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In the low-sparsity approach, the attack vector can be constructed based on the convex optimization as a sparse term where only few components are nonzeros, which indicates that a small number of sensors need to be controlled.
This paper focuses on designing a target detection model that uses compressive measurements to find a sparse representation of image pixels from spectral information-based dictionaries.
Recent research has demonstrated that sparse coding (or sparse representation) is a powerful image representation model.
According to this natural sparsity, Cevher and Baraniuk modeled the sound source localization problem as a sparse approximation problem [19] based on the sound propagation model and provided better positioning performance.
A key problem in CS is to recover a nearly sparse signal from considerably fewer linear measurements.
The core theory about CS consists of sparse representation and dictionary design.
In recent years, the research of compressed sensing(CS) [1-2] has received more attention as a mean to process the sparse signal (i.e., the number of nonzero elements in the vector is small).
The GLS method with sparse matrices, is very fast and highly accurate, for estimating base generation allele frequencies compared to the current method.
The sentences are simple and the text is sparse, drawing the reader to the sympathetically drawn, grainy illustrations imbued with a sleepy, restful quality.
They include dense waves, sparse waves, dense sparse waves, intermittent and serrated waves.
In the field of probabilistic engineering mechanics, another approach named the sparse PC and based on the Least Angle Regression (LARS) algorithm [10] has been successfully used in order to quantify uncertainties in high-dimensional problems.