Claudia Corradino

Turing Patterns in Memristive Cellular Nonlinear Networks

The formation of ordered structures, in particular Turing patterns, in complex spatially extended systems has been observed in many different contexts, spanning from natural sciences (chemistry, physics, and biology) to technology (mechanics and electronics). In this paper, it is shown that the use of memristors in a simple cell of a spatially-extended circuit architecture allows us to design systems able to generate Turing patterns. In addition, the memristor parameters play a key role in the selection of the type and characteristics of the emerging pattern, which is also influenced by the initial conditions. The problem of finding the regions of parameters where Turing patterns may emerge in the proposed cellular architecture is solved in an analytic way, and numerical results are shown to illustrate the system behavior with respect to its parameters.

Nonideal Behavior of Analog Multipliers for Chaos Generation

In this brief, nonideal behaviors of analog multipliers are explicitly taken into account in the design of nonlinear electronic circuits. The nonidealities of the analog multipliers led to further nonlinear terms that, instead of being considered as parasitic effects, are here explicitly accounted for and exploited to generate new complex dynamics, including chaos. In fact, despite the accuracy of analog devices, nonideal effects are always present, even if often neglected. An analog circuit based on a new nonlinear system is designed and tested, in which the nonideal terms in the multiplier input/output relation are essential for the onset of chaos, thus demonstrating the suitability of the approach.

Thermal load analysis and real time hot spots recognition in TOKAMAK using cellular nonlinear networks

A recent innovative technology in the field of plasma-wall interaction in nuclear fusion experiments is represented by the Liquid Lithium Limiter (LLL), a Limiter with a cooling system based on Liquid Lithium. Since its performance depends on the spatial temperature distribution, a thermal load analysis is important for long term developments. Furthermore, temperature is often not uniformly distributed leading to hot spots formation, that should be detected in real time to avoid any plasma disruptions. In this paper, an approach based on the definition of a suitable Cellular Nonlinear Network algorithm for the real-time image processing of thermal images taken during a plasma experiment is introduced. It allows both to map the LLL temperature and to detect hot spots over the limiter surface. Offline testing of the proposed procedure reveals the effectiveness of the approach paving the way to the modeling of the limiter surface temperature providing reliable information.

Temperature Model Identification of FTU Liquid Lithium Limiter

In this paper, the model identification of the temperature over the surface of the limiter adopted in the Frascati Tokamak Upgrade (FTU) is presented. Tokamaks are considered as the most interesting facilities to study self-sustained nuclear fusion reactions. Recently, a Liquid Lithium Limiter (LLL) has been introduced in the FTU with the aim of reducing impurities in the plasma. However, the performance of the LLL are maximized when temperature over its surface is uniformly distributed. In this paper, we face the problem of modeling the thermal behavior of the limiter surface following two different data-driven approaches: a linear autoregressive model, and a nonlinear autoregressive model. A comparison among the two models will be given, showing also which physical quantities are relevant to the specific modeling problem.

A Memristor-Based Cell for Complexity

The theoretical importance of memristor goes much beyond the field, i.e., circuit theory, in which its discovery originated. In fact, neuroscience and nonlinear science in general are also interested as memristors have been found to be adequate model of both synapses and Hodgkin–Huxley axons. In this work, we discuss the use of memristors as the unique nonlinear element of the cell of a cellular architecture reproducing several phenomena of interest for nonlinear science such as autowave propagation and Turing pattern formation. We illustrate the model and present numerical simulations showing how the same cell structure can account for these different dynamical behaviors when its parameters are varied.

Modeling spatiotemporal complexity during plasma instabilities

Nuclear fusion is a highly challenging field of intense research. One of the not yet solved problems avoiding conditions for a self-sustained reaction is certainly the modeling and control of plasma instabilities. In this paper, we propose a novel approach towards the qualitative modeling of spatiotemporal phenomena occurring in plasma during instabilities. The modeling strategy adopted is based on the representation of the pedestal area of the plasma torus by means of connected cells, following the paradigm of reaction-diffusion cellular nonlinear networks. The dynamics of each portion of the area of interest is modeled by using a simple nonlinear dynamical system, whose behavior reproduces the peculiar features of real measurements, such as pressure gradient and magnetic field. The capability of the proposed model to replicate the behavior of pressure gradient profiles observed during instabilities in real experiments determines a first step in the attempt of proposing a new qualitative model of plasma behavior.

Temperature model identification on FTU liquid lithium limiter

In this brief, the thermal model identification of the limiter surface placed in nuclear fusion plants facilities is presented. Experimental data measured at the Frascati Tokamak Upgrade (FTU) have been considered in order to identify a nonlinear dynamical model of the temperature distribution over the cooled lithium limiter used in the FTU. Three data-driven approaches have been followed: 1) a linear autoregressive (ARX) model; 2) a nonlinear ARX model; and 3) a Hammerstein model. The selection procedure of the relevant physical quantities will be outlined and a comparison among the obtained models will be given.

Automatic preprocessing of EEG signals in long time scale

Electroencephalography (EEG) signals are highly affected by physiological artifacts. Establishing a robust and repeatable EEG pre-processing is fundamental to overcome this issue and be able to use fully EEG data especially in long time scale experiments. In this work, starting from the Independent Component Analysis (ICA) of the EEG data, a control feedback scheme aiming to manage the cleaning of the independent component signals in an automatic way avoiding cut-bind solutions is presented, both with and without co-registrations. The method implemented combines different approaches based on the residual artifact contents check, identification and cleaning. The results of this procedure are shown on a test dataset. This analysis tool is embedded as core module, in a platform that can manage the automatic clearing of EEG recordings for multiple-subjects studies.