Training neural networks has become an almost daily activity for researchers working on different fields. Preparing accurately the training patterns often appears to be fundamental in obtaining a good balance between learning performance and time needed to reach them. In this contribution, we explore a paradigm to organize patterns for training quaternion neural networks for image processing tasks. The basic idea is to exploit the working principle of Cellular Nonlinear Networks, where local interactions are fundamental, to determine an efficient learning of multidimensional neural network, thus merging the main characteristics of the two architectures. A robustness analysis and practical applications are also presented.

This contribution is oriented towards proposing a novel strategy to simulate and predict planetary orbits based on exploiting the optimized performance of hypercomplex neural networks. These structures perform a learning process based on quaternion algebra thus leading to lower the number of epochs required to obtain an adequate degree of approximation. Moreover, we propose a strategy for predicting the outcome of the learning phase by instantiating suitable hypercomplex auxiliary networks to predict the trends of the main weights of the network.

Neural networks based on back-propagation learning algorithms and gradient descent algorithms are the first and the easiest tools developed for machine learning. They are still widespread nowadays, so much so by exploiting a huge number of different coding languages, between which MatLab, Python or Java, we have the possibility of using these training tools. But as highlighted in the past, these traditional neural networks suffer from their slow convergence rate. Aim of this paper is to revisit an algorithm to improve the speed of the learning phase, by exploiting the power of parallel computing to train a suitable number of auxiliary neural networks which work concurrently with the principal network. The implementation of the proposed algorithm in MatLab is shown in order to make evident the main difference with the traditional learning algorithms. Several examples, related to modeling of technological datasets from industrial environment, confirm the suitability of the proposed procedure.

By leveraging the performances achievable combining MARTe2 framework and ARMv8 platforms, the approach presented in this contribution suggests a way to the rapid development of signal processing units. The approach is finalized to the realisation of devices which will be employed on the magnetics diagnostics of ITER, the nuclear fusion experimental site in construction in France. The paper retraces the steps and procedures followed to successfully port the MARTe2 framework on the Ultrascale platform. As the employed Ultrascale platform shares the CPU unit with the Raspberry Pi embedded platform, the achieved collateral was to also have MARTe2 running on a low cost and easily reachable computing unit.As the MARTe2 framework represents a solid and scalable framework for the development of real-time control systems applications, its porting may become valuable for the field of industrial automation. Moreover, its current structure and the posed porting challenges may become interesting in the field of applied computer science and industrial electronics engineering.The need to port MARTe2 on the specific platform, with onboard ARMv8 and FPGA are present, is dictated by the need of a configurable, non firmware-bound, platform where the FPGA processing power is flanked by the MARTe2 features, especially on configurability and signal processing offer.

This paper presents a Model-Based Design (MBD) methodology as a promising approach for the rapid and se-cure development of Embedded System applications, including those involving digital controllers. Based on existing hardware and software application-oriented tools by STMicroelectronics, a new modeling technique has been implemented to move from a traditional design workflow to an MBD one by using Mathworks®software platform. For a practical application of the proposed approach, a case study is reported to design a Permanent Magnet Synchronous Motor (PMSM) drive based on the STM32 MCU family. The main contribution of this paper is to make focus on concretely building a unique model architecture of a complex software/hardware system able to implement all the major aspects of MBD methodology such as executable requirements, Normal and Processor-In-the-Loop (PIL) simulation modes, continuous test and verification, automatic code generation. To achieve this purpose, a new Simulink®blockset dedicated to the STM32 Motor Control ecosystem was developed. It includes Simulink blocks for maths, algorithms, and IPs, electronic circuitries, MCU peripherals, speed sensors. Simulink®Embedded Coder tool has been used to automatically generate code for a specific STM32 MCU tar-get to overcome the time consuming and error-prone problems of the handwritten coding.

Plasma physics is an example of research field where many measurements carried out at very specific working conditions need to be collected and processed. By looking at the properties of these data, it can be possible to explore their hidden features in order to solve challenging problems that usually require high computational efforts, such as the tomographic reconstruction. In this paper, preliminary but nontrivial analyses of flux measurements produced in a Tokamak machine are shown and discussed, with the aim of introducing an application of some algorithms for feature selection to detect hidden, relevant relationships within given sets of channels. All the statistical details, and therefore the feature selection procedure itself, are introduced in view of further deepenings, such as the aforementioned problem of tomographically reconstructing plasma profiles from flux measurements or modelling the system in terms of its input-output relationship.

In this paper a dielectric structure made with Carbon Black has been designed to realize a Fractional Order Element (FOE). This device is hence used as capacitor to build a fractional-order Wien oscillator. FOE experimental frequency characterization, theoretical and experimental simulations on Wien oscillator are provided.

The concept of imperfection is always associated to a negative outlook. However, real devices are always imperfect and operate far from ideality. Despite this, real devices actually work. This is due to the fact that imperfections give rise to hidden dynamics, which can be excited to gain a positive effect on the overall behavior of the device. Human-machine systems are relevant examples of imperfect systems in which the presence of imperfections plays a crucial role, either negative or positive. In this paper, we focus on a paradigmatic example of imperfect systems, represented by a complex and imperfect electromechanical structure which supports and couples 15 coils rotating thanks to the electromagnetic interaction with associated magnets. The electrical and mechanical interaction between the coils and that between the coils and the structure generate complex patterns of vibration which may prevent the system from reaching the correct working conditions, i.e., all coils actually rotating. A control strategy to ensure coils rotation based on the excitation of the hidden dynamics induced by imperfections is discussed, characterizing its effect with respect to the control signal properties and to the power provided to the structure. It is worth to notice that the control strategy is essentially based on the excitation rather than on the suppression of the hidden dynamics, thus providing evidence on the possibility to control imperfect systems exploiting imperfections.

In this paper, a fractional order representation and the related hardware realization of the Duffing system are presented. The considered system can be defined as the general case of the standard Duffing system, which is a typical example of a non autonomous chaotic system. The aim of this paper is to confirm that an opportunely excited fractional order Duffing system, with order less than two, can have a chaotic behavior. This statement is supported with the circuital implementation of the circuit.

The past few decades have seen an explosion of satellite remote sensing techniques for the monitoring of volcanic thermal features. Here, we propose an artificial neural network approach for improving the cloud detection through imperfect multispectral satellite images analysis. The cloud detection algorithm has been tested on a data set of MSG-SEVIRI images acquired over the area of Etna volcano in Sicily (Italy) before and during the 2008 eruption. Results show that this approach is robust in terms of percentage of correctly classified pixels.

System-of-Systems (SoS) can be defined as a large-scale integration of many independent, self-contained systems having the common aim of satisfying a global need. Under this perspective, lots of systems of systems can be found in several fields, where a common final goal drives systems towards a final SoS state. In nuclear fusion research area, several examples of SoS applications can be made. From the integration of all the constituents of the TOKAMAK machines, which work together to achieve a sustained nuclear fusion reaction, to the circuits made of active analogue components mimicking plasma behavior, to the neural networks made of connected units working together in order to predict plasma variables behavior. In this paper, we aim at collecting several examples of how a SoS approach can be adopted in order to characterize and control TOKAMAK relevant scenarios.

In this paper we study the effects of noise in computing devices composed by coupled memristive oscillators. In particular, we will focus the attention on the emergence of spatiotemporal chaos in a computing architecture made of simple oscillators composed by three basic elements, namely a capacitor, an inductor and a memristor, driven by a sinusoidal external signal. The characterization of the dynamical behavior of the simple oscillator in terms of flux and charge will be discussed and the counterintuitive role of initial conditions on memristors will be highlighted. Aim of this contribution is to provide evidence that a robust synchronized emergent behavior can be attained as a direct positive consequence of the presence of unavoidable sources of noise on initial conditions.

In this communication, the first example of fractional order nonlinear system showing jump resonance is described, that is a system in which the order of the derivative is non-integer and whose frequency response is a multi-valued function in a given range of frequencies. Furthermore, a strategy to design fractional order systems showing jump resonance is presented along with the procedure to design and implement an analog circuit based on the approximation of the fractional order derivative. An extensive numerical analysis allows to asses that the phenomenon is robust to difference in the derivative order, enlightening the fact that a system with order lower than two is able to provide a jump resonance behavior.

The study on stochastic effects occurring at the nanoscales in VLSI memristive devices is a timely topic which is gaining a growing interest. Indeed, these effects are often linked to the occurrence of nonlinear phenomena, such as spatiotemporal chaos. In this paper, we present evidence that spatiotemporal chaos in VLSI memristive systems can also be tamed by exploiting the unavoidable sources of noise affecting the dynamical behavior of the device. In particular, we aim at investigating this scenario starting from the results observed in several nonlinear oscillators, where the presence of noise facilitates their synchronization. In particular, we focus on the role of noise acting on initial conditions in memristive nonlinear oscillators. In this paper, we focus on a nonautonomous memristive oscillator characterizing its dynamics with respect to the memristor initial conditions and deriving a suitable model in which they appear as further system parameters and exploiting this dependence for the synchronization of an array of coupled nonlinear chaotic memristive oscillators.

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.