Publications
Alternative cue and response modalities maintain the Simon effect but impact task performance
Summary
Summary
Inhibitory control, the ability to inhibit impulsive responses and irrelevant stimuli, enables high level functioning and activities of daily living. The Simon task probes inhibition using interfering stimuli, i.e., cues spatially presented on the opposite side of the indicated response; incongruent response times (RT) are slower than congruent RTs. Operational...
Radar-optimized wind turbine siting
Summary
Summary
A method for analyzing wind turbine-radar interference is presented. A model is used to derive layouts for siting wind turbines that reduces their impact on radar systems, potentially allowing for increased wind turbine development near radar sites. By choosing a specific wind turbine grid stagger based on a wind farm's...
Individuals differ in muscle activation patterns during early adaptation to a powered ankle exoskeleton
Summary
Summary
Exoskeletons have the potential to assist users and augment physical ability. To achieve these goals across users, individual variation in muscle activation patterns when using an exoskeleton need to be evaluated. This study examined individual muscle activation patterns during walking with a powered ankle exoskeleton. 60% of the participants were...
AI-enabled, ultrasound-guided handheld robotic device for femoral vascular access
Summary
Summary
Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions...
Adapting deep learning models to new meteorological contexts using transfer learning
Summary
Summary
Meteorological applications such as precipitation nowcasting, synthetic radar generation, statistical downscaling and others have benefited from deep learning (DL) approaches, however several challenges remain for widespread adaptation of these complex models in operational systems. One of these challenges is adequate generalizability; deep learning models trained from datasets collected in specific...
Keeping Safe Rust safe with Galeed
Summary
Summary
Rust is a programming language that simultaneously offers high performance and strong security guarantees. Safe Rust (i.e., Rust code that does not use the unsafe keyword) is memory and type safe. However, these guarantees are violated when safe Rust interacts with unsafe code, most notably code written in other programming...
Selective network discovery via deep reinforcement learning on embedded spaces
Summary
Summary
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream...
Capacity bounds for frequency-hopped BPSK
Summary
Summary
In some channels, such as the frequency-hop channel, the transmission may undergo abrupt transitions in phase. This can require the receiver to re-estimate the phase on each hop, or for the system to utilize modulation techniques that lend themselves to noncoherent detection. How well the receiver can estimate the phase...
Relationships between cognitive factors and gait strategy during exoskeleton-augmented walking
Summary
Summary
Individual variation in exoskeleton-augmented gait strategy may arise from differences in cognitive factors, e.g., ability to respond quickly to stimuli or complete tasks under divided attention. Gait strategy is defined as different approaches to achieving gait priorities (e.g., walking without falling) and is observed via changes in gait characteristics like...
Scalable and Robust Algorithms for Task-Based Coordination From High-Level Specifications (ScRATCHeS)
Summary
Summary
Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We...