In this paper, we investigate channel modeling for visible light communications (VLC) using non-sequential ray tracing simulation tools. We create three dimensional realistic simulation environments to depict indoor scenarios specifying the geometry of the environment, the objects inside, the reflection characteristics of the surface materials as well as the characteristics of the transmitter and receivers, i.e., LED sources and photodioes. Through ray tracing simulations, we compute the received optical power and the delay of direct/indirect rays which are then used to obtain the channel impulse response (CIR). Following this methodology, we present CIRs for a number of indoor environments including empty/furnished rectangular rooms with different sizes and wall/object materials (e.g., plaster, gloss paint, wood, aluminum metal, glass) assuming deployment of both single and multiple LED transmitters. We further quantify multipath channel parameters such as delay spread and channel DC gain for each configuration and provide insights into the effects of indoor environment parameters (e.g., size, wall/object materials, etc.), transmitter/receiver specifications (e.g., single vs. multiple transmitters, location, rotation etc.) on the channel.
Recently a new approach to Bayesian image segmentation has been proposed by Bouman and Shapiro, based on a multiscale random field (MSRF) model along with a sequential MAP (SMAP) estimator as an efficient and computationally feasible alternative to MAP segmentation. But their method is restricted to image models with observed pixels that are conditionally independent given their class labels. In this paper, we follow the approach of and extend the SMAP method for a more general class of random field models. The proposed scheme is recursive, yields the exact MAP estimate, and is readily applicable to a broad range of image models. We present simulations on synthetic images and conclude that the generalized algorithm performs better and requires much less computation than maximum likelihood segmentation.