Active Learning of Gaussian Processes for Spatial Functions in Mobile Sensor Networks

This paper proposes a spatial function modeling approach using mobile sensor networks, which potentially can be used for environmental surveillance applications. The mobile sensor nodes are able to sample the point observations of an 2D spatial function. On the one hand, they will use the observations to generate a predictive model of the spatial function. On the other hand, they will make collective motion decisions to move into the regions where high uncertainties of the predictive model exist. In the end, an accurate predictive model is obtained in the sensor network and all the mobile sensor nodes are distributed in the environment with an optimized pattern. Gaussian process regression is selected as the modeling technique in the proposed approach. The hyperparameters of Gaussian process model are learned online to improve the accuracy of the predictive model. The collective motion control of mobile sensor nodes is based on a locational optimization algorithm, which utilizes an information entropy of the predicted Gaussian process to explore the environment and reduce the uncertainty of predictive model. Simulation results are provided to show the performance of the proposed approach. © 2011 IFAC

An Early Warning System for Detecting H1N1 Disease Outbreak - A Spatio-temporal Approach

The outbreaks of new and emerging infectious diseases in recent decades have caused widespread social and economic disruptions in the global economy. Various guidelines for pandemic influenza planning are based upon traditional infection control, best practice and evidence. This article describes the development of an early warning system for detecting disease outbreaks in the urban setting of Hong Kong, using 216 confirmed cases of H1N1 influenza from 1 May 2009 to 20 June 2009. The prediction model uses two variables – daily influenza cases and population numbers – as input to the spatio-temporal and stochastic SEIR model to forecast impending disease cases. The fairly encouraging forecast accuracy metrics for the 1- and 2-day advance prediction suggest that the number of impending cases could be estimated with some degree of certainty. Much like a weather forecast system, the procedure combines technical and scientific skills using empirical data but the interpretation requires experience and intuitive reasoning.postprin

Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems