arXiv subjects

Aurelio La Corte

Publications and source records attributed to Aurelio La Corte.

7 recordsLinked to original sources

arXiv2021

Automatic crack classification by exploiting statistical event descriptors for Deep Learning

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this paper is to combine deep neural networks with Bidirectional Long Short Term Memory and advanced statistical analysis involving Instantaneous Frequency and Spectral Kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from acoustic emission events (cracks). We investigated on effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of future on structural health monitoring (SHM) technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

cs.LG↗

arXiv2020

A Novel Methodology for designing Policies in Mobile Crowdsensing Systems

Mobile crowdsensing is a people-centric sensing system based on users' contributions and incentive mechanisms aim at stimulating them. In our work, we have rethought the design of incentive mechanisms through a game-theoretic methodology. Thus, we have introduced a multi-layer social sensing framework, where humans as social sensors interact on multiple social layers and various services. We have proposed to weigh these dynamic interactions by including the concept of homophily and we have modelled the evolutionary dynamics of sensing behaviours by defining a mathematical framework based on multiplex EGT, quantifying the impact of homophily, network heterogeneity and various social dilemmas. We have detected the configurations of social dilemmas and network structures that lead to the emergence and sustainability of human cooperation. Moreover, we have defined and evaluated local and global Nash equilibrium points by including the concepts of homophily and heterogeneity. We have analytically defined and measured novel statistical measures of social honesty, QoI and users' behavioural reputation scores based on the evolutionary dynamics. We have defined the Decision Support System and a novel incentive mechanism by operating on the policies in terms of users' reputation scores, that also incorporate users' behaviours other than quality and quantity of contributions. Experimentally, we have considered the Waze dataset on vehicular traffic monitoring application and derived the disbursement of incentives comparing our method with baselines. Results demonstrate that our methodology, which also includes the local (microscopic) spatio-temporal distribution of behaviours, is able to better discriminate users' behaviours. This multi-scale characterisation of users represents a novel research direction and paves the way for novel policies on mobile crowdsensing systems.

cs.GT↗

arXiv2020

Pipeline for Advanced Contrast Enhancement (PACE) of chest X-ray in evaluating COVID-19 patients by combining bidimensional empirical mode decomposition and CLAHE

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in monitoring of the healthy status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with a significant success. However, this approach cannot be used massively mainly for both high risk and cost and in some countries also because this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease, this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post processing tool, named PACE, combining properly fast and adaptive bidimensional empirical mode decomposition and contrast limited adaptive histogram equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, which evaluate the images separately, and confirmed by CT-scans. Based on our findings this method is proved as a flexible and effective way for medical image enhancement and can be used as a post-processing step for medical image understanding and analysis.

physics.med-ph↗

arXiv2019

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression

The concept of mobility prediction represents one of the key enablers for an efficient management of future cellular networks, which tend to be progressively more elaborate and dense due to the aggregation of multiple technologies. In this letter we aim to investigate the problem of cellular traffic prediction over a metropolitan area and propose a deep regression (DR) approach to model its complex spatio-temporal dynamics. DR is instrumental in capturing multi-scale and multi-domain dependences of mobile data by solving an image-to-image regression problem. A parametric relationship between input and expected output is defined and grid search is put in place to isolate and optimize performance. Experimental results confirm that the proposed method achieves a lower prediction error against stateof-the-art algorithms. We validate forecasting performance and stability by using a large public dataset of a European Provider.

eess.SP↗

arXiv2018

Micromagnetic understanding of the skyrmion Hall angle current dependence in perpendicular magnetized ferromagnets

The understanding of the dynamical properties of skyrmion is a fundamental aspect for the realization of a competitive skyrmion based technology beyond CMOS. Most of the theoretical approaches are based on the approximation of a rigid skyrmion. However, thermal fluctuations can drive a continuous change of the skyrmion size via the excitation of thermal modes. Here, by taking advantage of the Hilbert-Huang transform, we demonstrate that at least two thermal modes can be excited which are non-stationary in time. In addition, one limit of the rigid skyrmion approximation is that this hypothesis does not allow for correctly describing the recent experimental evidence of skyrmion Hall angle dependence on the amplitude of the driving force, which is proportional to the injected current. In this work, we show that, in an ideal sample, the combined effect of field-like and damping-like torques on a breathing skyrmion can indeed give rise to such a current dependent skyrmion Hall angle. While here we design and control the breathing mode of the skyrmion, our results can be linked to the experiments by considering that the thermal fluctuations and/or disorder can excite the breathing mode. We also propose an experiment to validate our findings.

cond-mat.mes-hall↗

arXiv2017

A data-Oriented based Self-Calibration And Robust chemical-shift encoding by using clusterization (OSCAR) - Theory, Optimization and Clinical Validation in Neuromuscular disorders

Multi-echo Chemical Shift Encoded methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water-fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant, such as for neuromuscular disorders, because of the spatial dependence of the peak amplitudes. The present work aims to overcome this limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to carry out a data-driven segmentation of the input MRI dataset into multiple regions. The idea is to apply the clusterization for partitioning the multi-echo MRI dataset into a finite number of clusters whose internal voxels exhibit similar distance metrics. For each cluster, the estimation of the fat spectral properties are evaluated with a self-calibration technique and finally the fat-water percentages are computed via a non-linear fitting. The method is tested in ad-hoc and public datasets. The overall performance and results in terms of fitting accuracy, robustness and reproducibility are compared with other state-of-the-art CSE algorithms. This approach provides a more accurate and reproducible identification of chemical species, hence fat-water separation, when compared with other calibrated and non-calibrated approaches.

physics.med-ph↗

arXiv2014

Intrinsic synchronization of an array of spin-torque oscillators driven by the spin-Hall effect

This paper micromagnetically studies the magnetization dynamics driven by the spin-Hall effect in a Platinum/Permalloy bi-layer. For a certain field and current range, the excitation of a uniform mode, characterized by a power with a spatial distribution in the whole ferromagnetic cross section, is observed. We suggest to use the ferromagnet of the bi-layer as basis for the realization of an array of spin-torque oscillators (STOs): the Permalloy ferromagnet will act as shared free layer, whereas the spacers and the polarizers are built on top of it. Following this strategy, the frequency of the uniform mode will be the same for the whole device, creating an intrinsic synchronization. The synchronization of an array of parallely connected STOs will allow to increase the output power, as necessary for technological applications.

cond-mat.mtrl-sci↗