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This paper presents an optimized design of research-oriented ASVs and a systematic design evaluation methodology for reliable in-water sensing. The objective is to minimize the interference on sensor readings by any ASV maneuver. The design space includes motors and sensors locations. In addition, this paper analyzes modularity - i.e., the effects of new sensor's installation. All prototype designs are thoroughly tested using hydrostatic analyses, Computational Fluid Dynamics (CFD) simulations, and real-world field testings. Quantitative metrics, including trim, pitch, velocity magnitude of flow, and turbulence, are used to compare different configurations. Our experiments show that a motor configuration at the back part of the straights hulls is the most optimal design, resulting in high-quality data collection. 

This paper presents a holistic system to scale up the teaching and learning of vocabulary words of American Sign Language (ASL). The system leverages the most recent mixed-reality technology to allow the user to perceive her own hands in an immersive learning environment with first- and third-person views for motion demonstration and practice. Precise motion sensing is used to record and evaluate motion, providing real-time feedback tailored to the specific learner. As part of this evaluation, learner motions are matched to features derived from the Hamburg Notation System (HNS) developed by sign-language linguists. We develop a prototype to evaluate the efficacy of mixed-reality-based interactive motion teaching. Results with 60 participants show a statistically significant improvement in learning ASL signs when using our system, in comparison to traditional desktop-based, non-interactive learning. We expect this approach to ultimately allow teaching and guided practice of thousands of signs.