Software Sensors in Ocean Engineering

Dear Colleagues,

This Special Issue aims at the problem of software sensors and their application in ocean engineering. Software sensors can be implemented as virtual sensors to identify the state of the system or to measure latent (unobservable) variables. Likewise, software sensors can be used as estimators of real variables offering advantages such as the reduction in the number of physical sensors or enabling the use of less expensive sensors as proxies to estimate information normally obtained from more expensive devices or measurement endeavors. In ocean engineering, an example might be a UV sensor used as a proxy to estimate solar radiation, or a fusion of multiple sensors to estimate fish abundance, or using fiber optic underwater cables to monitor seismic activity. Other approaches may focus on resource optimization, such as the placement of sensors to achieve the optimal coverage, or selecting the optimal number of sensors to accurately monitor a predefined area. These can be extended to many different scenarios commonly associated with IoT, such as sensor energy optimization and (software) sensor veracity estimation. In addition to work that relates the topological architecture of a sensor network to the quality of the data acquired for a given domain, we are particularly looking forward to works that use deep neural networks or transfer learning (e.g., for satellite or multispectral data), as well as works that address signal reconstruction from sparse data (as in compressed sensing or one-shot learning).

Prof. Dr. Hrvoje Kalinić
Prof. Dr. Tomislav Petković
Guest Editors

Manuscript Submission Information

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• soft-sensors
• remote sensing
• data fusion
• data completion and reconstruction
• missing data estimation
• generative models
• deep neural network
• sparse data