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Conventional stereoscopic displays force an unnatural decoupling of the accommodation and convergence cues, which may contribute to various visual artifacts and have adverse effects on depth perception accuracy. In this paper, we present the design and implementation of a high-resolution optical see-through multi-focal-plane head-mounted display enabled by state-of-the-art freeform optics. The prototype system is capable of rendering nearly-correct focus cues for a large volume of 3D space, extending into a depth range from 0 to 3 diopters. The freeform optics, consisting of a freeform prism eyepiece and a freeform lens, demonstrates an angular resolution of 1.8 arcminutes across a 40-degree diagonal field of view in the virtual display path while providing a 0.5 arcminutes angular resolution to the see-through view.

a b s t r a c tAgent-based simulations are useful for studying people's movement and to help making decisions in situations like emergency evacuation in smart environments. These agent-based simulations are typically used as offline tools and do not assimilate real time data from the environment. With more and more smart buildings equipped with sensor devices, it is possible to utilize real time sensor data to dynamically inform the simulations to improve simulation results. In this paper, we propose a method to assimilate real time sensor data in agent-based simulation of smart environments based on Particle Filters (PFs). The data assimilation aims to estimate the system state, i.e., people's location information in real time, and use the estimated states to provide initial conditions for more accurate simulation/prediction of the system dynamics in the future. We develop a PF-based data assimilation framework and propose a new resampling method named as component set resampling to improve data assimilation for multiple agents. The proposed framework and method are demonstrated and evaluated through experiments using a sparsely populated smart environment.

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Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.

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Simulating wildfire spread and containment remains a challenging problem due to the complexity of fire behavior. In this paper, the authors present an integrated simulation environment for surface wildfire spread and containment called DEVS-FIRE. DEVS-FIRE is based on the discrete event system specification (DEVS) and uses a cellular space model for simulating wildfire spread and agent models for simulating wildfire containment. The cellular space model incorporates real spatial fuels data, terrain data and temporal weather data into the prediction of wildfire behavior across both time and space. DEVS-FIRE is designed to be integrated with stochastic optimization models that use the scenario results from the simulation to determine an optimal mix of firefighting resources to dispatch to a wildfire. Preliminary computational experiments with fuel, terrain and weather data for a real forest demonstrate the viability of the integrated simulation environment for wildfire spread and containment.

Variable structure refers to the ability of a system to dynamically change its structure according to different situations. It provides component-based modeling and simulation environments with powerful modeling capability and the flexibility to design and analyze complex systems. In this article, the authors discuss variable structure—specifically, the structure change and interface change capability—in DEVS-based modeling and simulation environments. The operations of structure change and interface change are discussed, and their respective operation boundaries are defined. Three examples are given to illustrate the role of variable structure and how it can be used to model and design adaptive complex systems. Principles for the implementation of variable structure are also presented and illustrated in the DEVSJAVA modeling and simulation environment.