optimal detector
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Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7321
Author(s):  
Oleksandr Grynko ◽  
Tristen Thibault ◽  
Emma Pineau ◽  
Alla Reznik

The photoconductor layer is an important component of direct conversion flat panel X-ray imagers (FPXI); thus, it should be carefully selected to meet the requirements for the X-ray imaging detector, and its properties should be clearly understood to develop the most optimal detector design. Currently, amorphous selenium (a-Se) is the only photoconductor utilized in commercial direct conversion FPXIs for low-energy mammographic imaging, but it is not practically feasible for higher-energy diagnostic imaging. Amorphous lead oxide (a-PbO) photoconductor is considered as a replacement to a-Se in radiography, fluoroscopy, and tomosynthesis applications. In this work, we investigated the X-ray sensitivity of a-PbO, one of the most important parameters for X-ray photoconductors, and examined the underlying mechanisms responsible for charge generation and recombination. The X-ray sensitivity in terms of electron–hole pair creation energy, W±, was measured in a range of electric fields, X-ray energies, and exposure levels. W± decreases with the electric field and X-ray energy, saturating at 18–31 eV/ehp, depending on the energy of X-rays, but increases with the exposure rate. The peculiar dependencies of W± on these parameters lead to a conclusion that, at electric fields relevant to detector operation (~10 V/μm), the columnar recombination and the bulk recombination mechanisms interplay in the a-PbO photoconductor.


Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 28
Author(s):  
Joan Y. Q. Li ◽  
Stephanie Duce ◽  
Karen E. Joyce ◽  
Wei Xiang

Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species.


2021 ◽  
pp. 1-1
Author(s):  
Majed Saad ◽  
Hussein Hijazi ◽  
Ali Chamas Al Ghouwayel ◽  
Faouzi Bader ◽  
Jacques Palicot

2020 ◽  
Vol 17 (7) ◽  
pp. 1129-1133
Author(s):  
Jianbo Wang ◽  
Jianyu Ye ◽  
Guang Hua
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