Abstract This paper proposes an approach for reducing the computational complexity of a model-predictive-control strategy for discrete-time hybrid systems with discrete inputs only...
Bostjan Potocnik, Gasper Music, Igor Skrjanc, Boru...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
—This paper introduces a new algorithm for probabilistic motion planning in arbitrary, uncertain vector fields, with emphasis on high-level planning for Montgolfier´e balloons...
Michael T. Wolf, Lars Blackmore, Yoshiaki Kuwata, ...
The paper proposes a new shape morphometry approach to combine advanced classification techniques with geometric features in order to identify morphological abnormalities on brain...
—Uncertain data is inherent in various important applications and reverse nearest neighbor (RNN) query is an important query type for many applications. While many different type...
Muhammad Aamir Cheema, Xuemin Lin, Wei Wang 0011, ...