Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least a...
In a reverberant scenario, phase transformed weighted algorithms are more robust than Maximum Likelihood (ML) because of the insufficiency of the data model to incorporate reverb...
In this paper, we present a novel framework for resampling and complexity reduction of tapped delay line channel models. In contrast to related algorithms in this field, our fram...
We recently reported a criterion for blind separation of non-negative sources, using a new concept called convex analysis for mixtures of non-negative sources (CAMNS). Under some ...
We address the design and optimization of an energy-efficient lifting-based 2D transform for wireless sensor networks with irregular spatial sampling. The 2D transform is designe...
Owing to the lack of resolution of the measurement and the randomness inherent in the signal and the measuring devices, the measurement noise is often signal-dependent. Although t...
We propose a new algorithm for Approximate Joint Diagonalization (AJD) with two main advantages over existing state-of-the-art algorithms: Improved overall running speed, especial...
We present a novel video fingerprinting method based on subspace embedding. The proposed method is particularly robust against frame-rate conversion attacks and geometric attacks...
In this study a self-steering beamformer with binaural output for a head-worn microphone array is investigated in simulated and realworld conditions. The influence of the underly...
Thomas Rohdenburg, Stefan Goetze, Volker Hohmann, ...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of measurements. The results in the literature have focuse...
Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Wills...