Generating high-quality depth maps with accurate values is a critical research topic, and various depth estimation methods, such as Time-of-Flight (ToF) and Monocular Depth Estimation (MDE), are advancing rapidly. However, each single-sensor approach is inherently limited by the characteristics of its respective modality. Meanwhile, fused approaches using multiple sensors or dedicated trained models are often plagued by system complexity and limited generalization. In this paper, we propose an adaptive depth processing system based on the foundation transformer and ToF fusion, aiming to harness the precision of ToF data and the high-quality depth priors provided by the foundation model in a monocular and zero-shot manner. A hardware–algorithm co-design partitions the computation between a dedicated ASIC for compute-intensive foundation transformer acceleration and an MPSoC FPGA for potential reconfigurable fusion, yielding 36.4 ms latency and 1.9 W power under a 27.6 GOPS/frame workload. Zero-shot evaluations on various ToF data sources including FLAT, TICaM, and in-house captures confirm strong cross-domain generalization, demonstrating the system’s adaptability, efficiency, and accuracy.

Metal welding is a craft manufacturing skill that can be unusually difficult to externalize and represent to novices. Building competency requires an apprentice to iteratively practice embodied skills and sensitize themselves to a sensorially complex practice. To explore these challenges, we organized a series of co-design workshops with a youth program in welding and fabrication. Working with eight instructors and four students, we identified opportunities for mixed reality, sensing, and tinyML processes to augment welding training and practice. This resulted in an extended reality (XR) welding helmet and torch that enhances the embodied learning of welding in three key ways: biometric sensing enhances mindfulness and stress management in sensorially challenging environments; acoustic sensing focuses learner attention on non-visual cues of weld performance; and combined motion-sensing and visual XR feedback helps improve proprioceptive and embodied learning. These features are assessed and we offer design implications for augmenting novice learning of craft-practice with XR approaches.

Vaccines have proven to be extremely effective in preventing the spread of COVID-19 and potentially ending the pandemic. Lack of access caused many people not getting vaccinated early, so states such as Virginia deployed mobile vaccination sites in order to distribute vaccines across the state. Here we study the problem of deciding where these facilities should be placed and moved over time in order to minimize the distance each person needs to travel in order to be vaccinated. Traditional facility location models for this problem fail to incorporate the fact that our facilities are mobile (i.e., they can move over time). To this end, we instead model vaccine distribution as the Dynamic k-Supplier problem and give the first approximation algorithms for this problem. We then run extensive simulations on real world datasets to show the efficacy of our methods. In particular, we find that natural baselines for Dynamic k-Supplier cannot take advantage of the mobility of the facilities, and perform worse than non-mobile k-Supplier algorithms.