|Data Analytics and Fusion||Intelligent systems are envisioned to have the capability of analyzing quasi-stationary sensor data (e.g., time series, images and videos) for achieving comprehensive real-time situation awareness. This data could be generated from heterogeneous sensing devices that observe different types of spatio-temporal evolving processes (e.g., dynamically changing ocean bathymetry, damage evolution on 3-D structural surfaces, growth of anomalies in biological tissues, development of microscopic faults in electromechanical equipment, unfolding of new features as a robot discovers the map of its surroundings, intruders entering in a sensor network, etc.).
Typically, these intelligent systems are part of a larger complex system, for example a networked-control system such as an aircraft engine control system, smart grid, robotic swarm, and distributed sensor network, and they are equipped with myriads of sensing devices. Consequently, large volumes of heterogeneous data are generated that need to be inferred via development of robust, scalable, and computationally efficient tools of optimal sensor selection, data reduction, visualization, fusion, and classification. In this regard, the research at LINKS is focused on developing novel methods of data analytics and information fusion using multidisciplinary concepts derived from machine learning, automata theory, and symbolic dynamics.