4,025 research outputs found
MAP: Medial Axis Based Geometric Routing in Sensor Networks
One of the challenging tasks in the deployment of dense wireless networks (like sensor networks) is in devising a routing scheme for node to node communication. Important consideration includes scalability, routing complexity, the length of the communication paths and the load sharing of the routes. In this paper, we show that a compact and expressive abstraction of network connectivity by the medial axis enables efficient and localized routing. We propose MAP, a Medial Axis based naming and routing Protocol that does not require locations, makes routing decisions locally, and achieves good load balancing. In its preprocessing phase, MAP constructs the medial axis of the sensor field, defined as the set of nodes with at least two closest boundary nodes. The medial axis of the network captures both the complex geometry and non-trivial topology of the sensor field. It can be represented compactly by a graph whose size is comparable with the complexity of the geometric features (e.g., the number of holes). Each node is then given a name related to its position with respect to the medial axis. The routing scheme is derived through local decisions based on the names of the source and destination nodes and guarantees delivery with reasonable and natural routes. We show by both theoretical analysis and simulations that our medial axis based geometric routing scheme is scalable, produces short routes, achieves excellent load balancing, and is very robust to variations in the network model
Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0
Within the context of Industry 4.0, mobile robot systems such as automated
guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major
areas challenging current communication and localization technologies. Due to
stringent requirements on latency and reliability, several of the existing
solutions are not capable of meeting the performance required by industrial
automation applications. Additionally, the disparity in types and applications
of unmanned vehicle (UV) calls for more flexible communication technologies in
order to address their specific requirements. In this paper, we propose several
use cases for UVs within the context of Industry 4.0 and consider their
respective requirements. We also identify wireless technologies that support
the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important
role in image guided interven- tion. However, the lack of clear boundary
specifically at the apex and base, and huge variation of shape and texture
between the images from different patients make the task very challenging. To
overcome these problems, in this paper, we propose a deeply supervised
convolutional neural network (CNN) utilizing the convolutional information to
accurately segment the prostate from MR images. The proposed model can
effectively detect the prostate region with additional deeply supervised layers
compared with other approaches. Since some information will be abandoned after
convolution, it is necessary to pass the features extracted from early stages
to later stages. The experimental results show that significant segmentation
accuracy improvement has been achieved by our proposed method compared to other
reported approaches.Comment: Due to a crucial sign error in equation
GUARDIANS final report
Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a
large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we
discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with
the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings
Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label
AI-assisted surgeries have drawn the attention of the medical image research
community due to their real-world impact on improving surgery success rates.
For image-guided surgeries, such as Cochlear Implants (CIs), accurate object
segmentation can provide useful information for surgeons before an operation.
Recently published image segmentation methods that leverage machine learning
usually rely on a large number of manually predefined ground truth labels.
However, it is a laborious and time-consuming task to prepare the dataset. This
paper presents a novel technique using a self-supervised 3D-UNet that produces
a dense deformation field between an atlas and a target image that can be used
for atlas-based segmentation of the ossicles. Our results show that our method
outperforms traditional image segmentation methods and generates a more
accurate boundary around the ossicles based on Dice similarity coefficient and
point-to-point error comparison. The mean Dice coefficient is improved by 8.51%
with our proposed method.Comment: conferenc
Greedy routing and virtual coordinates for future networks
At the core of the Internet, routers are continuously struggling with
ever-growing routing and forwarding tables. Although hardware advances
do accommodate such a growth, we anticipate new requirements e.g. in
data-oriented networking where each content piece has to be referenced
instead of hosts, such that current approaches relying on global
information will not be viable anymore, no matter the hardware
progress. In this thesis, we investigate greedy routing methods that
can achieve similar routing performance as today but use much less
resources and which rely on local information only. To this end, we
add specially crafted name spaces to the network in which virtual
coordinates represent the addressable entities. Our scheme enables participating
routers to make forwarding decisions using only neighbourhood information,
as the overarching pseudo-geometric name space structure already
organizes and incorporates "vicinity" at a global level.
A first challenge to the application of greedy routing on virtual
coordinates to future networks is that of "routing dead-ends"
that are local minima due to the difficulty of consistent coordinates
attribution. In this context, we propose a routing recovery scheme
based on a multi-resolution embedding of the network in low-dimensional Euclidean spaces.
The recovery is performed by routing greedily on a blurrier view of the network. The
different network detail-levels are obtained though the embedding of
clustering-levels of the graph. When compared with
higher-dimensional embeddings of a given network, our method shows a
significant diminution of routing failures for similar header and
control-state sizes.
A second challenge to the application of virtual coordinates and
greedy routing to future networks is the support of
"customer-provider" as well as "peering" relationships between
participants, resulting in a differentiated services
environment. Although an application of greedy routing within such a
setting would combine two very common fields of today's networking
literature, such a scenario has, surprisingly, not been studied so
far. In this context we propose two approaches to address this scenario.
In a first approach we implement a path-vector protocol similar to
that of BGP on top of a greedy embedding of the network. This allows
each node to build a spatial map associated with each of its
neighbours indicating the accessible regions. Routing is then
performed through the use of a decision-tree classifier taking the
destination coordinates as input. When applied on a real-world dataset
(the CAIDA 2004 AS graph) we demonstrate an up to 40% compression ratio of
the routing control information at the network's core as well as a computationally efficient
decision process comparable to methods such as binary trees and tries.
In a second approach, we take inspiration from consensus-finding in social
sciences and transform the three-dimensional distance data structure
(where the third dimension encodes the service differentiation) into a
two-dimensional matrix on which classical embedding tools can be used.
This transformation is achieved by agreeing on a set of
constraints on the inter-node distances guaranteeing an
administratively-correct greedy routing. The computed distances are
also enhanced to encode multipath support. We demonstrate a good
greedy routing performance as well as an above 90% satisfaction of multipath constraints
when relying on the non-embedded obtained distances on synthetic datasets.
As various embeddings of the consensus distances do not fully exploit their multipath potential, the use of compression techniques such as transform coding to
approximate the obtained distance allows for better routing performances
Introduction to \u3cem\u3eGuiding Global Order: G8 Governance in the Twenty First Century\u3c/em\u3e
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