The underlying algebra for a noncommutative geometry is taken to be a matrix algebra, and the set of derivatives the adjoint of a subset of traceless matrices. This is sufficient to calculate the dual 1-forms, and show that the space of 1-forms is a free module over the algebra of matrices. The concept of a generalised algebra is defined and it is shown that this is required in order for the sp...

R Upper-triangular matrix from QR decomposition v Direction unit vector: vj ; j ∈ [1, N ] Vinf Freestream velocity W Combined weight matrix Wb Derivative observation weight matrix: Wb i,j ≡ wi δi,j Wf Function observation weight matrix: Wf i,j ≡ wi δi,j x Vector of input quantities to f : xj ; j ∈ [1, N ] θ, φ Spherical polar coordinates {d} Vector of Taylor series derivatives: dk ; k ∈ [1, t] ...

in the present paper, a numerical method is considered for solving one-dimensionalheat equation subject to both neumann and dirichlet initial boundaryconditions. this method is a combination of collocation method and radial basis functions (rbfs). the operational matrix of derivative for laguerre-gaussians (lg) radial basis functions is used to reduce the problem to a set of algebraic equations...

A pseudo-Euclidean space, or Smarandache space is a pair (Rn, ω|− → O ). In this paper, considering the time scale concept on Smarandache space with ω|− → O (u) = 0 for ∀u ∈ E, i.e., the Euclidean space, we introduce the tangent vector and some properties according to directional derivative, the delta differentiable vector fields on regular curve parameterized by time scales and the Jacobian ma...

The principal result in this paper is concerned with the derivative of a vector with respect to a block vector or matrix. This is applied to the asymptotic Fisher information matrix (FIM) of a stationary vector autoregressive and moving average time series process (VARMA). Representations which can be used for computing the components of the FIM are then obtained. In a related paper [1], the de...

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...