A Wearable Fall Detection System based on LoRa LPWAN Technology

Several technological solutions now available in the market offer the possibility of increasing the independent life of people who by age or pathologies otherwise need assistance. In particular, internet-connected wearable solutions are of considerable interest, as they allow continuous monitoring of the user. However, their use poses different challenges, from the real usability of a device that must still be worn to the performance achievable in terms of radio connectivity and battery life. The acceptability of a technology solution, by a user who would still benefit from its use, is in fact often conditioned by practical problems that impact the person’s normal lifestyle. The technological choices adopted in fact strongly determine the success of the proposed solution, as they may imply limitations both to the person who uses it and to the achievable performance. In this document, targeting the case of a fall detection sensor based on a pair of sensorized shoes, the effectiveness of a real implementation of an Internet of Things technology is examined. It is shown how alarming events, generated in a metropolitan context, are effectively sent to a supervision system through Low Power Wide Area Network technology without the need for a portable gateway. The experimental results demonstrate the effectiveness of the chosen technology, which allows the user to take advantage of the support of a wearable sensor without being forced to substantially change his lifestyle

Application of algorithms for system performance increasing in ICT

In questa dissertazione vengono descritti diversi algoritmi utilizzati nell'ICT riguardanti l'intelligenza artificiale, il deep learning e gli algoritmi genetici per elaborare i segnali nelle tecnologie di comunicazione con lo scopo di aumentare le prestazioni del sistema. I progressi nei sistemi a microonde e a onde millimetriche hanno consentito di utilizzare tecniche di telerilevamento precedentemente utilizzate in applicazioni a lungo raggio in applicazioni a raggio relativamente ravvicinato come il rilevamento e la categorizzazione della presenza umana e la misurazione degli attributi umani grazie all'enorme larghezza di banda e alla breve momento della trasmissione del segnale. La progettazione di una rete WSN omogenea con struttura gerarchica è dimostrata con la priorità di coprire un ambiente con costi minimi, connessione elevata e massima longevità. Nella prima parte verranno presentate diverse tecniche per elaborare i segnali micro-Doppler provenienti dai radar automobilistici riguardanti la classificazione e il tracciamento dell'obiettivo ricavato dalle informazioni del bersaglio. La seconda parte presenterà il confronto tra l'apprendimento automatico e l'algoritmo di deep learning utilizzato ai fini della classificazione delle attività umane e l'ottenimento dei migliori risultati in termini di prestazioni, accuratezza, ecc. L'ultima parte è la dimostrazione di un algoritmo genetico avanzato per migliorare le prestazioni in gerarchie personalizzabili reti di sensori wireless che scelgono la combinazione di peso per generare la topologia più performante possibile.Different algorithms used in ICT are described in this dissertation regarding artificial intelligence, deep learning, and genetic algorithms to process signals in communication technologies with the purpose of increasing system performance. Advances in microwave and millimeter-wave systems have enabled remote sensing techniques previously utilized in long-range applications to be utilized in relatively close-range applications such as detection and categorization of human presence and measurement of human attributes thank to the huge bandwidth and the short time of signal transmission. The design of a WSN homogeneous network with hierarchical structure is demonstrated with the priority of covering an environment with minimal cost, high connection, and maximum longevity. In the first part will be presented different techniques to process the micro-Doppler signals coming from automotive radars regarding classification and tracking of the objective gained from the information of the target. The second part will present the comparison between machine learning and deep learning algorithm used for human activity classification purpose and getting the best results in terms of performance, accuracy etc. The last part is the demonstration of an enhanced genetic algorithm for improving performance in customizable hierarchical wireless sensor networks choosing weight combination to generate the most performable topology conceivable

Underwater Wireless Sensor Networks: Estimation of Acoustic Channel in Shallow Water

Underwater sensor networks (UWSN) include a large number of devices and sensors which are positioned in a specific area to carry out monitoring in cooperation with each other as well as data collection. In this paper it has been studied and simulated the performance of an extremely important parameter for communication in UWSN such as the acoustic channel capacity as function of water temperature and salinity arise. The performance’s knowledge on acoustic channel may be improved with a deep study of its dependence by season, weather conditions or environmental parameters variation. If an accurate estimation of the acoustic communication capacity utilization for a given area is required, we must consider also the bottom materials of this area. The simulation results presented in this study through an improved algorithm, will help to understand better the underwater acoustic channel performance as a function of all these factors. This is of particular importance for acoustic modems designing, in order to implement suitable functionalities able to adapt the data transmission capacity of the acoustic link to the structure of the oceanic bottom and its component material

Performance Evaluation of Vibrational Measurements through mmWave Automotive Radars

Thanks to the availability of a significant amount of inexpensive commercial Frequency Modulated Continuous Wave Radar sensors, designed primarily for the automotive domain, it is interesting to understand if they can be used in alternative applications. It is well known that with a radar system it is possible to identify the micro-Doppler feature of a target, to detect the nature of the target itself (what the target is) or how it is vibrating. In fact, thanks to their high transmission frequency, large bandwidth and very short chirp signals, radars designed for automotive applications are able to provide sub-millimeter resolution and a large detection bandwidth, to the point that it is here proposed to exploit them in the vibrational analysis of a target. The aim is to evaluate what information on the vibrations can be extracted, and what are the performance obtainable. In the present work, the use of a commercial Frequency Modulated Continuous Wave radar is described, and the performances achieved in terms of displacement and vibration frequency measurement of the target are compared with the measurement results obtained through a laser vibrometer, considered as the reference instrument. The attained experimental results show that the radar under test and the reference laser vibrometer achieve comparable outcomes, even in a cluttered scenario