This paper studies compressed sensing for the recovery of non-negative sparse vectors from a smaller number of measurements than the ambient dimension of the unknown vector. We fo...
The reuse of past coefficient vectors of the NLMS for reducing the steady-state MSD in a low signal-to-noise ratio (SNR) was proposed recently. Its convergence analysis has not b...
This paper investigates query translation in cross-lingual information retrieval, especially the challenges caused by ambiguity and polysemi. We base our ideas on feature vectors a...
The Single Instruction Multiple Data (SIMD) model for fine-grained parallelism was recently extended to support SIMD operations on disjoint vector elements. In this paper we demon...
The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on t...