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The deployment of deep learning models for real-time image classification on resource-constrained sensor devices presents significant challenges. These devices face strict limitations in computational power, energy capacity, and memory resources, making it difficult to achieve both high accuracy and low latency. Current approaches either compromise model performance through compression or incur substantial overhead by offloading computation to remote servers. We introduce a novel distributed progressive inference platform that addresses these limitations by dynamically balancing local and remote computation. Our system employs reinforcement learning to make intelligent decisions about when and where to perform inference. Experimental results across multiple standard datasets demonstrate that our approach achieves up to 3% higher accuracy while reducing network traffic and preserving battery life compared to existing methods. 

Recent innovations in virtual and mixed-reality (VR/MR) technologies have enabled innovative hands-on training applications in high-risk/high-value fields such as medicine, flight, and worker-safety. Here, we present a detailed description of a novel VR/MR tactile user interactions/interface (TUI) hardware and software development framework that enables the rapid and cost-effective no-code development, optimization, and distribution of fully authentic hands-on VR/MR laboratory training experiences in the physical and life sciences. We applied our framework to the development and optimization of an introductory pipette calibration activity that is often carried out in real chemistry and biochemistry labs. Our approach provides users with nuanced real-time feedback on both their psychomotor skills during data acquisition and their attention to detail when conducting data analysis procedures. The cost-effectiveness of our approach relative to traditional face-to-face science labs improves access to quality hands-on science lab experiences. Importantly, the no-code nature of this Hands-On Virtual-Reality (HOVR) Lab platform enables faculties to iteratively optimize VR/MR experiences to meet their student’s targeted needs without costly software development cycles. Our platform also accommodates TUIs using either standard virtual-reality controllers (VR TUI mode) or fully functional hand-held physical lab tools (MR TUI mode). In the latter case, physical lab tools are strategically retrofitted with optical tracking markers to enable tactile, experimental, and analytical authenticity scientific experimentation. Preliminary user study data highlights the strengths and weaknesses of our generalized approach regarding student affective and cognitive student learning outcomes. 

Active tracking enables higher precision in tracking the positions, orientations, and states of the virtualized objects. STEAMVR Lighthouse tracking base-stations can be used for tracking specific objects. However, current solutions are bulky and costly. The overall goal of this research work was to reduce the size and cost of active VR trackers to enable their attachment to ever smaller physical tools and objects to be tracked in the real world and displayed in a virtual reality environment.