The problem of jointly estimating time delay and frequency of a signal received at two separated sensors is addressed in this paper. These methods require eigenvalue decomposition (EVD) of the covariance matrix of received data. Thus, the computational load and complexity is high especially when the number of samples is large. In addition, the noises field is assumed to be uncorrelated with con...

We present a hybrid clustering algorithm of multiple information sources via tensor decomposition, which can be regarded an extension of the spectral clustering based on modularity maximization. This hybrid clustering can be solved by the truncated higher-order singular value decomposition (HOSVD). Experimental results conducted on the synthetic data have demonstrated the effectiveness. keyword...

in this paper, a numerical efficient method is proposed for the solution of time fractionalmobile/immobile equation. the fractional derivative of equation is described in the caputosense. the proposed method is based on a finite difference scheme in time and legendrespectral method in space. in this approach the time fractional derivative of mentioned equationis approximated by a scheme of order o...

The theory of the 2-D Wold decomposition of homogeneous random elds is eeective in image and video analysis, synthesis, and model-ing. However, a robust and computationally ef-cient decomposition algorithm is needed for use of the theory in practical applications. This paper presents a spectral 2-D Wold decomposition algorithm for homogeneous and near homogeneous random elds. The algorithm reli...

Spectral decomposition provides a canonical representation of an operator over a vector space in terms of its eigenvalues and eigenfunctions. The canonical form often facilitates discussions which, otherwise, would be complicated and involved. Spectral decomposition is of fundamental importance in many applications. The well-known GLR theory generalizes the classical result of eigendecompositio...

Temporal decomposition (TD) is an e ective technique to compress the spectral information of speech through orthogonalization of the matrix of spectral parameters leading to an e cient rate reduction in speech coding applications. The performance of TD is function of the parameters used. Although \decomposition suitability" of a parameter set is typically de ned on the basis of \phonetic releva...

The main obstacle for obtaining fast domain decomposition solvers for the spectral element discretizations of the 2-nd order elliptic equations was the lack of fast solvers for local internal problems on subdomains of decomposition and their faces. As was recently shown by Korneev/Rytov, such solvers can be derived on the basis of the specific interrelation between the stiffness matrices of the...

The theory of the D Wold decomposition of homogeneous random elds is e ective in im age and video analysis synthesis and model ing However a robust and computationally ef cient decomposition algorithm is needed for use of the theory in practical applications This pa per presents a spectral D Wold decomposition algorithm for homogeneous and near homoge neous random elds The algorithm relies on t...