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Visual training systems employing high fidelity image generation capabilities have gained immense popularity over the years in a variety of training applications. They offer economy of cost, and precise scenario execution with consistency across unlimited trials. Training systems using head mounted displays (HMD) are a subset of this fast proliferating technology. However, the implementation and use of HMDs poses a number of challenges. A significant one is the problem of correlating the images that are rendered on the HMD's visor in time and space with the background scenery rendered on a projection screen. In this paper we describe a method to solve this problem using Kalman filters, and show the accuracy of its implementation in practice.

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The rapid growth of the Internet has triggered an explosion in the number of applications that leverage its capabilities. Unfortunately, many are designed to burden or destroy the capabilities of their peers and the network's infrastructure. Hence, considerable effort has been focused on detecting and predicting the security breaches they propagate. However, the enormity of the Internet poses a formidable challenge to analyzing such attacks using scalable models. Furthermore, the lack of complete information on network vulnerabilities makes forecasting the systems that may be exploited by such applications in the future very hard. This paper presents a technique for deriving a scalable model for representing network attacks, and its application to identify actual attacks with greater certainty amongst false positives and false negatives. It also presents a method to forecast the propagation of security failures proliferated by an attack over time and its likely targets in the future.

As militaries across the world continue to evolve, the roles of humans in various theatres of operation are being increasingly targeted by military planners for substitution with automation. Forward observation and direction of supporting arms to neutralize threats from dynamic adversaries is one such example. However, contemporary tracking and targeting systems are incapable of serving autonomously for they do not embody the sophisticated algorithms necessary to predict the future positions of adversaries with the accuracy offered by the cognitive and analytical abilities of human operators. The need for these systems to incorporate methods characterizing such intelligence is therefore compelling. In this paper, we present a novel technique to achieve this goal by modeling the path of an entity as a continuous polynomial function of multiple variables expressed as a Taylor series with a finite number of terms. We demonstrate the method for evaluating the coefficient of each term to define this function unambiguously for any given entity, and illustrate its use to determine the entity's position at any point in time in the future.