High voltage electrophoretic deposition (HVEPD) has been used to obtain forests of aligned multi-walled carbon nanotubes (MWCNTs) on long strips of flexible, conductive substrates. Successful design and integration of a continuous HVEPD setup has enabled scalable fabrication of electrodes for electrochemical energy storage. The mechanism of continuous HVEPD has been investigated to ensure appropriate alignment. Well-aligned forests of MWCNTs were obtained using a conductive holding layer which helped reduce internal resistance and enhance the electrochemical performance of the electrodes.

Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the dataset. The universality of the learned features is also demonstrated by strong prediction performance when transferring from a more complex underwater acoustic channel to a simpler one.

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