62,057 research outputs found
Compressive Sensing for Dynamic XRF Scanning
X-Ray Fluorescence (XRF) scanning is a widespread technique of high
importance and impact since it provides chemical composition maps crucial for
several scientific investigations. There are continuous requirements for
larger, faster and highly resolved acquisitions in order to study complex
structures. Among the scientific applications that benefit from it, some of
them, such as wide scale brain imaging, are prohibitively difficult due to time
constraints. However, typically the overall XRF imaging performance is
improving through technological progress on XRF detectors and X-ray sources.
This paper suggests an additional approach where XRF scanning is performed in a
sparse way by skipping specific points or by varying dynamically acquisition
time or other scan settings in a conditional manner. This paves the way for
Compressive Sensing in XRF scans where data are acquired in a reduced manner
allowing for challenging experiments, currently not feasible with the
traditional scanning strategies. A series of different compressive sensing
strategies for dynamic scans are presented here. A proof of principle
experiment was performed at the TwinMic beamline of Elettra synchrotron. The
outcome demonstrates the potential of Compressive Sensing for dynamic scans,
suggesting its use in challenging scientific experiments while proposing a
technical solution for beamline acquisition software.Comment: 16 pages, 7 figures, 1 tabl
C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach
In many applications, maintaining a consistent dense map of the environment
is key to enabling robotic platforms to perform higher level decision making.
Several works have addressed the challenge of creating precise dense 3D maps
from visual sensors providing depth information. However, during operation over
longer missions, reconstructions can easily become inconsistent due to
accumulated camera tracking error and delayed loop closure. Without explicitly
addressing the problem of map consistency, recovery from such distortions tends
to be difficult. We present a novel system for dense 3D mapping which addresses
the challenge of building consistent maps while dealing with scalability.
Central to our approach is the representation of the environment as a
collection of overlapping TSDF subvolumes. These subvolumes are localized
through feature-based camera tracking and bundle adjustment. Our main
contribution is a pipeline for identifying stable regions in the map, and to
fuse the contributing subvolumes. This approach allows us to reduce map growth
while still maintaining consistency. We demonstrate the proposed system on a
publicly available dataset and simulation engine, and demonstrate the efficacy
of the proposed approach for building consistent and scalable maps. Finally we
demonstrate our approach running in real-time on-board a lightweight MAV.Comment: 8 pages, 5 figures, conferenc
Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties
In this paper, we construct a hierarchical model for spatial compositional
data, which is used to reconstruct past land-cover compositions (in terms of
coniferous forest, broadleaved forest, and unforested/open land) for five time
periods during the past years over Europe. The model consists of a
Gaussian Markov Random Field (GMRF) with Dirichlet observations. A block
updated Markov chain Monte Carlo (MCMC), including an adaptive Metropolis
adjusted Langevin step, is used to estimate model parameters. The sparse
precision matrix in the GMRF provides computational advantages leading to a
fast MCMC algorithm. Reconstructions are obtained by combining pollen-based
estimates of vegetation cover at a limited number of locations with scenarios
of past deforestation and output from a dynamic vegetation model. To evaluate
uncertainties in the predictions a novel way of constructing joint confidence
regions for the entire composition at each prediction location is proposed. The
hierarchical model's ability to reconstruct past land cover is evaluated
through cross validation for all time periods, and by comparing reconstructions
for the recent past to a present day European forest map. The evaluation
results are promising and the model is able to capture known structures in past
land-cover compositions
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