated by the combustion back to the fuel to create a feedback loop thereby sustain an increased burning rate. A 0.5 meter diameter prototype burner showing the viability of the design concept is discussed. Tests show that the submersed lower part of the conductive object can get hot enough to sustain nucleate boiling, significantly increasing the burning rate, when compared to the baseline pool fire, where vaporization is achieved solely by evaporation at the pool surface
Optimal Detector Randomization in Cognitive Radio Systems in the Presence of Imperfect Sensing Decisions
Cataloged from PDF version of article.In this study, optimal detector randomization is developed for secondary users in a cognitive radio system in the presence of imperfect spectrum sensing decisions. It is shown that the minimum average probability of error can be achieved by employing no more than four maximum a-posteriori probability (MAP) detectors at the secondary receiver. Optimal MAP detectors and generic expressions for their average probability of error are derived in the presence of possible sensing errors. Also, sufficient conditions are presented related to improvements due to optimal detector randomization. © 2014 IEEE
Optimum Power Allocation for Average Power Constrained Jammers in the Presense of Non-Gaussian Noise
Cataloged from PDF version of article.We study the problem of determining the optimum
power allocation policy for an average power constrained jammer
operating over an arbitrary additive noise channel, where the aim
is to minimize the detection probability of an instantaneously
and fully adaptive receiver employing the Neyman-Pearson (NP)
criterion. We show that the optimum jamming performance
can be achieved via power randomization between at most two
different power levels. We also provide sufficient conditions
for the improvability and nonimprovability of the jamming
performance via power randomization in comparison to a fixed
power jamming scheme. Numerical examples are presented to
illustrate theoretical results
Physics-based Shading Reconstruction for Intrinsic Image Decomposition
We investigate the use of photometric invariance and deep learning to compute
intrinsic images (albedo and shading). We propose albedo and shading gradient
descriptors which are derived from physics-based models. Using the descriptors,
albedo transitions are masked out and an initial sparse shading map is
calculated directly from the corresponding RGB image gradients in a
learning-free unsupervised manner. Then, an optimization method is proposed to
reconstruct the full dense shading map. Finally, we integrate the generated
shading map into a novel deep learning framework to refine it and also to
predict corresponding albedo image to achieve intrinsic image decomposition. By
doing so, we are the first to directly address the texture and intensity
ambiguity problems of the shading estimations. Large scale experiments show
that our approach steered by physics-based invariant descriptors achieve
superior results on MIT Intrinsics, NIR-RGB Intrinsics, Multi-Illuminant
Intrinsic Images, Spectral Intrinsic Images, As Realistic As Possible, and
competitive results on Intrinsic Images in the Wild datasets while achieving
state-of-the-art shading estimations.Comment: Submitted to Computer Vision and Image Understanding (CVIU
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