The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. I...

Considering non-negative characteristic of the particle size distribution (PSD), based on trust-regionreflective Newton method, two non-negative regularization methods of truncated singular value decomposition (TSVD) and Tikhonov (TIK) for photon correlation spectroscopy (PCS) are proposed in this paper. Combining two regularization parameter criterions of GCV and L-curve, two non-negative regu...

The PP-TSVD algorithm is a regularization algorithm based on the truncated singular value decomposition (TSVD) that computes piecewise polynomial (PP) solutions without any a priori information about the locations of the break points. Here we describe an extension of this algorithm designed for two-dimensional inverse problems based on a Kronecker-product formulation. We illustrate its use in c...

In this paper we present novel strategies for completion of 5D pre-stack seismic data, viewed as a 5D tensor or as a set of 4D tensors across temporal frequencies. In contrast to existing complexity penalized algorithms for seismic data completion, which employ matrix analogues of tensor decompositions such as HOSVD or use overlapped Schatten norms from different unfoldings (or matricization) o...

Quantitative cerebral blood flow (CBF) can be obtained from dynamic susceptibility contrast (DSC) MRI using for instance the truncated singular value decomposition (tSVD). Block-circulant SVD and reformulated SVD (rSVD) are modified SVD approaches. The purpose of this study is to compare the different approaches. The optimal truncation thresholds (PSVD) for tSVD and block-circulant SVD are dete...

Singular Value Decomposition (SVD) is a technique based on linear projection theory, which has been frequently used for data analysis. It constitutes an optimal (in the sense of least squares) decomposition of a matrix in the most relevant directions of the data variance. Usually, this information is used to reduce the dimensionality of the data set in a few principal projection directions, thi...

This paper presents for the first time the hardware design of low complexity detection algorithms for the recovery of Spectrally Efficient Frequency Division Multiplexing (SEFDM) signals. The work shows that a practical design is feasible using Field Programmable Gate Arrays (FPGAs). Two detection techniques can be implemented using the proposed system architecture, namely Zero Forcing (ZF) and...

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...