Zobrazeno 1 - 8
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pro vyhledávání: '"Bicer, Yunus"'
Autor:
Smedemark-Margulies, Niklas, Wang, Ye, Koike-Akino, Toshiaki, Liu, Jing, Parsons, Kieran, Bicer, Yunus, Erdogmus, Deniz
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using new regularization techniques during model training. We propose several
Externí odkaz:
http://arxiv.org/abs/2310.08762
Autor:
Smedemark-Margulies, Niklas, Bicer, Yunus, Sunger, Elifnur, Naufel, Stephanie, Imbiriba, Tales, Tunik, Eugene, Erdoğmuş, Deniz, Yarossi, Mathew
Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem transformation approach, in which actions were subset into two biomecha
Externí odkaz:
http://arxiv.org/abs/2309.12217
Autor:
Bicer, Yunus, Smedemark-Margulies, Niklas, Celik, Basak, Sunger, Elifnur, Orendorff, Ryan, Naufel, Stephanie, Imbiriba, Tales, Erdoğmuş, Deniz, Tunik, Eugene, Yarossi, Mathew
Publikováno v:
in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 1187-1197, 2024
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration. sEMG data were streamed into a machine-learning algorithm that clas
Externí odkaz:
http://arxiv.org/abs/2309.07289
Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) can be very complex, particularly when the rules from insect flight dynamics (fast flapping dynamics and light wings) are not applicable. In these situations, widely used averaging technique
Externí odkaz:
http://arxiv.org/abs/2110.01057
Publikováno v:
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 2629-2634
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to
Externí odkaz:
http://arxiv.org/abs/2007.14671
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel si
Externí odkaz:
http://arxiv.org/abs/1909.11538
Akademický článek
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Autor:
Smedemark-Margulies N; Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States of America., Wang Y; Mitsubishi Electric Research Labs (MERL), Cambridge, MA, United States of America., Koike-Akino T; Mitsubishi Electric Research Labs (MERL), Cambridge, MA, United States of America., Liu J; Mitsubishi Electric Research Labs (MERL), Cambridge, MA, United States of America., Parsons K; Mitsubishi Electric Research Labs (MERL), Cambridge, MA, United States of America., Bicer Y; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States of America., Erdoğmuş D; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States of America.
Publikováno v:
Journal of neural engineering [J Neural Eng] 2024 Dec 16; Vol. 21 (6). Date of Electronic Publication: 2024 Dec 16.
