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The adoption of Human-Robot (HR) teaming continues to increase within many high-risk fields (e.g., space exploration, medical treatment). This work solves an important problem in HR teaming by learning to interpret Mental Workload (MW) from human passive biosignals in the context of human performance. Using a previously designed experiment, we analyze GP kernels for human performance estimation. GP models help limit designer bias and provide prediction confidence intervals. This analysis offers the following contributions to this field: a heuristic to help understand which biosignals are informative, an evaluation of data, and a comparison of GP kernels. The experiment showed that smooth kernels yield lower performance prediction Root Mean Squared Error (RMSE) and Standard Error of the Mean (SEM) for each performance metric considered. As a result of this work, performance prediction model designers will have a guide for improving HR systems, passive MW monitoring, and performance estimation of HR teaming.

Compared to single-mode VCSELs, multimode VCSELs have not received much attention in models and characterizations for functional parameters, despite making up the majority of commercially available VCSELs [1]. In particular, the extraction of the linewidth enhancement factor for multimode VCSELs has been overlooked, likely due to difficulties in measurement. Additionally, multimode models for VCSELs have, until recently, omitted spectral characteristics such as linewidth [2]. This is the first work to report a measured linewidth enhancement factor value (lower bound) for a multimode VCSEL. A characterization for the functional parameters of a red multimode vertical-cavity surface-emitting laser (VCSEL) is shown herein. The extracted values form a complete working set of parameters for the laser rate equations. The techniques employed for extracting values include frequency responses, power versus current fittings, and optical spectral measurements. From the frequency responses at various bias currents, the relaxation oscillation frequency and damping factor are found. The power versus current curve is fitted to find parameters including the modal spontaneous emission rate and carrier density at threshold. The spectral measurements are used for evaluating the linewidth enhancement factor (LEF) also known as the alpha factor or Henry factor. These 5 methods have been applied previously to characterizing single-mode VCSELs [3]–[5]. The experimentally extracted parameters herein are important for creating accurate models and simulations for multimode VCSELs. Improved multimode VCSEL models are necessary for improving optical communication, especially for short-range optical interconnects [2]. The measured parameters for the characterized VCSEL are comparable to similar single-mode VCSELs characterized in other works. This is promising because multi-mode VCSELs have higher output power than their single-mode counterparts, thus these results may aid in improving short-range optical interconnects.