Results 61 to 70 of about 71,381 (220)

A contact covariant approach to optimal control with applications to sub-Riemannian geometry [PDF]

open access: yes, 2016
We discuss contact geometry naturally related with optimal control problems (and Pontryagin Maximum Principle). We explore and expand the observations of [Ohsawa, 2015], providing simple and elegant characterizations of normal and abnormal sub-Riemannian
Jóźwikowski, Michał   +1 more
core   +2 more sources

The rolling problem: overview and challenges

open access: yes, 2012
In the present paper we give a historical account -ranging from classical to modern results- of the problem of rolling two Riemannian manifolds one on the other, with the restrictions that they cannot instantaneously slip or spin one with respect to the ...
A Agrachev   +44 more
core   +2 more sources

Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion

open access: yesESAIM: Proceedings and Surveys, 2018
In this paper we review several algorithms for image inpainting based on the hypoelliptic diffusion naturally associated with a mathematical model of the primary visual cortex. In particular, we present one algorithm that does not exploit the information
Boscain Ugo   +4 more
doaj   +1 more source

Nonholonomic Systems and Sub-Riemannian Geometry [PDF]

open access: yesCommunications in Information and Systems, 2010
This paper presents several classical mechanical systems with nonholonomic con- straints from the point of view of sub-Riemannian geometry. For those systems that satisfy the bracket generating condition the system can move continuously between any two given states.
Calin, Ovidiu   +2 more
openaire   +2 more sources

Nilpotent Approximations of Sub-Riemannian Distances for Fast Perceptual Grouping of Blood Vessels in 2D and 3D [PDF]

open access: yes, 2017
We propose an efficient approach for the grouping of local orientations (points on vessels) via nilpotent approximations of sub-Riemannian distances in the 2D and 3D roto-translation groups $SE(2)$ and $SE(3)$.
Bekkers, Erik J.   +2 more
core   +2 more sources

A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery

open access: yesFrontiers in Human Neuroscience, 2021
Objective: Tangent Space Mapping (TSM) using the geometric structure of the covariance matrices is an effective method to recognize multiclass motor imagery (MI).
Fan Wu   +11 more
doaj   +1 more source

Sub-Laplacian eigenvalue bounds on sub-Riemannian manifolds [PDF]

open access: yes, 2015
We study eigenvalue problems for intrinsic sub-Laplacians on regular sub-Riemannian manifolds. We prove upper bounds for sub-Laplacian eigenvalues λk of conformal sub-Riemannian metrics that are asymptotically sharp as k→+∞. For Sasakian manifolds with a
Hassannezhad, A, Kokarev, G
core   +3 more sources

Screen Cauchy–Riemann (SCR)-lightlike submanifolds of metallic semi-Riemannian manifolds [PDF]

open access: yesArab Journal of Mathematical Sciences
PurposeThe screen Cauchy–Riemann (SCR)-lightlike submanifold is an important class of submanifolds of semi-Riemannian manifolds. It contains various other classes of submanifolds as its sub-cases. It has been studied under various ambient space.
Gauree Shanker   +2 more
doaj   +1 more source

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

open access: yesAdvanced Intelligent Systems, EarlyView.
Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationary. A Minimum Distance to Mean classifier on covariance manifolds uses geodesic distances to outperform convolutional neural networks while reducing ...
Arnau Marin‐Llobet   +9 more
wiley   +1 more source

Elastic Fast Marching Learning from Demonstration

open access: yesAdvanced Intelligent Systems, EarlyView.
This article presents Elastic Fast Marching Learning (EFML), a novel approach for learning from demonstration that combines velocity‐based planning with elastic optimization. EFML enables smooth, precise, and adaptable robot trajectories in both position and orientation spaces.
Adrian Prados   +3 more
wiley   +1 more source

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