The parameter estimation of moving-average (MA) signals from second-order statistics was deemed for a long time to be a di cult nonlinear problem for which no computationally convenient and reliable solution was possible. In this paper we show how the problem of MA parameter estimation from sample covariances can be formulated as a semide nite program which can be solved in polynomial time as e ciently as a linear program. Two methods are proposed which rely on two speci c (over)parametrizations of the MA covariance sequence, whose use makes the minimization of the covariance tting criterion a convex problem. The MA estimation algorithms proposed here are computationally fast, statistically accurate, and reliable (i.e. they \never" fail). None of the previously available algorithms for MA estimation (methods based on higher-order statistics included) shares all these desirable properties. Our methods can also be used to obtain the optimal least squares approximant of an invalid (estimated) MA spectrum (that takes on negative values at some frequencies), which was another long-standing problem in the signal processing literature awaiting a satisfactory solution.
The use of an over-parametrized state-space model for system identification has some clear advantages: A single model structure covers the entire class of multivariable systems up to a given order. The over-parametrization also leads to the possibility to choose a numerically stable parametrization. During the parametric optimization the gradient calculations constitute the main computational part of the algorithm. Consequently using more than the minimal number of parameters required slows down the algorithm. However, we show that for any chosen (over)-parametrization it is possible to reduce the gradient calculations to the minimal amount by constructing the parameter subspace which is orthonormal to the tangent space of the manifold representing equivalent models.
AbstractHuman challenge trials (HCTs) have been proposed as a means to accelerate SARS-CoV-2 vaccine development. We identify and discuss three potential use cases of HCTs in the current pandemic: evaluating efficacy, converging on correlates of protection, and improving understanding of pathogenesis and the human immune response. We outline the limitations of HCTs and find that HCTs are likely to be most useful for vaccine candidates currently in preclinical stages of development. We conclude that, while currently limited in their application, there are scenarios in which HCTs would be extremely beneficial. Therefore, the option of conducting HCTs to accelerate SARS-CoV-2 vaccine development should be preserved. As HCTs require many months of preparation, we recommend an immediate effort to (1) establish guidelines for HCTs for COVID-19; (2) take the first steps toward HCTs, including preparing challenge virus and making preliminary logistical arrangements; and (3) commit to periodically re-evaluating the utility of HCTs.
This paper is aimed at identifying a dynamical model of an acoustic enclosure, a duct with rectangular cross section, closed ends, and side mounted speaker enclosures. Acoustic enclosures are known to be resonant systems of high order. In order to design a high performance feedback controller for a duct or an acoustic enclosure, one needs to have an accurate model of the system. Subspace based system identification techniques have proven to be an efficient means for identifying dynamics of high order highly resonant systems such as flexible structures. In this paper a frequency domain subspace based method together with a second iterative optimization step minimizing a frequency domain least-squares criterion is successfully employed.
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