The frequency estimation of multiple complex sinusoids in the presence noise is important for many signal processing applications. As already discussed literature, this problem can be reformulated as a sparse representation problem. In letter, such formulation derived and an algorithm based on cyclic coordinate descent (SCCD) estimating parameters proposed. adaptively reduces size used grid, wh...

Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras) is also sparse. The method, which we call Sp...

given the critical role played by urban green spaces and the emergence of remote sensing as avaluable natural resource management tool, this study sought to identify trends in green spaces within thecontext of south africa’s transition period (1990 - 2000). using the city of port elizabeth as a case study, threesets of landsat - 5 thematic mapper images (1990, 1995 and 2000) were geo-processed,...

A full-rank under-determined linear system of equations Ax = b has in general infinitely many possible solutions. In recent years there is a growing interest in the sparsest solution of this equation—the one with the fewest non-zero entries, measured by ‖x‖0. Such solutions find applications in signal and image processing, where the topic is typically referred to as “sparse representation”. Con...

in this paper, we present a computational method for solving boundary integral equations with loga-rithmic singular kernels which occur as reformulations of a boundary value problem for the laplacian equation. themethod is based on the use of the galerkin method with cas wavelets constructed on the unit interval as basis.this approach utilizes the non-uniform gauss-legendre quadrature rule for ...

Texts are the major information carrier for internet users, from which learning latent representations has important research and practical value. Neural topic models have been proposed great performance in extracting interpretable topics of texts. However, there remain two limitations: (1) these methods generally ignore contextual texts limited feature representation ability due to shallow fee...