Multimodal distributions of some physics based model parameters are often encountered in engineering due to different situations such as a change environmental conditions, and the presence types damage nonlinearity. In statistical updating, for locally identifiable parameters, it can be anticipated that multi-modal posterior would found. The full characterization these is important methodologie...

In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across modalities (speech utterances, images and motion). We propose this computational model as an answer to the question of how some class of concepts can b...

Modality is a source or form of information. Through various modal information, humans can perceive the world from multiple perspectives. Simultaneously, observation remote sensing (RS) multimodal. We observe macroscopically through panchromatic, Lidar, and other sensors. Multimodal has become an active area, which beneficial for urban planning, monitoring, applications. Despite numerous advanc...

The modelling of baroclinic tides generated in the northern South China Sea is studied using a fully-nonlinear non-hydrostatic numerical model. The focus of the modelling efforts was on the vertical structure of internal waves in the vicinity of the Luzon Strait. The barotropic tidal flow interacting with a two-ridge bottom topography in the area of the Luzon Strait produces a complex baroclini...

1 Introduction Energy harvesting (EH) research area has been emerged to build self-powered wireless electrical devices such as wireless sensors by collecting ambient or wasted energy. This EH technology is highly demanding because wireless sensors are increasingly used in the areas of structural health monitoring (SHM), building automation, and so on. The battery, a current power source for wir...

In this text we will formalise a novel solution, the Bayesian Volumetric Map (BVM), as a framework for a metric, short-term, egocentric spatial memory for multimodal perception of 3D structure and motion. This solution will enable the implementation of top-down mechanisms of attention guidance of perception towards areas of high entropy/uncertainty, so as to promote active exploration of the en...

We introduce a system that recognizes concurrent activities from real-world data captured by multiple sensors of different types. The recognition is achieved in two steps. First, we extract spatial and temporal features from the multimodal data. We feed each datatype into a convolutional neural network that extracts spatial features, followed by a long-short term memory network that extracts te...