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pro vyhledávání: '"Su, Steven"'
This paper developed an efficient method for calibrating triaxial MEMS gyroscopes, which can be effectively utilized in the field environment. The core strategy is to utilize the criterion that the dot product of the measured gravity and the rotation
Externí odkaz:
http://arxiv.org/abs/2410.19571
The present study introduces an innovative approach to the synthesis of Electroencephalogram (EEG) signals by integrating diffusion models with reinforcement learning. This integration addresses key challenges associated with traditional EEG data acq
Externí odkaz:
http://arxiv.org/abs/2410.00013
This study introduces a novel approach to robot-assisted ankle rehabilitation by proposing a Dual-Agent Multiple Model Reinforcement Learning (DAMMRL) framework, leveraging multiple model adaptive control (MMAC) and co-adaptive control strategies. In
Externí odkaz:
http://arxiv.org/abs/2407.21734
Autor:
Tong, Yuhao, Su, Steven W.
This note explores the extension of D-stability to non-square matrices, applicable to distributed/decentralized controllability analysis. We first present a definition of D-stability for non-square matrices, directly extending from square matrices. W
Externí odkaz:
http://arxiv.org/abs/2406.15440
The calibration of MEMS triaxial gyroscopes is crucial for achieving precise attitude estimation for various wearable health monitoring applications. However, gyroscope calibration poses greater challenges compared to accelerometers and magnetometers
Externí odkaz:
http://arxiv.org/abs/2405.03393
Brain-Computer Interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph Neural Networks (GNNs) outperform Convolutional Neural Networks (CNNs) in this regard, by leverag
Externí odkaz:
http://arxiv.org/abs/2405.00723
Brain-Computer Interfaces connect the brain to external control devices, necessitating the accurate translation of brain signals such as from electroencephalography (EEG) into executable commands. Graph Neural Networks (GCN) have been increasingly ap
Externí odkaz:
http://arxiv.org/abs/2404.11075
Autor:
Su, Steven W.
In this note, we discuss the extension of several important stable square matrices, e.g., D-stable matrices, diagonal dominance matrices, Volterra-Lyapunov stable matrices, to their corresponding non-square matrices. The extension is motivated by som
Externí odkaz:
http://arxiv.org/abs/2401.00367
This paper aims to develop a new human-machine interface to improve rehabilitation performance from the perspective of both the user (patient) and the machine (robot) by introducing the co-adaption techniques via model-based reinforcement learning. P
Externí odkaz:
http://arxiv.org/abs/2305.02058
Autor:
Su, Steven H.1,2 (AUTHOR), Mitani, Yosuke2,3,4 (AUTHOR), Li, Tianxia2,3,4 (AUTHOR), Sachdeva, Uma5 (AUTHOR), Flashner, Samuel2,3,4 (AUTHOR), Klein-Szanto, Andres6 (AUTHOR), Dunbar, Karen J.2,3,4 (AUTHOR), Abrams, Julian2,3,4 (AUTHOR), Nakagawa, Hiroshi2,3,4 (AUTHOR), Gabre, Joel2,3,4 (AUTHOR) jg4262@cumc.columbia.edu
Publikováno v:
Biomolecules (2218-273X). Sep2024, Vol. 14 Issue 9, p1195. 16p.
