|Affiliation||Institute for Research on Next-generation Semiconductor and Sensing Science (IRES²)|
|Concurrent post||Research Center for Agrotechnology and Biotechnology
Department of Electrical and Electronic Information Engineering
|Degree||Ph. D. (Toyohashi University of Technology)|
Please append "tut.jp" to the end of the address above.
|Laboratory website URL||http://int.ee.tut.ac.jp/bio/|
|Researcher information URL（researchmap）||Researcher information|
Multimodal sensing systems integrated with different kind of sensors attract attention as a key element of new generation society based on IoT and big data analysis. This concept is proposed as "Society 5.0". In our laboratory, multi-modal sensors based on CMOS/MEMS technology are studied. Machine learning-based sensing system is also studied in parallel with sensor device development.
Multi-modal sensing become an important technology in the field of smart agriculture typified as the plant factory. In recent years, there are attempts to measure many items such as nutrients, moisture content, pH, and temperature to optimally control the growth of plants and maximize the productivity of crops. In this laboratory, we are studying sensing devices and systems that measure and visualize the important items, which are nutrients, moisture content, and distribution of various ions for example, by a multimodal measurement.
Theme2：Machine learning-based multimodal sensing
A novel sensing approach to realize multimodal sensing using machine learning technology to analyze the data measured by combining multiple sensors with different detection characteristics has been studied. We are focused on the realization of simultaneous measurement of multiple ions, multiple gases, and smell sensing by combining AI technology and imaging-based sensors fabricated with CMOS technology.