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Due to the COVID-19 crisis preventing face-to-face interaction, three National Science Foundation (NSF)-funded centers employed a virtual/remote format for their summer Research Experiences for Teachers (RET) Programs, reaching K-12 STEM teachers across the country. Teachers participated virtually from four different states by joining engineering research teams from four different universities in three different RET programs. Lab experiences depended on the nature of the research and institution-specific guidelines for in-lab efforts, resulting in some teachers conducting lab experiments with materials sent directly to their homes, some completing their experience fully online, and some completing portions of lab work in person on campus. Each teacher developed an engineering lesson plan based on the corresponding center’s research to be implemented either in person or virtually during the 2020-2021 academic school year. Research posters, created with support from graduate student and faculty mentors, were presented to industry partners, education partners, center members, and the NSF. Support for the teachers as they implement lessons, present posters, and disseminate their developed curricula, has continued throughout the year. Common survey and interview/focus group protocols, previously designed specifically for measuring the impact of engineering education programs, were adapted and used to separately evaluate each of the three virtual programs. Strengths and suggested areas of improvement will be explored and discussed to inform future use of the common evaluation instruments. Additionally, preliminary results, highlighting general successes and challenges of shifting RET programming to a virtual/remote format across the three centers, will be discussed. 

Image sensors with programmable region-of-interest (ROI) readout are a new sensing technology important for energyefficient embedded computer vision. In particular, ROIs can subsample the number of pixels being readout while performing single object tracking in a video. In this paper, we develop adaptive sampling algorithms which perform joint object tracking and predictive video subsampling. We utilize an object detection consisting of either mean shift tracking or a neural network, coupled with a Kalman filter for prediction. We show that our algorithms achieve mean average precision of 0.70 or higher on a dataset of 20 videos in software. Further, we implement hardware acceleration of mean shift tracking with Kalman filter adaptive subsampling on an FPGA. Hardware results show a 23× improvement in clock cycles and latency as compared to baseline methods and achieves 38FPS real-time performance. This research points to a new domain of hardware-software co-design for adaptive video subsampling in embedded computer vision. 

Photovoltaic (PV) array analytics and control have become necessary for remote solar farms and for intelligent fault detection and power optimization. The management of a PV array requires auxiliary electronics that are attached to each solar panel. A collaborative industry-university-government project was established to create a smart monitoring device (SMD) and establish associated algorithms and software for fault detection and solar array management. First generation smart monitoring devices (SMDs) were built in Japan. At the same time, Arizona State University initiated research in algorithms and software to monitor and control individual solar panels. Second generation SMDs were developed later and included sensors for monitoring voltage, current, temperature, and irradiance at each individual panel. The latest SMDs include a radio and relays which allow modifying solar array connection topologies. With each panel equipped with such a sophisticated SMD, solar panels in a PV array behave essentially as nodes in an Internet of Things (IoT) type of topology. This solar energy IoT system is currently programmable and can: a) provide mobile analytics, b) enable solar farm control, c) detect and remedy faults, d) optimize power under different shading conditions, and e) reduce inverter transients. A series of federal and industry grants sponsored research on statistical signal analysis, communications, and optimization of this system. A Cyber-Physical project, whose aim is to improve solar array efficiency and robustness using new machine learning and imaging methods, was launched recently