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