Spread spectrum for imaging techniques in radio interferometry

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Recent advances in signal processing, machine learning and deep learning with sparse intrinsic structure of data have paved the path for solving inverse problems in acoustics and audio. The main task of this thesis was to bridge the gap between the powerfu ...

The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations. These challenges can be addressed by enforcing sparsity in the series representation throug ...

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We study 1D continuous-domain inverse problems for multicomponent signals. The prior assumption on these signals is that each component is sparse in a different dictionary specified by a regularization operators. We introduce a hybrid regularization functi ...

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Spread spectrum for imaging techniques in radio interferometry

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Sparse and Parametric Modeling with Applications to Acoustics and Audio

Sparse Polynomial Chaos expansions using variational relevance vector machines

Deep Micro-Dictionary Learning and Coding Network

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

Low-rank and sparse subspace modeling of speech for DNN based acoustic modeling

Sparse Dictionaries for Continuous-Domain Inverse Problems

Sparse and Low-rank Modeling for Automatic Speech Recognition

Solving Continuous-Domain Problems Exactly with Multiresolution B-Splines