Quantum stochastic filters for nonlinear time-domain filtering of communication signals

Principles and performances of quantum stochastic filters are studied for nonlinear time-domain filtering of communication signals. Filtering is realized by combining neural networks with the nonlinear Schroedinger equation and the time-variant probability density function of signals is estimated by solution of the equation. It is shown that obviously different performances can be achieved by the control of weight coefficients of potential fields. Based on this characteristic, a novel filtering algorithm is proposed, and utilizing this algorithm, the nonlinear waveform distortion of output signals and the denoising capability of the filters can be compromised. This will make the application of quantum stochastic filters be greatly extended, such as in applying the filters to the processing of communication signals. The predominant performance of quantum stochastic filters is shown by simulation results.

Parameters estimate of recurrent quantum stochastic filter for time variant frequency periodic signals

Designing optimal time and spatial difference step size is the key technology for quantum-random filtering(QSF)to realize time-varying frequency periodic signal filtering.In this paper,it was proposed to use the short-time Fourier transform(STFT)to dynamically estimate the signal to noise ratio(SNR)and relative frequency of the input time-varying frequency periodic signal.Then the model of time and space difference step size and signal to noise ratio(SNR)and relative frequency of quantum random filter is established by least square method.Finally,the parameters of the quantum filter can be determined step by step by analyzing the characteristics of the actual signal.The simulation results of single-frequency signal and frequency time-varying signal show that the proposed method can quickly and accurately design the optimal filter parameters based on the characteristics of the input signal,and achieve significant filtering effects.

Optimal Reinsurance and Investment Strategies Under Mean-Variance Criteria:Partial and Full Information

This paper is concerned with an optimal reinsurance and investment problem for an insurance firm under the criterion of mean-variance. The driving Brownian motion and the rate in return of the risky asset price dynamic equation cannot be directly observed. And the short-selling of stocks is prohibited. The problem is formulated as a stochastic linear-quadratic control problem where the control variables are constrained. Based on the separation principle and stochastic filtering theory, the partial information problem is solved. Efficient strategies and efficient frontier are presented in closed forms via solutions to two extended stochastic Riccati equations. As a comparison, the efficient strategies and efficient frontier are given by the viscosity solution to the HJB equation in the full information case. Some numerical illustrations are also provided.

Fluid Flow Estimation with Multiscale Ensemble Filters Based on Motion Measurements Under Location Uncertainty

This paper proposes a novel multi-scale fluid flow data assimilation approach,which integrates and complements the advantages of a Bayesian sequential assimilationtechnique, the Weighted Ensemble Kalman filter (WEnKF) [27]. The data assimilation proposed in this work incorporates measurement brought by an efficient multiscalestochastic formulation of the well-known Lucas-Kanade (LK) estimator. This estimatorhas the great advantage to provide uncertainties associated to the motion measurements at different scales. The proposed assimilation scheme benefits from this multiscale uncertainty information and enables to enforce a physically plausible dynamicalconsistency of the estimated motion fields along the image sequence. Experimentalevaluations are presented on synthetic and real fluid flow sequences.