Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets

In the modern information age, the quantity and complexity of spatiotemporal data is increasing both rapidly and continuously. Sensor systems with multiple feeds that gather multidimensional spatiotemporal data will result in information clusters and overload, as well as a high cognitive load for users of these systems. To meet future safety-critical situations and enhance time-critical decision-making missions in dynamic environments, and to support the easy and effective managing, browsing, and searching of spatiotemporal data in a dynamic environment, we propose an asynchronous, scalable, and comprehensive spatiotemporal data organization, display, and interaction method that allows operators to navigate through spatiotemporal information rather than through the environments being examined, and to maintain all necessary global and local situation awareness. To empirically prove the viability of our approach, we developed the Event-Lens system, which generates asynchronous prioritized images to provide the operator with a manageable, comprehensive view of the information that is collected by multiple sensors. The user study and interaction mode experiments were designed and conducted. The Event-Lens system was discovered to have a consistent advantage in multiple moving-target marking-task performance measures. It was also found that participants’ attentional control, spatial ability, and action video gaming experience affected their overall performance

The Recursive Form of Error Bounds for RFS State and Observation with Pd<1

In the target tracking and its engineering applications, recursive state estimation of the target is of fundamental importance. This paper presents a recursive performance bound for dynamic estimation and filtering problem, in the framework of the finite set statistics for the first time. The number of tracking algorithms with set-valued observations and state of targets is increased sharply recently. Nevertheless, the bound for these algorithms has not been fully discussed. Treating the measurement as set, this bound can be applied when the probability of detection is less than unity. Moreover, the state is treated as set, which is singleton or empty with certain probability and accounts for the appearance and the disappearance of the targets. When the existence of the target state is certain, our bound is as same as the most accurate results of the bound with probability of detection is less than unity in the framework of random vector statistics. When the uncertainty is taken into account, both linear and non-linear applications are presented to confirm the theory and reveal this bound is more general than previous bounds in the framework of random vector statistics.In fact, the collection of such measurements could be treated as a random finite set (RFS)

The 2010 spring drought reduced primary productivity in southwestern China

Many parts of the world experience frequent and severe droughts. Summer drought can significantly reduce primary productivity and carbon sequestration capacity. The impacts of spring droughts, however, have received much less attention. A severe and sustained spring drought occurred in southwestern China in 2010. Here we examine the influence of this spring drought on the primary productivity of terrestrial ecosystems using data on climate, vegetation greenness and productivity. We first assess the spatial extent, duration and severity of the drought using precipitation data and the Palmer drought severity index. We then examine the impacts of the drought on terrestrial ecosystems using satellite data for the period 2000–2010. Our results show that the spring drought substantially reduced the enhanced vegetation index (EVI) and gross primary productivity (GPP) during spring 2010 (March–May). Both EVI and GPP also substantially declined in the summer and did not fully recover from the drought stress until August. The drought reduced regional annual GPP and net primary productivity (NPP) in 2010 by 65 and 46 Tg C yr−1, respectively. Both annual GPP and NPP in 2010 were the lowest over the period 2000–2010. The negative effects of the drought on annual primary productivity were partly offset by the remarkably high productivity in August and September caused by the exceptionally wet conditions in late summer and early fall and the farming practices adopted to mitigate drought effects. Our results show that, like summer droughts, spring droughts can also have significant impacts on vegetation productivity and terrestrial carbon cycling