Conditioning super-Brownian motion on its boundary statistics, and fragmentation

We condition super-Brownian motion on "boundary statistics" of the exit measure $X_D$ from a bounded domain $D$. These are random variables defined on an auxiliary probability space generated by sampling from the exit measure $X_D$. Two particular examples are: conditioning on a Poisson random measure with intensity $\beta X_D$ and conditioning on $X_D$ itself. We find the conditional laws as $h$-transforms of the original SBM law using Dynkin's formulation of $X$-harmonic functions. We give explicit expression for the (extended) $X$-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 $X_D=\nu$, in terms of a particle system, called the backbone. Each particle in the backbone is labeled by a measure $\tilde{\nu}$, representing its descendants' total contribution to the exit measure. The particle's spatial motion is an $h$-transform of Brownian motion, where $h$ depends on $\tilde{\nu}$. At the particle's death two new particles are born, and $\tilde{\nu}$ 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