A powerful approach to sparse representation, dictionary learning consists in finding a redundant frame in which the representation of a particular class of images is sparse. In practice, all algorithms performing dictionary learning iteratively estimate the dictionary and a sparse representation of the images using this dictionary. However, the numerical complexity of dictionary learning restr...

n-grams are simple learning models considered state-of-the-art in many sequential domains. They suffer from an exponential number of parameters in their width, n. We introduce undirected sparse n-grams, which store probability estimates only for some n-tuples from the unconditional joint distribution, specifically the most frequent. Experimental results show this sparse version can outperform a...

The second generation of digital video broadcasting (DVB-T2) standard utilizes orthogonal frequency division multiplexing (OFDM) system to reduce and to compensate the channel effects by utilizing its estimation. Since wireless channels are inherently sparse, it is possible to utilize sparse representation (SR) methods to estimate the channel. In addition to sparsity feature of the channel, the...

Abstract: This paper proposes a novel scheme for multi-static passive radar processing, based on soft-input soft-output processing and Bayesian sparse estimation. In this scheme, each receiver estimates the probability of target presence based on its received signal and the prior information received from a central processor. The resulting posterior target probabilities are transmitted to the c...

[1] Long-term research has provided a great deal of information regarding the influence of management on the equilibrium dynamics of soil organic matter (SOM), although short-term dynamics remain largely uninvestigated. An improved approach to characterizing SOM dynamics in managed ecosystems would consider both short-term and long-term changes in content and composition. This approach and its ...