Polytechnic University of Turin

About: Polytechnic University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Finite element method & Nonlinear system. The organization has 11553 authors who have published 41395 publications receiving 789320 citations. The organization is also known as: POLITO & Politecnico di Torino.

On stability of cellular neural networks with delay

Abstract: It is known that symmetric cellular neural networks (CNNs) are completely stable. It is shown that CNNs with delay (DCNNs), though symmetric, can become unstable if the delay is suitably chosen: actually such networks can exhibit periodic cycles. Moreover, a sufficient condition is presented to ensure complete stability: such a condition establishes a relation between the delay time and the parameters of the network. >

Designing distributed applications with mobile code paradigms

Abstract: Large scale distributed systems are becoming of paramount importance, due to the evolution of technology and to the interest of market. Their development, however, is not yet supported by a sound teclmological and methodological background, as the results developed for small size distributed systems often do not scale up. Recently, mobile code languages (MCLs) have been proposed as a technological answer to the problem. In this work, -we abstract away from the details of these languages by deriving design paradigms exploiting code mobility that are independent of any particular technology. We present such design paradigms, together with a discussion of their features, their application domain, and some hints about the selection of the correct paradigm for a given distributed application.

A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait

Abstract: The aim of this work is to present an original double-threshold detector of muscle activation, specifically developed for gait analysis. This detector operates on the raw myoelectric signal and, hence, it does not require any envelope detection. Its performances are fixed by the values of 3 parameters, namely, false-alarm probability (P/sub fa/), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the P/sub fa/, they yield higher detection probability; furthermore, they allow the user to select the couple false alarm-detection probability with a higher degree of freedom, thus, adapting the performances of the detector to the characteristics of the myoelectric signal of interest and of the experimental situation. Here, first the authors derive the detection algorithm and describe different strategies for selecting its parameters, then they present the performances of the proposed procedure evaluated by means of computer simulations, and finally they report an example of application to myoelectric signals recorded during gait. The characterization of the proposed double-threshold detector demonstrates that, in most practical situations, the bias of the estimates of the on-off transitions is smaller than 10 ms, the standard deviation may be kept lower than 15 ms, and the percentage of erroneous patterns is below 5%. These results show that this detection approach is satisfactory in research applications as well as in the clinical practice.