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Mitsubishi Electric and Kobe University Establish Industry-Academia Collaborative Course: "Smart Sensing Course"

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AI Summary (NQ-processed)

Mitsubishi Electric and Kobe University will launch the "Smart Sensing Course" on April 1, 2026, at the Kobe University College of System Informatics (CSI). The collaboration aims to develop battery-less IoT sensors that utilize energy harvesting technology to facilitate continuous data collection for environmental monitoring, disaster prevention, and infrastructure maintenance.

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Frequently Asked Questions

Q: When will the Smart Sensing Course established by Mitsubishi Electric and Kobe University begin operations?
A: The Smart Sensing Course established by Mitsubishi Electric and Kobe University will begin operations on April 1, 2026, within the Kobe University College of System Informatics.
Q: What is the primary technological focus of the Smart Sensing Course at Kobe University?
A: The Smart Sensing Course primarily focuses on the integration of sensing and energy harvesting technologies, especially for battery-less IoT sensors that require no wiring or battery replacements.
Q: Which university department will host the industry-academia collaborative Smart Sensing Course?
A: The Smart Sensing Course will be hosted within the Kobe University College of System Informatics, which was established in April 2025 as a hub for research and social implementation.
Q: What societal challenges does the Smart Sensing Course aim to address through its technology development?
A: The course aims to address challenges in environmental monitoring, disaster prevention, and infrastructure maintenance by enabling continuous data acquisition using battery-less IoT sensors in remote or hard-to-access locations.
Q: Why is autonomous power supply important for the sensors developed in this collaborative course?
A: Autonomous power supply is important because many sensor installation sites face difficulties with wiring and battery replacements, making energy harvesting essential for continuous and timely data collection.