Detecting plants on railway embankment. (2013)
Journal Article
Nyberg, R., Gupta, N. K., Yella, S., & Dougherty, M. (2013). Detecting plants on railway embankment. Journal of Software Engineering and Applications, 6, 8-12. https://doi.org/10.4236/jsea.2013.63b003

This paper investigates problems concerning vegetation along railways and proposes automatic means of detecting
ground vegetation. Digital images of railway embankments have been acquired and used for the purpose. The current
work mainly proposes t... Read More about Detecting plants on railway embankment..

Comparison of pattern recognition techniques for the classification of impact acoustic emissions (2007)
Journal Article
Yella, S., Gupta, N. K., & Dougherty, M. S. (2007). Comparison of pattern recognition techniques for the classification of impact acoustic emissions. Transportation Research Part C : Emerging Technologies, 15(6), 345-360. https://doi.org/10.1016/j.trc.2007.05.004

Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. These inspections are mainly done intuitively by skilled personnel. In thi... Read More about Comparison of pattern recognition techniques for the classification of impact acoustic emissions.

Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing (2006)
Journal Article
Yella, S., Dougherty, M. S., & Gupta, N. K. (2006). Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing. Insight - Non-Destructive Testing & Condition Monitoring, 48(1), 10-20. https://doi.org/10.1784/insi.2006.48.1.10

This paper attempts to summarise the findings of a large
number of research papers deploying artificial intelligence
(AI) techniques for the automatic interpretation of data from non-destructive testing (NDT). Problems in the rail transport domain... Read More about Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing.