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A Brain Computer Interface (BCI) is a useful instrument to support human communication. In recent years, BCI systems have been frequently implemented by using EEG. Regarding the communication paradigm used, there exists a very large number of strategies and, recently, the remembering of unpleasant odors has been also defined. However, the quality of the signals collected by this last paradigm is very poor, due to the absence of a real stimulus (the stimulus consists in remembering a disgusting situation). For this reason, a crucial node is the choice of a very efficient classification algorithm to improve the accuracy of the BCI. The present paper describes a and compares classification strategies for such type of BCI systems. The proposed methods and the experimental setup are described and experimental measurements are presented and discussed.

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Non-carious cervical lesions are characterized by the loss of dental hard tissue at the cement-enamel junction (CEJ). Exceeding stresses are therefore generated in the cervical region of the tooth that cause disruption of the bonds between the hydroxyapatite crystals, leading to crack formation and eventual loss of enamel and the underlying dentine. Damage identification was performed by image analysis techniques and allowed to quantitatively assess changes in teeth. A computerized two-step procedure was generated and applied to the first left maxillary human premolar. In the first step, dental images were digitally processed by a segmentation method in order to identify the damage. The considered morphological properties were the enamel thickness and total area, the number of fragments in which the enamel is chipped. The information retrieved by the data processing of the section images allowed to orient the stress investigation toward selected portions of the tooth. In the second step, a three-dimensional finite element model based on CT images of both the tooth and the periodontal ligament was employed to compare the changes occurring in the stress distributions in normal occlusion and malocclusion. The stress states were analyzed exclusively in the critical zones designated in the first step. The risk of failure at the CEJ and of crack initiation at the dentin-enamel junction through the quantification of first and third principal stresses, von Mises stress, and normal and tangential stresses, were also estimated.

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The problem of determining the pupil morphological parameters from pupillometric data is considered. These characteristics are of great interest for non-invasive early diagnosis of the central nervous system response to environmental stimuli of different nature, in subjects suffering some typical diseases such as diabetes, Alzheimer disease, schizophrenia, drug and alcohol addiction. Pupil geometrical features such as diameter, area, centroid coordinates, are estimated by a procedure based on an image segmentation algorithm. It exploits the level set formulation of the variational problem related to the segmentation. A discrete set up of this problem that admits a unique optimal solution is proposed: an arbitrary initial curve is evolved towards the optimal segmentation boundary by a difference equation; therefore no numerical approximation schemes are needed, as required in the equivalent continuum formulation usually adopted in the relevant literature.

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The aim of this paper is to provide a theoretical set up and a mathematical model for the problem of image reconstruction. The original image belongs to a family of two-dimensional (2-D) possibly discontinuous functions, but is blurred by a Gaussian point spread function introduced by the measurement device. In addition, the blurred image is corrupted by an additive noise. We propose a preprocessing of data which enhances the contribution of the signal discontinuous component over that one of the regular part, while damping down the effect of noise. In particular we suggest to convolute data with a kernel defined as the second order derivative of a Gaussian spread function. Finally, the image reconstruction is embedded in an optimal problem framework. Now convexity and compactness properties for the admissible set play a fundamental role. We provide an instance of a class of admissible sets which is relevant from an application point of view while featuring the desired properties.

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The in vivo infusion of the thymidine analogue bromodeoxyuridine (BrdUrd). followed by delayed biopsy and bivariate DNA-BrdUrd flow cytometry, makes it possible to estimate Steel's potential doubling time (Tpot) of human tumors. In the present paper, the expression of Steel's Tpot for a rather general cell population model, in which the distribution of cell loss is assumed to be nonuniform, is derived in terms of the model parameters. We show that Steel's Tpot of a population can be markedly different for the doubling time that would be exhibited by the population in the absence of cell loss. These doubling times, on the contrary, are equal when loss is uniform. Moreover, we studied the influence of modes of cell loss different from the uniform random loss on the estimation of Tpot by using the labeling index or the nu-function, quantities that can be determined from the bivariate DNA-BrdUrd distribution.

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