1,720,981 research outputs found
Analisi Matematica e Sperimentale di Tecnologie "Low Power Wide Area Network" in Scenari IoT Avanzati
Get PDFAttualmente 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
Feature stability and setup minimization for EEG-EMG-enabled monitoring systems
Get PDFDelivering 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
The digitization process and the evolution of Clinical Risk Management concept: The role of Clinical Engineering in the operational management of biomedical technologies
Get PDFIntroductionDigital transformation and technological innovation which have influenced several areas of social and productive life in recent years, are now also a tangible and concrete reality in the vast and strategic sector of public healthcare. The progressive introduction of digital technologies and their widespread diffusion in many segments of the population undoubtedly represent a driving force both for the evolution of care delivery methods and for the introduction of new organizational and management methods within clinical structures. MethodsThe CS Clinical Engineering of the "Spedali Civili Hospital in Brescia" decided to design a path that would lead to the development of a software for the management of biomedical technologies within its competence inside the hospital. The ultimate aim of this path stems from the need of Clinical Engineering Department to have up-to-date, realistic, and systematic control of all biomedical technologies present in the company. "Spedali Civili Hospital in Brescia" is not just one of the most important corporate realities in the city, but it is also the largest hospital in Lombardy and one of the largest in Italy. System development has followed the well-established phases: requirement analysis phase, development phase, release phase and evaluating and updating phase. ResultsFinally, cooperation between the various figures involved in the multidisciplinary working group led to the development of an innovative management software called "SIC Brescia". DiscussionThe contribution of the present paper is to illustrate the development of a complex implementation model for the digitization of processes, information relating to biomedical technologies and their management throughout the entire life cycle. The purpose of sharing this path is to highlight the methodologies followed for its realization, the results obtained and possible future developments. This may enable other realities in the healthcare context to undertake the same type of pathway inspired by an accomplished model. Furthermore, future implementation and data collection related to the proposed Key Performance Indicators, as well as the consequent development of new operational management models for biomedical technologies and maintenance processes will be possible. In this way, the Clinical Risk Management concept will also be able to evolve into a more controlled, safe, and efficient system for the patient and the user
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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