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All Outputs (2)

Small Sample Deep Learning for Newborn Gestational Age Estimation (2017)
Conference Proceeding
Torres Torres, M., Valstar, M. F., Henry, C., Ward, C., & Sharkey, D. (2017). Small Sample Deep Learning for Newborn Gestational Age Estimation. In Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017) (79-86). https://doi.org/10.1109/FG.2017.19

A baby’s gestational age determines whether or not they are preterm, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these machines are expensive, require traine... Read More about Small Sample Deep Learning for Newborn Gestational Age Estimation.

Non-Symbolic Fragmentation (2002)
Conference Proceeding
Ashman, H., Coupe, H., Smith, P., Neville-Smith, M., & Gilbert, M. (2002). Non-Symbolic Fragmentation.

This paper reports on the use of non-symbolic fragmentation of data for securing communications. Non-symbolic fragmentation, or NSF, relies on breaking up data into non-symbolic fragments, which are (usually irregularly-sized) chunks whose boundaries... Read More about Non-Symbolic Fragmentation.