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Potassium fluoroboratoberyllate KBe2BO3F2 (KBBF) has been revealed theoretically and experimentally as a novel ultraviolet nonlinear optical crystal, but it is found to be very difficult to grow in a large size, because of the weak binding interaction between the (Be2BO3)∞ units, which leads to an apparent layer habit in the growth. By using a molecular engineering approach, oxygen bridges when brought in to strengthen the binding between the infinite units are found to be useful to overcome the above shortcoming of KBBF, and in the light of it another new ultraviolet nonlinear optical crystal—strontium boratoberyllate Sr2Be2B2O7 (SBBO) has been discovered. The linear optical properties of SBBO are similar to KBBF’s, but its nonlinear optical properties are better than that of the latter. d22(SBBO)≂d22(β-BaB2O4), which is two times higher than d11 of KBBF. SBBO has very good mechanical properties, and it is also not deliquescent. So SBBO is expected to have great potential for the application in ultraviolet nonlinear optical devices.

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This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective, which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics (e.g., mean and covariance) conditioned on the system's measurement data. This article offers a systematic introduction of the Bayesian state estimation framework and reviews various Kalman filtering (KF) techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including the Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation forward to more complicated problems such as simultaneous state and parameter/input estimation.