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...

In this paper, we treat a design problem for IIR digital filters described by rational transfer function in discrete space. First, we form the filter design problem using the modified least-squares (MLS) criterion and express it as the quadratic form with respect to the numerator and denominator coefficients. Next, we show the relaxation method using the Lagrange multiplier method in order to s...

Reportedly, standard identiication algorithms do not guarantee the controllability of the estimated system. In this paper, a penalized least squares (PLS) identiication criterion is proposed to overcome this diiculty. The criterion is shown to provide estimated systems which exhibit an uniform controllability property through time. Moreover, the Lai and Wei upper bound for the least squares est...

Monotone single index models have gained over the past decades increasing popularity due to their flexibility and versatile use in diverse areas. Semi-parametric estimators such as least squares maximum likelihood of unknown monotone ridge function were considered make inference without having choose some tuning parameter. Description asymptotic behavior those crucially depends on acquiring a g...

This paper firstly presents an extended ambiguity resolution model that deals with an ill-posed problem and constraints between the estimated parameters. In the extended model, the regularization criterion is used instead of the traditional least squares in order to estimate the float ambiguities better. The existing models can be derived from the general model. Secondly, the paper examines the...

We present a new algorithm for approximate joint diagonalization of several symmetric matrices. While it is based on the classical least squares criterion, a novel intrinsic scale constraint leads to a simple and easily parallelizable algorithm, called LSDIC (Least squares Diagonalization under an Intrinsic Constraint). Numerical simulations show that the algorithm behaves well as compared to o...

In this paper, a new approach for MC-DCT (motion compensated – Discrete Cosine Transform) hybrid video coding based on true motion estimation is proposed. The true motion estimation technique employs the leastmedian-squares (LMedS) matching criterion in block matching process for motion estimation. The rationale for using such true motion estimator is that at low bit-rates, very few bits are av...

A novel approach for signal parameter estimation, named the Non-Linear Instantaneous Least Squares (NILS) estimator, is proposed and a high SNR statistical analysis of the estimates is presented. The algorithm is generally applicable to deterministic signal in noise models. However, it is of particular interest in applications where the “conventional” non-linear least squares criterion suffers ...

In the usual optical ow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical ow detec...