Abstract: EEG-based acceleration of second language learning is accomplished by measuring via single-trial EEG a learner's cognitive response to the presentation (visual or auditory) of language learning materials and updating a user model of latent traits related to language-learning skills in accordance with the cognitive response. The user model is suitably updated with each trial, each trial being triggered by learner fixation on a portion of visual materials and/or a next phoneme in auditory materials. Additional discrimination may be achieved through the use of saccades or fixation duration features.

Abstract: EEG-based acceleration of second language learning is accomplished by measuring via single-trial EEG a learner's cognitive response to the presentation (visual or auditory) of language learning materials and updating a user model of latent traits related to language-learning skills in accordance with the cognitive response. The user model is suitably updated with each trial, each trial being triggered by learner fixation on a portion of visual materials and/or a next phoneme in auditory materials. Additional discrimination may be achieved through the use of saccades or fixation duration features.

Abstract: Bayesian super-resolution techniques fuse multiple low resolution images (possibly from multiple bands) to infer a higher resolution image. The super-resolution and fusion concepts are portable to a wide variety of sensors and environmental models. The procedure is model-based inference of super-resolved information. In this approach, both the point spread function of the sub-sampling process and the multi-frame registration parameters are optimized simultaneously in order to infer an optimal estimate of the super-resolved imagery. The procedure involves a significant number of improvements, among them, more accurate likelihood estimates and a more accurate, efficient, and stable optimization procedure.