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We show that there exists an explicit descriptor state space format which actually describes all strictly passive transfer functions. A key advantage of this explicitly strictly passive descriptor state space format resides in its relation with congruence projection-based reduced order modeling, where the resulting reduced order model is also cast in this(More)
— This correspondence presents the Barankin bound as a fundamental statistical tool for the understanding of the threshold effect associated with the estimation of the frequency of a sinusoid in additive white Gaussian noise. It is shown that the threshold effect takes hold whenever the Barankin bound departs significantly from the Cramer–Rao bound. In(More)
A new model order reduction technique is presented which preserves passivity and non-expansivity. It is a projection-based method which exploits the solution of linear matrix inequalities to generate a descriptor state space format which preserves positive-realness and bounded-realness. In the case of both non-singular and singular systems, solving the(More)
— The zeros of predictor polynomials are shown to belong to the numerical range of a shift operator associated with the particular prediction problem under consideration. The numerical range consists of the classical field of values of the shift operator when the setting is Hilbert space, but a new definition is necessary when the setting is a general(More)
This paper deals with the pole-zero identification of a linear system from a measured input-output record. It is shown that the minimization of a modified version of the squared Kalman equation error can be implemented by an order recursive algorithm in the time domain. The algorithm is based on the Gram-Schmidt orthogonaliza-tion of intertwined Krylov(More)
—The Hankel transform of a function by means of a direct Mellin approach requires sampling on an exponential grid, which has the disadvantage of coarsely undersampling the tail of the function. A novel modified Hankel transform procedure that does not require exponential sampling is presented. The algorithm proceeds via a three-step Mellin approach to yield(More)
A class of statistical distance measures and their spectral counterparts are presented. They have strong physical foundations since they are based on the combinatorial law leading to Bose-Einstein statistics in statistical physics. It is shown that these distance measures are very closely related to the recently introduced Jensen-Shannon divergence measure.(More)
Surrogate models are data-driven models used to accurately mimic the complex behavior of a system. They are often used to approximate computationally expensive simulation code in order to speed up the exploration of design spaces. A crucial step in the building of surrogate models is finding a good set of hyperparameters, which determine the behavior of the(More)