A spectrally-accurate FVTD technique for complicated amplification and reconfigurable filtering EMC devices

The consistent and computationally economical analysis of demanding amplification and filtering structures is introduced in this paper via a new spectrally-precise finite-volume time-domain algorithm. Combining a family of spatial derivative approximators with controllable accuracy in general curvilinear coordinates, the proposed method employs a fully conservative field flux formulation to derive electromagnetic quantities in areas with fine structural details. Moreover, the resulting 3-D operators assign the appropriate weight to each spatial stencil at arbitrary media interfaces, while for periodic components the domain is systematically divided to a number of nonoverlapping subdomains. Numerical results from various real-world configurations verify our technique and reveal its universality

Audio-Based Event Detection at Different SNR Settings Using Two-Dimensional Spectrogram Magnitude Representations

Audio-based event detection poses a number of different challenges that are not encountered in other fields, such as image detection. Challenges such as ambient noise, low Signal-to-Noise Ratio (SNR) and microphone distance are not yet fully understood. If the multimodal approaches are to become better in a range of fields of interest, audio analysis will have to play an integral part. Event recognition in autonomous vehicles (AVs) is such a field at a nascent stage that can especially leverage solely on audio or can be part of the multimodal approach. In this manuscript, an extensive analysis focused on the comparison of different magnitude representations of the raw audio is presented. The data on which the analysis is carried out is part of the publicly available MIVIA Audio Events dataset. Single channel Short-Time Fourier Transform (STFT), mel-scale and Mel-Frequency Cepstral Coefficients (MFCCs) spectrogram representations are used. Furthermore, aggregation methods of the aforementioned spectrogram representations are examined; the feature concatenation compared to the stacking of features as separate channels. The effect of the SNR on recognition accuracy and the generalization of the proposed methods on datasets that were both seen and not seen during training are studied and reported. Document type: Articl

Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions. Document type: Articl

Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma

Background: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. Objective: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. Methods: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. Results: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system. Discussion: Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied

The holistic perspective of the INCISIVE project : artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions

Combinatory reconfigurable structures of complex periodic media and microelectromechanical systems with applications in electromagnetic bandgap materials and metamaterials

The scope of this doctoral thesis is the development of novel structures that combine radio frequency microelectromechanical systems (RF-MEMS) with artificially engineered electromagnetic media, such as electromagnetic bandgap (EBG) structures and metamaterials, to obtain reconfigurability and efficiently tackle the inherent bandwidth restrictions. Therefore, an efficient technique that utilizes the Generalized-Pencil-of-Function (GPOF) algorithm as a post processing procedure in the Finite-Difference Time-Domain (FDTD) method is introduced to facilitate the study of EBG devices and accurately derive their dispersion characteristics. Moreover, correction criteria are developed to deal with erroneous eigenfrequencies associated with computational noise. The performance of the proposed method exhibits acceptable levels of accuracy, achieving a remarkable decrease in computational time. Additionally, a novel set of reconfigurable microwave filters based on EBG-loaded microstrip lines is proposed, whereas tunability is achieved by means of RF-MEMS switching elements located at properly selected positions. Several configurations, also fabricated in a prototype form, are thoroughly investigated using both a combined FDTD - GPOF technique and experimental testing, while the data obtained from these tests support the numerical outcomes satisfactorily. Furthermore, a reconfigurable mu-negative (MNG) material, obtained from the combination of two-hot-arm electrothermal actuators with a split-ring resonator (SRR), is developed. The proposed device, which acts as a fundamental building block of metamaterials, overcomes the undesired bandwidth constraints of existing structures and establishes significant levels of tunability. It is emphasized that the actuator is realized as an integrated part of the resonator, while a novel double parallel actuated structure is developed to simplify the design of bias networks. The proposed implementations are numerically verified through several setups which prove their MNG behavior. Additionally, a controllable SRR is designed that allows altering the essence of the associated resonance by applying electrical current. Specifically, a rearrangement of the device's topology is feasible resulting in transition between double-positive (DPS) behavior and MNG performance. On the other hand, an investigation is performed regarding the exclusive exploitation of RF-MEMS components as unit cells in the synthesis of controllable periodic media. In this context, the double parallel actuated structure is utilized to compose a tunable DPS material, while a MEMS microgripper is adopted as a thermally reconfigurable SRR to implement a tunable MNG medium.Κύριος στόχος της παρούσας διατριβής είναι ο συνδυασμός των μικροηλεκτρομηχανικών συστημάτων ραδιοσυχνοτήτων (radio frequency microelectromechanical systems – RF-MEMS) με περιοδικές διατάξεις σύνθετων υλικών, όπως τα υλικά ηλεκτρομαγνητικού διακένου (electromagnetic bandgap – EBG) και τα μεταϋλικά, προκειμένου να καταστεί δυνατή η επίτευξη αναδιατάξιμων ιδιοτήτων, καθώς και η άρση των εγγενών συχνοτικών περιορισμών. Αναλυτικότερα, παρουσιάζεται η ανάπτυξη ενός αξιόπιστου υπολογιστικού εργαλείου για τον ταχύτερο υπολογισμό των διαγραμμάτων διασποράς των EBG διατάξεων, αλλά και την αντιμετώπιση του προβλήματος των πολλαπλών προσομοιώσεων, που προκύπτει κατά την ανάλυση μιας επαναπροσδιοριζόμενης δομής. Έτσι, προτείνεται η αξιοποίηση της τεχνικής generalized pencil-of-function (GPOF) ως στάδιο μεταεπεξεργασίας της μεθόδου των πεπερασμένων διαφορών στο πεδίο του χρόνου (finite-difference time-domain method – FDTD), επιτρέποντας τον απευθείας υπολογισμό των ιδιοσυχνοτήτων από τα χρονικά δείγματα. Επιπλέον, εισάγονται νέα κριτήρια αξιολόγησης και επιλογής των εκάστοτε ιδιοσυχνοτήτων, ενώ η συνδυασμένη τεχνική FDTD – GPOF αποδεικνύεται χρήσιμη και για την πρόβλεψη της χρονικής εξέλιξης ενός ηλεκτρομαγνητικού σήματος. Ακολούθως, η προτεινόμενη μέθοδος εφαρμόζεται για τον καθορισμό των διαγραμμάτων διασποράς προγραμματιζόμενων EBG δομών, που σχεδιάζονται μέσω RF-MEMS διακοπτικών στοιχείων. Αποδεικνύεται ότι καθίσταται περιττή αφενός η προσομοίωση μεγάλου αριθμού χρονικών βημάτων, αφετέρου η πρόβλεψη της χρονικής εξέλιξης των υφιστάμενων σημάτων, μειώνοντας σημαντικά το εμπλεκόμενο υπολογιστικό κόστος. Επιπλέον, προτείνονται ηλεκτρομαγνητικά φίλτρα μικροταινίας με ενδιαφέρουσες επαναπροσδιοριζόμενες ιδιότητες, μέσω περιοδικής φόρτισης της γραμμής με EBG μοναδιαία κελιά και RF-MEMS διακόπτες. Ακόμη, σχεδιάζονται διατάξεις με περισσότερους βαθμούς ελευθερίας, επιτρέποντας τη συγκέντρωση πρόσθετων ιδιοτήτων σε μια μόνο δομή. Στη συνέχεια, η απόκριση των δομών αυτών τεκμηριώνεται πειραματικά, μέσω της κατασκευής και μέτρησης αντίστοιχων πρωτοτύπων, επιδεικνύοντας αξιοσημείωτη ακρίβεια σε σύγκριση με τα αριθμητικά αποτελέσματα της συνδυασμένης τεχνικής FDTD – GPOF. Ακολούθως, προτείνεται ένα σύνολο νέων επαναπροσδιοριζόμενων συντονιστών διακεκομμένου δακτυλίου (split-ring resonator – SRR), μέσω RF-MEMS στοιχείων, ώστε να επεκταθεί το εύρος ζώνης λειτουργίας των μεταϋλικών, αξιοποιώντας την ικανότητα αναδιάταξης. Έτσι, αναλύεται διεξοδικά η συμπεριφορά του ηλεκτροθερμικού ενεργοποιητή θερμών βραχιόνων, μέσω προσομοιώσεων συζευγμένων φυσικών πεδίων, με τη μέθοδο των πεπερασμένων στοιχείων (finite element method – FEM). Στη συνέχεια, παρουσιάζεται η αξιοποίηση αυτού του RF-MEMS στοιχείου για τη σχεδίαση ενός προγραμματιζόμενου μαγνητικού συντονιστή, αποδεικνύοντας την άρση των υφιστάμενων συχνοτικών περιορισμών. Ακόμη, εισάγεται ο διπλός ηλεκτροθερμικός ενεργοποιητής θερμών βραχιόνων, ώστε να μειωθεί η πολυπλοκότητα των δικτύων πόλωσης, ενώ σχεδιάζεται μια αντίστοιχη συνδυαστική δομή SRR με ελεγχόμενο εύρος ζώνης λειτουργίας. Επιπροσθέτως, η σχεδίαση επεκτείνεται σε διατάξεις μεταϋλικών με περισσότερους βαθμούς ελευθερίας, καταδεικνύοντας, έτσι, τις δυνατότητες της τεχνολογίας RF-MEMS. Στη συνέχεια, προτείνεται για πρώτη φορά μια αναδιατάξιμη δομή μεταϋλικού διττής φύσης, που διαθέτει την ικανότητα μετάβασης ανάμεσα σε δύο διακριτές φύσεις με θετική και αρνητική μαγνητική διαπερατότητα, αντίστοιχα. Με αυτόν τον τρόπο, αποδεικνύεται ότι είναι δυνατό να μεταβληθεί η ηλεκτρομαγνητική φύση ενός σύνθετου υλικού, μέσω της εφαρμογής ηλεκτρικών ρευμάτων. Από την άλλη πλευρά, καταδεικνύεται η δυνατότητα αποκλειστικής χρήσης των RF-MEMS δομών ως μοναδιαίων στοιχείων για τη σύνθεση περιοδικών μέσων με ρυθμιζόμενες αποκρίσεις. Έτσι, προτείνεται η χρήση του διπλού ηλεκτροθερμικού ενεργοποιητή για τη σχεδίαση μιας αναδιατάξιμης δομής συμβατικής φύσης. Στη συνέχεια, σχεδιάζεται μια MEMS διάταξη σύλληψης μικροδομών, η οποία αξιοποιείται ως επαναπροσδιοριζόμενος συντονιστής SRR, μέσω ενός εξωτερικά επιβαλλόμενου θερμικού πεδίου, για τη σύνθεση υλικών με αρνητική μαγνητική διαπερατότητα

