1PTUFSCTUSBDU OVUPNBUFE3FBM UJNFBOE äPSEBCMF ... · 1ptufs"ctusbdu "o"vupnbufe3fbm ujnfboe"...
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Poster Abstract: An Automated Real-time and Aordable Airborne Pollen Sensing System Nam Cao Institute of Technical Informatics Graz University of Technology [email protected] Olga Saukh Institute of Technical Informatics TU Graz / CSH Vienna [email protected] Lothar य़iele Computer Engineering and Networks Lab, ETH Zurich [email protected] ABSTRACT In this paper, we present the design of our prototype of an auto- mated real-time and aordable pollen sensing system. य़e design consists of three main subsystems: (1) a trap with automatic l- tering, (2) a particle concentration system, and (3) a digital micro- scope with autofocus. य़e prototype shows eective particle gath- ering, ltering 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 rst prototype collects raw time-stamped data and transmits these to the backend server where we plan to run the detection and classication algorithms to extract accurate pollen counts from mi- croscopic images. य़e 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. य़e nal model can then run locally on embedded hardware. 1 INTRODUCTION Airborne pollen is one of the most common triggers of seasonal allergies. It aects over 24% of European population with children quota up to 40% [5]. य़us, allergic diseases are increasing with un- precedented complexity and severity. Global warming impacts pol- lination and in a long run may cause higher pollen concentrations and longer pollen seasons. Urban areas are particularly aected due to a binding of urban pollutants with allergenic proteins [2]. य़ese trends motivate the need for making timely, accurate and spatially resolved information about pollen distribution available to aerobiologiests, health experts, and the general public. In Europe pollen is monitored by a network of manually oper- ated 400+ pollen traps 1 . य़e most widely used pollen trap in the world is the volumetric Hirst-type. It exists in dierent versions such as VPPS1000, VPPS2000, VPPS2010 2 . Rotorod sampler is an- other popular pollen trap used for short-term exposure measure- ments. Ionic spore trap makes use of electrostatics to aract parti- cles. Since pollen is rarely present in the ambient air, the air ow 1 European Aeroallergen Network, https://www.ean-net.org 2 http://lanzoni.it/vpps.html generated by a trap impacts the overall systems eਖ਼ciency. य़e air ow rate of a Hirst-type trap is fairly low with about 10-20 l/min (liters per minute), whereas an Ionic spore trap achieves up to 660 l/min. Every 1-7 days gathered samples are processed in the labo- ratory by trained aerobiologists. Manual sample inspection limits the number of covered locations and the inherent resolution and timeliness of pollen exposure models and forecasts. In recent years, progress has been made in testing, evaluation and development of instruments and methods for measuring pollen in real-time. Amongst the proposed systems one can distinguish between devices based on automatic image recognition such as Hund BAA500 (2015) [4], systems based on air-ow cytometry such as Yamatronics KH-3000 (2007) [3] and Swisens Poleno (2018) 3 , and optical detectors that make use of the uorescence of bioparticles like Plair PA-300 (2016) [1]. य़ese systems are highly expensive and so far havent received wide acceptance. Our aim is to design an automated real-time and aordable pollen sensing system based on eਖ਼cient sampling, detection and classi- cation from microscopic images. In the next section we present our rst prototype and discuss the design choices we made. 2 AFFORDABLE POLLEN SENSING SYSTEM य़e overall architecture of our pollen sensing system is presented in Fig. 1(a). It consists of three subsystems: (1) a trap with a particle lter, (2) a particle concentrator, and (3) a digital microscope with autofocus. य़e images are transmied to the backend system to be processed by (4) detection, classication and counting algorithms. Below we describe the three subsystems running on the embedded device, whereas the backend design is leॏ for future work. Cyclone-based Trap and Particle Filter. Common pollen grain size falls in the range 10-100 m with a mass between 2575 g. We chose a cyclone-based particle gathering and ltering design since the method can lter particles with a diameter of down to 0.5 m 4 . Conventional cyclone-based pollen traps are directional: they have only one inlet and are equipped with a vane to rotate and suck in the surrounding air from a wind direction. य़is makes the pollen trap sample from one direction at a time. In contrast, our design adapts an omnidirectional cyclone by implementing six equally spaced inlets to cover all 360 degrees. To further increase the eਖ਼ciency we follow the ideas in [6]. य़e inlets are divided in 3 pairs with each pair comprising a main and an auxiliary inlet placed on the opposite sides of the cyclone body. An auxiliary in- let is located 5 mm higher than the main one to stimulate the air ow. A brushless fan for pumping the air is placed on top of the cy- clone. य़e system is capable of generating air ows up to 200 l/min. 3 Swisens, https://swisens.ch/introduction-to-swisens-poleno/ 4 Airborne pollen counting and sampling, https://bit.ly/2Dy3eWq
Transcript of 1PTUFSCTUSBDU OVUPNBUFE3FBM UJNFBOE äPSEBCMF ... · 1ptufs"ctusbdu "o"vupnbufe3fbm ujnfboe"...
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Particle trap andfiltering
Particleconcentration
Microscope withautofocus
Detection,classification, counting
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1. Stepper and focusing slider frame
2. Microscope
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4. Particles and glycerine remover
5. Waste tray6. Stepper of drum rotation7. Glycerine box8. Parallel light source
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9. Filtered particles
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