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Michele Magno

Researcher at ETH Zurich

Publications -  267
Citations -  5149

Michele Magno is an academic researcher from ETH Zurich. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 37, co-authored 201 publications receiving 3857 citations. Previous affiliations of Michele Magno include fondazione bruno kessler & University of Bologna.

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

TL;DR: In this paper, the authors investigated scavenging of human body heat and the optimization of the power conversion efficiency from body core to the application, and compared two approaches: (1) a high output voltage, low thermal resistance, low electric resistance mTEG in combination with a low-input voltage coupled inductors based DC-DC converter.
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Context-Adaptive Multimodal Wireless Sensor Network for Energy-Efficient Gas Monitoring

TL;DR: A wireless sensor network for monitoring indoor air quality, which is crucial for people's comfort, health, and safety because they spend a large percentage of time in indoor environments, is presented and a significant lifetime extension is demonstrated.
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Biodegradable and Highly Deformable Temperature Sensors for the Internet of Things

TL;DR: In this paper, temperature sensors whose material composition enables full biodegradation while the layout and ultrathin format ensure a response time of 10 ms and stable operation demonstrated by a resistance variation of less than 0.7% when the devices are crumpled, folded, and stretched up to 10%.
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A Low Cost, Highly Scalable Wireless Sensor Network Solution to Achieve Smart LED Light Control for Green Buildings

TL;DR: In this paper, a low cost, wireless, easy to install, adaptable, and smart LED lighting system was proposed to automatically adjust the light intensity to save energy and maintaining user satisfaction.
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Design, Implementation, and Performance Evaluation of a Flexible Low-Latency Nanowatt Wake-Up Radio Receiver

TL;DR: An optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components and demonstrating low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction.