Browsing by Author "Naladala, I."

Now showing 1 - 3 of 3

Corrosion Damage Identification and Lifetime Estimation of Ship Parts using Image Processing
(2018) Naladala, I.; Raju, A.; Aishwarya, C.; Koolagudi, S.G.
Corrosion is a process that leads to early failure of ship parts, high maintenance costs and a shortened service life of the ship, as a whole. Human visual inspection is currently the most widely used method to assess corrosion. In this paper, we propose the use of image processing to determine the extent of corrosion and estimate the time period within which the ship parts have to be replaced. In the case of availability of pre-corrosion images, the histograms of the pre-corrosion and post-corrosion images are compared and their similarity is quantified as the Sum of Squared Distances (SSD) value. Our method then produces a numerical output which signifies the level of corrosion. We then correlate extent of damage and ship part replacement period. In the absence of pre-corrosion images, we classify superpixels in the post-corrosion image as undamaged or damaged with an accuracy of 92 per cent, using Random Forest classifier. We have also evaluated the performance of corrosion prevention measures such as galvanization, painting, etc on different parts of the ship, for example, parts exposed to only air and parts exposed to both saline water and air. � 2018 IEEE.
Corrosion Damage Identification and Lifetime Estimation of Ship Parts using Image Processing
(Institute of Electrical and Electronics Engineers Inc., 2018) Naladala, I.; Raju, A.; Aishwarya, C.; Koolagudi, S.G.
Corrosion is a process that leads to early failure of ship parts, high maintenance costs and a shortened service life of the ship, as a whole. Human visual inspection is currently the most widely used method to assess corrosion. In this paper, we propose the use of image processing to determine the extent of corrosion and estimate the time period within which the ship parts have to be replaced. In the case of availability of pre-corrosion images, the histograms of the pre-corrosion and post-corrosion images are compared and their similarity is quantified as the Sum of Squared Distances (SSD) value. Our method then produces a numerical output which signifies the level of corrosion. We then correlate extent of damage and ship part replacement period. In the absence of pre-corrosion images, we classify superpixels in the post-corrosion image as undamaged or damaged with an accuracy of 92 per cent, using Random Forest classifier. We have also evaluated the performance of corrosion prevention measures such as galvanization, painting, etc on different parts of the ship, for example, parts exposed to only air and parts exposed to both saline water and air. Â© 2018 IEEE.
Efficient Pull-based Mobile Video Streaming leveraging In-Network Functions
(Institute of Electrical and Electronics Engineers Inc., 2020) Matsuzono, K.; Asaeda, H.; Naladala, I.; Turletti, T.
There has been a considerable increase in the demand for high quality mobile video streaming services, while at the same time, the video traffic volume is expected to grow exponentially. Consequently, maintaining high quality of experience (QoE) and saving network resources are becoming crucial challenges to solve. In this paper, we propose a name-based mobile streaming scheme that allows efficient video content delivery by exploiting a smart pulling mechanism designed for information-centric networks (ICNs). The proposed mechanism enables fast packet loss recovery by leveraging in-network caching and coding. Through an experimental evaluation of our mechanism over an open wireless testbed and the Internet, we demonstrate that the proposed scheme leads to higher QoE levels than classical ICN and TCP-based streaming mechanisms. Â© 2020 IEEE.