Pregled bibliografske jedinice broj: 1214429
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System // Proceedings of the 2022 ACM Conference on Information Technology for Social Good
New York (NY): The Association for Computing Machinery (ACM), 2022. str. 258-264 doi:10.1145/3524458.3547266 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1214429 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
Autori
Buss, Eduard ; Rabbel, Tim-Lucas ; Horvat, Viktor ; Krizmancic, Marko ; Bogdan, Stjepan ; Wahby, Mostafa ; Hamann, Heiko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 2022 ACM Conference on Information Technology for Social Good
/ - New York (NY) : The Association for Computing Machinery (ACM), 2022, 258-264
ISBN
9781450392846
Skup
ACM International Conference on Information Technology for Social Good (GoodIT 2018)
Mjesto i datum
Limassol, Cipar, 07.09.2022. - 09.09.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
stimulus classification ; phytosensing ; biopotential ; neural networks
Sažetak
Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments, we gather electrophysiological data with sensor nodes. We expose the plant Zamioculcas zamiifolia to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data, we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Interdisciplinarne biotehničke znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
