2,200 research outputs found
PhD Forum: LoRaWAN networks evaluation through extensive ns-3 simulations
No full textIn Internet of Things (IoT) applications, hundreds of sensors collect and transmit data to a central gateway, allowing applications such as smart cities, smart home, and smart agriculture. Scalability and energy efficiency are two common requirements for this type of IoT systems that can be tackled by providing the sensor nodes with energy harvesting capabilities, so that they can operate even without batteries, exploiting the renewable energy sources, with a dramatic reduction in the manufacturing, management, and decommissioning costs. In this work, we outline our approach for network-wide and energy-aware performance optimization of energy harvesting IoT networks employing LoRaWAN, one of the most widespread technologies for the IoT. We describe a viable method to enable more efficient energy-aware operations on battery-less IoT devices, presenting promising preliminary results
Analisi Matematica e Sperimentale di Tecnologie "Low Power Wide Area Network" in Scenari IoT Avanzati
No full textAttualmente il concetto di Internet of Things (IoT) è applicato a diversi ambiti, che spaziano da applicazioni di Città Intelligenti (Smart City) a quelle in ambito industriale e agricolo (Smart Industry e Smart Agriculture). Per rispondere agli specifici requisiti di questi scenari sono state progettate le tecnologie Low Power Wide Area Network (LPWAN), tra le quali Long Range Wide Area Network (LoRaWAN) e Narrowband IoT (NB-IoT) hanno un ruolo predominante. Obiettivo di questa tesi è valutare le prestazioni di queste tecnologie, considerando sia scenari IoT “tradizionali”, sia casi d’uso più impegnativi, come il monitoraggio di sistemi industriali e la localizzazione di droni da remoto, dove i requisiti di comunicazione in termini di affidabilità e latenza sono più stringenti. Per stimare le prestazioni di rete in questi ambiti sono stati impiegati modelli matematici e simulazioni di rete che utilizzano il modulo lorawan di ns-3. Da queste valutazioni emerge che un’appropriata configurazione della tecnologia di comunicazione ha un impatto significativo sulle prestazioni del sistema, e che vari fattori devono essere considerati quando si implementa un sistema IoT. Un altro aspetto considerato in questa tesi è quello del consumo energetico: infatti, nonostante le tecnologie LPWAN siano progettate per avere basso consumo, la valutazione in sistemi reali può contribuire a verificare il corretto comportamento del nodo e l’impatto dei settaggi di rete sul ciclo di vita del dispositivo. Inoltre, molti dispositivi IoT sono attualmente alimentati a batterie, un approccio poco sostenibile economicamente e con grande impatto ecologico. Pertanto, una possibile alternativa è implementare sistemi di Green IoT, equipaggiando i nodi IoT con meccanismi che permettono di assorbire energia da sorgenti rinnovabili. La tesi applica questo concetto a dispositivi LoRaWAN e ne discute la fattibilità utilizzando simulazioni ns-3 e esperimenti con dispositivi reali.The Internet of Things (IoT) paradigm is nowadays applied to multiple domains, including Smart Cities, Smart Industry and Smart Agriculture. To support the specific requirements of these scenarios, Low Power Wide Area Network (LPWAN) technologies have been developed, among which Long Range Wide Area Network (LoRaWAN) and Narrowband IoT (NB-IoT) play a dominant role. This thesis aims at evaluating the performance of these technologies by considering both traditional IoT scenarios and more challenging use cases, such as industrial monitoring or remote drone tracking, which have strict communication requirements in terms of reliability and delay. To estimate the network performance in all these domains, mathematical modeling and network simulations have been used, leveraging the ns-3 lorawan module. From these evaluations, it is appearent that a proper technology configuration has a significant impact on the system’s performance, and that multiple elements should be taken into account when implementing an IoT system. Another aspect considered in this thesis regards the energy efficiency: indeed, although LPWAN technologies are designed to be low power, the evaluation on real testbeds can help in assessing the correctness of the node’s behavior and the impact of the network settings on the device lifetime. Furthermore, most of the IoT devices are currently battery-powered, an approach that is not economically sustainalble, nor environmental friendly. A possible alternative is to implement Green IoT systems by providing IoT nodes with a mechanism that allows them to harvest power from renewable sources. The thesis applies this concept to LoRaWAN devices, and discusses its feasibility by leveraging ns-3 simulations and experiments on real testbeds
Italian "Racial Laws" and Jewish Community of Fiume
No full textIl contributo ricostruisce le vicende della comunità ebraica di Fiume (oggi Rijeka) a partire dall'applicazione nel 1938 delle leggi razziali e dei successivi provvedimenti legislativi nell'allora Provincia del Carnaro, dove complessivamente erano residenti circa 2.200 ebrei, a cui poi si aggiunsero gli ebrei provenienti dalla Germania nazista e dall'Europa orientale, specialmente dall'Ungheria qui giunti per sfuggire nei loro paesi alla persecuzione antisemita.
