S
Simon O'Keefe
Researcher at University of York
Publications - 68
Citations - 1022
Simon O'Keefe is an academic researcher from University of York. The author has contributed to research in topics: Artificial neural network & Content-addressable memory. The author has an hindex of 14, co-authored 65 publications receiving 849 citations. Previous affiliations of Simon O'Keefe include Universities UK.
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Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey
TL;DR: Practical engineering solutions are focused on which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies, which identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.
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A survey of spectrogram track detection algorithms
Thomas Lampert,Simon O'Keefe +1 more
TL;DR: An extensive survey and an algorithm taxonomy is presented and each algorithm is reviewed according to a set of criteria relating to their success in application, concluding that none of these algorithms fully meets these criteria.
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A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
TL;DR: It is shown that the use of QC samples can lead to problems and non-QC correction methods are compared with standard QC correction and demonstrated their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.
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Using isoelectric point to determine the pH for initial protein crystallization trials
TL;DR: It is shown that a better estimate of the true pH can be predicted by considering not only the buffer pH but also any other chemicals in the crystallization solution, which helps investigate the disputed relationship between the pI of a protein and the pH at which it crystallizes.
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Hadoop neural network for parallel and distributed feature selection
TL;DR: A theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets is introduced which is underpinned by an associative memory (binary) neural network which is highly amenable to Parallel and distributed processing and fits with the Hadoops paradigm.