Regularization approaches based on spectral filtering can be highly effective in solving ill-posed inverse problems. These methods, however, require computing the singular value decomposition (SVD) and choosing appropriate regularization parameters. These tasks can be prohibitively expensive for large-scale problems. In this paper, we present a framework that uses operator approximations to eff...

This paper presents two techniques for the design of digital filters with wordlength-constrained coefficients, the bit-flipping algorithm improved since it was first presented in [l] and the Downhill Simplex Method [2] modified to deliver fixed-point filter coefficients. Both techniques can be applied to design of filter coefficients from scratch or to the optimisation of their floatingpoint ve...

In this paper, we set forth a new family of regularizing filters based on the magnitude response function of Chebyshev-I lowpass filter. The corresponding regularization strategies for inverse problem are constructed. The optimum asymptotic order of the regularized solution is obtained by a priori choice of the regularization parameter. Finally, numerical results are given to demonstrate the ef...

This paper proposes a modified linear prediction method for speech analysis, using two system identification methods—the least-square method and the instrument variable method—for the estimation of the coefficients of an all-pole filter. Whereas the linear prediction method estimates the coefficients of all-pole filters from speech signals, which are observed output signals, the system identifi...

The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we ex...

Abstract An ARMA(n,m) model is uniquely determined by its n first covariances and m first cepstrum coefficients. However, there does not always exist a model matching an estimated set of these parameters. We propose a method determining an asymptotically stable minimum phase model that match the covariances exactly and the cepstrum parameters approximatively. A convex barrier term is used for t...

space-time adaptive processing is used for clutter mitigation in airborne radars. in this paper, deterministic clutter subspace and direct data domain methods are introduced due to the fact that space-time adaptive processing algorithm requires estimating covariance matrix, limited training data and high complexity in processing. in first proposed method, filter coefficients are calculated by e...

In the solution of ill-posed problems by means of regularization methods, a crucial issue is the computation of the regularization parameter. In this work, we focus on the Truncated Singular Value Decomposition (TSVD) and Tikhonov method, and we define a method for computing the regularization parameter based on the behavior of Fourier coefficients. We compute a safe index for truncating the TS...