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422
Highly Dynamic DestinationSequenced DistanceVector Routing (DSDV) for Mobile Computers
, 1994
"... An adhoc network is the cooperative engagement of a collection of Mobile Hosts without the required intervention of any centralized Access Point. In this paper we present an innovative design for the operation of such adhoc networks. The basic idea of the design is to operate each Mobile Host as a ..."
Abstract

Cited by 2076 (8 self)
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as a specialized router, which periodically advertises its view of the interconnection topology with other Mobile Hosts within the network. This amounts to a new sort of routing protocol. We have investigated modifications to the basic BellmanFord routing mechanisms, as specified by RIP [5], to make
The capacity of wireless networks
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2000
"... When n identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput @ A obtainable by each node for a randomly chosen destination is 2 bits per second under a noninterference protocol. If the nodes are optimally p ..."
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Cited by 3243 (42 self)
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placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission’s range is optimally chosen, the bit–distance product that can be transported by the network per second is 2 @ A bitmeters per second. Thus even under optimal circumstances, the throughput is only 2 bits per
Simplification Envelopes
"... We propose the idea of simplification envelopes for generating a hierarchy of levelofdetail approximations for a given polygonal model. Our approach guarantees that all points of an approximation are within a userspecifiable distance # from the original model and that all points of the original m ..."
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Cited by 206 (17 self)
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We propose the idea of simplification envelopes for generating a hierarchy of levelofdetail approximations for a given polygonal model. Our approach guarantees that all points of an approximation are within a userspecifiable distance # from the original model and that all points of the original
A Simple Algorithm for Nearest Neighbor Search in High Dimensions
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1997
"... Abstract—The problem of finding the closest point in highdimensional spaces is common in pattern recognition. Unfortunately, the complexity of most existing search algorithms, such as kd tree and Rtree, grows exponentially with dimension, making them impractical for dimensionality above 15. In ne ..."
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Cited by 153 (1 self)
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. In nearly all applications, the closest point is of interest only if it lies within a userspecified distance e. We present a simple and practical algorithm to efficiently search for the nearest neighbor within Euclidean distance e. The use of projection search combined with a novel data structure
A SUBSET SELECTION PROCEDURE UNDER INPUT PARAMETER UNCERTAINTY
"... This paper considers a stochastic system simulation with unknown input distribution parameters and assumes the availability of a limited amount of historical data for parameter estimation. We investigate how to account for parameter uncertainty — the uncertainty that is due to the estimation of the ..."
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of the input distribution parameters from historical data of finite length — in a subset selection procedure that identifies the stochastic system designs whose sample means are within a userspecified distance of the best mean performance measure. We show that even when the number of simulation replications
An Asynchronous Complete Method for Distributed Constraint Optimization
 In AAMAS
, 2003
"... We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to pr ..."
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Cited by 132 (30 self)
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to provide theoretical guarantees on global solution quality while operating both efficiently and asynchronously. Adopt is guaranteed to find an optimal solution, or a solution within a userspecified distance from the optimal, while allowing agents to execute asynchronously and in parallel. Adopt obtains
ARTICLE IN PRESS ComputerAided Design ( ) –
, 2007
"... Curves on surfaces play an important role in computeraided geometric design. Because of the considerably high degree of exact curves on surfaces, approximation algorithms are preferred in CAD systems. To approximate the exact curve with a reasonably low degree curve which also lies completely on th ..."
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on the Bspline surface, an algorithm is presented in this paper. The Hausdorff distance between the approximate curve and the exact curve is controlled under the userspecified distance tolerance. The approximate curve is εT –G1 continuous, where εT is the userspecified angle tolerance. Examples
ABSTRACT
"... This paper presents a new error bound simplification algorithm for complex geometric models. A lower polygon count approximation of the input model is generated by performing edge collapse operations. The collapse vertex is constrained to lie within a localised tolerance volume built around the edge ..."
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the edge collapse neighbourhood. This constraint ensures that all points on the simplified surface are within a user specified distance from the surface after the previous edge collapse.
Generating Levels of Detail for LargeScale Polygonal Models
, 1995
"... We present an efficient algorithm for generating various levelsofdetail approximations for a given polygonal model. Our algorithm guarantees that all points of an approximation are within a userspecifiable distance ffl from the original model and all points of the original model are within a dist ..."
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Cited by 6 (0 self)
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We present an efficient algorithm for generating various levelsofdetail approximations for a given polygonal model. Our algorithm guarantees that all points of an approximation are within a userspecifiable distance ffl from the original model and all points of the original model are within a
Closest Point Search in High Dimensions
 Proc. of IEEE Conference on Computer Vision and Pattern Recognition, (CVPR 96
, 1996
"... The problem of finding the closest point in highdimensional spaces is common in computational vision. Unfortunately, the complexity of most existing search algorithms, such as kd tree and Rtree, grows exponentially with dimension, making them impractical for dimensionality above 15. In nearly all ..."
Abstract

Cited by 13 (0 self)
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applications, the closest point is of interest only if it lies within a user specified distance ffl. We present a simple and practical algorithm to efficiently search for the nearest neighbor within Euclidean distance ffl. Our algorithm uses a projection search technique along with a novel data structure
Results 1  10
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