Abstract: The Bayesian Computational Sensor Network methodology is applied to small-scale structural health monitoring. A mobile robot equipped with vision and ultrasound sensor maps small-scale structures for damage localizes itself and the damage in the map. The combination of vision and ultrasound reduces the uncertainty in damage localization. The data storage and analysis takes place exploiting cloud computing mechanisms, and there is also an off-line computational model calibration component which returns information to the robot concerning updated on-board models as well as proposed sampling points. The approach is validated in a set of physical experiments.

Abstract: A triangulation of points in E^2, or a tetrahedronization of points in E^3, is used in many applications. It is not necessary to fulfill the Delaunay criteria in all cases. For large data (more then 5∙ 〖10〗^7 points), parallel methods are used for the purpose of decreasing run time. A new approach for fast, effective and highly parallel CPU and GPU triangulation, or tetrahedronization, of large data sets in E^2 or E^3 suitable for in core and out core memory processing, is proposed. Experimental results proved that the resulting triangulation/tetrahedralization, is close to the Delaunay triangulation/tetrahedralization. It also demonstrates the applicability of the method proposed in applications.

Abstract: Future cyber information systems are required to determine network performance including trust, resiliency, and timeliness. Using the Dynamic Data-Driven Application Systems (DDDAS) concepts; we develop a method for crisis management that incorporates sensed data, performance models, theoretical analysis, and service-based software. Using constructs from security and resiliency theories, the motivating concept is Resilient-DDDAS-as-a-Cloud Service (rDaaS). Service-based approaches allow a system to react as needed to the dynamics of the situation. The Resilient Cloud Middleware supports the analysis the data stored and retrieved in the cloud, management of processes, and coordination with the end user/application. The r-DaaS concept is demonstrated with a nuclear plant example for emergency response that demonstrates the importance of the DDDAS system level performance.

Abstract: Many Dynamic Data-Driven Application Systems (DDDAS) use replicated simulations to project possible future system states. In many cases there are substantial similarities among these different replications. In other cases output statistics are independent of certain simulation computations. This paper explores computational methods to exploit these properties to improve efficiency. We discuss a new algorithm to speed up the execution of replicated vehicle traffic simulations, where the output statistics of interest focus on one or more attributes such as the trajectory of a certain “target” vehicle. By focusing on correctly reproducing the behavior of the target vehicle and its interaction with other modeled entities across the different replications and modifying the event handling mechanism the execution time can be reduced on both serial and parallel machines. A speculative execution method using a tagging mechanism allows this speedup to occur without loss of accuracy in the output statistics.

Abstract: Stream processing (SP) became relevant mainly due to inexpensive and hence ubiquitous deployment of sensors in many domains (e.g., environmental monitoring, battle field monitoring). Other continuous data generators (web clicks, traffic data, network packets, mobile devices) have also prompted processing and analysis of these streams for applications such as traffic congestion/ accidents, network intrusion detection, and personalized marketing. Image processing has been researched for several decades. Recently there is emphasis on video stream analysis for situation monitoring due to the ubiquitous deployment of video cameras and unmanned aerial vehicles for security and other applications. This paper elaborates on the research and development issues that need to be addressed for extending the traditional stream processing framework for video analysis, especially for situation awareness. This entails extensions to: data model, operators and language for expressing complex situations, QoS specifications and algorithms needed for their satisfaction. Specifically, this paper demonstrates inadequacy of current data representation (e.g., relation and arrable) and querying capabilities to infer long-term research and development issues.