We investigate the singular value decomposition (SVD) of a rectangular matrix of functions
that are analytic on an annulus that includes at least the unit circle. Such matrices occur, eg …

This paper investigates the detection of a weak transient broadband signal. We compare a
polynomial subspace detection approach to a likelihood ratio test. While the later is …

Multi-channel signals captured by spatially separated sensors often contain a high level of
data redundancy. A compact signal representation enables more efficient storage and …

Broadband sensor array problems can be formulated using parahermitian polynomial
matrices, and the optimal solution to these problems can be based on the eigenvalue …

The emerging vision foundation model (VFM) has inherited the ability to generalize to
unseen images. Nevertheless, the key challenge of domain-generalized semantic …

This paper presents a novel method for calculating a compact order singular value
decomposition (SVD) of polynomial matrices, building upon the recently proven existence of …

In order to determine the analytic eigenvalues of a parahermitian matrix, the state-of-the-art
algorithm offers proven convergence but its complexity grows factorially with the matrix …

The polynomial power method repeatedly multiplies a polynomial vector by a para-
Hermitian matrix containing spectrally majorised eigenvalue to estimate the dominant …

The proof of existence of an analytic singular value decomposition (SVD) has been formally
established. This mo-tivates the need to devise an algorithm which retrieves analytic …

Calculating the eigenvalues and eigenvectors of a polynomial matrix has proved to be an
important problem in signal processing, in recent years. There exists various iterative …