Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 $\pm 2.6{}^{\circ }$ C and 0.22 $\pm 0.59$ m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.

In this article, we demonstrate the ability to recognize hand gestures in a noncontact wireless fashion using only incoherent light signals reflected from a human subject. Fundamentally distinguished from radar, lidar, and camera-based sensing systems, this sensing modality uses only a low-cost light source (e.g., LED) and a sensor (e.g., photodetector). The lightwave-based gesture recognition system identifies different gestures from the variations in light intensity reflected from the subject's hand within a short (20–35 cm) range. As users perform different gestures, scattered light forms unique, statistically repeatable, time-domain signatures. These signatures can be learned by repeated sampling to obtain the training model against which unknown gesture signals are tested and categorized. These time-domain variations of the lightwave signals reflected from hand are denoised, standardized, and then classified by using machine learning classification tools such as $K$ -nearest neighbors and support vector machine. Performance evaluations have been conducted with eight gestures, five subjects, different distances and lighting conditions, and visible and infrared light sources. The results demonstrate the best hand gesture recognition performance of infrared sensing at 20 cm with an average of 96% accuracy. The developed gesture recognition system is low-cost, effective, and noncontact technology for numerous human–computer interaction applications.

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased datasets. While curated datasets can help, challenges remain in generalizability and real-world transfer. Meanwhile, large-scale ’in-the-wild’ video datasets have driven progress in computer vision using self-supervised techniques. Translating this to robotics, recent works have explored learning manipulation skills using abundant passive videos sourced online. Showing promising results, such video-based learning paradigms provide scalable supervision while reducing dataset bias. This survey reviews foundations such as video feature representation learning techniques, object affordance understanding, 3D hand/body modeling, and large-scale robot resources, as well as emerging techniques for acquiring robot manipulation skills from uncontrolled video demonstrations.We discuss how learning from only observing large-scale human videos can enhance generalization and sample efficiency for robotic manipulation. The survey summarizes video-based learning approaches, analyzes their benefits over standard datasets, survey metrics, and benchmarks, and discusses open challenges and future directions in this nascent domain at the intersection of computer vision, natural language processing, and robot learning.

This paper explores automated modification of text to make it more accessible for people with dyslexia, a reading disorder affecting a significant percentage of the global population. The modifications are both in terms of changing the appearance of text and simplification of the words, grammar, and length of textual material. For simplification of text, we built a dataset with original and dyslexia-friendly text verified by human readers that it improve their reading experience by 27% on average. Then we developed a pipeline to generate dyslexia-friendly text automatically using transfer learning. The model learns styles appropriate for dyslexic users and generates dyslexia-friendly text from arbitrary textual data, which is easier for people with dyslexia to read and interpret.