Smooth pulse recovery based on hybrid wavelet threshold denoising and first derivative adaptive smoothing filter

Based on the pulse-shaping unit in the front end of high-power laser facilities,we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery,so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve,and enhance the calibration accuracy of the pulse shape in the feedback control system.The related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing filter.The implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB,and the filtered pulse is smooth without modulation,with smoothness of about 98.8%.

Dark-field detection method of shallow scratches on the super-smooth optical surface based on the technology of adaptive smoothing and morphological differencing

In recent years, modern optical processing technologies, such as single point diamond turning, ion beam etching, and magneto-theological finishing, arc getting break- throughs. Machining precisions of super-smooth optics have also been significantly improved. However, with increasing demands for the optical surface quality,

Adaptive Endpoint Detection Based on Subband Speech

An adaptive endpoint detection algorithm based on band energy and adaptive smoothing algorithm is described. This algorithm utilizes the capability of adaptive smoothing algorithm that intensifies the discontinuity between local areas. The band energy features are selected because of their usefulness in detecting high energy regions (in the incoming signal) and making the distinction between speech and noise. Heuristic 'edge-focusing' is used to endpoint detection to save the time in iteration.

Adaptive practical output tracking of a class of nonlinear systems

Focus is laid on the adaptive practical output-tracking problem of a class of nonlinear systems with high-order lower-triangular structure and uncontrollable unstable linearization. Using the modified adaptive addition of a power integrator technique as a basic tool, a new smooth adaptive state feedback controller is designed. This controller can ensure all signals of the closed-loop systems are globally bounded and output tracking error is arbitrary small.

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter （SVSF） is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy. This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters （EKF）, nonlinear H∞ and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system （SINS） and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width. Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle （UAV） position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.

Adaptive quantile regression with precise risk bounds

An adaptive local smoothing method for nonpaxametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in the sense that no prior information about the structure of the model is assumed. The fully adaptive feature not only allows varying bandwidths to accommodate jumps or instantaneous slope changes, but also al- lows the algorithm to be spatially adaptive. Under general conditions, precise risk bounds for homogeneous and heterogeneous cases of the underlying conditional quantile curves are established. An automatic selection algo- rithm for locally adaptive bandwidths is also given, which is applicable to higher dimensional cases. Simulation studies and data analysis confirm that the proposed methodology works well.

Adaptive Local Linear Quantile Regression

In this paper we propose a new method of local linear adaptive smoothing for nonparametric conditional quantile regression. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on a simulated example and compare it with other methods. The simulation results demonstrate a reasonable performance of our method proposed especially in situations when the underlying image is piecewise linear or can be approximated by such images. Generally speaking, our method outperforms most other existing methods in the sense of the mean square estimation （MSE） and mean absolute estimation （MAE） criteria. The procedure is very stable with respect to increasing noise level and the algorithm can be easily applied to higher dimensional situations.

Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion

On highways,vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles.To ensure their safety,predicting the sideslip trajectories of such vehicles is crucial.However,the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction.Hence,this study uses a physical model-based approach to predict vehicle sideslip trajectories.Nevertheless,the traditional physical model-based method relies on constant input assumption,making its long-term prediction accuracy poor.To address this challenge,this study presents the time-series analysis and interacting multiple model-based(IMM)sideslip trajectory prediction(TSIMMSTP)method,which encompasses time-series analysis and multi-physical model fusion,for the prediction of vehicle sideslip trajectories.Firstly,we use the proposed adaptive quadratic exponential smoothing method with damping(AQESD)in the time-series analysis module to predict the input state sequence required by kinematic models.Then,we employ an IMM approach to fuse the prediction results of various physical models.The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories.The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios,and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.

Mapping Seismic Hazard for Canadian Sites Using Spatially Smoothed Seismicity Model

The estimated seismic hazard based on the delineated seismic source model is used as the basis to assign the seismic design loads in Canadian structural design codes.An alternative for the estimation is based on a spatially smoothed source model.However,a quantification of differences in the Canadian seismic hazard maps(CanSHMs)obtained based on the delineated seismic source model and spatially smoothed model is unavailable.The quantification is valuable to identify epistemic uncertainty in the estimated seismic hazard and the degree of uncertainty in the CanSHMs.In the present study,we developed seismic source models using spatial smoothing and historical earthquake catalogue.We quantified the differences in the estimated Canadian seismic hazard by considering the delineated source model and spatially smoothed source models.For the development of the spatially smoothed seismic source models,we considered spatial kernel smoothing techniques with or without adaptive bandwidth.The results indicate that the use of the delineated seismic source model could lead to under or over-estimation of the seismic hazard as compared to those estimated based on spatially smoothed seismic source models.This suggests that an epistemic uncertainty caused by the seismic source models should be considered to map the seismic hazard.