target detection, a number of sensors N are deployed to Target detection and field surveillance are among the most monitor a Field of Interest Fol. The sensor deployment can prominent applications of Sensor Networks (SN). The qualbe either stochastic or deterministic depending on the appliity of detection achieved by a SN can be quantified by evalu cation and the Fol. Stochastic deployment is preferred when ating the probability of detecting a mobile target crossing a the Fol is not under the designer's control at the time of Field of Interest (Fol). In this paper, we analytically evaludeployment (hostile environment), or when it is more costate the detection probability of mobile targets when N sen effective to randomly deploy the sensors than systematically sors are stochastically deployed to monitor a Fol. We map place them (large-scale networks) [3,6,19]. the target detection problem to a line-set intersection probOnce the SN is deployed, targets are detected using one or 1 1 1 * 1 * 1 o 1 * 1 r T 1 more sensing modalities such as optical, mechanical, acoustic, lem and derive analytical formulas using tools from Integral g Geometry and Geometric Probability. We show that the thermal, RF and magnetic sensing. In fact, to ensure robustdetection probability depends on the length of the perimeness and enhance performance, oftentimes a sensor fusion ters of the sensing areas of the sensors and not their shape. approach is required [15]. As an example, a surveillance Hence, compared to prior work, our formulation allows us to system can be realized via fusion of data aggregated from consider a heterogeneous sensing model, where each sensor infrared, CCD, pressure and acoustic sensors.can have an arbitrary sensing area. We also evaluate the

This case study describes the process of fusing the data from several wind tunnel experiments into a single coherent visualization. Each experiment was conducted independently and was designed to explore different flow features around airplane landing gear. In the past, it would have been very difficult to correlate results from the different experiments. However, with a single 3-D visualization representing the fusion of the three experiments, significant insight into the composite flowfield was observed that would have been extremely difficult to obtain by studying its component parts. The results are even more compelling when viewed in an immersive environment.