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We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solutio ...
We consider bearing estimation of multiple narrow-band plane waves impinging on an array of sensors. For this problem, bearing estimation algorithms such as minimum variance distortionless response (MVDR), multiple signal classification, and maximum likeli ...
In prediction error identification, the information matrix plays a central role. Specifically, when the system is in the model set, the covariance matrix of the parameter estimates converges asymptotically, up to a scaling factor, to the inverse of the infor ...
Nowadays, the general trend towards to minimally invasive interventions is present in all the medical domains. For the surgical intervertebral spinal disc cutting or removal domain, it is particularly a necessity because the manual methods currently employ ...
For good performance in practice, real-time optimization schemes need to be able to deal with the inevitable model-mismatch problem. Unlike the two-step schemes combining parameter estimation and optimization, the modifier-adaptation approach uses experime ...
This paper shows the construction of an algorithm to calculate the roots of both, the cross-product of Bessel functions respectively of their first derivatives. The algorithm has shown globally convergent behavior for a large range of values of the argumen ...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve ...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve ...
The aim of this project is to integrate uncertainty analysis in a thermo-economic optimization framework to be used as decision making support in the design of energy systems.Tree comprehensive thermo-economic models of fuel cells systems have been develop ...
Tensegrity structures are lightweight structures composed of cables in tension and struts in compression. Since tensegrity systems exhibit geometrically nonlinear behavior, finding optimal structural designs is difficult. This paper focuses on the use of s ...