Geometric Methods for Shape Recovery from Line Drawings of Polyhedra
Journal of Mathematical Imaging and Vision
Direct data-driven recursive controller unfalsification with analytic update
Automatica (Journal of IFAC)
Distributed adaptive sampling using bounded-errors
Proceedings of the 1st international conference on Robot communication and coordination
Feedforward neural networks training with optimal bounded ellipsoid algorithm
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
WSEAS Transactions on Computers
Control for Localization of Targets using Range-only Sensors
International Journal of Robotics Research
Recurrent neural networks training with stable bounding ellipsoid algorithm
IEEE Transactions on Neural Networks
Kinematic-sensitivity indices for dimensionally nonhomogeneous Jacobian matrices
IEEE Transactions on Robotics
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Presents an ellipsoidal calculus based solely on two basic operations: propagation and fusion. Propagation refers to the problem of obtaining an ellipsoid that must satisfy an affine relation with another ellipsoid, and fusion to that of computing the ellipsoid that tightly bounds the intersection of two given ellipsoids. These two operations supersede the Minkowski sum and difference, affine transformation and intersection tight bounding of ellipsoids on which other ellipsoidal calculi are based. Actually, a Minkowski operation can be seen as a fusion followed by a propagation and an affine transformation as a particular case of propagation. Moreover, the presented formulation is numerically stable in the sense that it is immune to degeneracies of the involved ellipsoids and/or affine relations. Examples arising when manipulating uncertain geometric information in the context of the spatial interpretation of line drawings are extensively used as a testbed for the presented calculus