With the rapid proliferation of small unmanned aircraft systems (UAS), the risk of mid-air collisions is growing, as is the risk associated with the malicious use of these systems. Airborne Detect-and-Avoid (ABDAA) and counter-UAS technologies have similar sensing requirements to detect and track airborne threats, albeit for different purposes: to avoid a collision or to neutralize a threat, respectively. These systems typically include a variety of sensors, such as electro-optical or infrared (EO/IR) cameras, RADAR, or LiDAR, and they fuse the data from these sensors to detect and track a given threat and to predict its trajectory. Camera imagery can be an effective method for detection as well as for pose estimation and threat classification, though a single camera cannot resolve range to a threat without additional information, such as knowledge of the threat geometry. To support ABDAA and counter-UAS applications, we consider a merger of two image-based sensing methods that mimic human vision: (1) a "peripheral vision" camera (i.e., with a fisheye lens) to provide a large field-of-view and (2) a "central vision" camera (i.e., with a perspective lens) to provide high resolution imagery of a specific target. Beyond the complementary ability of the two cameras to support detection and classification, the pair form a heterogeneous stereo vision system that can support range resolution. This paper describes the initial development and testing of a peripheral-central vision system to detect, localize, and classify an airborne threat and finally to predict its path using knowledge of the threat class.
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Formative assessment through think alouds
We describe how teachers used a formative assessment approach called whole class think alouds, which may happen at any time during everyday instruction and foster evidence-based instructional practices. They allow students to collaborate and orally communicate their problem solving with a goal of assessing to promote learning.
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- Award ID(s):
- 1720646
- PAR ID:
- 10252691
- Editor(s):
- Barlow, A.
- Date Published:
- Journal Name:
- Mathematics teaching and learning
- Volume:
- 114
- Issue:
- 8
- ISSN:
- 2589-6016
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Environmental data science (EDS) is a nascent STEM sub-discipline where we have the opportunity to shape the culture, to work to create an environment that welcomes broad participation, and to build a culture of inclusivity. Like many STEM disciplines, some may be excluded from participating in EDS due to historical legacies, systemic barriers, and social prejudices that create unequal opportunities and access. To better understand barriers to participation, and to identify solutions and priorities, we conducted a survey of the participants of the first Environmental Data Science Summit. We identified three barriers to participation that matched with three solutions and priorities for the field. The most commonly identified barrier was an unsupportive work environment for minorities and a male-dominated culture; creating a supportive community and work environment, particularly for minorities, was identified as both a solution and a priority for broadening participation in EDS. The second most commonly identified barrier pertained to training and maintaining relevance— specifically, late or informal training experiences and time constraints limiting time to upskill. The solution and priority proposed included access to good mentors and teachers, open data and educational materials, and increased applicability of projects. Finally, the third most commonly identified barrier, solution, and priority relate to financial concerns and the funding landscape, with both the solution and priority identified as improving funding and salary conditions. The results of this study identify the key barrier to participation in EDS and highlight potential solutions to lower these barriers to build a more equitable future.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5951/MTLT.2020.0245
