16,890 research outputs found
Searching the Efficient Frontier for the Coherent Covering Location Problem
In this article, we will try to find an efficient boundary
approximation for the bi-objective location problem with coherent
coverage for two levels of hierarchy (CCLP). We present the
mathematical formulation of the model used. Supported efficient
solutions and unsupported efficient solutions are obtained by solving
the bi-objective combinatorial problem through the weights method
using a Lagrangean heuristic. Subsequently, the results are validated
through the DEA analysis with the GEM index (Global efficiency
measurement)
The Search for Extraterrestrial Intelligence (SETI)
A bibliography of reports concerning the Search for Extraterrestrial Intelligence is presented. Cosmic evolution, space communication, and technological advances are discussed along with search strategies and search systems
Snowmass CF1 Summary: WIMP Dark Matter Direct Detection
As part of the Snowmass process, the Cosmic Frontier WIMP Direct Detection
subgroup (CF1) has drawn on input from the Cosmic Frontier and the broader
Particle Physics community to produce this document. The charge to CF1 was (a)
to summarize the current status and projected sensitivity of WIMP direct
detection experiments worldwide, (b) motivate WIMP dark matter searches over a
broad parameter space by examining a spectrum of WIMP models, (c) establish a
community consensus on the type of experimental program required to explore
that parameter space, and (d) identify the common infrastructure required to
practically meet those goals.Comment: Snowmass CF1 Final Summary Report: 47 pages and 28 figures with a 5
page appendix on instrumentation R&
Multiobjective metaheuristic approaches for mean-risk combinatorial optimisation with applications to capacity expansion
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200
Planning the Future of U.S. Particle Physics (Snowmass 2013): Chapter 1: Summary
These reports present the results of the 2013 Community Summer Study of the
APS Division of Particles and Fields ("Snowmass 2013") on the future program of
particle physics in the U.S. Chapter 1 contains the Executive Summary and the
summaries of the reports of the nine working groups.Comment: 51 page
Online Mapping and Perception Algorithms for Multi-robot Teams Operating in Urban Environments.
This thesis investigates some of the sensing and perception challenges faced
by multi-robot teams equipped with LIDAR and camera
sensors. Multi-robot teams are ideal for deployment in large,
real-world environments due to their ability to parallelize exploration,
reconnaissance or mapping tasks.
However, such domains also impose additional requirements, including the
need for a) online algorithms (to eliminate stopping and waiting for
processing to finish before proceeding) and b) scalability (to handle
data from many robots distributed over a large area).
These general requirements give rise to specific algorithmic challenges, including 1) online maintenance of large, coherent
maps covering the explored area, 2) online estimation of communication properties
in the presence of buildings and other interfering structure, and 3)
online fusion and segmentation of multiple sensors to aid in object detection.
The contribution of this thesis is the introduction of novel
approaches that leverage grid-maps and sparse multi-variate gaussian
inference to augment the capability of multi-robot teams operating in
urban, indoor-outdoor environments by improving the state of the art
of map rasterization, signal strength prediction, colored point cloud
segmentation, and reliable camera calibration.
In particular, we introduce a map rasterization technique for large
LIDAR-based occupancy grids that makes online updates possible when
data is arriving from many robots at once. We also introduce new
online techniques for robots to predict the signal strength to their
teammates by combining LIDAR measurements with signal strength
measurements from their radios. Processing fused LIDAR+camera point
clouds is also important for many object-detection pipelines. We
demonstrate a near linear-time online segmentation algorithm to this
domain. However, maintaining the calibration of a fleet of 14 robots
made this approach difficult to employ in practice.
Therefore we introduced a robust and repeatable
camera calibration process that grounds the camera model uncertainty in pixel
error, allowing the system to guide novices and experts alike to reliably produce accurate calibrations.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113516/1/jhstrom_1.pd
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