Open AccessBook
Stochastic Geometry and Its Applications
TLDR
Random Closed Sets I--The Boolean Model. Random Closed Sets II--The General Case.Abstract:
Mathematical Foundation. Point Processes I--The Poisson Point Process. Random Closed Sets I--The Boolean Model. Point Processes II--General Theory. Point Processes III--Construction of Models. Random Closed Sets II--The General Case. Random Measures. Random Processes of Geometrical Objects. Fibre and Surface Processes. Random Tessellations. Stereology. References. Indexes.read more
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Journal ArticleDOI
Sampling-based algorithms for optimal motion planning
Sertac Karaman,Emilio Frazzoli +1 more
TL;DR: In this paper, the authors studied the asymptotic behavior of the cost of the solution returned by stochastic sampling-based path planning algorithms as the number of samples increases.
Journal ArticleDOI
A Tractable Approach to Coverage and Rate in Cellular Networks
TL;DR: The proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate, and the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.
Book
Stochastic Geometry for Wireless Networks
TL;DR: This rigorous introduction to stochastic geometry will enable you to obtain powerful, general estimates and bounds of wireless network performance and make good design choices for future wireless architectures and protocols that efficiently manage interference effects.
Journal ArticleDOI
spatstat: An R Package for Analyzing Spatial Point Patterns
Adrian Baddeley,Rolf Turner +1 more
TL;DR: This paper is a general description of spatstat and an introduction for new users.
Posted Content
Sampling-based Algorithms for Optimal Motion Planning
Sertac Karaman,Emilio Frazzoli +1 more
TL;DR: The main contribution of the paper is the introduction of new algorithms, namely, PRM and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum.