World's Best Scientists 2026 revealed!
Michele Magno

Michele Magno

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
47
Citations
7421
World Ranking
3277
National Ranking
61

Michele Magno publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Michele Magno sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 280 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Michele Magno D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Michele Magno sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 47 D-Index — 54th percentile

54% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Overview

Michele Magno is affiliated with ETH Zurich in Switzerland and has contributed extensively to research in engineering and computer science. Their work spans several subfields including electrical and electronic engineering, computer vision and pattern recognition, aerospace engineering, biomedical engineering, and artificial intelligence.

The primary research areas addressed by Michele Magno include robotics and sensor-based localization, indoor and outdoor localization technologies, advanced memory and neural computing, energy harvesting in wireless networks, CCD and CMOS imaging sensors, underwater vehicles and communication systems, and context-aware activity recognition systems.

Selected recent publications illustrate the scope and focus of their research:

  • "FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things" (2020, IEEE Internet of Things Journal)
  • "NB-IoT Versus LoRaWAN: An Experimental Evaluation for Industrial Applications" (2020, IEEE Transactions on Industrial Informatics)
  • "TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition With Short Range Radars" (2021, IEEE Internet of Things Journal)
  • "Neuromorphic Edge Computing for Biomedical Applications: Gesture Classification Using EMG Signals" (2022, IEEE Sensors Journal)
  • "The relevance of rock shape over mass-implications for rockfall hazard assessments" (2021, Nature Communications)

Michele Magno has collaborated frequently with a number of researchers, including:

  • Luca Benini
  • Tommaso Polonelli
  • Nicolas Baumann
  • Philipp Mayer
  • Vlad Niculescu

Their publications are found predominantly in venues such as:

  • arXiv (Cornell University)
  • IEEE Internet of Things Journal
  • IEEE Sensors Journal
  • IEEE Transactions on Instrumentation and Measurement
  • Zenodo (CERN European Organization for Nuclear Research)

Best Publications

  • Human body heat for powering wearable devices: From thermal energy to application

    Moritz Thielen;Lukas Sigrist;Michele Magno;Michele Magno;Christofer Hierold

  • Biodegradable and Highly Deformable Temperature Sensors for the Internet of Things

    Giovanni A. Salvatore;Jenny Sülzle;Filippo Dalla Valle;Giuseppe Cantarella

  • Context-Adaptive Multimodal Wireless Sensor Network for Energy-Efficient Gas Monitoring

    V. Jelicic;M. Magno;D. Brunelli;G. Paci

  • A Low Cost, Highly Scalable Wireless Sensor Network Solution to Achieve Smart LED Light Control for Green Buildings

    Michele Magno;Tommaso Polonelli;Luca Benini;Emanuel Popovici

  • Design, Implementation, and Performance Evaluation of a Flexible Low-Latency Nanowatt Wake-Up Radio Receiver

    Michele Magno;Vana Jelicic;Bruno Srbinovski;Vedran Bilas

  • Beyond duty cycling: Wake-up radio with selective awakenings for long-lived wireless sensing systems

    Dora Spenza;Michele Magno;Stefano Basagni;Luca Benini

  • FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

    Xiaying Wang;Michele Magno;Lukas Cavigelli;Luca Benini

  • b+WSN

    Fiona Edwards-Murphy;Michele Magno;Pádraig M. Whelan;John O'Halloran

  • A survey of multi-source energy harvesting systems

    Alex S. Weddell;Michele Magno;Geoff V. Merrett;Davide Brunelli

  • Accelerating real-time embedded scene labeling with convolutional networks

    Lukas Cavigelli;Michele Magno;Luca Benini

  • Extended Wireless Monitoring Through Intelligent Hybrid Energy Supply

    Michele Magno;David Boyle;Davide Brunelli;Brendan O'Flynn

  • Analytic comparison of wake-up receivers for WSNs and benefits over the wake-on radio scheme

    Vana Jelicic;Michele Magno;Davide Brunelli;Vedran Bilas

  • On-Demand LoRa: Asynchronous TDMA for Energy Efficient and Low Latency Communication in IoT.

    Rajeev Piyare;Amy L. Murphy;Michele Magno;Luca Benini

  • InfiniTime: Multi-sensor wearable bracelet with human body harvesting

    Michele Magno;Michele Magno;Davide Brunelli;Lukas Sigrist;Renzo Andri

  • Benefits of Wake-up Radio in Energy-Efficient Multimodal Surveillance Wireless Sensor Network

    Vana Jelicic;Michele Magno;Davide Brunelli;Vedran Bilas

  • Ensuring Survivability of Resource-Intensive Sensor Networks Through Ultra-Low Power Overlays

    Michele Magno;David Boyle;Davide Brunelli;Emanuel Popovici

  • An ultra low power high sensitivity wake-up radio receiver with addressing capability

    Michele Magno;Luca Benini

  • WULoRa: An energy efficient IoT end-node for energy harvesting and heterogeneous communication

    Michele Magno;Faycal Ait Aoudia;Matthieu Gautier;Olivier Berder

  • A low-power wireless video sensor node for distributed object detection

    Aliaksei Kerhet;Michele Magno;Francesco Leonardi;Andrea Boni

  • NB-IoT Versus LoRaWAN: An Experimental Evaluation for Industrial Applications

    Massimo Ballerini;Tommaso Polonelli;Davide Brunelli;Michele Magno

  • Dynamic energy burst scaling for transiently powered systems

    Andres Gomez;Lukas Sigrist;Michele Magno;Luca Benini

Frequent Co-Authors

Luca Benini
Luca Benini ETH Zurich
Davide Brunelli
Davide Brunelli University of Trento
Emanuel Popovici
Emanuel Popovici University College Cork
Chiara Petrioli
Chiara Petrioli Sapienza University of Rome
Brendan O'Flynn
Brendan O'Flynn Tyndall National Institute
Alessandra Costanzo
Alessandra Costanzo University of Bologna
Luigi Di Stefano
Luigi Di Stefano University of Bologna
Maurizio Valle
Maurizio Valle University of Genoa
Giovanni A. Salvatore
Giovanni A. Salvatore Ca Foscari University of Venice

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