28,312 research outputs found
Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model
Many interesting natural phenomena are sparsely distributed and discrete.
Locating the hotspots of such sparsely distributed phenomena is often difficult
because their density gradient is likely to be very noisy. We present a novel
approach to this search problem, where we model the co-occurrence relations
between a robot's observations with a Bayesian nonparametric topic model. This
approach makes it possible to produce a robust estimate of the spatial
distribution of the target, even in the absence of direct target observations.
We apply the proposed approach to the problem of finding the spatial locations
of the hotspots of a specific phytoplankton taxon in the ocean. We use
classified image data from Imaging FlowCytobot (IFCB), which automatically
measures individual microscopic cells and colonies of cells. Given these
individual taxon-specific observations, we learn a phytoplankton community
model that characterizes the co-occurrence relations between taxa. We present
experiments with simulated robot missions drawn from real observation data
collected during a research cruise traversing the US Atlantic coast. Our
results show that the proposed approach outperforms nearest neighbor and
k-means based methods for predicting the spatial distribution of hotspots from
in-situ observations.Comment: To appear in ICRA 2017, Singapor
Cooperative Authentication in Underwater Acoustic Sensor Networks
With the growing use of underwater acoustic communications (UWAC) for both
industrial and military operations, there is a need to ensure communication
security. A particular challenge is represented by underwater acoustic networks
(UWANs), which are often left unattended over long periods of time. Currently,
due to physical and performance limitations, UWAC packets rarely include
encryption, leaving the UWAN exposed to external attacks faking legitimate
messages. In this paper, we propose a new algorithm for message authentication
in a UWAN setting. We begin by observing that, due to the strong spatial
dependency of the underwater acoustic channel, an attacker can attempt to mimic
the channel associated with the legitimate transmitter only for a small set of
receivers, typically just for a single one. Taking this into account, our
scheme relies on trusted nodes that independently help a sink node in the
authentication process. For each incoming packet, the sink fuses beliefs
evaluated by the trusted nodes to reach an authentication decision. These
beliefs are based on estimated statistical channel parameters, chosen to be the
most sensitive to the transmitter-receiver displacement. Our simulation results
show accurate identification of an attacker's packet. We also report results
from a sea experiment demonstrating the effectiveness of our approach.Comment: Author version of paper accepted for publication in the IEEE
Transactions on Wireless Communication
Modelling foraging movements of diving predators : A theoretical study exploring the effect of heterogeneous landscapes on foraging efficiency
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