Kenneth Kreutz-Delgado

TL;DR: This work considers in depth the extension of two classes of algorithms-Matching Pursuit and FOCal Underdetermined System Solver-to the multiple measurement case so that they may be used in applications such as neuromagnetic imaging, where multiple measurement vectors are available, and solutions with a common sparsity structure must be computed.

TL;DR: In this article, a general framework for the analysis of the attitude tracking control problem for a rigid body is presented and a large family of globally stable control laws are obtained by using the globally nonsingular unit quaternion representation in a Lyapunov function candidate whose form is motivated by the consideration of the total energy of the rigid body.

TL;DR: Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave negative log priors, showing improved performance over other independent component analysis methods.

TL;DR: The ICLabel classifier improves upon existing methods by improving the accuracy of the computed label estimates and by enhancing its computational efficiency by outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories.

TL;DR: A methodology is developed to derive algorithms for optimal basis selection by minimizing diversity measures proposed by Wickerhauser (1994) and Donoho (1994), which include the p-norm-like (l/sub (p/spl les/1)/) diversity measures and the Gaussian and Shannon entropies.