In this paper, a retuning strategy for a controller is proposed in order to improve the reference BIS tracking in patients, by means of simultaneous administration of propofol and of remifentanil , in the presence of ...
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In this paper, a retuning strategy for a controller is proposed in order to improve the reference BIS tracking in patients, by means of simultaneous administration of propofol and of remifentanil , in the presence of model uncertainties. This strategy proves to be useful as is shown by simulations.
Applying Monte Carlo method on Fisher's exact test is a prominent choice in estimating an existent statistical effect in large data. When used to analyze classification results, the method, which is widely known a...
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Applying Monte Carlo method on Fisher's exact test is a prominent choice in estimating an existent statistical effect in large data. When used to analyze classification results, the method, which is widely known as permutation testing, works by testing the null hypothesis after generating a permutation distribution (PD) of classification accuracies/errors that is centered around chance-level. In principle, these PDs should behave in accord with the central limit theorem (CLT) if the independence condition in the cross-validation classification error (test statistic) is fulfilled. Permutation testing has been widely used in pattern classification applied to neuroimaging studies to eradicate chance performance. In this work, we used Anderson-Darling test to evaluate the accordance level of PDs of classification accuracies to normality expected under CLT. An exhaustive simulation study was carried out using functional magnetic resonance imaging data that were collected while human subjects responded to visual stimulation paradigms and the following classifiers were considered: support vector machines, logistic regression, ridge logistic regression, Gaussian Naive Bayes, sparse multinomial logistic regression, and artificial neural networks. Our results showed that while the standard normal distribution does not adequately fit to PDs, it tends to fit well when the mean classification accuracy averaged over a set of independent classifiers is considered. We also found that across-run lk motion correction of the fMRI data weakens the accordance of PDs with CLT and this phenomenon could be due to the across runs dependence resulting from motion correction.
In this paper, two-dimensional convolutional codes constituted by sequences in Fn) Z2 where F is a finite field, are considered. In particular, we restrict to codes with rate 1/n and we investigate the problem of mini...
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In this paper we introduce two integral transforms involving the Legendre function in the kernel (see the operators I0+α,β,μ,v and I−α,β,μ,v . defined below) which generalize the classical Liouville fractional i...
In this paper we introduce two integral transforms involving the Legendre function in the kernel (see the operators I0+α,β,μ,v and I−α,β,μ,v . defined below) which generalize the classical Liouville fractional integrals. Then, we study their boundedness as operators mapping the space ℒv,r into the spaces ℒv−α,r. Moreover, we calculate the Mellin transform of the fractional integrals presented in this paper.
The purpose of this paper is to prove an interpolation theorem which arises in a method of coupling of a finite element and an analytical solution for boundary value problems with singularities.
The purpose of this paper is to prove an interpolation theorem which arises in a method of coupling of a finite element and an analytical solution for boundary value problems with singularities.
For modified Bessel heat equations subjected to an initial condition, we investigate integral transforms with kernels related to the solutions of those equations by using the theory of reproducing kernels. In particul...
For modified Bessel heat equations subjected to an initial condition, we investigate integral transforms with kernels related to the solutions of those equations by using the theory of reproducing kernels. In particular, a new framework within reproducing kernel Hilbert spaces is proposed where we construct the unique solutions of the corresponding initial value problems.
This paper is devoted to the study of variational problems with Hadamard type fractional integrals. We present two approaches to solve such type of variational problems: indirect and direct. In the first case we deriv...
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We propose and analyze an optimal control problem where the control system is a mathematical model for tuberculosis (TB) with different post-exposure interventions considered. We analyze the impact of transmission int...
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We investigate Chargaff's second parity rule and its extensions in the human genome, and evaluate its statistical significance. This phenomenon has been previously investigated in the reference human genome, but t...
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The recursive identification of a parsimonious nonlinear Wiener model for the neuromuscular blockade in closed-loop anesthesia is considered. The performance of two popular nonlinear estimation techniques, namely the ...
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The recursive identification of a parsimonious nonlinear Wiener model for the neuromuscular blockade in closed-loop anesthesia is considered. The performance of two popular nonlinear estimation techniques, namely the extended Kalman filter (EKF) and the particle filter (PF), is evaluated on synthetic and clinical data. The parameter estimates obtained with the PF, that does not rely on model linearization, exhibit less bias and shorter settling time than the ones produced by the EKF. This behavior persists when the parameter tracking capabilities of both estimation algorithms are assessed for the model in hand. Taking advantage of the model parameters that were recursively estimated from clinical data, it is demonstrated that the main source of intra-patient variability lies in the nonlinear pharmacodynamic part of the model. The distance to a bifurcation phenomenon leading to nonlinear oscillations of the Wiener model under PID feedback is also evaluated.
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