Dopo la caduta di Mussolini, Fiume veniva incorporata nella zona di Operazioni del Litorale Adriatico e annessa al II Reich. A seguito di ciò la situazione degli ebrei a Fiume, italiani e stranieri, divenne difficile e pericolosa. Nel gennaio del 1944 la grande sinagoga cittadina, costruita nel 1903, venne distrutta dai nazisti e anche la sinagoga degli ebrei ortodossi subì gravi danni.
Il 67% degli ebrei di Fiume venne deportato nei campi di sterminio con la collaborazione della polizia fascista della Repubblica Sociale Italiana che, in molti casi, contribuì alla loro cattura. Dei 1.473 degli ebrei della comunità di Fiume tornarono dai lager nazisti soltanto in 42. Particolarmente persecutoria fu l'azione posta in essere da prefetto Temistocle Testa che collaborò con le autorità naziste
Feature stability and setup minimization for EEG-EMG-enabled monitoring systems
No full textDelivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ([Formula: see text] ), with very few and stable MSC features ([Formula: see text] of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm’s muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition
Feature selection for gesture recognition in Internet-of-Things for healthcare
No full textInternet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance. Although Feature Selection with Consensus (FeSC) implements a very robust architecture for feature selection and classification, results are still negatively affected by the limited size of the dataset. In the future, further investigations could determine to what extent size could cause a drop in the performance of FeSC in this and other gesture recognition applications
Martina Drijverová and her literary works for children (author´s portrait)
No full textThis thesis Martina Drijverova and her literatur for children (the author´s portrait) is engaged in work of writer and screenwriter Martina Drijeverová. She is an excellent writer of literature for children. In the first part of this work her story writing is mentioned and the second part deals with her fairy-tale writing. The other author´s work written for children is in the third part. The conclusion of this thesis appreciates the author´s credit in literature for chidlren. Analysis of some books are available. The supplementary part is composed of autor´s biography and her photograph, some book covers, list of the autor´s work {--} televiews, radio plays and serials, audio tapes and CDs, stage plays, books written in Braille
HERStory Makers 2022: Martina Čagalj
No full textMartina Čagalj is a PhD candidate at the University of Split studying seaweeds as a potential source of bioactive compounds. She took part in HERStory Makers 2022.What is HERStory Makers?HERStory Makers is a social media competition for female-identifying early career researchers to share their research, their career journeys, and to inspire the next generation. Winners are selected by public vote. HERStory Makers is also part of EXPLORATHON, Scotland's contribution to European Researchers' Night.In 2022-23, EXPLORATHON was supported by the Engineering & Physical Sciences Research Council [grant number EP/X020894/1].Author contributions to contentMartina Čagalj conceived, planned, and recorded the video content. Kirsty Ross edited the video content to insert HERStory Maker credits, add subtitles, and maintain video length below Twitter/X limit of 2 mins and 20 secs, prior to scheduling the social media posts.</p
Impact of the First Phase of the COVID-19 Pandemic on the Acquisition of Goods and Services in the Italian Health System
No full textThe emergency caused by the escalation in the COVID-19 pandemic, which became widespread starting on 31 January 2020, put a strain on the Italian National Health System and forced purchasing centres to deviate from the ordinary general principles dictated by current legislation. The aim of this paper is to describe how Spedali Civili Hospital in Brescia challenged the crisis, structured itself optimally, followed simplified procedures, launched new processes, and opened up more Intensive Care Unit beds to accommodate the high number of COVID cases. From an analysis of the equipment variation in terms of increased purchases, subsequent installations, and tests carried out compared with the pre-pandemic period, we report the difficulties that hospitals had to face in the first phase of the pandemic and how they were able to respond to their needs. Our data clearly displayed how the pandemic situation led to a deep internal reorganisation and that the drafting of simpler, effective, and adaptable procedures represents a first key element to ensure receptivity and responsiveness in the management of ordinary and non-ordinary events such as this pandemic condition
Remote tracking of UAV swarms via3D mobility models and LoRaWAN communications
No full textOver the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. We analyze the trade-off in setting the main parameters of the communication system and Adaptive Data Rate (ADR) scheme, showing how our tracking system can handle large swarms of drones at distances up to 4 km. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a swarm of UAVs, significantly outperforming baseline tracking approaches.</p
Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking
No full textOver the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a UAV, significantly outperforming baseline tracking approaches
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