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The proposed robust filters are based on a regularized least-squares formulation and guarantee minimum state error variances.
The proposed strategy is based on a regularized model where the transport velocity in the advective fluxes is shifted proportionally to the pressure potential gradient.
We present a speech denoising algorithm based on a regularized non-negative matrix factorization (NMF), in which several constraints are defined to describe the background noise in a generic way.
The proposed developments are integrated into a computational platform based on a regularized boundary integral equation (BIE) method for 3D elastodynamics, and illustrated via a set of canonical numerical experiments.
Based on a regularized version of these slender body equations that is asymptotically equivalent to the original formulation, we construct a numerical method which uses a combination of finite differences, implicit time-stepping to avoid severe stability constraints, and special quadrature methods for nearly singular integrals.
For that, we propose a novel approach based on a regularized criterion.
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In this paper we outline a new particle-mesh method for rapidly rotating shallow water flows based on a set of regularized equations of motion.
The first algorithm is based on a tensor product of regularized one-dimensional delta functions.
In our work we have adopted this new methodology and proposed a regularized greedy algorithm based on a backward elimination procedure.
In this paper, a non-local regularized function based on a non-local idea is proposed.
In particular, the method is based on a multisensor system and exploits an advanced imaging technique, which takes advantages from a regularized linear inversion scheme.
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