86,694 research outputs found
Routing Unmanned Vehicles in GPS-Denied Environments
Most of the routing algorithms for unmanned vehicles, that arise in data
gathering and monitoring applications in the literature, rely on the Global
Positioning System (GPS) information for localization. However, disruption of
GPS signals either intentionally or unintentionally could potentially render
these algorithms not applicable. In this article, we present a novel method to
address this difficulty by combining methods from cooperative localization and
routing. In particular, the article formulates a fundamental combinatorial
optimization problem to plan routes for an unmanned vehicle in a GPS-restricted
environment while enabling localization for the vehicle. We also develop
algorithms to compute optimal paths for the vehicle using the proposed
formulation. Extensive simulation results are also presented to corroborate the
effectiveness and performance of the proposed formulation and algorithms.Comment: Publised in International Conference on Umanned Aerial System
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
Formulations of the Image Decomposition Problem as a Multicut Problem (MP)
w.r.t. a superpixel graph have received considerable attention. In contrast,
instances of the MP w.r.t. a pixel grid graph have received little attention,
firstly, because the MP is NP-hard and instances w.r.t. a pixel grid graph are
hard to solve in practice, and, secondly, due to the lack of long-range terms
in the objective function of the MP. We propose a generalization of the MP with
long-range terms (LMP). We design and implement two efficient algorithms
(primal feasible heuristics) for the MP and LMP which allow us to study
instances of both problems w.r.t. the pixel grid graphs of the images in the
BSDS-500 benchmark. The decompositions we obtain do not differ significantly
from the state of the art, suggesting that the LMP is a competitive formulation
of the Image Decomposition Problem. To demonstrate the generality of the LMP,
we apply it also to the Mesh Decomposition Problem posed by the Princeton
benchmark, obtaining state-of-the-art decompositions
Layout Decomposition for Quadruple Patterning Lithography and Beyond
For next-generation technology nodes, multiple patterning lithography (MPL)
has emerged as a key solution, e.g., triple patterning lithography (TPL) for
14/11nm, and quadruple patterning lithography (QPL) for sub-10nm. In this
paper, we propose a generic and robust layout decomposition framework for QPL,
which can be further extended to handle any general K-patterning lithography
(K4). Our framework is based on the semidefinite programming (SDP)
formulation with novel coloring encoding. Meanwhile, we propose fast yet
effective coloring assignment and achieve significant speedup. To our best
knowledge, this is the first work on the general multiple patterning
lithography layout decomposition.Comment: DAC'201
Competent genetic-evolutionary optimization of water distribution systems
A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms - a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms
SAT Modulo Monotonic Theories
We define the concept of a monotonic theory and show how to build efficient
SMT (SAT Modulo Theory) solvers, including effective theory propagation and
clause learning, for such theories. We present examples showing that monotonic
theories arise from many common problems, e.g., graph properties such as
reachability, shortest paths, connected components, minimum spanning tree, and
max-flow/min-cut, and then demonstrate our framework by building SMT solvers
for each of these theories. We apply these solvers to procedural content
generation problems, demonstrating major speed-ups over state-of-the-art
approaches based on SAT or Answer Set Programming, and easily solving several
instances that were previously impractical to solve
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