1,214,424 research outputs found
Experimental Design of a Prescribed Burn Instrumentation
Observational data collected during experiments, such as the planned Fire and
Smoke Model Evaluation Experiment (FASMEE), are critical for progressing and
transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM
into operational use. Historical meteorological data, representing typical
weather conditions for the anticipated burn locations and times, have been
processed to initialize and run a set of simulations representing the planned
experimental burns. Based on an analysis of these numerical simulations, this
paper provides recommendations on the experimental setup that include the
ignition procedures, size and duration of the burns, and optimal sensor
placement. New techniques are developed to initialize coupled fire-atmosphere
simulations with weather conditions typical of the planned burn locations and
time of the year. Analysis of variation and sensitivity analysis of simulation
design to model parameters by repeated Latin Hypercube Sampling are used to
assess the locations of the sensors. The simulations provide the locations of
the measurements that maximize the expected variation of the sensor outputs
with the model parameters.Comment: 35 pages, 4 tables, 28 figure
Continuum limit of total variation on point clouds
We consider point clouds obtained as random samples of a measure on a
Euclidean domain. A graph representing the point cloud is obtained by assigning
weights to edges based on the distance between the points they connect. Our
goal is to develop mathematical tools needed to study the consistency, as the
number of available data points increases, of graph-based machine learning
algorithms for tasks such as clustering. In particular, we study when is the
cut capacity, and more generally total variation, on these graphs a good
approximation of the perimeter (total variation) in the continuum setting. We
address this question in the setting of -convergence. We obtain almost
optimal conditions on the scaling, as number of points increases, of the size
of the neighborhood over which the points are connected by an edge for the
-convergence to hold. Taking the limit is enabled by a transportation
based metric which allows to suitably compare functionals defined on different
point clouds
Optimal stopping times for estimating Bernoulli parameters with applications to active imaging
We address the problem of estimating the parameter of a Bernoulli process. This arises in many applications, including photon-efficient active imaging where each illumination period is regarded as a single Bernoulli trial. We introduce a framework within which to minimize the mean-squared error (MSE) subject to an upper bound on the mean number of trials. This optimization has several simple and intuitive properties when the Bernoulli parameter has a beta prior. In addition, by exploiting typical spatial correlation using total variation regularization, we extend the developed framework to a rectangular array of Bernoulli processes representing the pixels in a natural scene. In simulations inspired by realistic active imaging scenarios, we demonstrate a 4.26 dB reduction in MSE due to the adaptive acquisition, as an average over many independent experiments and invariant to a factor of 3.4 variation in trial budget.Accepted manuscrip
Network Coding Channel Virtualization Schemes for Satellite Multicast Communications
In this paper, we propose two novel schemes to solve the problem of finding a
quasi-optimal number of coded packets to multicast to a set of independent
wireless receivers suffering different channel conditions. In particular, we
propose two network channel virtualization schemes that allow for representing
the set of intended receivers in a multicast group to be virtualized as one
receiver. Such approach allows for a transmission scheme not only adapted to
per-receiver channel variation over time, but to the network-virtualized
channel representing all receivers in the multicast group. The first scheme
capitalizes on a maximum erasure criterion introduced via the creation of a
virtual worst per receiver per slot reference channel of the network. The
second scheme capitalizes on a maximum completion time criterion by the use of
the worst performing receiver channel as a virtual reference to the network. We
apply such schemes to a GEO satellite scenario. We demonstrate the benefits of
the proposed schemes comparing them to a per-receiver point-to-point adaptive
strategy
Compression and Conditional Emulation of Climate Model Output
Numerical climate model simulations run at high spatial and temporal
resolutions generate massive quantities of data. As our computing capabilities
continue to increase, storing all of the data is not sustainable, and thus it
is important to develop methods for representing the full datasets by smaller
compressed versions. We propose a statistical compression and decompression
algorithm based on storing a set of summary statistics as well as a statistical
model describing the conditional distribution of the full dataset given the
summary statistics. The statistical model can be used to generate realizations
representing the full dataset, along with characterizations of the
uncertainties in the generated data. Thus, the methods are capable of both
compression and conditional emulation of the climate models. Considerable
attention is paid to accurately modeling the original dataset--one year of
daily mean temperature data--particularly with regard to the inherent spatial
nonstationarity in global fields, and to determining the statistics to be
stored, so that the variation in the original data can be closely captured,
while allowing for fast decompression and conditional emulation on modest
computers
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