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  1. 21

    Comparison of MIMO based on high capacity LPWAN technology TurMass<sup>TM</sup> and LoRa by Hao JIANG, Hongming CHEN, Yilong CAO, Haoyang CUI

    Published 2021-12-01
    “…With the rapid development of low power wide area network (LPWAN) technologies, more and more IoT terminals need to access the network.In order to deal with the challenge of massive random access, grant-free random access with massive MIMO (MGFRA) was proposed for LPWAN scenarios.The MGFRA was introduced and the performance of MGFRA to validate its performance advantage of greatly improving the channel access capacity with extreme low signaling overhead was theoretically analyzed.Further, TurMass<sup>TM</sup>was introduced, which is a high-capacity LPWAN technology based on MGFRA.To demonstrate the advantages of TurMass<sup>TM</sup>in practical applications, TurMass<sup>TM</sup>and LoRa in signal coverage, communication rate, network capacity, low power consumption and low cost in typical LPWAN communication scenarios were compared.The comparison results show that TurMass<sup>TM</sup>has outstanding advantages in signal coverage, communication rate, network capacity, low power consumption and low cost compared with LoRa.…”
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  2. 22

    Monitoring Environmental and Structural Parameters in Historical Masonry Buildings Using IoT LoRaWAN-Based Wireless Sensors by Noëlla Dolińska, Gabriela Wojciechowska, Łukasz Bednarz

    Published 2025-01-01
    “…This study investigates the impact of environmental conditions on the structural integrity and energy dynamics of historical masonry buildings using an IoT (Internet of Things) LoRaWAN-based (Long Range Wide Area Network) wireless sensor system. …”
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  3. 23

    PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling by Atiye Sarabi-Jamab, Babak N. Araabi

    Published 2011-01-01
    “…Comparing to LoLiMoT and PLN learning, our proposed improved learning algorithm shows the ability to construct models with less number of rules at comparable modeling errors. …”
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