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New classes of fast lower bounds for bin packing problems
TLDR
A simple generic approach for obtaining new fast lower bounds for the bin packing problem based on dual feasible functions is presented, which proves an asymptotic worst-case performance of 3/4 for a bound that can be computed in linear time for items sorted by size.
A Combinatorial Characterization of Higher-Dimensional Orthogonal Packing
TLDR
A new approach for modeling packings is presented, using a graph-theoretical characterization of feasible packings, that allows it to deal with classes of packings that share a certain combinatorial structure, instead of having to consider one packing at a time.
A General Framework for Bounds for Higher-Dimensional Orthogonal Packing Problems
TLDR
This work presents a new approach for obtaining classes of lower bounds for higher-dimensional packing problems; its bounds improve and simplify several well-known bounds from previous literature and provides an easy framework for proving correctness of new bounds.
An Exact Algorithm for Higher-Dimensional Orthogonal Packing
TLDR
A two-level tree search algorithm for solving higher-dimensional packing problems to optimality is developed, combining the use of the data structure for characterizing feasible packings with new classes of lower bounds, and other heuristics.
DyNoC: A dynamic infrastructure for communication in dynamically reconfugurable devices
TLDR
A new paradigm to support the communication among modules dynamically placed on a reconfigurable device at run-time is presented and the unrestricted reachability of components and pins, the deadlock-freeness and the feasibility are proved.
Neighborhood-Based Topology Recognition in Sensor Networks
TLDR
This work considers a crucial aspect of self-organization of a sensor network consisting of a large set of simple sensor nodes with no location hardware and only very limited communication range, and describes algorithmic approaches for determining the structure of boundary nodes of the region, and the topology of the Region.
Deterministic boundary recognition and topology extraction for large sensor networks
TLDR
The objective is to develop algorithms and protocols that allow self-organization of the swarm into large-scale structures that reflect the structure of the street network, setting the stage for global routing, tracking and guiding algorithms.
Shawn: The fast, highly customizable sensor network simulator
TLDR
Shawn is a discrete event simulator for sensor networks that is extremely fast but can be tuned to any accuracy that is required by the simulation or application.
Algorithms for Rapidly Dispersing Robot Swarms in Unknown Environments
TLDR
This work develops and analyze algorithms for dispersing a swarm of primitive robots in an unknown environment, R, and develops and analyzes algorithms for minimizing the makespan, that is, the time to fill the entire region.
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