For the lifted input–output representation of general dual-rate sampled-data systems, this paper presents a decomposition based recursive least squares (D-LS) identification algorithm using the hierarchical identification principle. Compared with the recursive least squares (RLS) algorithm, the proposed D-LS algorithmdoes not require computing the covariancematriceswith large sizes andmatrix in...

A novel least-squares (LS) scheme to estimate the channel frequency response (CFR) for orthogonal frequency division multiplexing (OFDM) over slow fading channels is presented. We take the advantage of slow fading to use repeated OFDM training blocks. This not only can facilitate the LS formulation but also improve the channel estimation performance as compared to the conventional one block LS ...

We address the problem of joint Schur decomposition (JSD) of several matrices. This problem is of great importance for many signal processing applications such as sonar, biomedicine, and mobile communications. We rst present a least-squares (LS) approach for computing the JSD. The LS approach is shown to coincide with that proposed intuitively by Haardt et al, thus establishing the optimality o...

a truly meshless local petrov-galerkin (mlpg) method is developed for solving 3d elasto-static problems. using the general mlpg concept, this method is derived through the local weak forms of the equilibrium equations, by using test functions, namely, the heaviside function. the moving least squares (mls) are chosen to construct the shape functions, for the mlpg method. the penalty approximatio...

A QR-decomposition based algorithm is presented for unbiased, equation error adaptive IIR filtering. The algorithm is based on casting the adaptive IIR filtering in a mixed Least Squares Total Least Squares (LS-TLS) framework. This formulation is shown to be equivalent to the minimization of the mean-square equation error subject to a unit norm constraint on the denominator parameter vector. An...

Support vector machines (SVMs), have proven to be effective for solving learning problems, and have been successfully applied to a large number of tasks. Lately a new technique, the Least Squares SVM (LS-SVM) has been introduced. This least squares version simplifies the required computation, but sparseness –a really attractive feature of the standard SVM– is lost. To reach a sparse model, furt...

In this paper, we propose a novel framework called rigid body localization for joint position and orientation estimation of a rigid body. We consider a setup in which a few sensors are mounted on a rigid body. The absolute position of the sensors on the rigid body, or the absolute position of the rigid body itself is not known. However, we know how the sensors are mounted on the rigid body, i.e...

A least-squares finite element method for second-order elliptic boundary value problems having interfaces due to discontinuous media properties is proposed and analyzed. Both Dirichlet and Neumann boundary data are treated. The boundary value problems are recast into a firstorder formulation to which a suitable least-squares principle is applied. Among the advantages of the method are that nonc...

Amino acids are quite important indices to indicate the growth status of oilseed rape under herbicide stress. Near infrared (NIR) spectroscopy combined with chemometrics was applied for fast determination of glutamic acid in oilseed rape leaves. The optimal spectral preprocessing method was obtained after comparing Savitzky-Golay smoothing, standard normal variate, multiplicative scatter corre...