where 1≤ p ≤∞ and μ is normalized Lebesgue measure on (−π,π). LetV be the space of trigonometric polynomials of degree n and let P be the Fourier projection from Lp(−π,π) onto V . It is well known (see [2, 3, 16]) that P is a minimal projection, that is, P has the least norm among all projections from Lp(−π,π) onto V . For p = 1,2 and p =∞, it was proved that P is a unique projection that has t...

This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...

Many problems, in diverse areas of science and engineering, involve the solution largescale sparse systems linear equations. In most these scenarios, they are also a computational bottleneck, therefore their efficient on parallel architectureshas motivated tremendous volume research.This dissertation targets use GPUs to enhance performance using iterative methods complemented with state-of-the-...

this article presents a new subspace-based technique for reducing the noise ofsignals in time-series. in the proposed approach, the signal is initially representedas a data matrix. then using singular value decomposition (svd), noisy datamatrix is divided into signal subspace and noise subspace. in this subspace division,each derivative of the singular values with respect to rank order is used ...