In recent years, the development of intelligent transportation systems (ITS) has involved the
input of various kinds of heterogeneous data in real time and from multiple sources, which …

Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …

Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable
and accurate information. Comparing with a range of classical probabilistic data fusion …

With the continuous expansion of the Industrial Internet of Things (IIoT) and the increasing
connectivity among the various intelligent devices or systems, the control of access and …

Without integrating various sensor data sets, sensing information in the presence of leakage
for large-scale pipeline systems is very challenging. A data fusion methodology, wherein …

Data fusion enables the characterisation of an object using multiple datasets collected by
various sensors. To improve optical coordinate measurement using data fusion, researchers …

Prediction of expected life of a composite structure especially at the initial stages of
degradation is challenging owing to inherent heterogeneity and lack of robust damage …

In Mobile Robotics, visual tracking is an extremely important sub-problem. Some solutions
found to reduce the problems arising from partial and total occlusion are the use of multiple …

Predictive analytics has become an essential area of research in health informatics. The
availability of multi-source and multi-modal data in healthcare has made the disease …

Data fusion generates fused data by combining multiple sources, resulting in information
that is more consistent, accurate, and useful than any individual source and more reliable …