Poster Abstract: An Automated Real-time and Affordable Airborne Pollen Sensing System

Research output: Contribution to conferencePosterResearchpeer-review

Abstract

In this paper, we present the design of our prototype of an automated real-time and affordable pollen sensing system. The design consists of three main subsystems: (1) a trap with automatic filtering, (2) a particle concentration system, and (3) a digital microscope with autofocus. The prototype shows effective particle gathering, filtering and concentration in a tiny sized area. As a result, we reduce particle loss and improve image quality taken by the optical system when searching and autofocusing on pollen grains. Our first prototype collects raw time-stamped data and transmits these to the backend server where we plan to run the detection and classification algorithms to extract accurate pollen counts from microscopic images. The key advantage of processing images at the backend is that we let the experts undertake corrective actions and help the system learn to detect and classify pollen using state-of- the-art interactive imitation learning algorithms. The final model can then run locally on embedded hardware.
Original languageEnglish
Number of pages2
Publication statusPublished - 16 Apr 2019
EventInternational Conference on Information Processing in Sensor Networks - Montreal, Canada
Duration: 16 Apr 201918 Apr 2019

Conference

ConferenceInternational Conference on Information Processing in Sensor Networks
Abbreviated titleIPSN
CountryCanada
CityMontreal
Period16/04/1918/04/19

Fingerprint

Optical systems
Learning algorithms
Image quality
Image processing
Microscopes
Servers
Hardware

Fields of Expertise

  • Information, Communication & Computing

Cite this

Nam, C. N. K., Saukh, O., & Thiele, L. (2019). Poster Abstract: An Automated Real-time and Affordable Airborne Pollen Sensing System. Poster session presented at International Conference on Information Processing in Sensor Networks, Montreal, Canada.

Poster Abstract: An Automated Real-time and Affordable Airborne Pollen Sensing System. / Nam, Cao Nguyen Khoa; Saukh, Olga; Thiele, Lothar.

2019. Poster session presented at International Conference on Information Processing in Sensor Networks, Montreal, Canada.

Research output: Contribution to conferencePosterResearchpeer-review

Nam, CNK, Saukh, O & Thiele, L 2019, 'Poster Abstract: An Automated Real-time and Affordable Airborne Pollen Sensing System' International Conference on Information Processing in Sensor Networks, Montreal, Canada, 16/04/19 - 18/04/19, .
Nam CNK, Saukh O, Thiele L. Poster Abstract: An Automated Real-time and Affordable Airborne Pollen Sensing System. 2019. Poster session presented at International Conference on Information Processing in Sensor Networks, Montreal, Canada.
Nam, Cao Nguyen Khoa ; Saukh, Olga ; Thiele, Lothar. / Poster Abstract: An Automated Real-time and Affordable Airborne Pollen Sensing System. Poster session presented at International Conference on Information Processing in Sensor Networks, Montreal, Canada.2 p.
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abstract = "In this paper, we present the design of our prototype of an automated real-time and affordable pollen sensing system. The design consists of three main subsystems: (1) a trap with automatic filtering, (2) a particle concentration system, and (3) a digital microscope with autofocus. The prototype shows effective particle gathering, filtering and concentration in a tiny sized area. As a result, we reduce particle loss and improve image quality taken by the optical system when searching and autofocusing on pollen grains. Our first prototype collects raw time-stamped data and transmits these to the backend server where we plan to run the detection and classification algorithms to extract accurate pollen counts from microscopic images. The key advantage of processing images at the backend is that we let the experts undertake corrective actions and help the system learn to detect and classify pollen using state-of- the-art interactive imitation learning algorithms. The final model can then run locally on embedded hardware.",
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