10,538 research outputs found
Network Localization by Shadow Edges
Localization is a fundamental task for sensor networks. Traditional network
construction approaches allow to obtain localized networks requiring the nodes
to be at least tri-connected (in 2D), i.e., the communication graph needs to be
globally rigid. In this paper we exploit, besides the information on the
neighbors sensed by each robot/sensor, also the information about the lack of
communication among nodes. The result is a framework where the nodes are
required to be bi-connected and the communication graph has to be rigid. This
is possible considering a novel typology of link, namely Shadow Edges, that
account for the lack of communication among nodes and allow to reduce the
uncertainty associated to the position of the nodes.Comment: preprint submitted to 2013 European Control Conference, July 17-19
2013, Zurich, Switzerlan
A graphical, scalable and intuitive method for the placement and the connection of biological cells
We introduce a graphical method originating from the computer graphics domain
that is used for the arbitrary and intuitive placement of cells over a
two-dimensional manifold. Using a bitmap image as input, where the color
indicates the identity of the different structures and the alpha channel
indicates the local cell density, this method guarantees a discrete
distribution of cell position respecting the local density function. This
method scales to any number of cells, allows to specify several different
structures at once with arbitrary shapes and provides a scalable and versatile
alternative to the more classical assumption of a uniform non-spatial
distribution. Furthermore, several connection schemes can be derived from the
paired distances between cells using either an automatic mapping or a
user-defined local reference frame, providing new computational properties for
the underlying model. The method is illustrated on a discrete homogeneous
neural field, on the distribution of cones and rods in the retina and on a
coronal view of the basal ganglia.Comment: Corresponding code at https://github.com/rougier/spatial-computatio
Robust Localization from Incomplete Local Information
We consider the problem of localizing wireless devices in an ad-hoc network
embedded in a d-dimensional Euclidean space. Obtaining a good estimation of
where wireless devices are located is crucial in wireless network applications
including environment monitoring, geographic routing and topology control. When
the positions of the devices are unknown and only local distance information is
given, we need to infer the positions from these local distance measurements.
This problem is particularly challenging when we only have access to
measurements that have limited accuracy and are incomplete. We consider the
extreme case of this limitation on the available information, namely only the
connectivity information is available, i.e., we only know whether a pair of
nodes is within a fixed detection range of each other or not, and no
information is known about how far apart they are. Further, to account for
detection failures, we assume that even if a pair of devices is within the
detection range, it fails to detect the presence of one another with some
probability and this probability of failure depends on how far apart those
devices are. Given this limited information, we investigate the performance of
a centralized positioning algorithm MDS-MAP introduced by Shang et al., and a
distributed positioning algorithm, introduced by Savarese et al., called
HOP-TERRAIN. In particular, for a network consisting of n devices positioned
randomly, we provide a bound on the resulting error for both algorithms. We
show that the error is bounded, decreasing at a rate that is proportional to
R/Rc, where Rc is the critical detection range when the resulting random
network starts to be connected, and R is the detection range of each device.Comment: 40 pages, 13 figure
- …