Quantum Clone and States Estimation for n-state System

We derive a lower bound for the optimal fidelity for deterministic cloning a set of n pure states. In connection with states estimation, we obtain a lower bound about average maximum correct states estimation probability.Comment: 4 Pages, No Figur

Remarks on a parameter estimation for von Mises--Fisher distributions

We point out an error in the proof of the main result of the paper of Tanabe et al. (2007) concerning a parameter estimation for von Mises--Fisher distributions, we correct the proof of the main result and we present a short alternative proof.Comment: 3 page

Stochastic Attribute-Value Grammars

Probabilistic analogues of regular and context-free grammars are well-known in computational linguistics, and currently the subject of intensive research. To date, however, no satisfactory probabilistic analogue of attribute-value grammars has been proposed: previous attempts have failed to define a correct parameter-estimation algorithm. In the present paper, I define stochastic attribute-value grammars and give a correct algorithm for estimating their parameters. The estimation algorithm is adapted from Della Pietra, Della Pietra, and Lafferty (1995). To estimate model parameters, it is necessary to compute the expectations of certain functions under random fields. In the application discussed by Della Pietra, Della Pietra, and Lafferty (representing English orthographic constraints), Gibbs sampling can be used to estimate the needed expectations. The fact that attribute-value grammars generate constrained languages makes Gibbs sampling inapplicable, but I show how a variant of Gibbs sampling, the Metropolis-Hastings algorithm, can be used instead.Comment: 23 pages, 21 Postscript figures, uses rotate.st

Distributed L1-state-and-fault estimation for Multi-agent systems

In this paper, we propose a distributed state-and-fault estimation scheme for multi-agent systems. The proposed estimator is based on an $\ell_1$-norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Two theoretical results are given to analyze the correctness of the proposed approach. First, we provide a necessary and sufficient condition such that state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. Second, we provide a sufficient condition for the estimation error of fault signals when numerical errors of solving the optimization problem are present. An illustrative example is given to validate the effectiveness of the proposed approach

Quantitative modeling of laser speckle imaging

We have analyzed the image formation and dynamic properties in laser speckle imaging (LSI) both experimentally and with Monte-Carlo simulation. We show for the case of a liquid inclusion that the spatial resolution and the signal itself are both significantly affected by scattering from the turbid environment. Multiple scattering leads to blurring of the dynamic inhomogeneity as detected by LSI. The presence of a non-fluctuating component of scattered light results in the significant increase in the measured image contrast and complicates the estimation of the relaxation time. We present a refined processing scheme that allows a correct estimation of the relaxation time from LSI data.Comment: submitted to Optics Letter