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Andrea Sciarrone

Bio: Andrea Sciarrone is an academic researcher from University of Genoa. The author has contributed to research in topics: Mobile device & Microwave imaging. The author has an hindex of 19, co-authored 60 publications receiving 1066 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper surveys and compares accelerometer signals classification methods to enable IoT for rehabilitation and elderly monitoring for active aging and considers two functions useful for such treatments: activity recognition and movement recognition.
Abstract: Rehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR.

130 citations

Journal ArticleDOI
TL;DR: This paper proposes a WiFi statistical fingerprint-based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks.
Abstract: The great availability of commercial drones has raised growing interest among people, since remotely piloted vehicles can be employed in numerous applications. The pervasive use of these devices has created many privacy and safety concerns that need to be addressed by means of proper surveillance systems able to cope with such threats. In this paper, we propose a WiFi statistical fingerprint-based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks. We present a performance analysis carried out through experimental tests, where our solution is able to achieve very good results in terms of correct recognitions in many real-life scenarios, with a peak true positive rate of ${\text{96}}$ %.

92 citations

Journal ArticleDOI
TL;DR: A WiFi-based approach aimed at detecting nearby aerial or terrestrial devices by performing statistical fingerprint analysis on wireless traffic is proposed, able to efficiently detect and identify intruder drones in all the considered experimental setups, making it a promising unmanned aerial vehicle detection approach in the framework of amateur drone surveillance.
Abstract: Amateur drones are enjoying great popularity in recent years due to the wide commercial diffusion of small, rather low-cost devices. More and more user-friendly, easy-to-pilot aerial and terrestrial drones are available off the shelf, and people can even remotely pilot them using their smartphones. This situation brings up the problem of keeping unauthorized drones away from private or sensitive areas, where they can represent a personal or public threat. With this motivation, after a survey of the existing solutions, we propose a WiFi-based approach aimed at detecting nearby aerial or terrestrial devices by performing statistical fingerprint analysis on wireless traffic. This novel detection technique, tested in a variety of real-life scenarios, proved able to efficiently detect and identify intruder drones in all the considered experimental setups, making it a promising unmanned aerial vehicle detection approach in the framework of amateur drone surveillance.

89 citations

Journal ArticleDOI
TL;DR: A novel approach, where the training data are obtained by means of finite-difference time-domain (FDTD) simulations of the electromagnetic propagation in the considered scenario, is presented and the performances of the method are assessed by Means of experimental results in a real scenario.
Abstract: Indoor localization of targets by using electromagnetic waves has attracted a lot of attention in the last few years. Thanks to the wide availability of electromagnetic sources deployed for various applications (e.g., WiFi), nowadays it is possible to perform this task by using low-cost mobile devices, such as smartphones. To this end, in order to achieve high positioning accuracy and reduce the computational resources used in the position estimation, fingerprinting approaches are usually employed. However, in this case, a time-consuming training phase, where a great number of measurements must be performed, is needed. In this letter, a novel approach, where the training data are obtained by means of finite-difference time-domain (FDTD) simulations of the electromagnetic propagation in the considered scenario, is presented. The performances of the method are assessed by means of experimental results in a real scenario.

88 citations

Journal ArticleDOI
TL;DR: The results highlight that the a priori knowledge of the speaker's gender allows a performance increase, and that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features.
Abstract: This paper proposes a system that allows recognizing a person's emotional state starting from audio signal registrations. The provided solution is aimed at improving the interaction among humans and computers, thus allowing effective human-computer intelligent interaction. The system is able to recognize six emotions(anger, boredom, disgust, fear, happiness, and sadness) and the neutral state. This set of emotional states is widely used for emotion recognition purposes. It also distinguishes a single emotion versus all the other possible ones, as proven in the proposed numerical results. The system is composed of two subsystems: 1) gender recognition(GR) and 2) emotion recognition(ER). The experimental analysis shows the performance in terms of accuracy of the proposed ER system. The results highlight that the a priori knowledge of the speaker's gender allows a performance increase. The obtained results show also that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features. Future developments of the proposed solution may include the implementation of this system over mobile devices such as smartphones.

77 citations


Cited by
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09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

2,069 citations

Proceedings Article
01 Jan 2007
TL;DR: In this paper, the Gaussian Process Latent Variable Model (GPLVM) is used to reconstruct a topological connectivity graph from a signal strength sequence, which can be used to perform efficient WiFi SLAM.
Abstract: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

488 citations

Journal ArticleDOI
19 Jan 2015-Sensors
TL;DR: This paper reviews the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors, and discusses their limitations and present various recommendations for future research.
Abstract: Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare Initially, one or more dedicated wearable sensors were used for such applications However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery The research on offline activity recognition has been reviewed in several earlier studies in detail However, work done on online activity recognition is still in its infancy and is yet to be reviewed In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors We discuss various aspects of these studies Moreover, we discuss their limitations and present various recommendations for future research

452 citations

Journal ArticleDOI
TL;DR: A unique taxonomy is provided, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses.
Abstract: The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

451 citations