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Biomedical subjects

L Fortuna

Publications and source records attributed to L Fortuna.

7 recordsLinked to original sources
PubMed2005

A programmable electronic circuit for modelling CO2 laser dynamics.

We introduce a programmable electronic circuit implementing the rich dynamics of CO2 laser models. The design and the implementation of the circuit are accomplished by using a programmable analog device, which permits an experimental characterization of the laser dynamics. The experimental results shown in the paper demonstrate that the circuit exhibits homoclinic chaos typical of CO2 laser with feedback modulation of cavity losses. Moreover, experimental results showing that noise regularizes the dynamical time scales of the system are reported.

Journal Article↗
PubMed2004

A nonlinear circuit architecture for magnetoencephalographic signal analysis.

OBJECTIVES: The objective of this paper was to face the complex spatio-temporal dynamics shown by Magnetoencephalography (MEG) data by applying a nonlinear distributed approach for the Blind Sources Separation. The effort was to characterize and differ-entiate the phases of a yogic respiratory exercise used in the treatment of obsessive compulsive disorders. METHODS: The patient performed a precise respiratory protocol, at one breath per minute for 31 minutes, with 10 minutes resting phase before and after. The two steps of classical Independent Component Approach have been performed by using a Cellular Neural Network with two sets of templates. The choice of the couple of suitable templates has been carried out using genetic algorithm optimization techniques. RESULTS: Performing BSS with a nonlinear distributed approach, the outputs of the CNN have been compared to the ICA ones. In all the protocol phases, the main components founded with CNN have similar trends compared with that ones obtained with ICA. Moreover, using this distributed approach, a spatial location has been associated to each component. CONCLUSIONS: To underline the spatio-temporal and the nonlinearly of the neural process a distributed nonlinear architecture has been proposed. This strategy has been designed in order to overcome the hypothesis of linear combination among the sources signals, that is characteristic of the ICA approach, taking advantage of the spatial information.

Algorithms↗
PubMed2000

Chaotic behavior in noninteger-order cellular neural networks

In this paper, a simple system showing chaotic behavior is introduced. It is based on the well-known concept of cellular neural networks (CNNs), which have already given good results in generating complex dynamics. The peculiarity of the CNN model consists in the fact that it replaces the traditional first-order cell with a noninteger-order one. The introduction of the fractional cell, with a suitable choice of the coupling parameters, leads to the onset of chaos in a simple two-cell system. A theoretical approach, based on the harmonic balance theory, has been used to investigate the existence of chaos. A circuit realization of the proposed fractional two-cell chaotic CNN is reported and the corresponding strange attractor is also shown.

Journal Article↗
PubMed1997

Multilayer Perceptrons to Approximate Quaternion Valued Functions.

In this paper a new type of multilayer feedforward neural network is introduced. Such a structure, called hypercomplex multilayer perceptron (HMLP), is developed in quaternion algebra and allows quaternionic input and output signals to be dealt with, requiring a lower number of neurons than the real MLP, thus providing a reduced computational complexity. The structure introduced represents a generalization of the multilayer perceptron in the complex space (CMLP) reported in the literature. The fundamental result reported in the paper is a new density theorem which makes HMLPs universal interpolators of quaternion valued continuous functions. Moreover the proof of the density theorem can be restricted in order to formulate a density theorem in the complex space. Due to the identity between the quaternion and the four-dimensional real space, such a structure is also useful to approximate multidimensional real valued functions with a lower number of real parameters, decreasing the probability of being trapped in local minima during the learning phase. A numerical example is also reported in order to show the efficiency of the proposed structure. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Journal Article↗
PubMed1996

Multi-layer neural network analysis of cerebrospinal fluid pressure patterns in idiopathic normal-pressure hydrocephalus.

The cerebrospinal fluid (CSF) pressure patterns have been reported as one of the most relevant indexes for the diagnosis and treatment of idiopathic normal-pressure hydrocephalus (INPH). Forty consecutive patients coming from our observations with the classic Hakim's triad underwent continuous CSF pressure monitoring via lumbar puncture for at least 12 hours. Twenty-eight patients were diagnosed as having INPH and underwent CSF shunt. A multi-layer neural network (perceptron) was employed to study the pressure patterns in order to try an alternative classification to the "expert" neurosurgeon one. Differences between expert and neural network classifications were indeed observed. Such differences may depend on the small group studied or on the inadequacy of CFS pressure patterns in correctly individuating those INPH patients who benefit from shunt surgery. The authors think that neural network processing of INPH could add relevant information to select the "responder" patients to surgery: in fact neural networks represent a powerful methodology for aiding the expert to select the proper choice on the basis of "what learnt" by the networks themselves.

Cerebrospinal Fluid↗
PubMed1995

Multilayer perceptrons to approximate complex valued functions.

In this paper the approximation capabilities of different structures of complex feedforward neural networks, reported in the literature, have been theoretically analyzed. In particular a new density theorem for Complex Multilayer Perceptrons with complex valued non-analytical sigmoidal activation functions has been proven. Such a result makes Multilayer Perceptrons with complex valued neurons universal interpolators of continuous complex valued functions. Moreover the approximation properties of superpositions of analytic activation functions have been investigated, proving that such combinations are not dense in the set of continuous complex valued functions. Several numerical examples have also been reported in order to show the advantages introduced by Complex Multilayer Perceptrons in terms of computational complexity with respect to the classical real MLP.

Neural Networks, Computer↗