16,561 research outputs found
Assessment of timber extraction distance and skid road network in steep karst terrain
This study aims to define a simple and effective method to calculate skidding distances on steep karst terrain, rich in ground obstacles (stoniness and rockiness) to support decision planning of secondary and primary forest infrastructure network for timber extraction in productive selective cut forests. Variations between geometrical extraction distances and actual distances were highlighted on the operational planning level (i.e., compartment level) through GIS-related calculation models, focusing on cable skidder timber extraction. Automation in defining geometrical and real extraction distances, as well as relative forest openness were achieved by geo-processing workflows in GIS environment. Due to variation of extraction correction factors at the compartment level from a minimum of 1.19 to a maximum of 5.05 in the same management unit, it can be concluded that planning harvesting operations (timber extraction) at operational level should not include the use of correction factors previously obtained for entire terrain (topographical) categories, sub-categories or even management units
Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle
We describe a sensor network deployment method using autonomous flying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.)
RSSI-Based Self-Localization with Perturbed Anchor Positions
We consider the problem of self-localization by a resource-constrained mobile
node given perturbed anchor position information and distance estimates from
the anchor nodes. We consider normally-distributed noise in anchor position
information. The distance estimates are based on the log-normal shadowing
path-loss model for the RSSI measurements. The available solutions to this
problem are based on complex and iterative optimization techniques such as
semidefinite programming or second-order cone programming, which are not
suitable for resource-constrained environments. In this paper, we propose a
closed-form weighted least-squares solution. We calculate the weights by taking
into account the statistical properties of the perturbations in both RSSI and
anchor position information. We also estimate the bias of the proposed solution
and subtract it from the proposed solution. We evaluate the performance of the
proposed algorithm considering a set of arbitrary network topologies in
comparison to an existing algorithm that is based on a similar approach but
only accounts for perturbations in the RSSI measurements. We also compare the
results with the corresponding Cramer-Rao lower bound. Our experimental
evaluation shows that the proposed algorithm can substantially improve the
localization performance in terms of both root mean square error and bias.Comment: Accepted for publication in 28th Annual IEEE International Symposium
on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017
Precise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systems
The rail industry in the UK is undergoing substantial changes in response to a modernisation vision for 2040. Development and implementation of these will lead to a highly automated and safe railway. Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone. Critically, maintenance will use intelligent trainborne and track-based systems. These will provide accurate and timely information for condition based intervention at precise track locations, reducing possession downtime and minimising the presence of workers in operating railways. Clearly, precise knowledge of trains’ real-time location is of paramount importance.
The positional accuracy demand of the future railway is less than 2m. A critical consideration of this requirement is the capability to resolve train occupancy in adjacent tracks, with the highest degree of confidence. A finer resolution is required for locating faults such as damage or missing parts, precisely.
Location of trains currently relies on track signalling technology. However, these systems mostly provide an indication of the presence of trains within discrete track sections. The standard Global Navigation Satellite Systems (GNSS), cannot precisely and reliably resolve location as required either.
Within the context of the needs of the future railway, state of the art location technologies and systems were reviewed and critiqued. It was found that no current technology is able to resolve location as required. Uncertainty is a significant factor. A new integrated approach employing complimentary technologies and more efficient data fusion process, can potentially offer a more accurate and robust solution. Data fusion architectures enabling intelligent self-aware rail-track maintenance systems are proposed
Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks
Localization in wireless sensor networks not only provides a node with its
geographical location but also a basic requirement for other applications such
as geographical routing. Although a rich literature is available for
localization in static WSN, not enough work is done for mobile WSNs, owing to
the complexity due to node mobility. Most of the existing techniques for
localization in mobile WSNs uses Monte-Carlo localization, which is not only
time-consuming but also memory intensive. They, consider either the unknown
nodes or anchor nodes to be static. In this paper, we propose a technique
called Dead Reckoning Localization for mobile WSNs. In the proposed technique
all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in
DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are
localized for the first time using three anchor nodes. For their subsequent
localizations, only two anchor nodes are used. The proposed technique estimates
two possible locations of a node Using Bezouts theorem. A dead reckoning
approach is used to select one of the two estimated locations. We have
evaluated DRLMSN through simulation using Castalia simulator, and is compared
with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201
Using Lidar Data to Analyse Sinkhole Characteristics Relevant for Understory Vegetation under Forest Cover\u2014Case Study of a High Karst Area in the Dinaric Mountains
In this article, we investigate the potential for detection and characterization of sinkholes
under dense forest cover by using airborne laser scanning data. Laser pulse returns from
the ground provide important data for the estimation of digital elevation model (DEM), which
can be used for further processing. The main objectives of this study were to map and determine
the geomorphometric characteristics of a large number of sinkholes and to investigate
the correlations between geomorphology and vegetation in areas with such characteristics.
The selected study area has very low anthropogenic influences and is particularly suitable
for studying undisturbed karst sinkholes. The information extracted from this study regarding
the shapes and depths of sinkholes show significant directionality for both orientation of
sinkholes and their distribution over the area. Furthermore, significant differences in vegetation
diversity and composition occur inside and outside the sinkholes, which indicates their
presence has important ecological impacts
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