EEG is a powerful and affordable brain sensing and imaging tool used extensively for the diagnosis of neurological disorders (e.g. epilepsy), brain computer interfacing, and basic neuroscience. Unfortunately, most EEG electrodes and systems are not designed to accommodate coarse and curly hair common in individuals of African descent. This can lead to poor quality data that might be discarded in scientific studies after recording from a broader population set, and for clinical diagnoses, lead to an uncomfortable and/or emotionally taxing experience, and, in the worst cases, misdiagnosis. In this work, we design a system to explicitly accommodate coarse and curly hair, and demonstrate that, across time, our electrodes, in conjunction with appropriate braiding, attain substantially (~10x) lower impedance than state-of-the-art systems. This builds on our prior work that demonstrated that braiding hair in patterns consistent with the clinical standard 10-20 arrangement leads to improved impedance with existing systems.I.
Photovoltaic solar panels are effective energy sources during periods of bright sunlight. Excess energy can be stored for later use at night or on cloudy days. The decision to use the stored energy now or later depends largely on being able to predict the weather on different timescales.Short term prediction of stored energy is challenging due to the non-trivial I-V characteristic of the solar cell. The erratic nature of the weather makes long term predictive energy management difficult. In this paper, we address these issues based on data collected from a solar panel, as well as its relationship to observations made of the weather. We observe that prediction, based on fuzzy decision trees, reduces the energy error by 22% compared to a constant prediction equal to the average on the studied period. Thus, exploiting the fuzzy classification provided by a fuzzy decision tree is a good improvement compared to the baseline.
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