Search Results for « Structured Dynamic Covariance Recovery »

Performing light field recovery from diffusing wave is difficult owing to its complex and randomized light behaviors imposed by scatters. The problem becomes even more challenging when a time-varying scattering medium is involved, because, the scattered light changes in space and time. Here we report theoretically and experimentally an approach to structured field recovery behind a dynamic scattering medium, both in the near-field and the far-field diffraction regimes. We exploit the temporal irregular scattering behaviors of the dynamic scatter to overcome light field distortions, without any prior knowledge or wavefront control technique. Of particular interest is that the technique can work with a fast response rate of the scattering change in the order of microsecond level, which is inaccessible with previous techniques. Furthermore, we demonstrate the possibility for recovering a higher-order vector vortex light field from a dynamic diffuser, which was not addressed before. This work shows a significant advance toward light field recovery behind the dynamic scatters and may find intriguing applications.

Summary Covariance matrices with a linear structure are widely used in multivariate analysis. The choice of the most appropriate covariance structure can be made from a class of possible linear structures. Once we have made the choice, an important question is how we can estimate the covariance matrix for a given covariance structure. This article describes methods used to estimate the structured covariance matrix, and indicates the advantages and disadvantages of the selected methods.

Summary Linearly structured covariance matrices are widely used in multivariate analysis. The covariance structure can be chosen from a class of linear structures. Therefore, the optimal structure is identified in terms of minimizing the discrepancy function. In this research, the entropy loss function is used as the discrepancy function. We give a methodology and algorithm for determining the optimal structure from the class of structures under consideration. The accuracy of the proposed method is checked using a simulation study.

Summary Covariance matrices with a linear structure are widely used in multivariate analysis. The choice of covariance structure can be made from a set of possible linear structures. As a result, the most appropriate structure is determined by minimizing the discrepancy function. This paper is a continuation of previous work on identifying linear structures with an entropy loss function as a discrepancy function. We present extensive simulation studies on the correctness of identification with the assumed pentagonal banded Toeplitz structure.