52 research outputs found
SNR and RSSI from an urban LoRa Network
No full text<p>The dataset contains various SNR and RSSI reading from a LoRa Network in the city of Portland, Maine. The dataset consists in a single sheet file, where each row corresponds to a specific LoRa node. Note that: i) the first row corresponds to the LoRa Gateway; ii) each row contains information over the node ID, its coordinates, the packet frequency, bandwidth, , Transmission Power, Spreading Factor, Coding Rate, SNR and RSSI.</p>
SDR-LoRa, an open-source, full-fledged implementation of LoRa on Software-Defined-Radios: Design and potential exploitation
No full textIn this paper, we present SDR-LoRa, an open-source, full-fledged Software Defined Radio (SDR) implementation of a LoRa transceiver. First, we conduct a thorough analysis of the LoRa physical layer (PHY) functionalities, encompassing processes such as packet modulation, demodulation, and preamble detection. Then, we leverage on this analysis to create a pioneering SDR-based LoRa PHY implementation. Accordingly, we thoroughly describe all the implementation details. Moreover, we illustrate how SDR-LoRa can help boost research on the LoRa protocol by presenting three exemplary key applications that can be built on top of our implementation, namely fine-grained localization, interference cancellation, and enhanced link reliability. To validate SDR-LoRa and its applications, we test it on two different platforms: (i) a physical setup involving USRP radios and off-the-shelf commercial devices, and (ii) the Colosseum wireless channel emulator. Our experimental findings reveal that (i) SDR-LoRa performs comparably to conventional commercial LoRa systems, and (ii) all the aforementioned applications can be successfully implemented on top of SDR-LoRa with remarkable results. The complete details of the SDR-LoRa implementation code have been publicly shared online, together with a plug-and-play Colosseum container
La definizione e la gestione del prezzo
No full textNel capitolo, dopo aver illustrato il modello costi-concorrenza-valore per il cliente (paragrafo 1), ci si concentra sul valore per il cliente come punto di riferimento “principe” per la definizione del prezzo. Solo la misurazione del valore per il cliente, la conseguente definizione di un prezzo customer based e la gestione del prezzo in una più ampia prospettiva di customer va-lue management possono permettere l’alimentazione di una vera relazione fiduciaria tra impresa e cliente. In particolare vengono approfondite la definizione e la gestione del prezzo di vendita, nonchè i metodi di definizione del prezzo Customer Based (paragrafo 2) – EVC, tecnica à la Fishbein, Conjoint Analysis. Quindi ci si focalizza sulla gestione del prezzo Customer Based, orientato alla creazione di relazioni fiduciarie con la domanda (paragrafi 3 e 4), illustrando il passaggio dalla Price Competition alla Value Competition
"Customer Value Metrics"
No full textThis paper focuses on customer value analysis and measurement, framing customer value management as one of the main antecedents of the company value-creation process. The paper builds on three main pillars. First, the paper highlights the critical role of customer value in business-to-business markets, focusing on the links between the company’s ability to manage customer value creation processes and the positive financial and economic outcomes generated by loyalty effects. Secondly, the paper develops key analytical stages for an understanding of customer value. The focus is on the customer value-chain concept, including consideration of the customer information and acquisition process and its decision rules. Third, the paper illustrates the measurement process, offering an organizational framework for selecting the most suitable methodology for measuring perceived customer value. The methodological alternatives range from desk measures (e.g., technical computation of the TCO (total cost of ownership)) to field analysis, like those considered under both compositional and the decomposition approaches (e.g., conjoint analysis). The paper concludes with remarks on the managerial implications of these measures, as well as offering suggestions for further research on value for the customer
Prezzo e Valore per il cliente. Tecniche di misurazione ed applicazioni manageriali
No full textDescrizione ed applicazione delle principali tecniche di misurazione del valore per il cliente.
Analisi e definizione delle principali scelte di prezzo delle imprese.
Casi ed esempi applicativi
A randomized trial of two laparoscopic treatments of endometriomas: cystectomy versus drainage-coagulation
No full textDesign and implementation of machine learning techniques for modeling and managing battery energy storage systems
No full textThe fast technological evolution and industrialization that have interested the humankind since the fifties has caused a progressive and exponential increase of CO2 emissions and Earth temperature. Therefore, the research community and the political authorities have recognized the need of a deep technological revolution in both the transportation and the energy distribution systems to hinder climate changes. Thus, pure and hybrid electric powertrains, smart grids, and microgrids are key technologies for achieving the expected goals. Nevertheless, the development of the above mentioned technologies require very effective and performing Battery Energy Storage Systems (BESSs), and even more effective Battery Management Systems (BMSs).
Considering the above background, this Ph.D. thesis has focused on the development of an innovative and advanced BMS that involves the use of machine learning techniques for improving the BESS effectiveness and efficiency. Great attention has been paid to the State of Charge (SoC) estimation problem, aiming at investigating solutions for achieving more accurate and reliable estimations. To this aim, the main contribution has concerned the development of accurate and flexible models of electrochemical cells.
Three main modeling requirements have been pursued for ensuring accurate SoC estimations: insight on the cell physics, nonlinear approximation capability, and flexible system identification procedures. Thus, the research activity has aimed at fulfilling these requirements by developing and investigating three different modeling approaches, namely black, white, and gray box techniques.
Extreme Learning Machines, Radial Basis Function Neural Networks, and Wavelet Neural Networks were considered among the black box models, but none of them were able to achieve satisfactory SoC estimation performances. The white box Equivalent Circuit Models (ECMs) have achieved better results, proving the benefit that the insight on the cell physics provides to the SoC estimation task. Nevertheless, it has appeared clear that the linearity of ECMs has reduced their effectiveness in the SoC task. Thus, the gray box Neural Networks Ensemble (NNE) and the white box Equivalent Neural Networks Circuit (ENNC) models have been developed aiming at exploiting
the neural networks theory in order to achieve accurate models, ensuring at the same time very flexible system identification procedures together with nonlinear approximation capabilities.
The performances of NNE and ENNC have been compelling. In particular, the white box ENNC has reached the most effective performances, achieving accurate SoC estimations, together with a simple architecture and a flexible system identification procedure.
The outcome of this thesis makes it possible the development of an interesting scenario in which a suitable cloud framework provides remote assistance to several BMSs in order to adapt the managing algorithms to the aging of BESSs, even considering different and distinct applications
Target Wake Time Scheduling for Time-sensitive and Energy-efficient Wi-Fi Networks
No full textTime Sensitive Networking (TSN) is fundamental for
the reliable, low-latency networks that will enable the Industrial
Internet of Things (IIoT). Wi-Fi has historically been considered
unfit for TSN, as channel contention and collisions prevent
deterministic transmission delays. However, this issue can be
overcome by using Target Wake Time (TWT), which enables the
access point to instruct Wi-Fi stations to wake up and transmit
in non-overlapping TWT Service Periods (SPs), and sleep in
the remaining time. In this paper, we first formulate the TWT
Acceptance and Scheduling Problem (TASP), with the objective to
schedule TWT SPs that maximize traffic throughput and energy
efficiency while respecting Age of Information (AoI) constraints.
Then, due to TASP being NP-hard, we propose the TASP Efficient
Resolver (TASPER), a heuristic strategy to find near-optimal
solutions efficiently. Using a TWT simulator based on ns-3,
we compare TASPER to several baselines, including HSA, a
state-of-the-art solution originally designed for WirelessHART
networks. We demonstrate that TASPER obtains up to 24.97%
lower mean transmission rejection cost and saves up to 14.86%
more energy compared to the leading baseline, ShortestFirst, in
a challenging, large-scale scenario. Additionally, when compared
to HSA, TASPER also reduces the energy consumption by 34%
and reduces the mean rejection cost by 26%. Furthermore,
we validate TASPER on our IIoT testbed, which comprises 10
commercial TWT-compatible stations, observing that our solution
admits more transmissions than the best baseline strategy,
without violating any AoI deadline
Randomized clinical trial of two laparoscopic treatments of endometriomas : cystectomy versus drainage and coagulation
No full textTo assess the efficacy of two laparoscopic methods for the management of endometriomas with regard to pain relief, pregnancy rate, and disease recurrence
Target Wake Time Scheduling for Time-Sensitive Networking in the Industrial IoT
No full textTime Sensitive Networking (TSN) is fundamental for the low-latency, reliable, and energy-efficient networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. How- ever, this issue can be overcome using Target Wake Time (TWT) to instruct Wi-Fi stations to wake up and transmit in non- overlapped TWT Service Periods (SPs) and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), whose objective is to schedule TWT SPs as to maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, since the TASP is NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Finally, we compare TASPER with several baselines through numerical analysis and simulations, which we performed using a TWT-compatible simulator based on ns-3. We demonstrate that TASPER schedules traffic with up to 21.23% higher priority-weighted admission ratio and saves up to 7.42% energy compared to the ShortestFirst strategy, all while satisfying AoI constraints for 99.5% of transmissions
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