Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Jan Wülfing"'
Autor:
Sreedhar S Kumar, Jan Wülfing, Samora Okujeni, Joschka Boedecker, Martin Riedmiller, Ulrich Egert
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 8, p e1005054 (2016)
Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulat
Externí odkaz:
https://doaj.org/article/e7297c58244742ab8049b0539d8d7e9e
Publikováno v:
Neurocomputing. 342:66-74
Driving activity patterns in the brain to desired levels is an important therapeutic strategy for many neurological disorders. The highly dynamic nature of neuronal networks and changes with disease progression create an urgent need for closed-loop c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10f1d3ba277805192716b0eee89f1b0a
https://doi.org/10.1016/j.neucom.2018.10.084
https://doi.org/10.1016/j.neucom.2018.10.084
Autor:
Joachim Hertzberg, Kai Lingemann, Andreas Nüchter, Thomas Wiemann, Stefan Stiene, Jan Wülfing
Publikováno v:
Scopus-Elsevier
This paper reports a method and results for solving the following problem: Given a 3D polygonal indoor map and a mobile robot equipped with a 3D time of flight (ToF) camera, localize at frame rate the 6D robot pose with respect to the map. To solve t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d09ac86458ed24e3a84cc5156b16af86
https://doi.org/10.3182/20100906-3-it-2019.00018
https://doi.org/10.3182/20100906-3-it-2019.00018
Publikováno v:
ADPRL
In this paper we present a fully automated approach to (approximate) optimal control of non-linear systems. Our algorithm jointly learns a non-parametric model of the system dynamics — based on Gaussian Process Regression (GPR) — and performs rec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de79360105758b721e4af5bf1ce5793a
https://doi.org/10.1109/adprl.2014.7010608
https://doi.org/10.1109/adprl.2014.7010608
Publikováno v:
ICRA
In this work we address the problem of feature extraction for object recognition in the context of cameras providing RGB and depth information (RGB-D data). We consider this problem in a bag of features like setting and propose a new, learned, local
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21ebf27de97867d833ebef1586504b43
https://doi.org/10.1109/icra.2012.6225188
https://doi.org/10.1109/icra.2012.6225188