Antonino Cuce

Remote house calls [disease detector]

An innovative bio-robotic system DDX for neuropsychophysical health-condition detection is presented. The proposed fuzzy logic solution is portable without losing efficiency and accuracy in diagnosis and also provides the ability to transfer diagnoses through a remote communication interface in order to monitor the daily health of a patient. The system is an intelligent machine based on soft computing techniques, and its efficiency can be improved considering more patterns of examples for functions, calibration, or, moreover, by using self-learning techniques.

Fuzzy blood pressure measurement

In this paper, an intelligent system for blood pressure measurement is posed together with a possible implementation using an eight bit fuzzy processor. The system can automatically determine the ideal cuff inflation level eliminating the discomfort and misreading caused by incorrect cuff inflation. Using statistics distribution of the systolic and diastolic blood pressure, in the inflation phase, a fuzzy rule system determine the pressure levels at which checking the presence of heart beat in order to exceed the systolic pressure with the minimum gap. The heart beats, characterized through pressure variations, are recognized by a fuzzy classifier.

Implementation of multidimensional fuzzy terms on hardware device

In this paper, we will propose a method to represent multidimensional fuzzy terms on a particular hardware device, the processor W.A.R.P. 1 (weight associative rule processor). The choice of this hardware depends on its characteristics here described together with its structure. In order to explain the method and its advantages with respect to the traditional approach using aggregation operators, we will show how a simple fuzzy rules system, concerning a real robotics problem, is implemented in both cases.

System identification through neuro-fuzzy methodologies

The aim of the present work is to propose a way to identify the behaviour of an induction motor supplied by using a DC/AC converter controlled through a pulse width modulation (PWM) technique. Although a mathematical description of the motor is well-known in literature, the model is sensitive to parameters variations. Moreover it is impossible to modelize in a mathematical way the system composed by the motor and the inverter together. A neuro fuzzy network, trained with a set of I/O measures, it is able to identify the whole system. The results proposed show how the behaviour of the identified system matches the real one.