A Multimodal AI-Leveraged Counter-UAV Framework for Diverse Environments

Unmanned Aerial Vehicles (UAVs) have become a major part of everyday life, as well as an emerging research field, by establishing their versatility in a variety of applications. Nevertheless, this rapid spread of UAVs reputation has provoked serious security issues that can probably affect homeland security. Defence communities have started to investigate large field-of-view sensor-based methods to enable various civil protection applications, including the detection and localisation of flying threat objects. Counter-UAV (c-UAV) detection challenges may be granted from a fusion of sensors to enhance the confidence of flying threats identification. The real-time monitoring of the environment is absolutely rigorous and demands accurate methods to detect promptly the occurrence of harmful conditions. Deep learning (DL) based techniques are capable of tackling the challenges that are associated with generic objects detection and explicitly UAV identification. In this paper, we present a novel multimodal DL methodology that combines data from individual unimodal approaches that are associated with UAV detection. Specifically, this work aims to identify and classify potential targets of UAVs based on fusion methods in two different cases of operational environments, i.e. rural and urban scenarios. A dedicated architecture is designed based on the development of deep neural networks (DNNs) frameworks that has been trained and validated employing real UAV flights scenarios. The proposed approach has achieved prominent detection accuracies over different background environments, exhibiting potential employment even in major defence applications

Cybersecurity Aspects of 5G Connectivity in Smart Cities Ecosystem via Connected and Autonomous Vehicles Use Cases

This work presents the necessity of the implementation of mitigation techniques to counter the cybersecurity issues and threats that arise from the fifth generation (5G) embodiment in a smart city ecosystem. During the past few years, the popularity and growth of cellular technology has led the 5G networks to be considered as the emerging domain of future’s communication architecture. Moreover, the connected and autonomous vehicles constitute an essential part of smart cities infrastructure, providing the answer for the city’s mobility demands. With human safety being at stake, the security assurance is of the utmost importance. The requirement of ensuring a safe transportation system with complete trust to the smart city ecosystem is sufficiently described, while proper counter-measures within the scope of 5G connectivity are proposed. This work was supported by the European Union's Horizon 2020 Research and Innovation Programme Autonomous Vehicles to Evolve to a New Urban Experience (AVENUE) under Grant Agreement No 769033

Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions