A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment.

With the growing rate of urban population and transport congestion, it is important for a city to have bike riding as an attractive travel choice but one of its biggest barriers for people is the perceived lack of safety. To improve the safety of urban cycling, identification of high-risk location and routes are major obstacles for safety countermeasures. Risk assessment is performed by crash data analysis, but the lack of data makes that approach less effective when applied to cyclist safety. Furthermore, the availability of data collected with the modern technologies opens the way to different approaches. This research aim is to analyse data needs and capability to identify critical cycling safety events for urban context where bicyclist behaviour can be recorded with different equipment and bicycle used as a probe vehicle to collect data. More specifically, three different sampling frequencies have been investigated to define the minimum one able to detect and recognize hard breaking. In details, a novel signal processing procedure has been proposed to correctly deal with speed and acceleration signals. Besides common signal filtering approaches, wavelet transformation and Dynamic Time Warping (DTW) techniques have been applied to remove more efficiently the instrument noise and align the signals with respect to the reference. The Euclidean distance of the DTW has been introduced as index to get the best filter parameters configuration. Obtained results, both during the calibration and the investigated real scenario, confirm that at least a GPS signal with a sampling frequency of 1Hz is needed to track the rider's behaviour to detect events. In conclusion, with a very cheap hardware setup is possible to monitor riders' speed and acceleration.

A new model of Hopfield network with fractional-order neurons for parameter estimation.

In this work, we study an application of fractional-order Hopfield neural networks for optimization problem solving. The proposed network was simulated using a semi-analytical method based on Adomian decomposition,, and it was applied to the on-line estimation of time-varying parameters of nonlinear dynamical systems. Through simulations, it was demonstrated how fractional-order neurons influence the convergence of the Hopfield network, improving the performance of the parameter identification process if compared with integer-order implementations. Two different approaches for computing fractional derivatives were considered and compared as a function of the fractional-order of the derivatives: the Caputo and the Caputo-Fabrizio definitions. Simulation results related to different benchmarks commonly adopted in the literature are reported to demonstrate the suitability of the proposed architecture in the field of on-line parameter estimation.

Implementation of a fully analog feedback loop with a Carbon-Black-based fractional order controller.

Fractional calculus is a mathematical framework that has attracted considerable interest from mathematicians, physicists, and engineers. Among its applications, the use of fractional calculus in the automatic control field has led to interesting results, such as more robust controllers, compared to their integer-order counterparts. The proposed work utilizes the physical realization of a solid-state fractional-order capacitor for the implementation of a fractional-order lead compensator. The proposed capacitor is realized using a carbon black-based dielectric. Therefore, a fully analog closed-loop system implementation is realized. A suitable case study is conducted to validate the controller performance, both from simulations and experimentally. The obtained results further confirm the possibility of realizing and applying a fully analog fractional-order controller.

SC-CNNs for chaotic signal applications in secure communication systems.

In this paper a CNNs based circuit for the generation of hyperchaotic signals is proposed. The circuit has been developed for applications in secure communication systems. An Saito oscillator has been designed by using a suitable configuration of a four-cells State-Controlled CNNs. A cryptography system based on the Saito oscillator has been implemented by using inverse system synchronization. The proposed circuit implementation and experimental results are given.

Identification of IPMC nonlinear model via single and multi-objective optimization algorithms.

Ionic Polymer-Metal Composites (IPMCs) are electro-active polymers transforming mechanical forces into electric signals and vice versa. This paper proposes an improved electro-mechanical grey-box model for IPMC membrane working as actuator. In particular the IPMC nonlinearity has been characterized through experimentation and included within the electric model. Moreover identification of the model parameters has been performed via optimization algorithms using both single- and multi-objective formulation. Minimization was attained via the Nelder-Mead simplex and the Genetic Algorithms considering as cost functions the error between the experimental and modeled absorbed current and the error between experimental and modeled displacement. The obtained results for the different formulations have been then compared.

Mapping heart dynamics by using nonlinear indicators.

A novel approach for the nonlinear characterization of Electrocardiogram (ECG) signals has been developed. The new developed methodology is based on a numerical algorithm that extracts the value of dinfinity (d-infinite) characterizing the asymptotic chaotic behavior of a system. This algorithm also extracts a measure of the maximum Lyapunov exponent and it is applicable to time series where the knowledge of the system structure and laws is not necessary. In order to prove the significance of the extracted parameters, the presented algorithm was applied on a statistically significant number of ECG signals taken from the MIT-BIH database and including normal subjects and subjects affected by arrhythmia and ventricular arrhythmia. A systematic study, analyzing how dinfinity varies with initial condition was performed showing the sensitivity of such parameter to the initial conditions. Furthermore, two maps, one presenting the maximum Lyapunov exponent and the other the dinfinity versus a control parameter II, as a measure of the rate variation, were drawn using the parameters extracted by the experimental data. They clearly show three distinguishable zones where the normal subjects and the subjects affected by the two different pathologies can be mapped and discriminated. Concluding, the newly presented algorithm, thanks to its implementation features and its effectiveness, it lends itself to future real-time implementation for clinical application in the early diagnosis of cardiac pathologies.