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Space-efficient parallel merging

J. Katajainen, C. Levcopoulos, O. Petersson (1993)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

Spatially-distributed coverage optimization and control with limited-range interactions

Jorge Cortés, Sonia Martínez, Francesco Bullo (2005)

ESAIM: Control, Optimisation and Calculus of Variations

This paper presents coordination algorithms for groups of mobile agents performing deployment and coverage tasks. As an important modeling constraint, we assume that each mobile agent has a limited sensing or communication radius. Based on the geometry of Voronoi partitions and proximity graphs, we analyze a class of aggregate objective functions and propose coverage algorithms in continuous and discrete time. These algorithms have convergence guarantees and are spatially distributed with respect...

Spatially-distributed coverage optimization and control with limited-range interactions

Jorge Cortés, Sonia Martínez, Francesco Bullo (2010)

ESAIM: Control, Optimisation and Calculus of Variations

This paper presents coordination algorithms for groups of mobile agents performing deployment and coverage tasks. As an important modeling constraint, we assume that each mobile agent has a limited sensing or communication radius.
Based on the geometry of Voronoi partitions and proximity graphs, we analyze a class of aggregate objective functions and propose coverage algorithms in continuous and discrete time.
These algorithms have convergence guarantees and are spatially distributed with...

Wildfires identification: Semantic segmentation using support vector machine classifier

Pecha, Marek, Langford, Zachary, Horák, David, Tran Mills, Richard (2023)

Programs and Algorithms of Numerical Mathematics

This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with 𝓁 1 -loss and 𝓁 2 -loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks.

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