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Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important ...
Part & x00A0;I of this paper considered optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie ...
Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relies on f ...
Principal component analysis (PCA) finds the best linear representation of data and is an indispensable tool in many learning and inference tasks. Classically, principal components of a dataset are interpreted as the directions that preserve most of its "e ...
The human hand is a versatile and complex system with dexterous manipulation capabilities. For the transfer of human grasping capabilities to humanoid robotic and prosthetic hands, an understanding of the dynamic characteristics of grasp motions is fundame ...
This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional ...
Here we present an electroencephalographic (EEG) collection of 71-channel datasets recorded from 14 subjects (7 males, 7 females, aged 20–40 years) while performing a visual working memory task with a T set of 150 Independent Component Analysis (ICA) decom ...
We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire sam ...
This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that ...
Towards the goal of improving acoustic modeling for automatic speech recognition (ASR), this work investigates the modeling of senone subspaces in deep neural network (DNN) posteriors using low-rank and sparse modeling approaches. While DNN posteriors are ...