Field of view expansion for 3-D holographic display using a single spatial light modulator with scanning reconstruction light

Increasing the field of view of a holographic display while maintaining adequate image size is a difficult task. To address this problem, we designed a system that tessellates several sub-holograms into one large hologram at the output. The sub-holograms we generate is similar to a kinoform but without the paraxial approximation during computation. The sub-holograms are loaded onto a single spatial light modulator consecutively and relayed to the appropriate position at the output through a combination of optics and scanning reconstruction light. We will review the method of computer generated hologram and describe the working principles of our system. Results from our proof-of-concept system are shown to have an improved field of view and reconstructed image size. ©2009 IEEE.

Symmetrical 2-D Hermite-Gaussian square launch for high bit rate transmission in multimode fiber links

A 2-D Hermite-Gaussian square launch is demonstrated to show improved systems capacity over multimode fiber links. It shows a bandwidth improvement over both center and offset launches and exhibits ±5 μm misalignment tolerance. © 2011 Optical Society of America.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.