Abstract This paper presents an open-source aerial neuromorphic dataset that captures pedestrians and vehicles moving in an urban environment. The dataset, titled NU-AIR, features over 70 min of event footage acquired with a 640 $$\times $$ × 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment. Crowds of pedestrians, different types of vehicles, and street scenes featuring busy urban environments are captured at different elevations and illumination conditions. Manual bounding box annotations of vehicles and pedestrians contained in the recordings are provided at a frequency of 30 Hz, yielding more than 93,000 labels in total. A baseline evaluation for this dataset was performed using three Spiking Neural Networks (SNNs) and ten Deep Neural Networks (DNNs). All data and Python code to voxelize the data and subsequently train SNNs/DNNs has been open-sourced.

This paper reports on the design, motivations, and preliminary development outcomes of an ongoing NSF-funded study to create an Internet of Things (IoT) based CS curriculum tailored for high school students. The curriculum adopts the 4-layer IoT model and emphasizes an immersive, hands-on pedagogy using single-board devices and the Python programming language. Prior to designing the curriculum, focus groups were conducted using Keller's ARCS model of motivation as a conceptual framework. Qualitative data analysis about student attention, relevance, confidence, and satisfaction revealed actionable insights used to design the curriculum. Preliminary curriculum implementation has underscored IoT's promise in engaging high school CS students and developing modern computing skills applicable to college and career pathways.